interview in quantitative research

9.3 Quantitative Interview Techniques and Considerations

Learning objectives.

• Define and describe standardized interviews.
• Describe how quantitative interviews differ from qualitative interviews.
• Describe the process and some of the drawbacks of telephone interviewing techniques.
• Describe how the analysis of quantitative interview works.
• Identify the strengths and weaknesses of quantitative interviews.

Quantitative interviews are similar to qualitative interviews in that they involve some researcher/respondent interaction. But the process of conducting and analyzing findings from quantitative interviews also differs in several ways from that of qualitative interviews. Each approach also comes with its own unique set of strengths and weaknesses. We’ll explore those differences here.

Conducting Quantitative Interviews

Much of what we learned in the previous chapter on survey research applies to quantitative interviews as well. In fact, quantitative interviews are sometimes referred to as survey interviews because they resemble survey-style question-and-answer formats. They might also be called standardized interviews Interviews during which the same questions are asked of every participant in the same way, and survey-style question-and-answer formats are utilized. . The difference between surveys and standardized interviews is that questions and answer options are read to respondents rather than having respondents complete a questionnaire on their own. As with questionnaires, the questions posed in a standardized interview tend to be closed ended. See Chapter 8 "Survey Research: A Quantitative Technique" for the definition of closed ended. There are instances in which a quantitative interviewer might pose a few open-ended questions as well. In these cases, the coding process works somewhat differently than coding in-depth interview data. We’ll describe this process in the following subsection.

In quantitative interviews, an interview schedule A document containing the list of questions and answer options that quantitative interviewers read to respondents. is used to guide the researcher as he or she poses questions and answer options to respondents. An interview schedule is usually more rigid than an interview guide. It contains the list of questions and answer options that the researcher will read to respondents. Whereas qualitative researchers emphasize respondents’ roles in helping to determine how an interview progresses, in a quantitative interview, consistency in the way that questions and answer options are presented is very important. The aim is to pose every question-and-answer option in the very same way to every respondent. This is done to minimize interviewer effect Occurs when an interviewee is influenced by how or when questions and answer options are presented by an interviewer. , or possible changes in the way an interviewee responds based on how or when questions and answer options are presented by the interviewer.

Quantitative interviews may be recorded, but because questions tend to be closed ended, taking notes during the interview is less disruptive than it can be during a qualitative interview. If a quantitative interview contains open-ended questions, however, recording the interview is advised. It may also be helpful to record quantitative interviews if a researcher wishes to assess possible interview effect. Noticeable differences in responses might be more attributable to interviewer effect than to any real respondent differences. Having a recording of the interview can help a researcher make such determinations.

Quantitative interviewers are usually more concerned with gathering data from a large, representative sample. As you might imagine, collecting data from many people via interviews can be quite laborious. Technological advances in telephone interviewing procedures can assist quantitative interviewers in this process. One concern about telephone interviewing is that fewer and fewer people list their telephone numbers these days, but random digit dialing (RDD) takes care of this problem. RDD programs dial randomly generated phone numbers for researchers conducting phone interviews. This means that unlisted numbers are as likely to be included in a sample as listed numbers (though, having used this software for quantitative interviewing myself, I will add that folks with unlisted numbers are not always very pleased to receive calls from unknown researchers). Computer-assisted telephone interviewing (CATI) programs have also been developed to assist quantitative survey researchers. These programs allow an interviewer to enter responses directly into a computer as they are provided, thus saving hours of time that would otherwise have to be spent entering data into an analysis program by hand.

Conducting quantitative interviews over the phone does not come without some drawbacks. Aside from the obvious problem that not everyone has a phone, research shows that phone interviews generate more fence-sitters than in-person interviews (Holbrook, Green, & Krosnick, 2003). Holbrook, A. L., Green, M. C., & Krosnick, J. A. (2003). Telephone versus face-to-face interviewing of national probability samples with long questionnaires: Comparisons of respondent satisficing and social desirability response bias. Public Opinion Quarterly, 67 , 79–125. Responses to sensitive questions or those that respondents view as invasive are also generally less accurate when data are collected over the phone as compared to when they are collected in person. I can vouch for this latter point from personal experience. While conducting quantitative telephone interviews when I worked at a research firm, it was not terribly uncommon for respondents to tell me that they were green or purple when I asked them to report their racial identity.

Analysis of Quantitative Interview Data

As with the analysis of survey data, analysis of quantitative interview data usually involves coding response options numerically, entering numeric responses into a data analysis computer program, and then running various statistical commands to identify patterns across responses. Section 8.5 "Analysis of Survey Data" of Chapter 8 "Survey Research: A Quantitative Technique" describes the coding process for quantitative data. But what happens when quantitative interviews ask open-ended questions? In this case, responses are typically numerically coded, just as closed-ended questions are, but the process is a little more complex than simply giving a “no” a label of 0 and a “yes” a label of 1.

In some cases, quantitatively coding open-ended interview questions may work inductively, as described in Section 9.2.2 "Analysis of Qualitative Interview Data" . If this is the case, rather than ending with codes, descriptions of codes, and interview excerpts, the researcher will assign a numerical value to codes and may not utilize verbatim excerpts from interviews in later reports of results. Keep in mind, as described in Chapter 1 "Introduction" , that with quantitative methods the aim is to be able to represent and condense data into numbers. The quantitative coding of open-ended interview questions is often a deductive process. The researcher may begin with an idea about likely responses to his or her open-ended questions and assign a numerical value to each likely response. Then the researcher will review participants’ open-ended responses and assign the numerical value that most closely matches the value of his or her expected response.

Strengths and Weaknesses of Quantitative Interviews

Quantitative interviews offer several benefits. The strengths and weakness of quantitative interviews tend to be couched in comparison to those of administering hard copy questionnaires. For example, response rates tend to be higher with interviews than with mailed questionnaires (Babbie, 2010). Babbie, E. (2010). The practice of social research (12th ed.). Belmont, CA: Wadsworth. That makes sense—don’t you find it easier to say no to a piece of paper than to a person? Quantitative interviews can also help reduce respondent confusion. If a respondent is unsure about the meaning of a question or answer option on a questionnaire, he or she probably won’t have the opportunity to get clarification from the researcher. An interview, on the other hand, gives the researcher an opportunity to clarify or explain any items that may be confusing.

As with every method of data collection we’ve discussed, there are also drawbacks to conducting quantitative interviews. Perhaps the largest, and of most concern to quantitative researchers, is interviewer effect. While questions on hard copy questionnaires may create an impression based on the way they are presented, having a person administer questions introduces a slew of additional variables that might influence a respondent. As I’ve said, consistency is key with quantitative data collection—and human beings are not necessarily known for their consistency. Interviewing respondents is also much more time consuming and expensive than mailing questionnaires. Thus quantitative researchers may opt for written questionnaires over interviews on the grounds that they will be able to reach a large sample at a much lower cost than were they to interact personally with each and every respondent.

Key Takeaways

• Unlike qualitative interviews, quantitative interviews usually contain closed-ended questions that are delivered in the same format and same order to every respondent.
• Quantitative interview data are analyzed by assigning a numerical value to participants’ responses.
• While quantitative interviews offer several advantages over self-administered questionnaires such as higher response rates and lower respondent confusion, they have the drawbacks of possible interviewer effect and greater time and expense.
• The General Social Survey (GSS), which we’ve mentioned in previous chapters, is administered via in-person interview, just like quantitative interviewing procedures described here. Read more about the GSS at http://www.norc.uchicago.edu/GSS+Website .
• Take a few of the open-ended questions you created after reading Section 9.2 "Qualitative Interview Techniques and Considerations" on qualitative interviewing techniques. See if you can turn them into closed-ended questions.

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Quantitative Interview Techniques & Considerations

61 Conducting Quantitative Interviews

Much of what we learned in the previous chapter on survey research applies to quantitative interviews as well. In fact, quantitative interviews are sometimes referred to as survey interviews because they resemble survey-style question-and-answer formats. They might also be called standardized interviews . The difference between surveys and standardized interviews is that questions and answer options are read to respondents in a standardized interview, rather than having respondents complete a survey on their own. As with surveys, the questions posed in a standardized interview tend to be closed ended. There are instances in which a quantitative interviewer might pose a few open-ended questions as well. In these cases, the coding process works somewhat differently than coding in-depth interview data. We will describe this process in the following section.

In quantitative interviews, an interview schedule is used to guide the researcher as he or she poses questions and answer options to respondents. An interview schedule is usually more rigid than an interview guide. It contains the list of questions and answer options that the researcher will read to respondents. Whereas qualitative researchers emphasize respondents’ roles in helping to determine how an interview progresses, in a quantitative interview, consistency in the way that questions and answer options are presented is very important. The aim is to pose every question-and-answer option in the very same way to every respondent. This is done to minimize interviewer effect, or possible changes in the way an interviewee responds based on how or when questions and answer options are presented by the interviewer.

Quantitative interviews may be recorded, but because questions tend to be closed ended, taking notes during the interview is less disruptive than it can be during a qualitative interview. If a quantitative interview contains open-ended questions, recording the interview is advised. It may also be helpful to record quantitative interviews if a researcher wishes to assess possible interview effect. Noticeable differences in responses might be more attributable to interviewer effect than to any real respondent differences. Having a recording of the interview can help a researcher make such determinations.

Quantitative interviewers are usually more concerned with gathering data from a large, representative sample. As you might imagine, collecting data from many people via interviews can be quite laborious. In the past, telephone interviewing was quite common; however, the growth in use mobile phones has raised concern regarding whether or not traditional landline telephone interviews and surveys are now representative of the general population (Busse & Fuchs, 2012). Indeed, there are other drawbacks to telephone interviews. Aside from the obvious problem that not everyone has a phone (mobile or landline), research shows that phone interview respondents were less cooperative, less engaged in the interview, and more likely to express dissatisfaction with the length of the interview than were face-to-face respondents (Holbrook, Green, & Krosnick, 2003, p. 79). Holbrook et al.’s research also demonstrated that telephone respondents were more suspicious of the interview process and more likely than face-to-face respondents to present themselves in a socially desirable manner.

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• This chapter is an adaptation of Chapter 9.3 in Principles of Sociological Inquiry , which was adapted by the Saylor Academy without attribution to the original authors or publisher, as requested by the licensor. © Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License .

An Introduction to Research Methods in Sociology Copyright © 2019 by Valerie A. Sheppard is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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• Types of Interviews in Research | Guide & Examples

Types of Interviews in Research | Guide & Examples

Published on 4 May 2022 by Tegan George . Revised on 10 October 2022.

An interview is a qualitative research method that relies on asking questions in order to collect data . Interviews involve two or more people, one of whom is the interviewer asking the questions.

There are several types of interviews, often differentiated by their level of structure. Structured interviews have predetermined questions asked in a predetermined order. Unstructured interviews are more free-flowing, and semi-structured interviews fall in between.

Interviews are commonly used in market research, social science, and ethnographic research.

Table of contents

What is a structured interview, what is a semi-structured interview, what is an unstructured interview, what is a focus group, examples of interview questions, advantages and disadvantages of interviews, frequently asked questions about types of interviews.

Structured interviews have predetermined questions in a set order. They are often closed-ended, featuring dichotomous (yes/no) or multiple-choice questions. While open-ended structured interviews exist, they are much less common. The types of questions asked make structured interviews a predominantly quantitative tool.

Asking set questions in a set order can help you see patterns among responses, and it allows you to easily compare responses between participants while keeping other factors constant. This can mitigate biases and lead to higher reliability and validity. However, structured interviews can be overly formal, as well as limited in scope and flexibility.

• You feel very comfortable with your topic. This will help you formulate your questions most effectively.
• You have limited time or resources. Structured interviews are a bit more straightforward to analyse because of their closed-ended nature, and can be a doable undertaking for an individual.
• Your research question depends on holding environmental conditions between participants constant

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Semi-structured interviews are a blend of structured and unstructured interviews. While the interviewer has a general plan for what they want to ask, the questions do not have to follow a particular phrasing or order.

Semi-structured interviews are often open-ended, allowing for flexibility, but follow a predetermined thematic framework, giving a sense of order. For this reason, they are often considered ‘the best of both worlds’.

However, if the questions differ substantially between participants, it can be challenging to look for patterns, lessening the generalisability and validity of your results.

• You have prior interview experience. It’s easier than you think to accidentally ask a leading question when coming up with questions on the fly. Overall, spontaneous questions are much more difficult than they may seem.
• Your research question is exploratory in nature. The answers you receive can help guide your future research.

An unstructured interview is the most flexible type of interview. The questions and the order in which they are asked are not set. Instead, the interview can proceed more spontaneously, based on the participant’s previous answers.

Unstructured interviews are by definition open-ended. This flexibility can help you gather detailed information on your topic, while still allowing you to observe patterns between participants.

However, so much flexibility means that they can be very challenging to conduct properly. You must be very careful not to ask leading questions, as biased responses can lead to lower reliability or even invalidate your research.

• You have a solid background in your research topic and have conducted interviews before
• Your research question is exploratory in nature, and you are seeking descriptive data that will deepen and contextualise your initial hypotheses
• Your research necessitates forming a deeper connection with your participants, encouraging them to feel comfortable revealing their true opinions and emotions

A focus group brings together a group of participants to answer questions on a topic of interest in a moderated setting. Focus groups are qualitative in nature and often study the group’s dynamic and body language in addition to their answers. Responses can guide future research on consumer products and services, human behaviour, or controversial topics.

Focus groups can provide more nuanced and unfiltered feedback than individual interviews and are easier to organise than experiments or large surveys. However, their small size leads to low external validity and the temptation as a researcher to ‘cherry-pick’ responses that fit your hypotheses.

• Your research focuses on the dynamics of group discussion or real-time responses to your topic
• Your questions are complex and rooted in feelings, opinions, and perceptions that cannot be answered with a ‘yes’ or ‘no’
• Your topic is exploratory in nature, and you are seeking information that will help you uncover new questions or future research ideas

Depending on the type of interview you are conducting, your questions will differ in style, phrasing, and intention. Structured interview questions are set and precise, while the other types of interviews allow for more open-endedness and flexibility.

Here are some examples.

• Semi-structured
• Unstructured
• Focus group
• Do you like dogs? Yes/No
• Do you associate dogs with feeling: happy; somewhat happy; neutral; somewhat unhappy; unhappy
• If yes, name one attribute of dogs that you like.
• If no, name one attribute of dogs that you don’t like.
• What feelings do dogs bring out in you?
• When you think more deeply about this, what experiences would you say your feelings are rooted in?

Interviews are a great research tool. They allow you to gather rich information and draw more detailed conclusions than other research methods, taking into consideration nonverbal cues, off-the-cuff reactions, and emotional responses.

However, they can also be time-consuming and deceptively challenging to conduct properly. Smaller sample sizes can cause their validity and reliability to suffer, and there is an inherent risk of interviewer effect arising from accidentally leading questions.

Here are some advantages and disadvantages of each type of interview that can help you decide if you’d like to utilise this research method.

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order.
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when:

• You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
• You are constrained in terms of time or resources and need to analyse your data quickly and efficiently
• Your research question depends on strong parity between participants, with environmental conditions held constant

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

• You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
• Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

• You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions
• Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
• You are seeking descriptive data, and are ready to ask questions that will deepen and contextualise your initial thoughts and hypotheses
• Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

Center for Career Development

Quantitative Interview Preparation

A quantitative (quant) interview is designed to help the interviewer understand how you think, and may include specific industry references including financial terms, economic theories or established mathematical models. Interviewers assess these skills through computations, logic problems and brain teasers.

What traits are firms seeking in quant candidates?

Organizations hiring for analytical roles seek candidates with logical reasoning processes, exceptional computational skills and strong statistical abilities. Because such organizations often operate at a fast pace and the decisions staff make can have far-reaching economic and financial impacts, interviewers seek to assess an applicant’s mathematical mastery, critical thinking skill and creativity, as well as the ability to perform under pressure. 

What does a quant interview look like?

Organizations may ask you to complete a 30-minute online assessment before interviewing with a company representative. The most common tools for online assessments are Pymetrics, a platform that gamifies calculations and logical reasoning, and Hirevue, a video question bank of behavioral, mathematical and industry-specific questions. 

Through the use of AI, a firm can assess the speed and accuracy of your analytical skills and/or your interest in and knowledge of the field before considering your resume.

A preliminary quant interpersonal interview is typically 45-60 minutes, and may be in person or virtual. For in-person interviews, you may be handed a pencil and paper or markers for a whiteboard for your calculations. For virtual interviews, you might work on a shared virtual whiteboard or Google Doc.

Your interviewer will provide instructions to either take time to work through a problem first and then share your solution, or to work and explain your rationale as you go. In either case, you should be mindful not to get hung up on terms you don’t know or calculations you can’t compute. Instead, communicate what you are thinking and why.

With the initial interview largely assessing analytical skills, subsequent interviews may focus on in-depth quant concepts related to the industry and to broader personality characteristics, such as how you weigh a given set of options, how you manage your time and why you’re drawn to working in that particular industry.

How should I prepare?

Although each company will have its own interview protocol, here are steps you can take to prepare for quant interviews regardless of the organization:

• Keep current on business issues and financial markets to understand trends
• Cultivate a basic financial vocabulary
• Practice mental math so you can work with quantitative data more easily
• Review brain teasers and practice solving them
• Connect with alumni who work in quant roles to learn more and/or practice interviewing 
• Attend information sessions with quant employers to learn about their firm’s interview processes
• Read about the specific organization with whom you are interviewing to understand their clients and the services, and the philosophy that guides their practice 
• Be prepared to devote time over a number of weeks to become fluent in financial terms and proficient with the different types of quant interview questions

Review company resources

Some major investment banks and trading firms have interview preparation tips on their websites, typically in the “Careers” or “Work with Us” sections.

Some may also post these resources on platforms such as YouTube and host live events, which are usually listed in  Handshake . These resources can be a valuable tool in your interview preparation, as they are tailored to the specific company for which you are interviewing.

Find external resources

A quick online search for “quant interview prep” will yield ample videos, podcasts and documents offering tips and advice. Be cautious as you explore these tools, as some may be free snippets that are really commercials for their paid products.

The content on sites like Quora, Reddit and Glassdoor are populated by group members and may not accurately reflect the content or conditions under which your interview will take place.

Princeton University maintains a subscription to  Firsthand , a site that provides in-depth intelligence on what it’s really like to work within an industry, company, or profession. Firsthand's Guide to Finance Interviews and descriptions of quantitative analyst roles across industries are helpful resources for learning about the field.

There are many books on the topic of quant interview prep. A few recent books include: 

• 150 Most Frequently Asked Questions on Quant Interviews, 2nd Edition (2019) by Dan Stefanica, Radoš Radoičić and Tai-Ho Wang
• Cracking the Finance Quant Interview: 75 Interview Questions and Solutions (2020) by Jean Peyre

Examples of quant interview questions

Industry-specific questions.

• How might you forecast future prices using a Monte Carlo simulation technique?
• When you begin a new tech project, what types of systems requirements do you gather?
• In what ways might fiscal policy influence economic growth?

Mathematical computation questions

• What is the sum of the numbers from 1 to 100?
• With a three-cup jug and a five-cup jug, how do you measure out one cup of water?
• If a train conductor issues 27 tickets in 30 minutes, how many tickets can they issue in eight hours?

Logic problems

• If you have 18 blue socks and 14 black socks in a drawer, in the dark, how many do you have to pull out before you have a matching pair?
• If I roll two dice, what is the probability that I will get a two on the second roll?

Brain teasers

• Why are manhole covers round?
• How many tennis balls fit in an airplane?
• When might you use a linear equation in your daily life?

Don’t fret if you haven’t memorized banking vocabulary or the specific nuances of a particular mathematical formula. The most important trait to demonstrate in these types of interviews is your ability to think quickly and to explain the rationale behind your decision-making.

Related Resources

A Complete Guide to Quantitative Research Methods

Numbers are everywhere and drive our day-to-day lives. We take decisions based on numbers, both at work and in our personal lives. For example, an organization may rely on sales numbers to see if it’s succeeding or failing, and a group of friends planning a vacation may look at ticket prices to pick a place.

In the social domain, numbers are just as important. They help identify what interventions are needed, whether ongoing projects are effective, and more. But how do organizations in the social domain get the numbers they need?

This is where quantitative research comes in. Quantitative research is the process of collecting numerical data through standardized techniques, then applying statistical methods to derive insights from it.

When is quantitative research useful?

The goal of quantitative research methods is to collect numerical data from a group of people, then generalize those results to a larger group of people to explain a phenomenon. Researchers generally use quantitative research when they want get objective, conclusive answers.

For example, a chocolate brand may run a survey among a sample of their target group (teenagers in the United States) to check whether they like the taste of the chocolate. The result of this survey would reveal how all teenagers in the U.S. feel about the chocolate.

Similarly, an organization running a project to improve a village’s literacy rate may look at how many people came to their program, how many people dropped out, and each person’s literacy score before and after the program. They can use these metrics to evaluate the overall success of their program.

Unlike  qualitative research , quantitative research is generally not used in the early stages of research for exploring a question or scoping out a problem. It is generally used to answer clear, pre-defined questions in the advanced stages of a research study.

How can you plan a quantitative research exercise?

• Identify the research problem . An example would be, how well do New Delhi’s government schools ensure that students complete their education?
• Prepare the research questions that need to be answered to address the research problem. For example, what percentage of students drop out of government schools in New Delhi?
• Review existing literature on the research problem and questions to ensure that there is no duplication. If someone has already answered this, you can rely on their results.
• Develop a research plan . This includes identifying the target group, sample , and method of data collection ; conducting data analysis; collating recommendations; and arriving at a conclusion.

What are the advantages of quantitative research methods?

• Quantitative research methods provide an relatively conclusive answer to the research questions.
• When the data is collected and analyzed in accordance with standardized, reputable methodology, the results are usually trustworthy.
• With statistically significant sample sizes, the results can be generalized to an entire target group.

Samples have to be carefully designed and chosen, else their results can’t be generalized. Learn how to choose the right sampling technique for your survey.

What are the limitations of quantitative research methods?

• Does not account for people’s thoughts or perceptions about what you’re evaluating.
• Does not explore the “why” and “how” behind a phenomenon.

What quantitative research methods can you use?

Here are four quantitative research methods that you can use to collect data for a quantitative research study:

Questionnaires

This is the most common way to collect quantitative data. A questionnaire (also called a survey) is a series of questions, usually written on paper or a digital form. Researchers give the questionnaire to their sample, and each participant answers the questions. The questions are designed to gather data that will help researchers answer their research questions.

Typically, a questionnaire has closed-ended questions — that is, the participant chooses an answer from the given options. However, a questionnaire may also have quantitative open-ended questions. In the open-ended example above, the participants could write a simple number like “4”, a range like “I usually go one or two times per week” or a more complex response like “Most weeks I go twice, but this week I went 4 times because I kept forgetting my grocery list. During the winter, I only go once a week.”

Understanding closed and open-ended questions is crucial to designing a great survey and collecting high quality data. Learn more with our complete guide about when and how to use closed and open-ended questions.

A good questionnaire should have clear language, correct grammar and spelling, and a clear objective.

Advantages:

• Questionnaires are often less time consuming than interviews or other in-person quantitative research methods.
• They’re a common, fairly simple way to collect data.
• They can be a cost-effective option for gathering data from a large sample.

Limitations:

• Responses may lack depth and provide limited information.
• Respondents may lose interest or quit if the questionnaire is long.
• Respondents may not understand all questions, which would lead to inaccurate responses.

Response bias — a set of factors that lead participants answer a question incorrectly — can be deadly for data quality. Learn how it happens and how to avoid it.

An interview for quantitative research involves verbal communication between the participant and researcher, whose goal is to gather numerical data. The interview can be conducted face-to-face or over the phone, and it can be structured or unstructured.

In a structured interview, the researcher asks a fixed set of questions to every participant. The questions and their order are pre-decided by the researcher. The interview follows a formal pattern. Structured interviews are more cost efficient and can be less time consuming.

In an unstructured interview, the researcher thinks of his/her questions as the interview proceeds. This type of interview is conversational in nature and can last a few hours. This type of interview allows the researcher to be flexible and ask questions depending on the participant’s responses. This quantitative research method can provide more in-depth information, since it allows researchers to delve deeper into a participant’s response.

• Interviews can provide more in-depth information.
• Interviews are more flexible than questionnaires, since interviewers can adapt their questions to each participant or ask follow-up questions.
• Interviewers can clarify participants’ questions, which will help them get clearer, more accurate data.
• Interviewing one person at a time can be time-consuming.
• Travel, interviewer salaries and other expenses can make interviews an expensive data collection tool.
• With unstructured interviews, it can be difficult to quantify some responses.

One way to speed up interviews is to conduct them with multiple people at one time in a focus group discussion. Learn more about how to conduct a great FGD.

Observation

Observation is a systematic way to collect data by observing people in natural situations or settings. Though it is mostly used for collecting qualitative data, observation can also be used to collect quantitative data.

Observation can be simple or behavioral. Simple observations are usually numerical, like how many cars pass through a given intersection each hour or how many students are asleep during a class. Behavioral observation, on the other hand, observes and interprets people’s behavior, like how many cars are driving dangerously or how engaging a lecturer is.

Simple observation can be a good way to collect numerical data. This can be done by pre-defining clear numerical variables that can be collected during observation — for example, what time employees leave the office. This data can be collected by observing employees over a period of time and recording when each person leaves.

• Observation is often an inexpensive way to collect data.
• Since researchers are recording the data themselves (rather than participants reporting the data), most of the collected data will generally be usable.
• Data collection can be stopped and started by researchers at any time, making it a flexible data collection tool.
• Researchers need to be extensively trained to undertake observation and record data correctly.
• Sometimes the environment or research may bias the data, like when participants know they’re being observed.
• If the situation to be observed sometimes doesn’t happen, researchers may waste a lot of time during data collection.

Simple vs. behavioral is just one type of observation. Learn more about the 5 different types of observation and when you should use each to collect different types of data.

Since quantitative research depends on numerical data, records (also known as external data) can provide critical information to answer research questions. Records are numbers and statistics that institutions use to track activities, like attendance in a school or the number of patients admitted in a hospital.

For example, the Government of India conducts the Census every 10 years, which is a record of the country’s population. This data can be used by a researcher who is addressing a population-related research problem.

• Records often include comprehensive data captured over a long period of time.
• Data collection time is minimal, since the data has already been collected and recorded by someone else.
• Records often only provide numerical data, not the reason or cause behind the data.
• Cleaning badly structured or formatted records can take a long time.
• If a record is incomplete or inaccurate, there is often no way to fix it.

Summing it up

Quantitative research methods are one of the best tools to identify a problem or phenomenon, how widespread it is, and how it is changing over time. After identifying a problem, quantitative research can also be used to come up with a trustworthy solution, identified using numerical data collected through standardized techniques.

Image credits:  Curtis MacNewton ,  Brijesh Nirmal ,  Charles Deluvio , and Atlan.

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14 comments.

Very useful for research

Very easy to read and informative book. Well written. Thany thanks for the download.

It is concise and practical as well as easy to understand.

Nice book but I kind find a way to download it. Kindly let me know how to download it. Thanks

Hello Micah Nalianya Greetings! Kindly tell me how to download the book. Simeon

Hi Micah and Simeon! You can download our data collection ebook here: https://socialcops.com/ebooks/data-collection/

I have loved reviewing the brief write up. Good revision for me. Thanks

The text contains concise and important tips on data collection techniques.

Thanks for an explicit and precise outline of data collection methods.

thank you very much, this guide is really useful and easy to understand. Specially for students that just have started research.

Thank you so much for sharing me this very important material.

I am highly impressed with the simply ways you explain methods of collecting data. I am a Monitoring and Evaluation Specialist and I will like to be receiving your regular publications.

i have benefited from the work. well organized .thank you

interview is a qualitative method not quantitative.

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This is “Interviews: Qualitative and Quantitative Approaches”, chapter 9 from the book Sociological Inquiry Principles: Qualitative and Quantitative Methods (v. 1.0). For details on it (including licensing), click here .

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Normally, the author and publisher would be credited here. However, the publisher has asked for the customary Creative Commons attribution to the original publisher, authors, title, and book URI to be removed. Additionally, per the publisher's request, their name has been removed in some passages. More information is available on this project's attribution page .

For more information on the source of this book, or why it is available for free, please see the project's home page . You can browse or download additional books there. To download a .zip file containing this book to use offline, simply click here .

Chapter 9 Interviews: Qualitative and Quantitative Approaches

Why interview research.

Today’s young men are delaying their entry into adulthood. That’s a nice way of saying they are “totally confused”; “cannot commit to their relationships, work, or lives”; and are “obsessed with never wanting to grow up.” These quotes come from a summary of reviews on the website dedicated to Kimmel’s book, Guyland : http://www.guyland.net . But don’t take my word for it. Take sociologist Michael Kimmel’s word. He interviewed 400 young men, ages 16 to 26, over the course of 4 years across the United States to learn how they made the transition from adolescence into adulthood. Since the results of Kimmel’s research were published in 2008, Kimmel, M. (2008). Guyland: The perilous world where boys become men . New York, NY: Harper Collins. his book has made quite a splash. Featured in news reports, on blogs, and in many book reviews, some claim Kimmel’s research “could save the humanity of many young men,” This quote from Gloria Steinem is provided on the website dedicated to Kimmel’s book, Guyland : http://www.guyland.net . while others suggest that its conclusions can only be applied to “fraternity guys and jocks.” This quote comes from “Thomas,” who wrote a review of Kimmel’s book on the following site: http://yesmeansyesblog.wordpress.com/2010/03/12/review-guyland . Whatever your take on Kimmel’s research, one thing remains true: We surely would not know nearly as much as we now do about the lives of many young American men were it not for interview research.

Thanks to interview research, we know something about how young men today do (or do not) make the transition into adulthood.

© Thinkstock

9.1 Interview Research: What Is It and When Should It Be Used?

Learning objectives.

• Define interviews from the social scientific perspective.
• Identify when it is appropriate to employ interviews as a data-collection strategy.

Knowing how to create and conduct a good interview is one of those skills you just can’t go wrong having. Interviews are used by market researchers to learn how to sell their products, journalists use interviews to get information from a whole host of people from VIPs to random people on the street. Regis Philbin (a sociology major in college This information comes from the following list of famous sociology majors provided by the American Sociological Association on their website: http://www.asanet.org/students/famous.cfm . ) used interviews to help television viewers get to know guests on his show, employers use them to make decisions about job offers, and even Ruth Westheimer (the famous sex doctor who has an MA in sociology Read more about Dr. Ruth, her background, and her credentials at her website: http://www.drruth.com . ) used interviews to elicit details from call-in participants on her radio show. Interested in hearing Dr. Ruth’s interview style? There are a number of audio clips from her radio show, Sexually Speaking , linked from the following site: http://www.cs.cmu.edu/~chuck/ruthpg . Warning: some of the images and audio clips on this page may be offensive to some readers. It seems everyone who’s anyone knows how to conduct an interview.

Social scientists use interviews as do many others, from journalists to talk show hosts to businesses making hiring decisions.

From the social scientific perspective, interviews A method of data collection that involves two or more people exchanging information through a series of questions and answers. are a method of data collection that involves two or more people exchanging information through a series of questions and answers. The questions are designed by a researcher to elicit information from interview participant(s) on a specific topic or set of topics. Typically interviews involve an in-person meeting between two people, an interviewer and an interviewee. But as you’ll discover in this chapter, interviews need not be limited to two people, nor must they occur in person.

In social science, interviews are a method of data collection that involves two or more people exchanging information through a series of questions and answers.

The question of when to conduct an interview might be on your mind. Interviews are an excellent way to gather detailed information. They also have an advantage over surveys; with a survey, if a participant’s response sparks some follow-up question in your mind, you generally don’t have an opportunity to ask for more information. What you get is what you get. In an interview, however, because you are actually talking with your study participants in real time, you can ask that follow-up question. Thus interviews are a useful method to use when you want to know the story behind responses you might receive in a written survey.

Interviews are also useful when the topic you are studying is rather complex, when whatever you plan to ask requires lengthy explanation, or when your topic or answers to your questions may not be immediately clear to participants who may need some time or dialogue with others in order to work through their responses to your questions. Also, if your research topic is one about which people will likely have a lot to say or will want to provide some explanation or describe some process, interviews may be the best method for you. For example, I used interviews to gather data about how people reach the decision not to have children and how others in their lives have responded to that decision. To understand these “how’s” I needed to have some back-and-forth dialogue with respondents. When they begin to tell me their story, inevitably new questions that hadn’t occurred to me from prior interviews come up because each person’s story is unique. Also, because the process of choosing not to have children is complex for many people, describing that process by responding to closed-ended questions on a survey wouldn’t work particularly well.

In sum, interview research is especially useful when the following are true:

• You wish to gather very detailed information
• You anticipate wanting to ask respondents for more information about their responses
• You plan to ask questions that require lengthy explanation
• The topic you are studying is complex or may be confusing to respondents
• Your topic involves studying processes

Key Takeaways

• Understanding how to design and conduct interview research is a useful skill to have.
• In a social scientific interview, two or more people exchange information through a series of questions and answers.
• Interview research is often used when detailed information is required and when a researcher wishes to examine processes.
• Think about a topic about which you might wish to collect data by conducting interviews. What makes this topic suitable for interview research?

9.2 Qualitative Interview Techniques and Considerations

• Identify the primary aim of in-depth interviews.
• Describe what makes qualitative interview techniques unique.
• Define the term interview guide and describe how to construct an interview guide.
• Outline the guidelines for constructing good qualitative interview questions.
• Define the term focus group and identify one benefit of focus groups.
• Identify and describe the various stages of qualitative interview data analysis.
• Identify the strengths and weaknesses of qualitative interviews.

Qualitative interviews are sometimes called intensive or in-depth interviews A semistructured meeting between a researcher and respondent in which the researcher asks a series of open-ended questions; questions may be posed to respondents in slightly different ways or orders. . These interviews are semistructured; the researcher has a particular topic about which he or she would like to hear from the respondent, but questions are open ended and may not be asked in exactly the same way or in exactly the same order to each and every respondent. In in-depth interviews, the primary aim is to hear from respondents about what they think is important about the topic at hand and to hear it in their own words. In this section, we’ll take a look at how to conduct interviews that are specifically qualitative in nature, analyze qualitative interview data, and use some of the strengths and weaknesses of this method. In Section 9.4 "Issues to Consider for All Interview Types" , we return to several considerations that are relevant to both qualitative and quantitative interviewing.

Conducting Qualitative Interviews

Qualitative interviews might feel more like a conversation than an interview to respondents, but the researcher is in fact usually guiding the conversation with the goal in mind of gathering information from a respondent. A key difference between qualitative and quantitative interviewing is that qualitative interviews contain open-ended questions Questions for which a researcher does not provide answer options; questions that require respondents to answer in their own words. . The meaning of this term is of course implied by its name, but just so that we’re sure to be on the same page, I’ll tell you that open-ended questions are questions that a researcher poses but does not provide answer options for. Open-ended questions are more demanding of participants than closed-ended questions, for they require participants to come up with their own words, phrases, or sentences to respond.

In a qualitative interview, the researcher usually develops a guide in advance that he or she then refers to during the interview (or memorizes in advance of the interview). An interview guide A list of topics or questions that an interviewer hopes to cover during the course of an interview. is a list of topics or questions that the interviewer hopes to cover during the course of an interview. It is called a guide because it is simply that—it is used to guide the interviewer, but it is not set in stone. Think of an interview guide like your agenda for the day or your to-do list—both probably contain all the items you hope to check off or accomplish, though it probably won’t be the end of the world if you don’t accomplish everything on the list or if you don’t accomplish it in the exact order that you have it written down. Perhaps new events will come up that cause you to rearrange your schedule just a bit, or perhaps you simply won’t get to everything on the list.

You might think of an interview guide as you would your to-do list. Perhaps it contains the list of things you hope to accomplish, in the order you hope to accomplish them, but there may be new events or new information that cause you to alter your list or to change its order just a bit.

Interview guides should outline issues that a researcher feels are likely to be important, but because participants are asked to provide answers in their own words, and to raise points that they believe are important, each interview is likely to flow a little differently. While the opening question in an in-depth interview may be the same across all interviews, from that point on what the participant says will shape how the interview proceeds. This, I believe, is what makes in-depth interviewing so exciting. It is also what makes in-depth interviewing rather challenging to conduct. It takes a skilled interviewer to be able to ask questions; actually listen to respondents; and pick up on cues about when to follow up, when to move on, and when to simply let the participant speak without guidance or interruption.

I’ve said that interview guides can list topics or questions. The specific format of an interview guide might depend on your style, experience, and comfort level as an interviewer or with your topic. I have conducted interviews using different kinds of guides. In my interviews of young people about their experiences with workplace sexual harassment, the guide I used was topic based. There were few specific questions contained in the guide. Instead, I had an outline of topics that I hoped to cover, listed in an order that I thought it might make sense to cover them, noted on a sheet of paper. That guide can be seen in Figure 9.4 "Interview Guide Displaying Topics Rather Than Questions" .

Figure 9.4 Interview Guide Displaying Topics Rather Than Questions

In my interviews with child-free adults, the interview guide contained questions rather than brief topics. One reason I took this approach is that this was a topic with which I had less familiarity than workplace sexual harassment. I’d been studying harassment for some time before I began those interviews, and I had already analyzed much quantitative survey data on the topic. When I began the child-free interviews, I was embarking on a research topic that was entirely new for me. I was also studying a topic about which I have strong personal feelings, and I wanted to be sure that I phrased my questions in a way that didn’t appear biased to respondents. To help ward off that possibility, I wrote down specific question wording in my interview guide. As I conducted more and more interviews, and read more and more of the literature on child-free adults, I became more confident about my ability to ask open-ended, nonbiased questions about the topic without the guide, but having some specific questions written down at the start of the data collection process certainly helped. The interview guide I used for the child-free project is displayed in Figure 9.5 "Interview Guide Displaying Questions Rather Than Topics" .

Figure 9.5 Interview Guide Displaying Questions Rather Than Topics

As you might have guessed, interview guides do not appear out of thin air. They are the result of thoughtful and careful work on the part of a researcher. As you can see in both of the preceding guides, the topics and questions have been organized thematically and in the order in which they are likely to proceed (though keep in mind that the flow of a qualitative interview is in part determined by what a respondent has to say). Sometimes qualitative interviewers may create two versions of the interview guide: one version contains a very brief outline of the interview, perhaps with just topic headings, and another version contains detailed questions underneath each topic heading. In this case, the researcher might use the very detailed guide to prepare and practice in advance of actually conducting interviews and then just bring the brief outline to the interview. Bringing an outline, as opposed to a very long list of detailed questions, to an interview encourages the researcher to actually listen to what a participant is telling her. An overly detailed interview guide will be difficult to navigate through during an interview and could give respondents the misimpression that the interviewer is more interested in her questions than in the participant’s answers.

When beginning to construct an interview guide, brainstorming is usually the first step. There are no rules at the brainstorming stage—simply list all the topics and questions that come to mind when you think about your research question. Once you’ve got a pretty good list, you can begin to pare it down by cutting questions and topics that seem redundant and group like questions and topics together. If you haven’t done so yet, you may also want to come up with question and topic headings for your grouped categories. You should also consult the scholarly literature to find out what kinds of questions other interviewers have asked in studies of similar topics. As with quantitative survey research, it is best not to place very sensitive or potentially controversial questions at the very beginning of your qualitative interview guide. You need to give participants the opportunity to warm up to the interview and to feel comfortable talking with you. Finally, get some feedback on your interview guide. Ask your friends, family members, and your professors for some guidance and suggestions once you’ve come up with what you think is a pretty strong guide. Chances are they’ll catch a few things you hadn’t noticed.

In terms of the specific questions you include on your guide, there are a few guidelines worth noting. First, try to avoid questions that can be answered with a simple yes or no, or if you do choose to include such questions, be sure to include follow-up questions. Remember, one of the benefits of qualitative interviews is that you can ask participants for more information—be sure to do so. While it is a good idea to ask follow-up questions, try to avoid asking “why” as your follow-up question, as this particular question can come off as confrontational, even if that is not how you intend it. Often people won’t know how to respond to “why,” perhaps because they don’t even know why themselves. Instead of “why,” I recommend that you say something like, “Could you tell me a little more about that?” This allows participants to explain themselves further without feeling that they’re being doubted or questioned in a hostile way.

Also, try to avoid phrasing your questions in a leading way. For example, rather than asking, “Don’t you think that most people who don’t want kids are selfish?” you could ask, “What comes to mind for you when you hear that someone doesn’t want kids?” Or rather than asking, “What do you think about juvenile delinquents who drink and drive?” you could ask, “How do you feel about underage drinking?” or “What do you think about drinking and driving?” Finally, as noted earlier in this section, remember to keep most, if not all, of your questions open ended. The key to a successful qualitative interview is giving participants the opportunity to share information in their own words and in their own way.

Even after the interview guide is constructed, the interviewer is not yet ready to begin conducting interviews. The researcher next has to decide how to collect and maintain the information that is provided by participants. It is probably most common for qualitative interviewers to take audio recordings of the interviews they conduct.

Recording interviews allows the researcher to focus on her or his interaction with the interview participant rather than being distracted by trying to take notes. Of course, not all participants will feel comfortable being recorded and sometimes even the interviewer may feel that the subject is so sensitive that recording would be inappropriate. If this is the case, it is up to the researcher to balance excellent note-taking with exceptional question asking and even better listening. I don’t think I can understate the difficulty of managing all these feats simultaneously. Whether you will be recording your interviews or not (and especially if not), practicing the interview in advance is crucial. Ideally, you’ll find a friend or two willing to participate in a couple of trial runs with you. Even better, you’ll find a friend or two who are similar in at least some ways to your sample. They can give you the best feedback on your questions and your interview demeanor.

Ideally, you will take an audio recording of your interviews so that you can pay attention to your participants during the interview and so that you have a verbatim record of the interview.

All interviewers should be aware of, give some thought to, and plan for several additional factors, such as where to conduct an interview and how to make participants as comfortable as possible during an interview. Because these factors should be considered by both qualitative and quantitative interviewers, we will return to them in Section 9.4 "Issues to Consider for All Interview Types" after we’ve had a chance to look at some of the unique features of each approach to interviewing.

Although our focus here has been on interviews for which there is one interviewer and one respondent, this is certainly not the only way to conduct a qualitative interview. Sometimes there may be multiple respondents present, and occasionally more than one interviewer may be present as well. When multiple respondents participate in an interview at the same time, this is referred to as a focus group Multiple respondents participate in an interview at the same time. . Focus groups can be an excellent way to gather information because topics or questions that hadn’t occurred to the researcher may be brought up by other participants in the group. Having respondents talk with and ask questions of one another can be an excellent way of learning about a topic; not only might respondents ask questions that hadn’t occurred to the researcher, but the researcher can also learn from respondents’ body language around and interactions with one another. Of course, there are some unique ethical concerns associated with collecting data in a group setting. We’ll take a closer look at how focus groups work and describe some potential ethical concerns associated with them in Chapter 12 "Other Methods of Data Collection and Analysis" .

Analysis of Qualitative Interview Data

Analysis of qualitative interview data typically begins with a set of transcripts of the interviews conducted. Obtaining said transcripts requires having either taken exceptionally good notes during an interview or, preferably, recorded the interview and then transcribed it. Transcribing interviews is usually the first step toward analyzing qualitative interview data. To transcribe Creating a complete, written copy of a recorded interview by playing the recording back and typing in each word that is spoken on the recording, noting who spoke which words. an interview means that you create, or someone whom you’ve hired creates, a complete, written copy of the recorded interview by playing the recording back and typing in each word that is spoken on the recording, noting who spoke which words. In general, it is best to aim for a verbatim transcription, one that reports word for word exactly what was said in the recorded interview. If possible, it is also best to include nonverbals in an interview’s written transcription. Gestures made by respondents should be noted, as should the tone of voice and notes about when, where, and how spoken words may have been emphasized by respondents.

If you have the time (or if you lack the resources to hire others), I think it is best to transcribe your interviews yourself. I never cease to be amazed by the things I recall from an interview when I transcribe it myself. If the researcher who conducted the interview transcribes it himself or herself, that person will also be able to make a note of nonverbal behaviors and interactions that may be relevant to analysis but that could not be picked up by audio recording. I’ve seen interviewees roll their eyes, wipe tears from their face, and even make obscene gestures that spoke volumes about their feelings but that could not have been recorded had I not remembered to include these details in their transcribed interviews.

Sometimes a respondents’ body language during an interview provides as much information as her spoken words. Qualitative researchers often note respondents’ gestures and other nonverbal cues during an interview.

The goal of analysis The process of arriving at some inferences, lessons, or conclusions by condensing large amounts of data into relatively smaller bits of understandable information. is to reach some inferences, lessons, or conclusions by condensing large amounts of data into relatively smaller, more manageable bits of understandable information. Analysis of qualitative interview data often works inductively (Glaser & Strauss, 1967; Charmaz, 2006). For an additional reminder about what an inductive approach to analysis means, see Chapter 2 "Linking Methods With Theory" . If you would like to learn more about inductive qualitative data analysis, I recommend two titles: Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory: Strategies for qualitative research . Chicago, IL: Aldine; Charmaz, K. (2006). Constructing grounded theory: A practical guide through qualitative analysis . Thousand Oaks, CA: Sage. To move from the specific observations an interviewer collects to identifying patterns across those observations, qualitative interviewers will often begin by reading through transcripts of their interviews and trying to identify codes. A code A shorthand representation of some more complex set of issues or ideas. is a shorthand representation of some more complex set of issues or ideas. In this usage, the word code is a noun. But it can also be a verb. The process of identifying codes in one’s qualitative data is often referred to as coding . Coding involves identifying themes across interview data by reading and rereading (and rereading again) interview transcripts until the researcher has a clear idea about what sorts of themes come up across the interviews.

Qualitative researcher and textbook author Kristin Esterberg (2002) Esterberg, K. G. (2002). Qualitative methods in social research . Boston, MA: McGraw-Hill. describes coding as a multistage process. Esterberg suggests that there are two types of coding: open coding and focused coding. To analyze qualitative interview data, one can begin by open coding The first stage of developing codes in qualitative data; involves reading data with an open mind and jotting down themes or categories that various bits of data seem to suggest. transcripts. This means that you read through each transcript, line by line, and make a note of whatever categories or themes seem to jump out to you. At this stage, it is important that you not let your original research question or expectations about what you think you might find cloud your ability to see categories or themes. It’s called open coding for a reason—keep an open mind. Open coding will probably require multiple go-rounds. As you read through your transcripts, it is likely that you’ll begin to see some commonalities across the categories or themes that you’ve jotted down. Once you do, you might begin focused coding.

Focused coding A later stage of developing codes in qualitative data; occurs after open coding and involves collapsing or narrowing themes and categories identified in open coding, succinctly naming them, describing them, and identifying passages of data that represent them. involves collapsing or narrowing themes and categories identified in open coding by reading through the notes you made while conducting open coding. Identify themes or categories that seem to be related, perhaps merging some. Then give each collapsed/merged theme or category a name (or code), and identify passages of data that fit each named category or theme. To identify passages of data that represent your emerging codes, you’ll need to read through your transcripts yet again (and probably again). You might also write up brief definitions or descriptions of each code. Defining codes is a way of making meaning of your data and of developing a way to talk about your findings and what your data mean. Guess what? You are officially analyzing data!

As tedious and laborious as it might seem to read through hundreds of pages of transcripts multiple times, sometimes getting started with the coding process is actually the hardest part. If you find yourself struggling to identify themes at the open coding stage, ask yourself some questions about your data. The answers should give you a clue about what sorts of themes or categories you are reading. In their text on analyzing qualitative data, Lofland and Lofland (1995) Lofland, J., & Lofland, L. H. (1995). Analyzing social settings: A guide to qualitative observation and analysis (3rd ed.) Belmont, CA: Wadsworth. identify a set of questions that I find very useful when coding qualitative data. They suggest asking the following:

• Of what topic, unit, or aspect is this an instance?
• What question about a topic does this item of data suggest?
• What sort of answer to a question about a topic does this item of data suggest (i.e., what proposition is suggested)?

Asking yourself these questions about the passages of data that you’re reading can help you begin to identify and name potential themes and categories.

Still feeling uncertain about how this process works? Sometimes it helps to see how interview passages translate into codes. In Table 9.1 "Interview Coding Example" , I present two codes that emerged from the inductive analysis of transcripts from my interviews with child-free adults. I also include a brief description of each code and a few (of many) interview excerpts from which each code was developed.

Table 9.1 Interview Coding Example

As you might imagine, wading through all these data is quite a process. Just as quantitative researchers rely on the assistance of special computer programs designed to help with sorting through and analyzing their data, so, too, do qualitative researchers. Where quantitative researchers have SPSS and MicroCase (and many others), qualitative researchers have programs such as NVivo ( http://www.qsrinternational.com ) and Atlasti ( http://www.atlasti.com ). These are programs specifically designed to assist qualitative researchers with organizing, managing, sorting, and analyzing large amounts of qualitative data. The programs work by allowing researchers to import interview transcripts contained in an electronic file and then label or code passages, cut and paste passages, search for various words or phrases, and organize complex interrelationships among passages and codes.

In sum, the following excerpt, from a paper analyzing the workplace sexual harassment interview data I have mentioned previously, summarizes how the process of analyzing qualitative interview data often works:

All interviews were tape recorded and then transcribed and imported into the computer program NVivo. NVivo is designed to assist researchers with organizing, managing, interpreting, and analyzing non-numerical, qualitative data. Once the transcripts, ranging from 20 to 60 pages each, were imported into NVivo, we first coded the data according to the themes outlined in our interview guide. We then closely reviewed each transcript again, looking for common themes across interviews and coding like categories of data together. These passages, referred to as codes or “meaning units” (Weiss, 2004), Weiss, R. S. (2004). In their own words: Making the most of qualitative interviews. Contexts, 3 , 44–51. were then labeled and given a name intended to succinctly portray the themes present in the code. For this paper, we coded every quote that had something to do with the labeling of harassment. After reviewing passages within the “labeling” code, we placed quotes that seemed related together, creating several sub-codes. These sub-codes were named and are represented by the three subtitles within the findings section of this paper. Our three subcodes were the following: (a) “It’s different because you’re in high school”: Sociability and socialization at work; (b) Looking back: “It was sexual harassment; I just didn’t know it at the time”; and (c) Looking ahead: New images of self as worker and of workplace interactions. Once our sub-codes were labeled, we re-examined the interview transcripts, coding additional quotes that fit the theme of each sub-code. (Blackstone, Houle, & Uggen, 2006) Blackstone, A., Houle, J., & Uggen, C. “At the time, I thought it was great”: Age, experience, and workers’ perceptions of sexual harassment. Presented at the Annual Meeting of the American Sociological Association, Montreal, QC, August 2006. Currently under review.

Strengths and Weaknesses of Qualitative Interviews

As the preceding sections have suggested, qualitative interviews are an excellent way to gather detailed information. Whatever topic is of interest to the researcher employing this method can be explored in much more depth than with almost any other method. Not only are participants given the opportunity to elaborate in a way that is not possible with other methods such as survey research, but they also are able share information with researchers in their own words and from their own perspectives rather than being asked to fit those perspectives into the perhaps limited response options provided by the researcher. And because qualitative interviews are designed to elicit detailed information, they are especially useful when a researcher’s aim is to study social processes, or the “how” of various phenomena. Yet another, and sometimes overlooked, benefit of qualitative interviews that occurs in person is that researchers can make observations beyond those that a respondent is orally reporting. A respondent’s body language, and even her or his choice of time and location for the interview, might provide a researcher with useful data.

Of course, all these benefits do not come without some drawbacks. As with quantitative survey research, qualitative interviews rely on respondents’ ability to accurately and honestly recall whatever details about their lives, circumstances, thoughts, opinions, or behaviors are being asked about. As Esterberg (2002) puts it, “If you want to know about what people actually do, rather than what they say they do, you should probably use observation [instead of interviews].” Esterberg, K. G. (2002). Qualitative methods in social research . Boston, MA: McGraw-Hill. Further, as you may have already guessed, qualitative interviewing is time intensive and can be quite expensive. Creating an interview guide, identifying a sample, and conducting interviews are just the beginning. Transcribing interviews is labor intensive—and that’s before coding even begins. It is also not uncommon to offer respondents some monetary incentive or thank-you for participating. Keep in mind that you are asking for more of participants’ time than if you’d simply mailed them a questionnaire containing closed-ended questions. Conducting qualitative interviews is not only labor intensive but also emotionally taxing. When I interviewed young workers about their sexual harassment experiences, I heard stories that were shocking, infuriating, and sad. Seeing and hearing the impact that harassment had had on respondents was difficult. Researchers embarking on a qualitative interview project should keep in mind their own abilities to hear stories that may be difficult to hear.

• In-depth interviews are semistructured interviews where the researcher has topics and questions in mind to ask, but questions are open ended and flow according to how the participant responds to each.
• Interview guides can vary in format but should contain some outline of the topics you hope to cover during the course of an interview.
• NVivo and Atlas.ti are computer programs that qualitative researchers use to help them with organizing, sorting, and analyzing their data.
• Qualitative interviews allow respondents to share information in their own words and are useful for gathering detailed information and understanding social processes.
• Drawbacks of qualitative interviews include reliance on respondents’ accuracy and their intensity in terms of time, expense, and possible emotional strain.
• Based on a research question you have identified through earlier exercises in this text, write a few open-ended questions you could ask were you to conduct in-depth interviews on the topic. Now critique your questions. Are any of them yes/no questions? Are any of them leading?
• Read the open-ended questions you just created, and answer them as though you were an interview participant. Were your questions easy to answer or fairly difficult? How did you feel talking about the topics you asked yourself to discuss? How might respondents feel talking about them?

9.3 Quantitative Interview Techniques and Considerations

• Define and describe standardized interviews.
• Describe how quantitative interviews differ from qualitative interviews.
• Describe the process and some of the drawbacks of telephone interviewing techniques.
• Describe how the analysis of quantitative interview works.
• Identify the strengths and weaknesses of quantitative interviews.

Quantitative interviews are similar to qualitative interviews in that they involve some researcher/respondent interaction. But the process of conducting and analyzing findings from quantitative interviews also differs in several ways from that of qualitative interviews. Each approach also comes with its own unique set of strengths and weaknesses. We’ll explore those differences here.

Conducting Quantitative Interviews

Much of what we learned in the previous chapter on survey research applies to quantitative interviews as well. In fact, quantitative interviews are sometimes referred to as survey interviews because they resemble survey-style question-and-answer formats. They might also be called standardized interviews Interviews during which the same questions are asked of every participant in the same way, and survey-style question-and-answer formats are utilized. . The difference between surveys and standardized interviews is that questions and answer options are read to respondents rather than having respondents complete a questionnaire on their own. As with questionnaires, the questions posed in a standardized interview tend to be closed ended. See Chapter 8 "Survey Research: A Quantitative Technique" for the definition of closed ended. There are instances in which a quantitative interviewer might pose a few open-ended questions as well. In these cases, the coding process works somewhat differently than coding in-depth interview data. We’ll describe this process in the following subsection.

In quantitative interviews, an interview schedule A document containing the list of questions and answer options that quantitative interviewers read to respondents. is used to guide the researcher as he or she poses questions and answer options to respondents. An interview schedule is usually more rigid than an interview guide. It contains the list of questions and answer options that the researcher will read to respondents. Whereas qualitative researchers emphasize respondents’ roles in helping to determine how an interview progresses, in a quantitative interview, consistency in the way that questions and answer options are presented is very important. The aim is to pose every question-and-answer option in the very same way to every respondent. This is done to minimize interviewer effect Occurs when an interviewee is influenced by how or when questions and answer options are presented by an interviewer. , or possible changes in the way an interviewee responds based on how or when questions and answer options are presented by the interviewer.

Quantitative interviews may be recorded, but because questions tend to be closed ended, taking notes during the interview is less disruptive than it can be during a qualitative interview. If a quantitative interview contains open-ended questions, however, recording the interview is advised. It may also be helpful to record quantitative interviews if a researcher wishes to assess possible interview effect. Noticeable differences in responses might be more attributable to interviewer effect than to any real respondent differences. Having a recording of the interview can help a researcher make such determinations.

Quantitative interviewers are usually more concerned with gathering data from a large, representative sample. As you might imagine, collecting data from many people via interviews can be quite laborious. Technological advances in telephone interviewing procedures can assist quantitative interviewers in this process. One concern about telephone interviewing is that fewer and fewer people list their telephone numbers these days, but random digit dialing (RDD) takes care of this problem. RDD programs dial randomly generated phone numbers for researchers conducting phone interviews. This means that unlisted numbers are as likely to be included in a sample as listed numbers (though, having used this software for quantitative interviewing myself, I will add that folks with unlisted numbers are not always very pleased to receive calls from unknown researchers). Computer-assisted telephone interviewing (CATI) programs have also been developed to assist quantitative survey researchers. These programs allow an interviewer to enter responses directly into a computer as they are provided, thus saving hours of time that would otherwise have to be spent entering data into an analysis program by hand.

Conducting quantitative interviews over the phone does not come without some drawbacks. Aside from the obvious problem that not everyone has a phone, research shows that phone interviews generate more fence-sitters than in-person interviews (Holbrook, Green, & Krosnick, 2003). Holbrook, A. L., Green, M. C., & Krosnick, J. A. (2003). Telephone versus face-to-face interviewing of national probability samples with long questionnaires: Comparisons of respondent satisficing and social desirability response bias. Public Opinion Quarterly, 67 , 79–125. Responses to sensitive questions or those that respondents view as invasive are also generally less accurate when data are collected over the phone as compared to when they are collected in person. I can vouch for this latter point from personal experience. While conducting quantitative telephone interviews when I worked at a research firm, it was not terribly uncommon for respondents to tell me that they were green or purple when I asked them to report their racial identity.

Analysis of Quantitative Interview Data

As with the analysis of survey data, analysis of quantitative interview data usually involves coding response options numerically, entering numeric responses into a data analysis computer program, and then running various statistical commands to identify patterns across responses. Section 8.5 "Analysis of Survey Data" of Chapter 8 "Survey Research: A Quantitative Technique" describes the coding process for quantitative data. But what happens when quantitative interviews ask open-ended questions? In this case, responses are typically numerically coded, just as closed-ended questions are, but the process is a little more complex than simply giving a “no” a label of 0 and a “yes” a label of 1.

In some cases, quantitatively coding open-ended interview questions may work inductively, as described in Section 9.2.2 "Analysis of Qualitative Interview Data" . If this is the case, rather than ending with codes, descriptions of codes, and interview excerpts, the researcher will assign a numerical value to codes and may not utilize verbatim excerpts from interviews in later reports of results. Keep in mind, as described in Chapter 1 "Introduction" , that with quantitative methods the aim is to be able to represent and condense data into numbers. The quantitative coding of open-ended interview questions is often a deductive process. The researcher may begin with an idea about likely responses to his or her open-ended questions and assign a numerical value to each likely response. Then the researcher will review participants’ open-ended responses and assign the numerical value that most closely matches the value of his or her expected response.

Strengths and Weaknesses of Quantitative Interviews

Quantitative interviews offer several benefits. The strengths and weakness of quantitative interviews tend to be couched in comparison to those of administering hard copy questionnaires. For example, response rates tend to be higher with interviews than with mailed questionnaires (Babbie, 2010). Babbie, E. (2010). The practice of social research (12th ed.). Belmont, CA: Wadsworth. That makes sense—don’t you find it easier to say no to a piece of paper than to a person? Quantitative interviews can also help reduce respondent confusion. If a respondent is unsure about the meaning of a question or answer option on a questionnaire, he or she probably won’t have the opportunity to get clarification from the researcher. An interview, on the other hand, gives the researcher an opportunity to clarify or explain any items that may be confusing.

As with every method of data collection we’ve discussed, there are also drawbacks to conducting quantitative interviews. Perhaps the largest, and of most concern to quantitative researchers, is interviewer effect. While questions on hard copy questionnaires may create an impression based on the way they are presented, having a person administer questions introduces a slew of additional variables that might influence a respondent. As I’ve said, consistency is key with quantitative data collection—and human beings are not necessarily known for their consistency. Interviewing respondents is also much more time consuming and expensive than mailing questionnaires. Thus quantitative researchers may opt for written questionnaires over interviews on the grounds that they will be able to reach a large sample at a much lower cost than were they to interact personally with each and every respondent.

• Unlike qualitative interviews, quantitative interviews usually contain closed-ended questions that are delivered in the same format and same order to every respondent.
• Quantitative interview data are analyzed by assigning a numerical value to participants’ responses.
• While quantitative interviews offer several advantages over self-administered questionnaires such as higher response rates and lower respondent confusion, they have the drawbacks of possible interviewer effect and greater time and expense.
• The General Social Survey (GSS), which we’ve mentioned in previous chapters, is administered via in-person interview, just like quantitative interviewing procedures described here. Read more about the GSS at http://www.norc.uchicago.edu/GSS+Website .
• Take a few of the open-ended questions you created after reading Section 9.2 "Qualitative Interview Techniques and Considerations" on qualitative interviewing techniques. See if you can turn them into closed-ended questions.

9.4 Issues to Consider for All Interview Types

• Identify the main issues that both qualitative and quantitative interviewers should consider.
• Describe the options that interviewers have for balancing power between themselves and interview participants.
• Describe and define rapport.
• Define the term probe and describe how probing differs in qualitative and quantitative interviewing.

While quantitative interviews resemble survey research in their question/answer formats, they share with qualitative interviews the characteristic that the researcher actually interacts with her or his subjects. The fact that the researcher interacts with his or her subjects creates a few complexities that deserve attention. We’ll examine those here.

First and foremost, interviewers must be aware of and attentive to the power differential between themselves and interview participants. The interviewer sets the agenda and leads the conversation. While qualitative interviewers aim to allow participants to have some control over which or to what extent various topics are discussed, at the end of the day it is the researcher who is in charge (at least that is how most respondents will perceive it to be). As the researcher, you are asking someone to reveal things about themselves they may not typically share with others. Also, you are generally not reciprocating by revealing much or anything about yourself. All these factors shape the power dynamics of an interview.

A number of excellent pieces have been written dealing with issues of power in research and data collection. Feminist researchers in particular paved the way in helping researchers think about and address issues of power in their work (Oakley, 1981). Oakley, A. (1981). Interviewing women: A contradiction in terms. In H. Roberts (Ed.), Doing feminist research (pp. 30–61). London, UK: Routledge & Kegan Paul. Suggestions for overcoming the power imbalance between researcher and respondent include having the researcher reveal some aspects of her own identity and story so that the interview is a more reciprocal experience rather than one-sided, allowing participants to view and edit interview transcripts before the researcher uses them for analysis, and giving participants an opportunity to read and comment on analysis before the researcher shares it with others through publication or presentation (Reinharz, 1992; Hesse-Biber, Nagy, & Leavy, 2007). Reinharz, S. (1992). Feminist methods in social research . New York, NY: Oxford University Press; Hesse-Biber, S. N., & Leavy, P. L. (Eds.). (2007). Feminist research practice: A primer . Thousand Oaks, CA: Sage. On the other hand, some researchers note that sharing too much with interview participants can give the false impression that there is no power differential, when in reality researchers retain the ability to analyze and present participants’ stories in whatever way they see fit (Stacey, 1988). Stacey, J. (1988). Can there be a feminist ethnography? Women’s Studies International Forum, 11 , 21–27.

However you feel about sharing details about your background with an interview participant, another way to balance the power differential between yourself and your interview participants is to make the intent of your research very clear to the subjects. Share with them your rationale for conducting the research and the research question(s) that frame your work. Be sure that you also share with subjects how the data you gather will be used and stored. Also, be sure that participants understand how their privacy will be protected including who will have access to the data you gather from them and what procedures, such as using pseudonyms, you will take to protect their identities. Many of these details will be covered by your institutional review board’s informed consent procedures and requirements, but even if they are not, as researchers we should be attentive to how sharing information with participants can help balance the power differences between ourselves and those who participate in our research.

Feminist researchers offer several strategies for balancing the power between interviewers and their research participants.

There are no easy answers when it comes to handling the power differential between the researcher and researched, and even social scientists do not agree on the best approach for doing so. It is nevertheless an issue to be attentive to when conducting any form of research, particularly those that involve interpersonal interactions and relationships with research participants.

Location, Location, Location

One way to balance the power between researcher and respondent is to conduct the interview in a location of the participants’ choosing, where he or she will feel most comfortable answering your questions. Interviews can take place in any number of locations—in respondents’ homes or offices, researchers’ homes or offices, coffee shops, restaurants, public parks, or hotel lobbies, to name just a few possibilities. I have conducted interviews in all these locations, and each comes with its own set of benefits and its own challenges. While I would argue that allowing the respondent to choose the location that is most convenient and most comfortable for her or him is of utmost importance, identifying a location where there will be few distractions is also important. For example, some coffee shops and restaurants are so loud that recording the interview can be a challenge. Other locations may present different sorts of distractions. For example, I have conducted several interviews with parents who, out of necessity, spent more time attending to their children during an interview than responding to my questions (of course, depending on the topic of your research, the opportunity to observe such interactions could be invaluable). As an interviewer, you may want to suggest a few possible locations, and note the goal of avoiding distractions, when you ask your respondents to choose a location.

Of course, the extent to which a respondent should be given complete control over choosing a location must also be balanced by accessibility of the location to you, the interviewer, and by your safety and comfort level with the location. I once agreed to conduct an interview in a respondent’s home only to discover on arriving that the living room where we conducted the interview was decorated wall to wall with posters representing various white power organizations displaying a variety of violently racist messages. Though the topic of the interview had nothing to do with the topic of the respondent’s home décor, the discomfort, anger, and fear I felt during the entire interview consumed me and certainly distracted from my ability to carry on the interview. In retrospect, I wish I had thought to come up with some excuse for needing to reschedule the interview and then arranged for it to happen in a more neutral location. While it is important to conduct interviews in a location that is comfortable for respondents, doing so should never come at the expense of your safety.

Researcher-Respondent Relationship

Finally, a unique feature of interviews is that they require some social interaction, which means that to at least some extent, a relationship is formed between interviewer and interviewee. While there may be some differences in how the researcher-respondent relationship works depending on whether your interviews are qualitative or quantitative, one essential relationship element is the same: R-E-S-P-E-C-T. You should know by now that I can’t help myself. If you, too, now have Aretha Franklin on the brain, feel free to excuse yourself for a moment to enjoy a song and dance: http://www.youtube.com/watch?v=z0XAI-PFQcA . A good rapport between you and the person you interview is crucial to successful interviewing. Rapport The sense of connection a researcher establishes with a participant. is the sense of connection you establish with a participant. Some argue that this term is too clinical, and perhaps it implies that a researcher tricks a participant into thinking they are closer than they really are (Esterberg, 2002). Esterberg, K. G. (2002). Qualitative methods in social research . Boston, MA: McGraw-Hill. While it is unfortunately true that some researchers might adopt this misguided approach to rapport, that is not the sense in which I use the term here nor is that the sort of rapport I advocate researchers attempt to establish with their subjects. Instead, as already mentioned, it is respect that is key.

There are no big secrets or tricks for how to show respect for research participants. At its core, the interview interaction should not differ from any other social interaction in which you show gratitude for a person’s time and respect for a person’s humanity. It is crucial that you, as the interviewer, conduct the interview in a way that is culturally sensitive. In some cases, this might mean educating yourself about your study population and even receiving some training to help you learn to effectively communicate with your research participants. Do not judge your research participants; you are there to listen to them, and they have been kind enough to give you their time and attention. Even if you disagree strongly with what a participant shares in an interview, your job as the researcher is to gather the information being shared with you, not to make personal judgments about it. In case you still feel uncertain about how to establish rapport and show your participants respect, I will leave you with a few additional bits of advice.

Developing good rapport requires good listening. In fact, listening during an interview is an active, not a passive, practice. Active listening Occurs when an interviewer demonstrates that he or she understands what an interview participant has said; requires probes or follow-up questions that indicate such understanding. means that you, the researcher, participate with the respondent by showing that you understand and follow whatever it is that he or she is telling you (Devault, 1990). For more on the practice of listening, especially in qualitative interviews, see Devault, M. (1990). Talking and listening from women’s standpoint: Feminist strategies for interviewing and analysis. Social Problems, 37 , 96–116. The questions you ask respondents should indicate that you’ve actually heard what they’ve just said. Active listening probably means that you will probe the respondent for more information from time to time throughout the interview. A probe A request, on the part of an interviewer, for more information from an interview participant. is a request for more information. Both qualitative and quantitative interviewers probe respondents, though the way they probe usually differs. In quantitative interviews, probing should be uniform. Often quantitative interviewers will predetermine what sorts of probes they will use. As an employee at the research firm I’ve mentioned before, our supervisors used to randomly listen in on quantitative telephone interviews we conducted. We were explicitly instructed not to use probes that might make us appear to agree or disagree with what respondents said. So “yes” or “I agree” or a questioning “hmmmm” were discouraged. Instead, we could respond with “thank you” to indicate that we’d heard a respondent. We could use “yes” or “no” if, and only if, a respondent had specifically asked us if we’d heard or understood what they had just said.

In some ways qualitative interviews better lend themselves to following up with respondents and asking them to explain, describe, or otherwise provide more information. This is because qualitative interviewing techniques are designed to go with the flow and take whatever direction the respondent goes during the interview. Nevertheless, it is worth your time to come up with helpful probes in advance of an interview even in the case of a qualitative interview. You certainly do not want to find yourself stumped or speechless after a respondent has just said something about which you’d like to hear more. This is another reason that practicing your interview in advance with people who are similar to those in your sample is a good idea.

• While there are several key differences between qualitative and quantitative interviewing techniques, all interviewers using either technique should take into consideration the power differential between themselves and respondents, should take care in identifying a location for an interview, and should take into account the fact that an interview is, to at least some extent, a form of relationship.
• Feminist researchers paved the way for helping interviewers think about how to balance the power differential between themselves and interview participants.
• Interviewers must always be respectful of interview participants.
• Imagine that you will be conducting interviews. What are some possible locations in your area you think might be good places to conduct interviews? What makes those locations good?
• What do you think about the suggestions for balancing power between interviewers and interviewees? How much of your own story do you think you’d be likely to share with interview participants? Why? What are the possible consequences (positive and negative) of revealing information about yourself when you’re the researcher?

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Quantitative and qualitative interviewing, overview of quantitative research methods, questionnaire/survey methods, quantitative analysis, suggested books for further research.

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Common Quantitative Terms

• ANOVA (Analysis of Variance)
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• Random Control Trial
• Correlational Design
• Descriptive Statistics
• Partial Correlaton
• Linear Regression
• Repeated Measures ANOVA
• Effect Size
• Overview Video Broad-based overview of the quantitative process, not limited to interviews
• Using Structured Interview Techniques In-depth document covering every aspect of the Quantitative Interview

Survey - data collection tool for gather information from a group of individuals. Data can be factual or opinions. Administration options includes structured interview or participant/respondent completing survey on their own.  

• Questionnaire (face-to-face or telephone) - structured questions for individuals to obtain information
• Market survey
• Explanation of Questionnaires/surveys
• Example of the results of a Questionnaire/survey 
• Using EXCEL How do you analyze your quantitative data? You might try using Microsoft EXCEL.

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Quantitative Research Interview questions: Why they're important, and what to ask

In this blog post, we're sharing potential interview questions for assessing quantitative research candidates. These questions are designed to reveal analytical abilities, problem-solving skills, and suitability for roles where data analysis takes center stage.

Interview question 1: What statistical software or programming languages are you proficient in, and can you provide examples of projects where you've used them?

 Why this question is important: This question evaluates a candidate’s fit with the role's requirements. It also assesses adaptability, verifies resume claims (this is huge!), and gauges their ability to communicate technical concepts effectively. Look for answers that can explain multiple languages in depth, and can give realistic answers of how they’re applied.

Example answer: " I am experienced in Python, R, and MATLAB. In my previous role, I used Python for risk assessment, analyzing financial data. R was essential for predictive modeling and visualization of customer behavior. For engineering projects, I relied on MATLAB to design control algorithms, like the one for an autonomous vehicle prototype. I also have experience with SQL and distributed computing frameworks like ApacheSpark, making me well-equipped to tackle quantitative engineering challenges."

Interview question 2: How would you handle missing data in a dataset you're analyzing?

 Why this question is important: Asking quantitative research candidates about handling missing data is crucial because it assesses their technical competence, problem-solving skills, and understanding of quality assurance. Their response reveals if they can produce reliable results and adapt to specific project needs. Additionally, it highlights their commitment to ethics and transparency.- which is essential in data roles.

Example answer: “I would handle missing data in a dataset through careful assessment of the missing data pattern. If it's random or related to other variables, I'd consider imputation methods like mean, median, or regression-based imputation. If data loss is minimal, I'd consider deletion methods. I'd document my approach and conduct sensitivity analysis to validate the results, prioritizing accuracy and transparency.”

Interview question 3: Can you describe your experience with time series analysis and forecasting?

‍ Why this question is important: Asking quantitative researchers about their experience with time series analysis and forecasting  provides insights into a candidate's ability to analyze historical data, identify patterns, and make future predictions - demonstrating their proficiency in quantitative techniques.

Additionally, this question allows interviewers to assess a candidate's adaptability. Methods and tools for time series analysis and forecasting are constantly evolving, making it vital for engineers to stay up-to-date with the latest approaches and technologies.

Example answer: Answers here may vary, but look for responses that mention historical data, applying neural networks, and model evaluation. Also look for answers that address risk assessment and portfolio optimization – as this indicates that candidates make strong data-driven decisions.  

Interview question 4: Walk me through a quantitative research project you worked on from start to finish. What was the problem, and how did you approach it?

 Why this question is important: It's crucial for a quantitative researcher to answer the question about a past research project because it demonstrates their ability to apply their quantitative skills in a practical context. By outlining the project's problem, approach, and execution from start to finish, the candidate showcases their problem-solving capabilities and clear-headed thinking. Additionally, it offers the interviewer a clear understanding of the candidate's research process, which is crucial to know when evaluating fit.

Example answer: " I recently worked on a quantitative research project focused on analyzing customer churn in a subscription-based service. The problem was declining customer retention rates which was ultimately impacting company revenue. I started by gathering historical data, conducting exploratory data analysis to identify key factors influencing churn, and then built predictive models using logistic regression and decision trees. After analyzing the results, we successfully reduced churn rates by 15% within six months."  

Interview question 5: Tell me about a time when you encountered unexpected results in your analysis. How did you handle it, and what did you learn from the experience?

 Why this question is important: Things are always going to go wrong at some point in every job, but how you address it makes all the difference! Their response to this question showcases their analytical and critical-thinking skills and highlights how they manage unexpected findings, which can still lead to new insights or the refinement of research methodologies.

Example answer: "During a research project analyzing the impact of advertising campaigns on product sales, I encountered unexpected results when one campaign that was anticipated to have a significant positive effect showed a negative impact instead. To address this, I dove deeper into the data and discovered a data quality issue in the advertising spend records. I corrected the discrepancies, re-ran the analysis, and found that the campaign indeed had a positive effect, aligning with expectations.This experience taught me the importance of thorough data validation and the potential for data quality issues to lead to misleading conclusions in quantitative research."

These interview questions should help you assess a candidate's technical proficiency, problem-solving abilities, and their ability to work effectively in a quantitative research role. Learn more about Quantitative Research roles here .

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• Qualitative vs. Quantitative Research | Differences, Examples & Methods

Qualitative vs. Quantitative Research | Differences, Examples & Methods

Published on April 12, 2019 by Raimo Streefkerk . Revised on June 22, 2023.

When collecting and analyzing data, quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions.

Quantitative research is at risk for research biases including information bias , omitted variable bias , sampling bias , or selection bias . Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews that explore concepts and theories.

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs. quantitative research, how to analyze qualitative and quantitative data, other interesting articles, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyze data, and they allow you to answer different kinds of research questions.

Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observational studies or case studies , your data can be represented as numbers (e.g., using rating scales or counting frequencies) or as words (e.g., with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

• Surveys :  List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
• Experiments : Situation in which different types of variables are controlled and manipulated to establish cause-and-effect relationships.
• Observations : Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

• Interviews : Asking open-ended questions verbally to respondents.
• Focus groups : Discussion among a group of people about a topic to gather opinions that can be used for further research.
• Ethnography : Participating in a community or organization for an extended period of time to closely observe culture and behavior.
• Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

• Use quantitative research if you want to confirm or test something (a theory or hypothesis )
• Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs. deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: “on a scale from 1-5, how satisfied are your with your professors?”

You can perform statistical analysis on the data and draw conclusions such as: “on average students rated their professors 4.4”.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: “How satisfied are you with your studies?”, “What is the most positive aspect of your study program?” and “What can be done to improve the study program?”

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analyzed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analyzing quantitative data

Quantitative data is based on numbers. Simple math or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

• Average scores ( means )
• The number of times a particular answer was given
• The correlation or causation between two or more variables
• The reliability and validity of the results

Analyzing qualitative data

Qualitative data is more difficult to analyze than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analyzing qualitative data include:

• Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
• Thematic analysis : Closely examining the data to identify the main themes and patterns
• Discourse analysis : Studying how communication works in social contexts

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Top 20 Quantitative Interview Questions & Answers

Master your responses to Quantitative related interview questions with our example questions and answers. Boost your chances of landing the job by learning how to effectively communicate your Quantitative capabilities.

Embarking on a career in the quantitative field means immersing yourself in a world where numbers, data analysis, and algorithmic thinking are paramount. Whether you’re aiming for a role in finance, research, or another sector that relies heavily on quantitative skills, it’s imperative to demonstrate not just your technical acumen but also your ability to apply complex mathematical concepts to real-world problems.

During an interview for a quantitative position, expect to encounter questions designed to probe your expertise in statistical methods, your experience with programming languages, and your knack for critical thinking under pressure. To help you navigate these challenges and convey your quantitative prowess effectively, we’ve curated a selection of typical interview questions tailored for the quantitatively inclined professional, coupled with strategic advice on formulating compelling responses.

Common Quantitative Interview Questions

1. how would you validate a statistical model you’ve developed for predicting market trends.

Understanding the validation of a statistical model is essential, particularly when predicting market trends. It’s not just about accuracy but also about the model’s adaptability to changing conditions and the robustness of the underlying data. Candidates should be prepared to discuss their experience with statistical validation techniques and their ability to foresee potential issues.

When responding, highlight your approach to model validation, which might include split-sample testing, cross-validation, or out-of-time validation. Discuss the importance of using relevant and high-quality data, as well as the need to test the model against unseen data to gauge its generalizability. Emphasize your understanding of key performance metrics—like R-squared, RMSE, or MAE—that can help quantify the model’s accuracy. Show that you are also mindful of the model’s limitations and the importance of continuous monitoring and updating to maintain its predictive relevance over time.

Example: “ To validate a statistical model designed for predicting market trends, I would employ a combination of split-sample testing and cross-validation techniques to ensure the model’s robustness and generalizability. Initially, I would partition the dataset into training and testing subsets, using the training set to build the model and the testing set to evaluate its predictive power. This approach helps to mitigate overfitting and provides an initial assessment of the model’s performance on unseen data.

Subsequently, I would implement k-fold cross-validation, which further refines the validation process by averaging the model’s performance across different partitions of the data, offering a more comprehensive view of its predictive capabilities. Throughout this process, I would closely monitor key performance metrics such as R-squared for assessing the proportion of variance explained by the model, and RMSE or MAE for quantifying the prediction errors, adjusting the model accordingly to optimize these metrics.

It’s crucial to recognize that model validation is not a one-time task but an ongoing process. As market conditions evolve, the model should be periodically revalidated using fresh, out-of-time data to ensure its continued relevance. Moreover, I would remain vigilant about the quality of data inputs, as the predictive accuracy of the model is inherently dependent on the relevance and integrity of the data it is trained on. Continuous monitoring and updating of the model are essential to account for new patterns or structural changes in the market, ensuring the model’s long-term predictive validity.”

2. Describe an experience where you had to interpret complex data sets without clear patterns.

For roles that require a high degree of analytical rigor, candidates must be adept at interpreting ambiguous data. This question probes the candidate’s persistence, creativity, and ability to communicate complex findings to non-technical stakeholders.

When responding, candidates should outline the steps they took to analyze the data, including any specific methodologies or tools they employed. They should discuss how they identified variables of interest, dealt with missing or noisy data, and the reasoning behind the particular analytical approaches they chose. It’s also beneficial to explain how they validated their conclusions and the impact of their findings on the decision-making process. A story that conveys the complexity of the situation, the approach taken, and the eventual outcome or learning experience will demonstrate their competence in handling such challenges.

Example: “ In an experience dealing with a multifaceted data set lacking apparent patterns, I approached the analysis through a combination of exploratory data analysis (EDA) and advanced statistical techniques. Initially, I employed EDA methods, such as visualizing the data through histograms, boxplots, and scatter plots, to identify any underlying structures or anomalies. This preliminary step was crucial in formulating hypotheses about potential relationships within the data.

Subsequently, I used dimensionality reduction techniques like Principal Component Analysis (PCA) to distill the data into its most informative components. This was followed by implementing machine learning algorithms, including clustering methods like K-means and DBSCAN, to detect any subtle groupings or trends that were not immediately obvious. To address missing or noisy data, I applied imputation methods and robust statistical measures to minimize bias.

Throughout the analysis, I rigorously validated the findings using cross-validation techniques and sensitivity analysis to ensure the robustness of the results. The insights gained from this comprehensive approach led to the identification of several key drivers that were previously obscured. These findings informed strategic decisions, resulting in optimized processes and a significant improvement in the overall efficiency of the project. The experience underscored the importance of a methodical and iterative approach to data analysis, especially when faced with complex and initially inscrutable data sets.”

3. What metrics would you prioritize when assessing the financial health of a tech startup?

A tech startup’s financial health is not just about current profitability. Candidates should be ready to discuss how they evaluate a startup’s potential for growth, scalability, and other industry-specific KPIs that reflect the unique challenges of the technology sector.

When responding to this question, candidates should focus on a balanced set of metrics such as cash flow, runway, customer acquisition cost (CAC), customer lifetime value (CLV), monthly recurring revenue (MRR), and churn rate. It’s important to articulate why each metric is relevant and how it would influence strategic decisions. For instance, a high CAC relative to CLV could signal unsustainable growth, while a short runway might necessitate immediate fundraising or cost-cutting measures. Demonstrating an understanding of how these metrics interplay can show your analytical skills and your ability to guide a startup towards financial stability and growth.

Example: “ When evaluating the financial health of a tech startup, I would prioritize a mix of liquidity, profitability, and growth metrics. Cash flow is paramount as it indicates the company’s ability to sustain operations and grow without external financing. I would assess the runway by comparing the current burn rate with available cash to estimate the time before additional capital is required. This metric is critical for understanding the immediacy of fundraising needs or the necessity for cost optimization.

Simultaneously, I would analyze customer-centric metrics such as CAC and CLV to gauge the efficiency of the startup’s growth strategies. A lower CAC relative to CLV suggests a sustainable acquisition strategy and a healthy potential for long-term profitability. MRR and churn rate offer insights into the recurring revenue stability and customer retention, respectively. High MRR growth coupled with low churn rates often indicates product-market fit and a loyal customer base, which are strong indicators of a startup’s upward trajectory. These metrics collectively provide a comprehensive view of the startup’s financial performance and can inform strategic decisions to ensure both short-term survival and long-term success.”

4. Outline your process for conducting a Monte Carlo simulation in portfolio risk assessment.

Conducting a Monte Carlo simulation is a key skill for effective portfolio risk assessment. Candidates should be prepared to explain their approach to this stochastic technique and how it helps them understand the impact of risk and uncertainty in financial models.

When responding, you should clearly outline the key steps: defining a domain of possible inputs, generating inputs randomly from a probability distribution that reflects the risk or uncertainty being modeled, running a deterministic computation with those inputs, and aggregating the results to get a probability distribution of the output. It’s important to detail your experience with relevant software or programming languages, explain how you ensure the accuracy and reliability of the data, and discuss how you interpret the results to inform risk management decisions. Demonstrating an understanding of the limitations and assumptions of the model will also show depth of knowledge.

Example: “ In conducting a Monte Carlo simulation for portfolio risk assessment, I begin by defining the domain of possible inputs, which typically includes historical return distributions for each asset class, correlations, and volatilities. I ensure these inputs are based on robust statistical analysis and are reflective of current market conditions. Using these distributions, I generate a large number of random scenarios for future returns, employing a pseudo-random number generator or a quasi-random sequence for better convergence properties.

Next, I run deterministic computations for each scenario, which involves calculating the portfolio returns and risk metrics such as VaR (Value at Risk) or CVaR (Conditional Value at Risk). This is done through a simulation engine that I either code in a language like Python or R, or by using specialized software such as @RISK or Crystal Ball, depending on the complexity and the specific requirements of the task.

The aggregation of results is critical; I analyze the output distribution to assess the risk profile of the portfolio, looking at the range of outcomes and the likelihood of extreme losses. I interpret these results within the context of the portfolio’s investment strategy and risk tolerance, providing actionable insights for risk management decisions.

Throughout the process, I am mindful of the assumptions and limitations inherent in the model, such as the assumption of a static correlation structure or the potential for model risk due to input uncertainty. I conduct sensitivity analyses to understand how changes in the inputs affect the outcomes, ensuring that the final recommendations are robust and account for a range of possible market conditions.”

5. In what ways have you utilized machine learning algorithms to enhance quantitative analysis?

In modern finance, marketing, or data science, machine learning is increasingly important. Candidates should be able to discuss how they use machine learning algorithms to enhance decision-making and drive innovation.

When responding to this question, a candidate should highlight specific projects or experiences where machine learning algorithms directly impacted the analysis. Discuss the type of algorithms used—such as decision trees, neural networks, or clustering techniques—and the outcomes they achieved. Explain the problem-solving process, including how the algorithm was selected, the data preparation involved, and how the results were validated and interpreted. It’s essential to articulate the value added through these techniques, such as increased accuracy of predictions, time saved, or improved profitability.

Example: “ In a recent project, I leveraged Random Forest algorithms to enhance the predictive accuracy of a quantitative trading strategy. By integrating a multitude of decision trees, the model was trained on historical market data to identify complex patterns and interactions between various financial indicators. The ensemble approach not only improved the robustness of the predictions against overfitting but also increased the out-of-sample Sharpe ratio significantly, leading to a more profitable and risk-adjusted return profile.

Furthermore, I employed neural networks for time-series forecasting, specifically LSTM (Long Short-Term Memory) models, to capture the temporal dependencies in asset price movements. The LSTM’s ability to remember information over extended periods was crucial for understanding the momentum and mean-reversion effects in the markets. The model’s forecasts were instrumental in optimizing trade execution and managing dynamic portfolio allocations, resulting in a marked decrease in slippage costs and enhanced overall portfolio performance. Validation of the models’ effectiveness was conducted through rigorous backtesting and forward performance monitoring, ensuring that the machine learning applications provided tangible benefits to the quantitative analysis framework.”

6. Detail a scenario where you significantly improved a model’s accuracy; what changes did you make?

Model improvement is a critical skill in quantitative roles. Candidates should be ready to showcase their problem-solving skills and their ability to enhance existing systems, demonstrating a deep understanding of data science methodologies.

When responding, candidates should outline the situation clearly, describing the model in question, the specific issues with its accuracy, and the steps taken to address them. They should discuss the data analysis performed, any algorithm adjustments, feature engineering, or cross-validation techniques employed. Articulating the reasoning behind each change and how it contributed to the overall improvement in accuracy will show a thoughtful and methodical approach to model optimization.

Example: “ In one scenario, the model’s performance was hindered by overfitting due to a high dimensionality of features relative to the number of observations. To address this, I implemented a combination of feature selection and regularization techniques. I started by applying a variance threshold to remove features with minimal variance, as they were unlikely to contribute significantly to the model’s predictive power. Then, I used a recursive feature elimination process with cross-validation (RFECV) to identify and retain the most impactful features.

Subsequently, I incorporated L1 regularization (Lasso) into the model to penalize the magnitude of the coefficients and encourage sparsity, effectively reducing the complexity of the model. This regularization technique not only helped in feature selection but also improved the model’s generalization by discouraging overfitting. The changes led to a more parsimonious model with a better balance between bias and variance, resulting in a significant uplift in out-of-sample accuracy, as confirmed by a stratified K-fold cross-validation approach.”

7. Walk us through a time when you had to explain quantitative findings to a non-technical audience.

Translating complex quantitative data into digestible insights is a valuable skill. Candidates should be prepared to discuss how they make complex numerical information accessible and actionable for those without a technical background.

When responding, begin by outlining the context of the quantitative findings you were dealing with, emphasizing the audience’s lack of technical expertise. Describe the steps you took to simplify the data, such as using analogies, visual aids, or breaking down the methodology into layman’s terms. Highlight your ability to engage with the audience, asking questions to gauge their understanding, and adjusting your explanation accordingly. Conclude by reflecting on the outcome—how your communication facilitated a better understanding and led to informed decisions or actions.

Example: “ In a recent project, I was tasked with presenting the results of a complex regression analysis that identified key factors affecting customer retention rates. The audience comprised department heads with varied backgrounds, most of whom lacked statistical training. To bridge the gap, I distilled the findings into the core message: which factors were most influential and by how much. I used a simple analogy, comparing the statistical model to a recipe where each ingredient’s quantity affects the final taste, to convey the idea of coefficients impacting the outcome.

I supplemented this with a clear, intuitive visualization—a bar chart showing the relative importance of each factor, avoiding technical jargon like “p-values” or “confidence intervals.” During the presentation, I engaged with the audience by asking if the visual representation made sense and if they could relate the findings to their experiences. This interaction helped me tailor the explanation further, ensuring clarity.

The outcome was a productive discussion that led to actionable strategies. The department heads grasped the key takeaways and were able to brainstorm targeted initiatives to improve customer retention, demonstrating that the quantitative findings had been successfully communicated and understood.”

8. Which quantitative research publication has most influenced your work and why?

The influence of quantitative research on your work can demonstrate scholarly rigor and breadth of knowledge. Candidates should be ready to discuss how they integrate complex data from research publications into their thought process.

To respond effectively, you should select a publication that is not only reputable but also closely related to your field of work or the position you are applying for. Discuss the publication’s findings or methodologies and clearly articulate how it has shaped your approach to data analysis or problem-solving. Be prepared to explain the publication’s impact on your thought process or professional practices, providing concrete examples of how you have applied its insights to your work.

Example: “ The publication that has most influenced my work is “The Econometrics of Financial Markets” by John Y. Campbell, Andrew W. Lo, and A. Craig MacKinlay. This seminal work provided a comprehensive framework for analyzing financial markets using econometric methods, which has been invaluable in both my approach to modeling market behaviors and in developing predictive analytics for asset pricing.

The rigorous treatment of topics such as time-series analysis, the Capital Asset Pricing Model (CAPM), and the Efficient Market Hypothesis (EMH) within this text has been particularly impactful. It has honed my ability to critically evaluate model assumptions and to integrate econometric techniques with machine learning algorithms for enhanced forecasting accuracy. For instance, leveraging the concepts from this publication, I’ve successfully implemented vector autoregression models to predict stock prices, accounting for the dynamic interplay between multiple economic indicators. The book’s empirical focus also encouraged a data-driven mindset that ensures the robustness and validity of my analyses, a principle that I’ve upheld in all my quantitative endeavors.”

9. What approach do you take to ensure compliance with data protection regulations during analysis?

Data protection is a critical concern, especially when handling sensitive information. Candidates should be prepared to discuss their knowledge of data protection laws and how they incorporate compliance into their daily work processes.

To respond effectively, outline specific steps you take to ensure compliance, such as anonymizing data, implementing access controls, and conducting regular audits. You may also mention staying updated on the latest regulations, attending training sessions, and collaborating with legal or compliance teams. It’s essential to demonstrate a proactive approach to data protection, showcasing that you prioritize ethical considerations alongside analytical rigor.

Example: “ In ensuring compliance with data protection regulations during analysis, my approach is both proactive and systematic. Initially, I conduct a thorough data inventory to classify the sensitivity of the data sets and understand the specific compliance requirements associated with each. Following this, I employ data minimization techniques, ensuring that only the necessary data for the analysis is processed, and anonymize or pseudonymize personal data to mitigate any privacy risks.

I implement robust access controls, restricting data access to authorized personnel and applying the principle of least privilege. To maintain the integrity of these measures, I regularly perform audits and monitor data access logs to detect any unauthorized attempts or non-compliance issues. Furthermore, I stay abreast of the evolving regulatory landscape by attending advanced training sessions and actively engaging with legal or compliance teams to ensure that my analysis practices are aligned with the latest data protection standards. This ongoing dialogue allows for the anticipation of regulatory changes and the seamless integration of new compliance requirements into the analytical workflow.”

10. Share an example of how you’ve used hypothesis testing to inform a business decision.

Hypothesis testing is a fundamental statistical method in decision-making. Candidates should be ready to explain how they conduct hypothesis tests and how they use the results to inform business strategies.

When responding to this question, articulate a clear scenario where hypothesis testing was central to resolving a business question or challenge. Describe the hypothesis you tested, the data you used, and the statistical method you employed. Most importantly, discuss the results of the test and how you interpreted them to make a well-informed business decision. Highlight how your analysis had a tangible impact on the business, such as improving a product, optimizing a process, or enhancing customer satisfaction. This will showcase your ability to bridge the gap between data analysis and practical business outcomes.

Example: “ In a recent project, we were faced with the challenge of optimizing the pricing strategy for a new product line. The hypothesis was that by increasing the price point by 10%, we would not significantly affect the sales volume, aiming to increase overall revenue without deterring customers. To test this hypothesis, we conducted an A/B test, where Group A was exposed to the current pricing and Group B to the increased price.

Utilizing a t-test for the difference in means between the two groups, we analyzed sales data over a four-week period. The results indicated that there was no statistically significant difference in sales volume (p > 0.05), suggesting that the price increase did not negatively impact sales. Based on this analysis, we advised the business to implement the new pricing strategy across all markets, which ultimately led to a 9% rise in revenue without a drop in sales volume. This decision was further supported by monitoring post-implementation sales data, confirming the sustainability of the new pricing model.”

11. When building predictive models, how do you address multicollinearity in your independent variables?

Multicollinearity can complicate the interpretation of predictive models. Candidates should be prepared to discuss how they diagnose and resolve issues of multicollinearity to ensure the accuracy of their models.

When responding to this question, you should demonstrate a clear understanding of the concept of multicollinearity and its implications for predictive modeling. Outline the methods you employ to detect multicollinearity, such as examining correlation matrices or variance inflation factors (VIF). Then, explain the strategies you apply to address it, which might include removing highly correlated predictors, combining them into a single variable, or using regularization techniques like Lasso or Ridge regression that can penalize coefficients for correlated variables and reduce overfitting. Your answer should convey a methodical and informed approach to maintaining the integrity of your predictive models.

Example: “ To address multicollinearity among independent variables in predictive modeling, I first employ diagnostic tools like correlation matrices and Variance Inflation Factor (VIF) thresholds to detect the presence and severity of multicollinearity. A VIF above 5 or 10, depending on the context and domain-specific thresholds, usually signals a multicollinearity issue that needs to be addressed.

Once identified, I tackle multicollinearity using a combination of domain knowledge and statistical techniques. If two variables are highly correlated and one is deemed less important based on domain knowledge, I might remove it to reduce redundancy. Alternatively, I might combine correlated variables into a single feature through principal component analysis or factor analysis, which preserves the information while mitigating the multicollinearity effect. In situations where variable selection is crucial, I might apply regularization methods like Lasso regression, which is designed to perform both variable selection and parameter shrinkage, effectively handling multicollinearity by penalizing the coefficients of correlated predictors and driving some to zero. This approach ensures the model remains robust and generalizable without compromising the predictive power.”

12. Describe a situation where you integrated qualitative insights into your quantitative analysis.

Blending quantitative and qualitative insights can lead to more effective analysis. Candidates should be ready to discuss how they incorporate qualitative factors like consumer behavior and market trends into their data analysis.

When responding, candidates should recount a specific scenario where they identified the need for qualitative input to complement their quantitative findings. They might discuss how they gathered qualitative data, such as customer feedback or expert opinions, and the methods used to integrate this information with quantitative results. The aim is to illustrate their thought process, the challenges they faced, and how the integration of both data types led to a more informed decision or recommendation. It’s important to convey adaptability, analytical depth, and a recognition that numbers tell a part of the story, but not the whole.

Example: “ In a recent analysis of customer churn, I recognized that while the quantitative data highlighted trends in cancellations, it didn’t provide the underlying reasons for customer dissatisfaction. To address this gap, I conducted a series of in-depth interviews and focus groups to gain qualitative insights. I used thematic analysis to identify common reasons for churn that weren’t apparent in the numerical data, such as perceived value and customer service interactions.

Integrating these qualitative insights with the quantitative data involved creating a framework that allowed for a holistic view of the factors influencing churn. By overlaying sentiment analysis on the churn rate across different customer segments, I was able to pinpoint specific service touchpoints that required improvement. This mixed-methods approach not only enriched the analysis but also led to targeted strategies that reduced churn by 15% over the next quarter. The process highlighted the importance of blending qualitative nuances with quantitative rigor to derive actionable intelligence.”

13. How do you determine the right sample size for a statistically significant survey study?

Determining the right sample size is crucial for survey studies. Candidates should be prepared to discuss their approach to calculating sample size and ensuring the reliability and validity of their research findings.

When responding to this question, one should discuss the factors that influence sample size determination, such as the desired confidence level, the margin of error, the population size, and the expected effect size or variability in the data. It’s important to articulate a systematic approach to sample size calculation, perhaps referencing specific statistical formulas or software used to make these estimations. Offering an example from past experience where you determined sample size for a study can showcase your practical application of these concepts, along with any lessons learned from the outcome of that research.

Example: “ Determining the right sample size for a statistically significant survey study hinges on balancing precision and practicality. Initially, I establish the desired confidence level, typically 95% for conventional standards, which sets the Z-score used in calculations. The margin of error, reflecting the maximum expected difference between the sample statistic and the population parameter, is chosen based on the level of precision required for the study’s objectives. For a general population survey, a 5% margin is common, but this might be tightened for more sensitive analyses.

I then consider the population size, especially relevant when dealing with finite populations, to apply the finite population correction if necessary. The expected effect size or variability in the data, informed by prior research or pilot studies, guides my estimation of the standard deviation, which is crucial for calculating the required sample size. Utilizing formulas for sample size calculation or software like G*Power or nQuery, I incorporate these parameters to yield a sample size that balances statistical rigor with resource constraints. For instance, in a previous study estimating the prevalence of a health behavior, I used a conservative estimate of variability to ensure the sample size was sufficient to detect even small prevalence rates, which was vital for the subsequent health intervention planning. The study’s success underscored the importance of meticulous sample size calculation in achieving meaningful and actionable results.”

14. What is your strategy for staying current with advancements in quantitative methods and tools?

Keeping up with the latest trends and tools is essential in the quantitative field. Candidates should be ready to discuss their commitment to continuous learning and how they stay current with new technologies and methodologies.

When responding to this question, a candidate should highlight their proactive approach to professional development. This could include subscribing to industry journals, attending workshops and conferences, participating in online courses, or being part of professional forums and networks. Demonstrating a systematic approach to integrating new knowledge and tools into one’s work routine will reassure employers that the candidate is both technically proficient and strategically prepared to contribute to the organization’s success.

Example: “ To stay abreast of the latest advancements in quantitative methods and tools, I maintain a disciplined approach to continuous learning and professional development. I regularly subscribe to and read key industry journals such as the “Journal of Quantitative Analysis” and “Quantitative Finance,” which provide insights into cutting-edge research and applications. Additionally, I leverage online platforms like Coursera and edX to enroll in relevant courses that sharpen my skills and deepen my understanding of new techniques.

I also prioritize attending annual conferences and workshops, which not only offer exposure to innovative methodologies but also provide opportunities to engage with thought leaders and practitioners in the field. This engagement is complemented by active participation in professional forums and networks, such as the Quantitative Finance Professional Group on LinkedIn, where I can exchange ideas and discuss practical challenges with peers. By systematically integrating new knowledge into my existing framework, I ensure that my quantitative analysis remains robust, relevant, and aligned with the state-of-the-art in the field.”

15. Provide an instance where you had to adapt your analytical approach due to unexpected data limitations.

Agility in problem-solving is key when data is incomplete or unexpected. Candidates should be prepared to discuss how they handle such challenges while maintaining data integrity and accuracy.

When responding, recount a specific scenario where you encountered data limitations, emphasizing how you evaluated the situation, identified the constraints, and decided on an alternative approach. Outline the steps you took to ensure the new approach still provided valuable insights, and if possible, share the outcome of your analysis. This will illustrate your adaptability, problem-solving skills, and commitment to delivering results despite challenges.

Example: “ In a project where I was modeling customer churn, I encountered a situation where the historical data was far less comprehensive than initially anticipated. The data lacked granularity on customer interactions and product usage, which were critical for building a robust predictive model. I quickly realized that traditional regression techniques would not yield the predictive power needed due to the sparsity of data.

To adapt, I pivoted to a survival analysis approach, leveraging what we did have—customer tenure and churn events. This allowed me to model churn risk over time without the need for detailed interaction data. I also employed bootstrapping methods to enhance the stability of the model given the limited dataset. By focusing on the time-to-event aspect, I could still provide the business with meaningful insights into customer retention patterns and identify key time intervals for intervention. The outcome was a strategic shift in customer engagement, informed by the survival model, which ultimately led to a measurable reduction in churn.”

16. In your view, what is the biggest challenge facing quantitative analysts in the finance sector today?

The finance sector’s constant evolution requires quantitative analysts to be adaptable. Candidates should be ready to discuss how they integrate new data sources and technologies into their financial models.

When responding to this question, candidates should articulate their understanding of the current financial landscape, highlighting specific challenges such as the need for real-time data analysis, cybersecurity threats, or the implications of global economic events. They should also discuss their approach to continuous learning and adaptation, perhaps by mentioning their engagement with new analytical tools, professional development courses, or forums that discuss emerging trends in finance. Demonstrating awareness of these challenges and a proactive approach to overcoming them will show interviewers that the candidate is both informed and forward-thinking.

Example: “ The most pressing challenge for quantitative analysts in the finance sector today is the rapid evolution of machine learning and artificial intelligence technologies. These advancements are constantly reshaping the landscape of data analysis and predictive modeling. The integration of AI into quantitative finance not only requires analysts to maintain a robust understanding of new algorithms and computational methods but also to ensure that these tools are employed without introducing systemic risk. As financial markets become increasingly complex and automated, the potential for AI-driven strategies to create feedback loops or unforeseen market dynamics grows, necessitating a deep understanding of both the financial instruments involved and the underlying technology.

To navigate this challenge, I actively engage with the latest research and developments in AI and machine learning, applying a critical eye to how these innovations can be leveraged responsibly in financial contexts. This involves not only staying abreast of the technical aspects but also understanding the broader economic implications and regulatory frameworks. By maintaining a dialogue with industry peers through forums and professional development opportunities, I ensure that my approach to quantitative analysis is both cutting-edge and grounded in a risk-aware perspective.”

17. How do you balance the need for timely results with the rigor of thorough quantitative analysis?

Delivering accurate data analysis promptly is essential in quantitative roles. Candidates should be prepared to discuss how they balance the need for speed with the need for precision in their work.

When responding to this question, candidates should articulate a structured approach to quantitative tasks that showcases their time management skills. They could mention specific methodologies or tools they use to streamline analysis, such as automating repetitive tasks, employing statistical software, or breaking projects into phases to allow for preliminary insights to be shared in advance of full analysis completion. It’s also important to communicate an understanding of when depth is crucial versus when an executive summary will suffice, and to provide examples from past experiences where this balance was successfully achieved.

Example: “ Balancing timeliness with rigor in quantitative analysis is a matter of prioritizing efficiency without compromising on the integrity of the results. I employ a phased approach, where I initially focus on exploratory data analysis to quickly identify patterns, outliers, and potential areas of interest. This allows for the generation of preliminary insights that can guide subsequent, more detailed investigations. I leverage statistical software and scripting to automate routine data processing tasks, which significantly cuts down on the time required for data cleaning and manipulation.

When deeper analysis is necessary, I judiciously apply sampling techniques or model-based approaches to extrapolate findings without having to crunch every data point, which can be time-consuming. I’m always conscious of the trade-off between accuracy and speed, and I communicate these considerations transparently with stakeholders. For instance, in a past project involving predictive modeling, I used ensemble methods to quickly generate a robust model, then iteratively refined it as time allowed, ensuring that stakeholders had a functional tool at their disposal while I worked on enhancing its precision.”

18. Illustrate how you would conduct a cost-benefit analysis on implementing new technology within an organization.

Conducting a cost-benefit analysis requires strategic thinking. Candidates should be ready to discuss how they evaluate the tangible and intangible factors that contribute to an organization’s ROI.

To respond, you should outline a structured approach, starting with defining the scope of the analysis and identifying all associated costs, such as purchase price, implementation expenses, training, and maintenance. Next, articulate how you would measure the anticipated benefits, which might include increased efficiency, revenue growth, or improved customer satisfaction. Explain how you would consider the time value of money in your analysis, possibly utilizing net present value (NPV) or internal rate of return (IRR) calculations. Then, discuss how you would weigh these factors against each other, possibly including a scenario analysis to account for uncertainty. Finally, describe how you would present your findings, emphasizing clear communication of the potential risks and rewards to stakeholders.

Example: “ In conducting a cost-benefit analysis for new technology implementation, I would begin by meticulously defining the scope and identifying all relevant costs, including upfront capital expenditure, operational costs, and any indirect costs such as potential downtime during the transition. I would then forecast the tangible benefits, such as productivity gains and cost savings, as well as intangible benefits like customer satisfaction and competitive advantage, quantifying these where possible.

To assess the financial viability, I would calculate the net present value (NPV) of the project, ensuring that future cash flows are discounted appropriately to reflect the time value of money. I would also consider the internal rate of return (IRR) to understand the project’s profitability relative to the cost of capital. To address uncertainty, I would perform sensitivity and scenario analyses, varying key assumptions to gauge the robustness of the project under different conditions. My findings would be synthesized into a clear, data-driven recommendation, articulating the strategic rationale and potential impact on the organization’s bottom line, ensuring that decision-makers are fully informed of the risks and potential returns.”

19. What techniques do you apply to forecast demand for a product with little historical data available?

Forecasting demand with limited historical data is challenging. Candidates should be prepared to discuss their approach to making informed predictions in such scenarios.

When responding, emphasize your systematic approach to the problem. You might start by explaining how you gather qualitative insights from market research, expert opinions, and competitive analysis to form initial hypotheses. Then, detail how you use quantitative methods such as time series analysis, regression models, or even newer techniques like machine learning to extrapolate from available data. Highlight any specific tools or software you are proficient with, and describe a past scenario where you successfully forecasted under similar constraints, focusing on the process and the outcome.

Example: “ To forecast demand for a product with limited historical data, I employ a combination of qualitative assessments and exploratory quantitative methods. Initially, I gather insights through market research, including customer surveys, focus groups, and Delphi method sessions with industry experts to establish a foundational understanding of potential demand drivers and market dynamics. Concurrently, I perform a competitive analysis to benchmark against similar products or services, which can provide valuable clues about the market’s response to analogous offerings.

Quantitatively, I lean towards employing Bayesian methods that allow for the incorporation of prior knowledge and expert opinion into the statistical model, which is particularly useful when data is scarce. This approach, coupled with bootstrapping techniques, helps in constructing confidence intervals around forecasts even with small datasets. Additionally, I utilize machine learning algorithms, such as random forests or gradient boosting machines, which can capture complex nonlinear relationships and interactions between variables, even when historical data is not robust. Tools like R or Python, with their extensive libraries for statistical and machine learning methods, are instrumental in this process.

A specific instance where this approach proved successful was when forecasting the demand for a niche technological product entering a new market. By synthesizing market research insights with a Bayesian model that integrated expert assessments, I was able to provide a demand estimate that was later validated to be within 10% of actual sales in the first quarter post-launch. This accuracy was pivotal in optimizing the supply chain and marketing strategy, ultimately contributing to a successful product introduction.”

20. Tell us about a project where you leveraged network analysis to uncover insights into customer behavior.

Network analysis can reveal strategic business insights. Candidates should be ready to discuss how they use network analysis to inform decisions on marketing, product development, and customer engagement.

When responding to this question, candidates should outline a specific project they worked on, detailing the objectives, the nature of the data, the network analysis techniques employed, and the software or tools used. It’s crucial to articulate the unique customer behavior insights gained from the analysis and how these insights led to actionable strategies or decisions within the project. Quantify the impact whenever possible, such as increased customer retention rates or improved product recommendations, to underscore the value of your analytical contributions.

Example: “ In a recent project, the objective was to understand the interconnectedness of customers within a subscription-based service to identify influential users and predict churn. Utilizing a combination of transactional data and social interaction metrics, I constructed a weighted undirected graph where nodes represented customers and edges signified the frequency and quality of interactions between them.

Employing network analysis techniques such as centrality measures and community detection algorithms, I identified clusters of highly interconnected users and pinpointed those with high eigenvector centrality as potential influencers. These insights were instrumental in developing targeted retention strategies. By engaging these key influencers with personalized incentives, we observed a 15% reduction in churn rate within their respective clusters. Additionally, the analysis of structural holes within the network revealed opportunities for cross-selling, leading to a 10% increase in uptake of additional services among identified bridging users. The project leveraged Python’s NetworkX library for the analysis, which facilitated a robust and scalable examination of the network’s properties.”

Top 20 Product Strategy Interview Questions & Answers

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16 Quantitative Research Analyst Interview Questions (With Example Answers)

It's important to prepare for an interview in order to improve your chances of getting the job. Researching questions beforehand can help you give better answers during the interview. Most interviews will include questions about your personality, qualifications, experience and how well you would fit the job. In this article, we review examples of various quantitative research analyst interview questions and sample answers to some of the most common questions.

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Common Quantitative Research Analyst Interview Questions

What motivated you to choose quantitative research as your area of focus, what do you think sets quantitative research apart from other research disciplines, what would you say is the most challenging aspect of quantitative research, what do you think is the most rewarding aspect of quantitative research, what do you think is the most important skill for a quantitative researcher to possess, what do you think is the most important attribute of successful quantitative research projects, what do you think is the most important factor to consider when designing a quantitative research study, what do you think is the most important element of data analysis in quantitative research, what do you think is the most important consideration when interpreting results from quantitative research studies, what do you think is the most important thing to remember when writing a report on quantitative research findings, what do you think is the most important advice you would give to someone who is new to conducting quantitative research, what do you think is the most important thing to keep in mind when working with clients or sponsors on quantitative research projects, what do you think is the most important factor to consider when planning a career in quantitative research, what do you think is the most important attribute of successful quantitative researchers, what do you think sets quantitative research apart from other types of research, what do you think is the most rewarding aspect of a career in quantitative research.

There are a few reasons why an interviewer might ask this question. First, they may be trying to gauge your interest in the field of quantitative research. Second, they may be trying to determine if you have the necessary skills and knowledge to be successful in this field. Finally, they may be trying to get a sense of your long-term career goals and how quantitative research fits into those goals.

It is important for the interviewer to know your motivation for choosing quantitative research as your area of focus because it will help them understand your level of commitment to the field and whether or not you are likely to stick with it for the long haul. Additionally, this question can give the interviewer some insight into your thought process and how you go about making decisions.

Example: “ I was motivated to choose quantitative research as my area of focus because it is a highly analytical and detail-oriented field that allows me to use my critical thinking skills to solve complex problems. Additionally, I am interested in the mathematical and statistical aspects of quantitative research, which makes this field even more appealing to me. ”

There are a few reasons why an interviewer might ask this question to a quantitative research analyst. First, it allows the interviewer to gauge the analyst's understanding of quantitative research methods. Second, it allows the interviewer to determine whether the analyst is familiar with the key differences between quantitative and other research disciplines. Finally, this question can help the interviewer to understand the analyst's thoughts on the strengths and weaknesses of quantitative research methods.

Quantitative research is a scientific approach to data collection and analysis that focuses on measuring and quantifying variables of interest. In contrast, qualitative research is a more exploratory and open-ended approach that emphasizes understanding and describing phenomena rather than measuring and quantifying them.

The key difference between quantitative and qualitative research lies in their respective goals. Quantitative research is typically used to test hypotheses or to answer questions about cause-and-effect relationships, while qualitative research is used to explore phenomena or to generate new hypotheses. Qualitative research is often more flexible and allows for more detailed data collection than quantitative methods, but it can be more difficult to draw clear and definitive conclusions from qualitative data.

Both quantitative and qualitative research play important roles in the scientific process, and each has its own strengths and weaknesses. Quantitative methods are often seen as more objective and rigorous, while qualitative methods are seen as more flexible and responsive to the complexities of real-world phenomena. Ultimately, the choice of which research method to use depends on the specific question being asked and the resources available.

Example: “ There are a few key things that set quantitative research apart from other research disciplines: 1. The focus on data and numbers. Quantitative researchers are interested in understanding relationships between variables using numerical data. This data can be collected through surveys, experiments, or other means. 2. The use of statistical methods. In order to analyze this data, quantitative researchers use statistical methods to identify patterns and relationships. 3. The use of formal models. Formal models are used to describe the relationships between variables and to make predictions about future behavior. 4. The focus on generalizability. One of the goals of quantitative research is to be able to generalize findings to a larger population. This requires careful design and analysis of data. ”

There are a few reasons why an interviewer might ask this question. First, they want to see if you are able to identify the challenges of quantitative research. This is important because it shows that you understand the limitations of this type of research and that you are aware of the potential difficulties that can arise. Second, they want to see how you would address these challenges if you were to encounter them in your work. This is important because it shows that you are proactive and that you have a plan for dealing with difficult situations. Finally, they want to see if you have a good understanding of the statistical methods that are used in quantitative research. This is important because it shows that you are knowledgeable about the topic and that you are able to apply these methods in a real-world setting.

Example: “ There are many challenges that can be faced when conducting quantitative research, but one of the most challenging is ensuring the data collected is accurate and representative of the population being studied. This can be difficult to achieve if the sample size is small or if there is a lot of variability in the data. Another challenge is designing experiments or surveys that accurately measure the phenomena being studied. This can be difficult if the phenomena are complex or if there are many variables that need to be considered. ”

There are a few reasons why an interviewer might ask this question to a quantitative research analyst. First, it allows the interviewer to gauge the analyst's understanding of the field of quantitative research. Second, it allows the interviewer to gauge the analyst's understanding of the benefits of quantitative research. Finally, it allows the interviewer to gauge the analyst's motivation for pursuing a career in quantitative research.

The most rewarding aspect of quantitative research is that it allows analysts to use their skills to help organizations make better decisions. Quantitative research provides organizations with data that can be used to improve policies, make more informed decisions, and allocate resources more effectively. By conducting quantitative research, analysts can have a direct and positive impact on the lives of people and organizations.

Example: “ There are many rewarding aspects of quantitative research, but I think the most rewarding is the ability to see the impact of your work on real-world problems. When you can see that your research is making a difference in the world, it is a very gratifying feeling. ”

Some possible reasons an interviewer might ask this question are to better understand the candidate's views on the role of a quantitative researcher, to gauge the candidate's level of experience, or to get a sense for how the candidate would approach problem-solving in this role. The most important skill for a quantitative researcher depends on the specific field or industry, but some essential skills might include the ability to effectively collect and analyze data, to develop hypotheses and test them using statistical methods, and to communicate findings clearly.

Example: “ There are many important skills that a quantitative researcher should possess, but some of the most important ones include: 1. Strong analytical and critical thinking skills: A quantitative researcher needs to be able to analyze data and identify patterns and trends. They also need to be able to think critically about the data and come up with hypotheses about what it might mean. 2. Strong math skills: A quantitative researcher needs to be able to understand and work with complex mathematical concepts. They need to be able to use statistical software to analyze data and draw conclusions from it. 3. Strong communication skills: A quantitative researcher needs to be able to communicate their findings clearly and concisely, both in writing and verbally. They need to be able to explain their findings to those who may not be familiar with the concepts involved. ”

There are many important attributes of successful quantitative research projects, but the most important attribute is probably methodological rigor. A rigorous quantitative research project is one that is carefully designed and executed, and which uses sound statistical methods to analyze the data. A rigorous quantitative research project can provide valuable insights into a wide variety of topics, and can help to improve decision-making in many different fields.

Example: “ There are a number of attributes that can contribute to the success of quantitative research projects, but some of the most important include: 1. A clear and concise research question that can be answered using quantitative methods. 2. A well-designed research plan that includes a detailed methodology and robust data collection and analysis procedures. 3. A commitment to rigorously following the research plan and ensuring that data is of high quality. 4. A willingness to iterate and refine the research design as needed in order to obtain accurate and meaningful results. 5. A thorough understanding of statistical methods and their application to the data at hand. 6. The ability to effectively communicate findings to both academic and non-academic audiences. ”

There are many factors to consider when designing a quantitative research study, but the most important factor is the research question. The research question should be clear and concise, and it should be possible to answer it with the data that is collected. Other important factors to consider include the population of interest, the sample size, and the type of data that is collected.

Example: “ The most important factor to consider when designing a quantitative research study is the research question. The research question should be clear and concise, and should be able to be answered by the data that is collected. Other important factors to consider when designing a quantitative research study include the population of interest, the sampling method, and the type of data that is collected. ”

The interviewer is likely looking for qualities that are important in a quantitative research analyst, such as attention to detail, strong mathematical skills, and the ability to draw conclusions from data. This question allows the interviewer to gauge the interviewee's understanding of the role of data analysis in quantitative research and their ability to articulate why it is important.

Example: “ There are many elements of data analysis in quantitative research, but I believe the most important element is accuracy. In order to produce accurate results, quantitative researchers need to have a strong understanding of statistics and be able to apply the proper statistical techniques to their data. They also need to be able to effectively communicate their findings to others. ”

There are a few reasons why an interviewer might ask this question to a quantitative research analyst. First, it is important to understand the limitations of quantitative research studies in order to properly interpret their results. Second, analysts need to be aware of potential sources of bias that can distort results. Finally, analysts need to understand how to effectively communicate results to those who may not be familiar with the technical details of the study.

The most important consideration when interpreting results from quantitative research studies is understanding the limitations of the study. Quantitative research studies are often limited in scope and cannot provide a complete picture of a phenomenon. For example, a quantitative study might only be able to measure a limited number of variables, or it might only be able to observe a phenomenon over a short period of time. As a result, analysts need to be careful not to overinterpret the results of a quantitative study.

Another important consideration when interpreting results from quantitative research studies is potential sources of bias. There are many potential sources of bias that can distort results, such as selection bias, measurement bias, and self-reporting bias. analysts need to be aware of these potential sources of bias and take them into account when interpreting results.

Finally, analysts need to understand how to effectively communicate results to those who may not be familiar with the technical details of the study. When presenting results from a quantitative study, analysts need to clearly explain the methodology used and the limitations of the study. They also need to provide context for the results, such as how the results compare to other studies on the same topic.

Example: “ There are a number of important considerations to take into account when interpreting results from quantitative research studies. Perhaps the most important consideration is the study's methodological quality. This includes factors such as the study's design, sample size, and statistical analysis. If a study has flaws in any of these areas, its results may not be accurate or reliable. Another important consideration is the context in which the study was conducted. This includes factors such as the population being studied, the setting in which the data was collected, and the specific research question that was being addressed. All of these factors can affect the results of a quantitative study and how they should be interpreted. Finally, it is also important to consider the implications of the results before drawing any conclusions. What do the results mean in terms of real-world applications? Are there any potential risks or benefits associated with implementing the findings? These are just some of the questions that need to be considered before making any decisions based on quantitative research results. ”

There are a few reasons why an interviewer might ask this question to a quantitative research analyst. First, it is important to remember that when writing a report on quantitative research findings, it is important to be clear and concise. The report should be easy to read and understand, and should not contain any superfluous information. Second, it is important to remember that the report should be objective and unbiased. The report should not be swayed by the researcher's personal opinions or biases. Third, the report should be accurate. All of the data and information included in the report should be accurate and up-to-date. Finally, the report should be well-organized. The information should be presented in a logical and easy-to-follow manner.

Example: “ There are a few things to keep in mind when writing a report on quantitative research findings: 1. Make sure to clearly state the research question that was being addressed in the study. 2. Present the data in a clear and concise manner, using tables and graphs as needed. 3. Be sure to discuss the implications of the findings and how they relate to the research question. 4. Finally, make sure to proofread the report carefully before submitting it. ”

There are a few reasons why an interviewer would ask this question to a quantitative research analyst. First, it allows the interviewer to gauge the analyst's level of experience and expertise in conducting quantitative research. Second, it allows the interviewer to understand the analyst's process for conducting quantitative research and how they go about acquiring data and analyzing it. Finally, it allows the interviewer to get a sense for the analyst's personal philosophies or methods for conducting research, which can be helpful in determining if they would be a good fit for the position.

Example: “ There are a few things to keep in mind when conducting quantitative research: 1. Make sure your data is of high quality. This means that it should be accurate, reliable, and representative of the population you are studying. 2. Choose the right statistical methods for your data and research question. There are many different statistical methods, and it is important to choose the one that is most appropriate for your data and question. 3. Be careful when interpreting results. Quantitative research is often complex, and it is easy to make mistakes when interpreting results. Make sure to carefully review your results before drawing any conclusions. ”

There are a few reasons why an interviewer might ask this question to a quantitative research analyst. Firstly, the interviewer wants to know if the analyst is aware of the importance of working closely with clients or sponsors on quantitative research projects. Secondly, the interviewer wants to know if the analyst has the ability to think critically about the project and identify the most important aspects that need to be considered. Finally, the interviewer wants to gauge the analyst's level of experience and knowledge in this area.

Quantitative research projects can be extremely complex, and it is crucial that analysts work closely with clients or sponsors in order to ensure that all of the necessary data is collected and analyzed correctly. Furthermore, analysts need to be able to identify the most important factors that will impact the results of the research in order to ensure that the project is successful. Therefore, it is essential that analysts have a strong understanding of both the quantitative research process and the specific needs of their clients or sponsors.

Example: “ There are a few things that are important to keep in mind when working with clients or sponsors on quantitative research projects: 1. It is important to clearly define the goals and objectives of the project from the outset. This will help to ensure that everyone is on the same page and that the project stays focused. 2. It is also important to be clear about who the target audience is for the research. This will help to ensure that the data collected is relevant and can be used to answer the research questions. 3. Another thing to keep in mind is that quantitative research can be expensive, so it is important to work with a budget in mind. This will help to ensure that the project stays within its financial constraints. 4. Finally, it is also important to keep in mind that quantitative research takes time. This means that it is important to plan for adequate time to collect and analyze data before presenting results. ”

There are many factors to consider when planning a career in quantitative research, but the most important factor is probably experience. The more experience you have in the field, the better equipped you will be to handle the challenges that come with it. Additionally, it is important to stay current on the latest methods and techniques used in quantitative research.

Example: “ There are many factors to consider when planning a career in quantitative research, but the most important factor is probably your own skills and interests. If you're not interested in the subject matter, it will be very difficult to succeed in this field. Likewise, if you don't have strong mathematical and analytical skills, you'll likely find it difficult to progress in this career. So, it's important to consider your own skills and interests when planning a career in quantitative research. ”

There are many important attributes of successful quantitative researchers, but some attributes are more important than others. The most important attribute of successful quantitative researchers is the ability to think critically and solve problems. Quantitative research is all about finding solutions to problems, so it is essential that quantitative researchers be able to think critically and solve problems. Other important attributes of successful quantitative researchers include the ability to communicate effectively, the ability to work independently, and the ability to work in a team.

Example: “ There are a few attributes that are important for successful quantitative researchers. Firstly, they need to be excellent at math and statistics. Secondly, they need to be able to think logically and solve problems efficiently. Thirdly, they need to be able to communicate their findings clearly and concisely. Lastly, they need to be able to work well under pressure and meet deadlines. ”

There are a few reasons why an interviewer might ask this question. First, it allows them to gauge the interviewee's understanding of quantitative research. Second, it allows them to see how the interviewee would explain the concept of quantitative research to someone who is not familiar with it. Finally, it allows the interviewer to get a sense of the interviewee's thought process and how they approach problem solving.

It is important for the interviewer to ask this question because it allows them to get a better understanding of the interviewee's skills and abilities. Additionally, it allows the interviewer to get a better sense of the interviewee's personality and whether or not they would be a good fit for the position.

Example: “ Quantitative research is a type of scientific research that focuses on the collection and analysis of numerical data. This data can be collected through surveys, experiments, or other methods of observation. Once collected, this data can be used to answer questions about the relationships between different variables, or to test hypotheses about how these variables interact with each other. One of the main things that sets quantitative research apart from other types of research is its focus on data. This data can be collected in a number of ways, but it must be numerical in order to be analyzed. This means that quantitative research is often more rigorous and objective than other types of research, as it relies on hard evidence rather than personal opinions or anecdotal evidence. Another thing that sets quantitative research apart is its focus on relationships between variables. This type of research is often used to test hypotheses about how different variables interact with each other. For example, a researcher might want to know if there is a relationship between income and happiness. By collecting data on both income and happiness levels, the researcher can test their hypothesis and see if there is a statistically significant relationship between the two variables. Overall, quantitative research is a powerful tool for understanding the world around us. By collecting and analyzing numerical data, we can ”

An interviewer might ask this question to gain insight into what motivates the research analyst and what they consider to be the most important part of their job. This can help the interviewer understand if the analyst is likely to be satisfied with the position and if they are likely to stay in the role for the long term. Additionally, this question can give the interviewer a sense of the research analyst's priorities and how they might approach their work.

Example: “ The most rewarding aspect of a career in quantitative research is the ability to make a real difference in the world. With the help of data and analysis, quantitative researchers are able to provide insights that can lead to positive change. They can help decision-makers understand complex problems and identify potential solutions. In addition, quantitative researchers often have the opportunity to work on cutting-edge projects that can have a real impact on people’s lives. ”

Related Interview Questions

• Quantitative Analyst
• Research Analyst
• Market Research Analyst
• Marketing Research Analyst
• Equity Research Analyst
• Operations Research Analyst

Qualitative vs Quantitative Research Methods & Data Analysis

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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What is the difference between quantitative and qualitative?

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.

Qualitative research , on the other hand, collects non-numerical data such as words, images, and sounds. The focus is on exploring subjective experiences, opinions, and attitudes, often through observation and interviews.

Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography.

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis .

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded .

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

• Mixed methods research
• Designing qualitative research
• Methods of data collection and analysis
• Introduction to quantitative and qualitative research
• Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
• Qualitative research in health care: Analysing qualitative data
• Qualitative data analysis: the framework approach
• Using the framework method for the analysis of
• Qualitative data in multi-disciplinary health research
• Content Analysis
• Grounded Theory
• Thematic Analysis

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Comprehensive guide to qualitative research analysis methods.

Home » Comprehensive guide to qualitative research analysis methods

Qualitative analysis techniques play a crucial role in understanding human behavior and experiences. When researchers explore complex topics, they often need to gather insights beyond numbers and statistics. This kind of depth allows for a richer understanding of people's thoughts, attitudes, and emotions.

In qualitative research, techniques such as interviews, focus groups, and content analysis are invaluable. They provide nuanced perspectives that quantitative methods alone cannot capture. By employing these techniques, researchers can effectively analyze and interpret data, revealing the underlying themes and patterns that inform decision-making. This guide serves as a comprehensive resource for mastering qualitative analysis techniques, empowering researchers to derive meaningful insights from their findings.

Overview of Qualitative Research Analysis

Qualitative analysis techniques are essential for understanding complex data derived from various qualitative research methods. These techniques help researchers extract meaningful insights and themes from interviews, focus groups, and open-ended surveys. By enabling researchers to delve deeper into participant perspectives, qualitative analysis techniques yield rich narratives that numerical data alone cannot provide.

Several core qualitative analysis techniques exist, each serving unique purposes. Thematic analysis identifies recurring themes within data, offering a broad overview of trends. Grounded theory focuses on generating theories rooted in data, while narrative analysis explores personal stories and experiences. Framework analysis organizes data for systematic comparison and interpretation. Lastly, discourse analysis examines language use and social context in communication. Understanding these techniques equips researchers to effectively analyze qualitative data and derive actionable insights that inform strategic decisions.

Importance of Qualitative Analysis Techniques

Qualitative analysis techniques are essential for gaining deep insights into human behavior, motivations, and emotions. Such techniques allow researchers to explore complex phenomena that quantitative methods cannot fully capture. They help in understanding the context and meaning behind the data, leading to richer, more comprehensive findings.

One significant importance of qualitative analysis techniques is their ability to uncover nuances within the data. Researchers can identify patterns, themes, and trends that might otherwise be overlooked in purely numerical analyses. Additionally, they foster a participatory approach, encouraging engagement and dialogue among participants, which can lead to more trustworthy insights. By utilizing these techniques, researchers can better interpret subjective experiences and provide actionable recommendations tailored to specific needs, ultimately enhancing the quality of their analyses.

Qualitative vs. Quantitative Methods

Qualitative analysis techniques offer in-depth insights that quantitative methods may miss. While quantitative methods focus on numerical data and statistical validation, qualitative techniques explore the meanings, themes, and experiences behind that data. This qualitative approach emphasizes understanding the participant's perspective, which adds rich context to survey findings or interviews.

When comparing qualitative and quantitative methods, three key distinctions emerge. First, qualitative research addresses open-ended questions, allowing participants to express their thoughts freely. Conversely, quantitative methods rely on pre-defined scales to measure responses. Second, qualitative data is often narrative and descriptive, while quantitative data is structured and expressed in numbers. Finally, qualitative research is more flexible, adapting dynamically while data collection is ongoing, while quantitative analysis follows a predetermined model. These differences highlight the complementary roles of qualitative and quantitative methods in gathering comprehensive insights.

Key Qualitative Analysis Techniques

Qualitative analysis techniques are essential for deriving meaningful insights from qualitative data. These techniques allow researchers to capture participants' experiences, opinions, and emotions, leading to a deeper understanding of complex subjects. The goal is to identify patterns and themes that reflect the nuances of human behavior and interaction, fostering both individual and collective understanding.

One widely used technique is thematic analysis, which involves identifying and analyzing patterns within qualitative data. Another important approach is grounded theory, where theories are developed based on the data collected, making the analysis inherently flexible. Additionally, narrative analysis focuses on the stories participants share, revealing their perspectives and contextual backgrounds. Lastly, content analysis helps quantify and analyze the presence of certain words or themes, offering a structured way to interpret qualitative material. Understanding these key qualitative analysis techniques empowers researchers to convert raw data into actionable insights.

Thematic Analysis

Thematic analysis is a qualitative analysis technique that facilitates the identification of patterns and themes within qualitative data. This method involves a systematic process where researchers review, analyze, and interpret data to uncover recurring themes that provide insights into the participants' experiences and perceptions. By focusing on these patterns, researchers can gain a deeper understanding of the data, which is essential for drawing meaningful conclusions.

To conduct a thematic analysis, follow these key steps:

• Familiarize yourself with the data.
• Generate initial codes.
• Identify themes from the codes.
• Review themes for coherence.
• Define and name the themes.

Each step is crucial for ensuring that the analysis is thorough and reflective of the data's complexity. Ultimately, thematic analysis empowers researchers to translate qualitative findings into key insights that can guide future research or inform practical applications.

Grounded Theory

Grounded Theory is a prominent qualitative analysis technique used to generate theories grounded in systematic data collection and analysis. This method focuses on discovering patterns and constructing theories based on the data rather than testing existing theories. Researchers employing Grounded Theory begin by collecting qualitative data through interviews, observations, or written materials, seeking to understand the underlying social processes within a specific context.

Once the data is collected, researchers engage in coding, a crucial step in identifying concepts and categories that emerge from the data. The iterative nature of Grounded Theory encourages researchers to return to the data multiple times, refining their insights and developing a robust theoretical framework. This flexibility makes Grounded Theory particularly valuable in exploring complex social phenomena, allowing researchers to build theories that reflect the perspectives of the participants rather than preconceived notions. Through this approach, Grounded Theory contributes to a deeper understanding of qualitative research and its capacity to illuminate intricate human experiences.

Implementing Qualitative Analysis Techniques in Research

When implementing qualitative analysis techniques in research, it is essential to adhere to structured methodologies that ensure thorough exploration of data. First, researchers should familiarize themselves with the core qualitative methods such as thematic analysis, grounded theory, and content analysis. Each method offers distinct advantages and should be selected based on research goals and data characteristics.

Next, it is important to follow a systematic approach to coding the data. This involves categorizing responses into meaningful themes or codes, which facilitates deeper understanding. Researchers must also engage in constant comparison, refining codes and themes as new data emerges. Finally, drawing interpretations from the analysis requires a critical evaluation of the findings, ensuring that conclusions are grounded in the data while acknowledging any potential biases. Adopting these qualitative analysis techniques will not only enhance research quality but also provide clearer insights into complex phenomena.

Data Collection Methods

Data collection methods play a pivotal role in qualitative analysis techniques, shaping the effectiveness of research. One popular method involves conducting in-depth interviews, allowing researchers to gather personal insights and experiences. Focus groups are another effective approach, where diverse participants discuss topics under the guidance of a moderator. This interactive setting encourages rich dialogue and collective insights, essential for understanding complex issues.

Another method is observational research, which involves observing behaviors in natural settings. This technique helps gather contextual information that surveys or interviews alone may overlook. Furthermore, researchers often utilize case studies, focusing on detailed examinations of specific instances or phenomena. Each of these methods contributes to a comprehensive understanding of qualitative data, aiding researchers in drawing valuable conclusions. Selecting the appropriate method depends on the research objectives and the type of information needed, ensuring a robust qualitative analysis.

Data Coding and Interpretation

Data coding serves as a pivotal component of qualitative analysis techniques, allowing researchers to systematically categorize and interpret their data. This process begins with the identification of key themes, patterns, and concepts embedded in the collected qualitative data. Using coding frameworks, researchers can assign labels to significant pieces of information, enabling a structured approach to understanding complex narratives and experiences.

Once coding is complete, interpretation follows. Researchers aim to derive meaningful insights from coded data, connecting these insights back to the research questions and objectives. This phase requires critical thinking and contextual understanding to ensure that interpretations are grounded in the data rather than preconceived notions. Ultimately, effective data coding and interpretation lead to richer, more nuanced findings that contribute to a deeper understanding of the research subject, making it an indispensable part of qualitative analysis techniques.

Conclusion on Mastering Qualitative Analysis Techniques

Mastering qualitative analysis techniques is a journey that enhances the richness of research. By navigating various methods, researchers gain deep insights into human behavior, opinions, and experiences. This understanding equips professionals with the necessary tools to interpret complex data effectively, guiding decisions that impact real-world outcomes.

Furthermore, continual practice and refinement of these techniques establish greater confidence in analysis capabilities. Engaging with diverse perspectives helps researchers remain adaptable and open-minded. Ultimately, mastering qualitative analysis techniques not only enriches the research process but also fosters a culture of inquiry and critical thinking in various fields.

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Qualitative research examples: How to unlock, rich, descriptive insights

Qualitative research uncovers in-depth user insights, but what does it look like? Here are seven methods and examples to help you get the data you need.

Armin Tanovic

Behind every what, there’s a why . Qualitative research is how you uncover that why. It enables you to connect with users and understand their thoughts, feelings, wants, needs, and pain points.

There’s many methods for conducting qualitative research, and many objectives it can help you pursue—you might want to explore ways to improve NPS scores, combat reduced customer retention, or understand (and recreate) the success behind a well-received product. The common thread? All these metrics impact your business, and qualitative research can help investigate and improve that impact.

In this article, we’ll take you through seven methods and examples of qualitative research, including when and how to use them.

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7 Qualitative research methods: An overview

There are various qualitative UX research methods that can help you get in-depth, descriptive insights. Some are suited to specific phases of the design and development process, while others are more task-oriented.

Here’s our overview of the most common qualitative research methods. Keep reading for their use cases, and detailed examples of how to conduct them.

1. User interviews

A user interview is a one-on-one conversation between a UX researcher, designer or Product Manager and a target user to understand their thoughts, perspectives, and feelings on a product or service. User interviews are a great way to get non-numerical data on individual experiences with your product, to gain a deeper understanding of user perspectives.

Interviews can be structured, semi-structured, or unstructured . Structured interviews follow a strict interview script and can help you get answers to your planned questions, while semi and unstructured interviews are less rigid in their approach and typically lead to more spontaneous, user-centered insights.

When to use user interviews

Interviews are ideal when you want to gain an in-depth understanding of your users’ perspectives on your product or service, and why they feel a certain way.

Interviews can be used at any stage in the product design and development process, being particularly helpful during:

• The discovery phase: To better understand user needs, problems, and the context in which they use your product—revealing the best potential solutions
• The design phase: To get contextual feedback on mockups, wireframes, and prototypes, helping you pinpoint issues and the reasons behind them
• Post-launch: To assess if your product continues to meet users’ shifting expectations and understand why or why not

How to conduct user interviews: The basics

• Draft questions based on your research objectives
• Recruit relevant research participants and schedule interviews
• Conduct the interview and transcribe responses
• Analyze the interview responses to extract insights
• Use your findings to inform design, product, and business decisions

💡 A specialized user interview tool makes interviewing easier. With Maze Interview Studies , you can recruit, host, and analyze interviews all on one platform.

User interviews: A qualitative research example

Let’s say you’ve designed a recruitment platform, called Tech2Talent , that connects employers with tech talent. Before starting the design process, you want to clearly understand the pain points employers experience with existing recruitment tools'.

You draft a list of ten questions for a semi-structured interview for 15 different one-on-one interviews. As it’s semi-structured, you don’t expect to ask all the questions—the script serves as more of a guide.

One key question in your script is: “Have tech recruitment platforms helped you find the talent you need in the past?”

Most respondents answer with a resounding and passionate ‘no’ with one of them expanding:

“For our company, it’s been pretty hit or miss honestly. They let just about anyone make a profile and call themselves tech talent. It’s so hard sifting through serious candidates. I can’t see any of their achievements until I invest time setting up an interview.”

You begin to notice a pattern in your responses: recruitment tools often lack easily accessible details on talent profiles.

You’ve gained contextual feedback on why other recruitment platforms fail to solve user needs.

2. Focus groups

A focus group is a research method that involves gathering a small group of people—around five to ten users—to discuss a specific topic, such as their’ experience with your new product feature. Unlike user interviews, focus groups aim to capture the collective opinion of a wider market segment and encourage discussion among the group.

When to use focus groups

You should use focus groups when you need a deeper understanding of your users’ collective opinions. The dynamic discussion among participants can spark in-depth insights that might not emerge from regular interviews.

Focus groups can be used before, during, and after a product launch. They’re ideal:

• Throughout the problem discovery phase: To understand your user segment’s pain points and expectations, and generate product ideas
• Post-launch: To evaluate and understand the collective opinion of your product’s user experience
• When conducting market research: To grasp usage patterns, consumer perceptions, and market opportunities for your product

How to conduct focus group studies: The basics

• Draft prompts to spark conversation, or a series of questions based on your UX research objectives
• Find a group of five to ten users who are representative of your target audience (or a specific user segment) and schedule your focus group session
• Conduct the focus group by talking and listening to users, then transcribe responses
• Analyze focus group responses and extract insights
• Use your findings to inform design decisions

The number of participants can make it difficult to take notes or do manual transcriptions. We recommend using a transcription or a specialized UX research tool , such as Maze, that can automatically create ready-to-share reports and highlight key user insights.

Focus groups: A qualitative research example

You’re a UX researcher at FitMe , a fitness app that creates customized daily workouts for gym-goers. Unlike many other apps, FitMe takes into account the previous day’s workout and aims to create one that allows users to effectively rest different muscles.

However, FitMe has an issue. Users are generating workouts but not completing them. They’re accessing the app, taking the necessary steps to get a workout for the day, but quitting at the last hurdle.

Time to talk to users.

You organize a focus group to get to the root of the drop-off issue. You invite five existing users, all of whom have dropped off at the exact point you’re investigating, and ask them questions to uncover why.

A dialog develops:

Participant 1: “Sometimes I’ll get a workout that I just don’t want to do. Sure, it’s a good workout—but I just don’t want to physically do it. I just do my own thing when that happens.”

Participant 2: “Same here, some of them are so boring. I go to the gym because I love it. It’s an escape.”

Participant 3: “Right?! I get that the app generates the best one for me on that specific day, but I wish I could get a couple of options.”

Participant 4: “I’m the same, there are some exercises I just refuse to do. I’m not coming to the gym to do things I dislike.”

Conducting the focus groups and reviewing the transcripts, you realize that users want options. A workout that works for one gym-goer doesn’t necessarily work for the next.

A possible solution? Adding the option to generate a new workout (that still considers previous workouts)and the ability to blacklist certain exercises, like burpees.

3. Ethnographic research

Ethnographic research is a research method that involves observing and interacting with users in a real-life environment. By studying users in their natural habitat, you can understand how your product fits into their daily lives.

Ethnographic research can be active or passive. Active ethnographic research entails engaging with users in their natural environment and then following up with methods like interviews. Passive ethnographic research involves letting the user interact with the product while you note your observations.

When to use ethnographic research

Ethnographic research is best suited when you want rich insights into the context and environment in which users interact with your product. Keep in mind that you can conduct ethnographic research throughout the entire product design and development process —from problem discovery to post-launch. However, it’s mostly done early in the process:

• Early concept development: To gain an understanding of your user's day-to-day environment. Observe how they complete tasks and the pain points they encounter. The unique demands of their everyday lives will inform how to design your product.
• Initial design phase: Even if you have a firm grasp of the user’s environment, you still need to put your solution to the test. Conducting ethnographic research with your users interacting with your prototype puts theory into practice.

How to conduct ethnographic research:

• Recruit users who are reflective of your audience
• Meet with them in their natural environment, and tell them to behave as they usually would
• Take down field notes as they interact with your product
• Engage with your users, ask questions, or host an in-depth interview if you’re doing an active ethnographic study
• Collect all your data and analyze it for insights

While ethnographic studies provide a comprehensive view of what potential users actually do, they are resource-intensive and logistically difficult. A common alternative is diary studies. Like ethnographic research, diary studies examine how users interact with your product in their day-to-day, but the data is self-reported by participants.

⚙️ Recruiting participants proving tough and time-consuming? Maze Panel makes it easy, with 400+ filters to find your ideal participants from a pool of 3 million participants.

Ethnographic research: A qualitative research example

You're a UX researcher for a project management platform called ProFlow , and you’re conducting an ethnographic study of the project creation process with key users, including a startup’s COO.

The first thing you notice is that the COO is rushing while navigating the platform. You also take note of the 46 tabs and Zoom calls opened on their monitor. Their attention is divided, and they let out an exasperated sigh as they repeatedly hit “refresh” on your website’s onboarding interface.

You conclude the session with an interview and ask, “How easy or difficult did you find using ProFlow to coordinate a project?”

The COO answers: “Look, the whole reason we turn to project platforms is because we need to be quick on our feet. I’m doing a million things so I need the process to be fast and simple. The actual project management is good, but creating projects and setting up tables is way too complicated.”

You realize that ProFlow ’s project creation process takes way too much time for professionals working in fast-paced, dynamic environments. To solve the issue, propose a quick-create option that enables them to move ahead with the basics instead of requiring in-depth project details.

4. Qualitative observation

Qualitative observation is a similar method to ethnographic research, though not as deep. It involves observing your users in a natural or controlled environment and taking notes as they interact with a product. However, be sure not to interrupt them, as this compromises the integrity of the study and turns it into active ethnographic research.

When to qualitative observation

Qualitative observation is best when you want to record how users interact with your product without anyone interfering. Much like ethnographic research, observation is best done during:

• Early concept development: To help you understand your users' daily lives, how they complete tasks, and the problems they deal with. The observations you collect in these instances will help you define a concept for your product.
• Initial design phase: Observing how users deal with your prototype helps you test if they can easily interact with it in their daily environments

How to conduct qualitative observation:

• Recruit users who regularly use your product
• Meet with users in either their natural environment, such as their office, or within a controlled environment, such as a lab
• Observe them and take down field notes based on what you notice

Qualitative observation: An qualitative research example

You’re conducting UX research for Stackbuilder , an app that connects businesses with tools ideal for their needs and budgets. To determine if your app is easy to use for industry professionals, you decide to conduct an observation study.

Sitting in with the participant, you notice they breeze past the onboarding process, quickly creating an account for their company. Yet, after specifying their company’s budget, they suddenly slow down. They open links to each tool’s individual page, confusingly switching from one tab to another. They let out a sigh as they read through each website.

Conducting your observation study, you realize that users find it difficult to extract information from each tool’s website. Based on your field notes, you suggest including a bullet-point summary of each tool directly on your platform.

5. Case study research

Case studies are a UX research method that provides comprehensive and contextual insights into a real-world case over a long period of time. They typically include a range of other qualitative research methods, like interviews, observations, and ethnographic research. A case study allows you to form an in-depth analysis of how people use your product, helping you uncover nuanced differences between your users.

When to use case studies

Case studies are best when your product involves complex interactions that need to be tracked over a longer period or through in-depth analysis. You can also use case studies when your product is innovative, and there’s little existing data on how users interact with it.

As for specific phases in the product design and development process:

• Initial design phase: Case studies can help you rigorously test for product issues and the reasons behind them, giving you in-depth feedback on everything between user motivations, friction points, and usability issues
• Post-launch phase: Continuing with case studies after launch can give you ongoing feedback on how users interact with the product in their day-to-day lives. These insights ensure you can meet shifting user expectations with product updates and future iterations

How to conduct case studies:

• Outline an objective for your case study such as examining specific user tasks or the overall user journey
• Select qualitative research methods such as interviews, ethnographic studies, or observations
• Collect and analyze your data for comprehensive insights
• Include your findings in a report with proposed solutions

Case study research: A qualitative research example

Your team has recently launched Pulse , a platform that analyzes social media posts to identify rising digital marketing trends. Pulse has been on the market for a year, and you want to better understand how it helps small businesses create successful campaigns.

To conduct your case study, you begin with a series of interviews to understand user expectations, ethnographic research sessions, and focus groups. After sorting responses and observations into common themes you notice a main recurring pattern. Users have trouble interpreting the data from their dashboards, making it difficult to identify which trends to follow.

With your synthesized insights, you create a report with detailed narratives of individual user experiences, common themes and issues, and recommendations for addressing user friction points.

Some of your proposed solutions include creating intuitive graphs and summaries for each trend study. This makes it easier for users to understand trends and implement strategic changes in their campaigns.

6. Secondary research

Secondary research is a research method that involves collecting and analyzing documents, records, and reviews that provide you with contextual data on your topic. You’re not connecting with participants directly, but rather accessing pre-existing available data. For example, you can pull out insights from your UX research repository to reexamine how they apply to your new UX research objective.

Strictly speaking, it can be both qualitative and quantitative—but today we focus on its qualitative application.

When to use secondary research

Record keeping is particularly useful when you need supplemental insights to complement, validate, or compare current research findings. It helps you analyze shifting trends amongst your users across a specific period. Some other scenarios where you need record keeping include:

• Initial discovery or exploration phase: Secondary research can help you quickly gather background information and data to understand the broader context of a market
• Design and development phase: See what solutions are working in other contexts for an idea of how to build yours

Secondary research is especially valuable when your team faces budget constraints, tight deadlines, or limited resources. Through review mining and collecting older findings, you can uncover useful insights that drive decision-making throughout the product design and development process.

How to conduct secondary research:

• Outline your UX research objective
• Identify potential data sources for information on your product, market, or target audience. Some of these sources can include: a. Review websites like Capterra and G2 b. Social media channels c. Customer service logs and disputes d. Website reviews e. Reports and insights from previous research studies f. Industry trends g. Information on competitors
• Analyze your data by identifying recurring patterns and themes for insights

Secondary research: A qualitative research example

SafeSurf is a cybersecurity platform that offers threat detection, security audits, and real-time reports. After conducting multiple rounds of testing, you need a quick and easy way to identify remaining usability issues. Instead of conducting another resource-intensive method, you opt for social listening and data mining for your secondary research.

Browsing through your company’s X, you identify a recurring theme: many users without a background in tech find SafeSurf ’s reports too technical and difficult to read. Users struggle with understanding what to do if their networks are breached.

After checking your other social media channels and review sites, the issue pops up again.

With your gathered insights, your team settles on introducing a simplified version of reports, including clear summaries, takeaways, and step-by-step protocols for ensuring security.

By conducting secondary research, you’ve uncovered a major usability issue—all without spending large amounts of time and resources to connect with your users.

7. Open-ended surveys

Open-ended surveys are a type of unmoderated UX research method that involves asking users to answer a list of qualitative research questions designed to uncover their attitudes, expectations, and needs regarding your service or product. Open-ended surveys allow users to give in-depth, nuanced, and contextual responses.

When to use open-ended surveys

User surveys are an effective qualitative research method for reaching a large number of users. You can use them at any stage of the design and product development process, but they’re particularly useful:

• When you’re conducting generative research : Open-ended surveys allow you to reach a wide range of users, making them especially useful during initial research phases when you need broad insights into user experiences
• When you need to understand customer satisfaction: Open-ended customer satisfaction surveys help you uncover why your users might be dissatisfied with your product, helping you find the root cause of their negative experiences
• In combination with close-ended surveys: Get a combination of numerical, statistical insights and rich descriptive feedback. You’ll know what a specific percentage of your users think and why they think it.

How to conduct open-ended surveys:

• Design your survey and draft out a list of survey questions
• Distribute your surveys to respondents
• Analyze survey participant responses for key themes and patterns
• Use your findings to inform your design process

Open-ended surveys: A qualitative research example

You're a UX researcher for RouteReader , a comprehensive logistics platform that allows users to conduct shipment tracking and route planning. Recently, you’ve launched a new predictive analytics feature that allows users to quickly identify and prepare for supply chain disruptions.

To better understand if users find the new feature helpful, you create an open-ended, in-app survey.

The questions you ask your users:

• “What has been your experience with our new predictive analytics feature?"
• “Do you find it easy or difficult to rework your routes based on our predictive suggestions?”
• “Does the predictive analytics feature make planning routes easier? Why or why not?”

Most of the responses are positive. Users report using the predictive analytics feature to make last-minute adjustments to their route plans, and some even rely on it regularly. However, a few users find the feature hard to notice, making it difficult to adjust their routes on time.

To ensure users have supply chain insights on time, you integrate the new feature into each interface so users can easily spot important information and adjust their routes accordingly.

💡 Surveys are a lot easier with a quality survey tool. Maze’s Feedback Surveys solution has all you need to ensure your surveys get the insights you need—including AI-powered follow-up and automated reports.

Qualitative research vs. quantitative research: What’s the difference?

Alongside qualitative research approaches, UX teams also use quantitative research methods. Despite the similar names, the two are very different.

Here are some of the key differences between qualitative research and quantitative research .

Before selecting either qualitative or quantitative methods, first identify what you want to achieve with your UX research project. As a general rule of thumb, think qualitative data collection for in-depth understanding and quantitative studies for measurement and validation.

Conduct qualitative research with Maze

You’ll often find that knowing the what is pointless without understanding the accompanying why . Qualitative research helps you uncover your why.

So, what about how —how do you identify your 'what' and your 'why'?

The answer is with a user research tool like Maze.

Maze is the leading user research platform that lets you organize, conduct, and analyze both qualitative and quantitative research studies—all from one place. Its wide variety of UX research methods and advanced AI capabilities help you get the insights you need to build the right products and experiences faster.

Frequently asked questions about qualitative research examples

What is qualitative research?

Qualitative research is a research method that aims to provide contextual, descriptive, and non-numerical insights on a specific issue. Qualitative research methods like interviews, case studies, and ethnographic studies allow you to uncover the reasoning behind your user’s attitudes and opinions.

Can a study be both qualitative and quantitative?

Absolutely! You can use mixed methods in your research design, which combines qualitative and quantitative approaches to gain both descriptive and statistical insights.

For example, user surveys can have both close-ended and open-ended questions, providing comprehensive data like percentages of user views and descriptive reasoning behind their answers.

Is qualitative or quantitative research better?

The choice between qualitative and quantitative research depends upon your research goals and objectives.

Qualitative research methods are better suited when you want to understand the complexities of your user’s problems and uncover the underlying motives beneath their thoughts, feelings, and behaviors. Quantitative research excels in giving you numerical data, helping you gain a statistical view of your user's attitudes, identifying trends, and making predictions.

What are some approaches to qualitative research?

There are many approaches to qualitative studies. An approach is the underlying theory behind a method, and a method is a way of implementing the approach. Here are some approaches to qualitative research:

• Grounded theory: Researchers study a topic and develop theories inductively
• Phenomenological research: Researchers study a phenomenon through the lived experiences of those involved
• Ethnography: Researchers immerse themselves in organizations to understand how they operate

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Strengths and Weaknesses of Quantitative Interviews

Quantitative interviews offer several benefits. The strengths and weakness of quantitative interviews tend to be couched in comparison to those of administering hard copy questionnaires. For example, response rates tend to be higher with interviews than with mailed questionnaires (Babbie, 2010). 1 That makes sense—don’t you find it easier to say no to a piece of paper than to a person? Quantitative interviews can also help reduce respondent confusion. If a respondent is unsure about the meaning of a question or answer option on a questionnaire, he or she probably won’t have the opportunity to get clarification from the researcher. An interview, on the other hand, gives the researcher an opportunity to clarify or explain any items that may be confusing.

As with every method of data collection we’ve discussed, there are also drawbacks to conducting quantitative interviews. Perhaps the largest, and of most concern to quantitative researchers, is interviewer effect. While questions on hard copy questionnaires may create an impression based on the way they are presented, having a person administer questions introduces a slew of additional variables that might influence a respondent. As I’ve said, consistency is key with quantitative data collection—and human beings are not necessarily known for their consistency. Interviewing respondents is also much more time consuming and expensive than mailing questionnaires. Thus quantitative researchers may opt for written questionnaires over interviews on the grounds that they will be able to reach a large sample at a much lower cost than were they to interact personally with each and every respondent.

KEY TAKEAWAYS

• Unlike qualitative interviews, quantitative interviews usually contain closed-ended questions that are delivered in the same format and same order to every respondent.
• Quantitative interview data are analyzed by assigning a numerical value to participants’ responses.
• While quantitative interviews offer several advantages over self-administered questionnaires such as higher response rates and lower respondent confusion, they have the drawbacks of possible interviewer effect and greater time and expense.
• The General Social Survey (GSS), which we’ve mentioned in previous chapters, is administered via in-person interview, just like quantitative interviewing procedures described here. Read more about the GSS at http://www.norc.uchicago.edu/GSS+Website .
• Take a few of the open-ended questions you created after reading " Qualitative Interview Techniques and Considerations " on qualitative interviewing techniques. See if you can turn them into closed-ended questions.
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• Designing Questionnaires KEY TAKEAWAYS EXERCISES
• From Completed Questionnaires to Analyzable Data
• Identifying Patterns KEY TAKEAWAYS EXERCISES
• Interview Research: What Is It and When Should It Be Used? LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISE
• Conducting Qualitative Interviews
• Analysis of Qualitative Interview Data
• Strengths and Weaknesses of Qualitative Interviews KEY TAKEAWAYS EXERCISES
• Conducting Quantitative Interviews
• Analysis of Quantitative Interview Data
• Strengths and Weaknesses of Quantitative Interviews KEY TAKEAWAYS EXERCISES
• Location, Location, Location
• Researcher-Respondent Relationship KEY TAKEAWAYS EXERCISES
• Field Research: What Is It and When to Use It? LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISES
• Strengths of Field Research
• Weaknesses of Field Research KEY TAKEAWAYS EXERCISES
• Choosing a Site
• Choosing a Role KEY TAKEAWAYS EXERCISES
• Writing in the Field
• Writing out of the Field KEY TAKEAWAYS EXERCISE
• From Description to Analysis KEY TAKEAWAYS EXERCISE
• Unobtrusive Research: What Is It and When to Use It? LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISES
• Strengths of Unobtrusive Research
• Weaknesses of Unobtrusive Research KEY TAKEAWAYS EXERCISES
• Content Analysis
• Indirect Measures
• Analysis of Unobtrusive Data Collected by You KEY TAKEAWAYS EXERCISES
• Analyzing Others’ Data LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISES
• Reliability in Unobtrusive Research LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISE
• Focus Groups LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISES
• Experiments LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISES
• Ethnomethodology and Conversation Analysis LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISE
• Sharing It All: The Good, the Bad, and the Ugly
• Knowing Your Audience KEY TAKEAWAYS EXERCISE
• Presenting Your Research LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISES
• Writing Up Research Results LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISES
• Disseminating Findings LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISES
• Reading Reports of Sociological Research LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISES
• Being a Responsible Consumer of Research LEARNING OBJECTIVE KEY TAKEAWAY EXERCISE
• Media Reports of Sociological Research LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISES
• Sociological Research: It’s Everywhere LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISE
• Evaluation Research
• Market Research
• Policy and Other Government Research KEY TAKEAWAY EXERCISE
• Doing Research for a Cause LEARNING OBJECTIVES KEY TAKEAWAYS EXERCISE
• Public Sociology LEARNING OBJECTIVE KEY TAKEAWAYS EXERCISES
• Transferable Skills
• Understanding Yourself, Your Circumstances, and Your World KEY TAKEAWAYS EXERCISES
•  Back Matter

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  Global Journal of Educational Research Journal / Global Journal of Educational Research / Vol. 23 No. 3 (2024) / Articles (function() { function async_load(){ var s = document.createElement('script'); s.type = 'text/javascript'; s.async = true; var theUrl = 'https://www.journalquality.info/journalquality/ratings/2408-www-ajol-info-gjedr'; s.src = theUrl + ( theUrl.indexOf("?") >= 0 ? "&" : "?") + 'ref=' + encodeURIComponent(window.location.href); var embedder = document.getElementById('jpps-embedder-ajol-gjedr'); embedder.parentNode.insertBefore(s, embedder); } if (window.attachEvent) window.attachEvent('onload', async_load); else window.addEventListener('load', async_load, false); })();  

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© Bachudo Science Co. Ltd. This work is licensed under the creative commons Attribution 4.0 International license.

Uchegbue Henrietta Osayi

Department of Educational Foundations, University of Calabar, Calabar,                                                     Nigeria

Otu Bernard Diwa

Department of Educational Foundations, University of Calabar, Calabar, Nigeria

Ari Judith Tano

Main article content, classroom variables and its influence on verbal reasoning outcomes in basic technology among secondary school students: application of discriminant analysis.

This study investigates the influence of classroom variables on verbal reasoning outcomes in basic technology among secondary school students in Cross River State, Nigeria. The research adopts a survey research design, utilizing both quantitative and qualitative data collection methods to evaluate the relationship between interactive teaching practices and student performance. A sample size of 1176 students from various secondary schools in the state was selected through stratified random sampling to ensure a diverse representation of the student population. One research question and one research hypothesis guided the quantitative part of this study. Structured questionnaires were administered to gather quantitative data on students' perceptions of classroom variables and their corresponding verbal reasoning outcomes in basic technology. Meanwhile, in-depth interviews and focus group discussions with teachers and students provided data on the dynamics of classroom interactions and their effects on learning. The study's findings indicate a significant positive correlation between the frequency and quality of classroom variables and improved verbal reasoning outcomes in basic technology. Furthermore, the research highlights the importance of interactive teaching methods, timely feedback, and supportive communication in enhancing students' understanding and retention of basic technology concepts. Based on the study findings, it was recommended that the policy makers and educators should invest in training programs that enhance interactive teaching skills and promote effective classroom communication.

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