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• Survey Research | Definition, Examples & Methods

Survey Research | Definition, Examples & Methods

Published on August 20, 2019 by Shona McCombes . Revised on June 22, 2023.

Survey research means collecting information about a group of people by asking them questions and analyzing the results. To conduct an effective survey, follow these six steps:

• Determine who will participate in the survey
• Decide the type of survey (mail, online, or in-person)
• Design the survey questions and layout
• Distribute the survey
• Analyze the responses
• Write up the results

Surveys are a flexible method of data collection that can be used in many different types of research .

Table of contents

What are surveys used for, step 1: define the population and sample, step 2: decide on the type of survey, step 3: design the survey questions, step 4: distribute the survey and collect responses, step 5: analyze the survey results, step 6: write up the survey results, other interesting articles, frequently asked questions about surveys.

Surveys are used as a method of gathering data in many different fields. They are a good choice when you want to find out about the characteristics, preferences, opinions, or beliefs of a group of people.

Common uses of survey research include:

• Social research : investigating the experiences and characteristics of different social groups
• Market research : finding out what customers think about products, services, and companies
• Health research : collecting data from patients about symptoms and treatments
• Politics : measuring public opinion about parties and policies
• Psychology : researching personality traits, preferences and behaviours

Surveys can be used in both cross-sectional studies , where you collect data just once, and in longitudinal studies , where you survey the same sample several times over an extended period.

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Before you start conducting survey research, you should already have a clear research question that defines what you want to find out. Based on this question, you need to determine exactly who you will target to participate in the survey.

Populations

The target population is the specific group of people that you want to find out about. This group can be very broad or relatively narrow. For example:

• The population of Brazil
• US college students
• Second-generation immigrants in the Netherlands
• Customers of a specific company aged 18-24
• British transgender women over the age of 50

Your survey should aim to produce results that can be generalized to the whole population. That means you need to carefully define exactly who you want to draw conclusions about.

Several common research biases can arise if your survey is not generalizable, particularly sampling bias and selection bias . The presence of these biases have serious repercussions for the validity of your results.

It’s rarely possible to survey the entire population of your research – it would be very difficult to get a response from every person in Brazil or every college student in the US. Instead, you will usually survey a sample from the population.

The sample size depends on how big the population is. You can use an online sample calculator to work out how many responses you need.

There are many sampling methods that allow you to generalize to broad populations. In general, though, the sample should aim to be representative of the population as a whole. The larger and more representative your sample, the more valid your conclusions. Again, beware of various types of sampling bias as you design your sample, particularly self-selection bias , nonresponse bias , undercoverage bias , and survivorship bias .

There are two main types of survey:

• A questionnaire , where a list of questions is distributed by mail, online or in person, and respondents fill it out themselves.
• An interview , where the researcher asks a set of questions by phone or in person and records the responses.

Which type you choose depends on the sample size and location, as well as the focus of the research.

Questionnaires

Sending out a paper survey by mail is a common method of gathering demographic information (for example, in a government census of the population).

• You can easily access a large sample.
• You have some control over who is included in the sample (e.g. residents of a specific region).
• The response rate is often low, and at risk for biases like self-selection bias .

Online surveys are a popular choice for students doing dissertation research , due to the low cost and flexibility of this method. There are many online tools available for constructing surveys, such as SurveyMonkey and Google Forms .

• You can quickly access a large sample without constraints on time or location.
• The data is easy to process and analyze.
• The anonymity and accessibility of online surveys mean you have less control over who responds, which can lead to biases like self-selection bias .

If your research focuses on a specific location, you can distribute a written questionnaire to be completed by respondents on the spot. For example, you could approach the customers of a shopping mall or ask all students to complete a questionnaire at the end of a class.

• You can screen respondents to make sure only people in the target population are included in the sample.
• You can collect time- and location-specific data (e.g. the opinions of a store’s weekday customers).
• The sample size will be smaller, so this method is less suitable for collecting data on broad populations and is at risk for sampling bias .

Oral interviews are a useful method for smaller sample sizes. They allow you to gather more in-depth information on people’s opinions and preferences. You can conduct interviews by phone or in person.

• You have personal contact with respondents, so you know exactly who will be included in the sample in advance.
• You can clarify questions and ask for follow-up information when necessary.
• The lack of anonymity may cause respondents to answer less honestly, and there is more risk of researcher bias.

Like questionnaires, interviews can be used to collect quantitative data: the researcher records each response as a category or rating and statistically analyzes the results. But they are more commonly used to collect qualitative data : the interviewees’ full responses are transcribed and analyzed individually to gain a richer understanding of their opinions and feelings.

Next, you need to decide which questions you will ask and how you will ask them. It’s important to consider:

• The type of questions
• The content of the questions
• The phrasing of the questions
• The ordering and layout of the survey

Open-ended vs closed-ended questions

There are two main forms of survey questions: open-ended and closed-ended. Many surveys use a combination of both.

Closed-ended questions give the respondent a predetermined set of answers to choose from. A closed-ended question can include:

• A binary answer (e.g. yes/no or agree/disagree )
• A scale (e.g. a Likert scale with five points ranging from strongly agree to strongly disagree )
• A list of options with a single answer possible (e.g. age categories)
• A list of options with multiple answers possible (e.g. leisure interests)

Closed-ended questions are best for quantitative research . They provide you with numerical data that can be statistically analyzed to find patterns, trends, and correlations .

Open-ended questions are best for qualitative research. This type of question has no predetermined answers to choose from. Instead, the respondent answers in their own words.

Open questions are most common in interviews, but you can also use them in questionnaires. They are often useful as follow-up questions to ask for more detailed explanations of responses to the closed questions.

The content of the survey questions

To ensure the validity and reliability of your results, you need to carefully consider each question in the survey. All questions should be narrowly focused with enough context for the respondent to answer accurately. Avoid questions that are not directly relevant to the survey’s purpose.

When constructing closed-ended questions, ensure that the options cover all possibilities. If you include a list of options that isn’t exhaustive, you can add an “other” field.

Phrasing the survey questions

In terms of language, the survey questions should be as clear and precise as possible. Tailor the questions to your target population, keeping in mind their level of knowledge of the topic. Avoid jargon or industry-specific terminology.

Survey questions are at risk for biases like social desirability bias , the Hawthorne effect , or demand characteristics . It’s critical to use language that respondents will easily understand, and avoid words with vague or ambiguous meanings. Make sure your questions are phrased neutrally, with no indication that you’d prefer a particular answer or emotion.

Ordering the survey questions

The questions should be arranged in a logical order. Start with easy, non-sensitive, closed-ended questions that will encourage the respondent to continue.

If the survey covers several different topics or themes, group together related questions. You can divide a questionnaire into sections to help respondents understand what is being asked in each part.

If a question refers back to or depends on the answer to a previous question, they should be placed directly next to one another.

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Before you start, create a clear plan for where, when, how, and with whom you will conduct the survey. Determine in advance how many responses you require and how you will gain access to the sample.

When you are satisfied that you have created a strong research design suitable for answering your research questions, you can conduct the survey through your method of choice – by mail, online, or in person.

There are many methods of analyzing the results of your survey. First you have to process the data, usually with the help of a computer program to sort all the responses. You should also clean the data by removing incomplete or incorrectly completed responses.

If you asked open-ended questions, you will have to code the responses by assigning labels to each response and organizing them into categories or themes. You can also use more qualitative methods, such as thematic analysis , which is especially suitable for analyzing interviews.

Statistical analysis is usually conducted using programs like SPSS or Stata. The same set of survey data can be subject to many analyses.

Finally, when you have collected and analyzed all the necessary data, you will write it up as part of your thesis, dissertation , or research paper .

In the methodology section, you describe exactly how you conducted the survey. You should explain the types of questions you used, the sampling method, when and where the survey took place, and the response rate. You can include the full questionnaire as an appendix and refer to it in the text if relevant.

Then introduce the analysis by describing how you prepared the data and the statistical methods you used to analyze it. In the results section, you summarize the key results from your analysis.

In the discussion and conclusion , you give your explanations and interpretations of these results, answer your research question, and reflect on the implications and limitations of the research.

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.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your sampling methods or criteria for selecting subjects
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

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Home » Survey Research – Types, Methods, Examples

Survey Research – Types, Methods, Examples

Table of Contents

Survey Research

Definition:

Survey Research is a quantitative research method that involves collecting standardized data from a sample of individuals or groups through the use of structured questionnaires or interviews. The data collected is then analyzed statistically to identify patterns and relationships between variables, and to draw conclusions about the population being studied.

Survey research can be used to answer a variety of questions, including:

• What are people’s opinions about a certain topic?
• What are people’s experiences with a certain product or service?
• What are people’s beliefs about a certain issue?

Survey Research Methods

Survey Research Methods are as follows:

• Telephone surveys: A survey research method where questions are administered to respondents over the phone, often used in market research or political polling.
• Face-to-face surveys: A survey research method where questions are administered to respondents in person, often used in social or health research.
• Mail surveys: A survey research method where questionnaires are sent to respondents through mail, often used in customer satisfaction or opinion surveys.
• Online surveys: A survey research method where questions are administered to respondents through online platforms, often used in market research or customer feedback.
• Email surveys: A survey research method where questionnaires are sent to respondents through email, often used in customer satisfaction or opinion surveys.
• Mixed-mode surveys: A survey research method that combines two or more survey modes, often used to increase response rates or reach diverse populations.
• Computer-assisted surveys: A survey research method that uses computer technology to administer or collect survey data, often used in large-scale surveys or data collection.
• Interactive voice response surveys: A survey research method where respondents answer questions through a touch-tone telephone system, often used in automated customer satisfaction or opinion surveys.
• Mobile surveys: A survey research method where questions are administered to respondents through mobile devices, often used in market research or customer feedback.
• Group-administered surveys: A survey research method where questions are administered to a group of respondents simultaneously, often used in education or training evaluation.
• Web-intercept surveys: A survey research method where questions are administered to website visitors, often used in website or user experience research.
• In-app surveys: A survey research method where questions are administered to users of a mobile application, often used in mobile app or user experience research.
• Social media surveys: A survey research method where questions are administered to respondents through social media platforms, often used in social media or brand awareness research.
• SMS surveys: A survey research method where questions are administered to respondents through text messaging, often used in customer feedback or opinion surveys.
• IVR surveys: A survey research method where questions are administered to respondents through an interactive voice response system, often used in automated customer feedback or opinion surveys.
• Mixed-method surveys: A survey research method that combines both qualitative and quantitative data collection methods, often used in exploratory or mixed-method research.
• Drop-off surveys: A survey research method where respondents are provided with a survey questionnaire and asked to return it at a later time or through a designated drop-off location.
• Intercept surveys: A survey research method where respondents are approached in public places and asked to participate in a survey, often used in market research or customer feedback.
• Hybrid surveys: A survey research method that combines two or more survey modes, data sources, or research methods, often used in complex or multi-dimensional research questions.

Types of Survey Research

There are several types of survey research that can be used to collect data from a sample of individuals or groups. following are Types of Survey Research:

• Cross-sectional survey: A type of survey research that gathers data from a sample of individuals at a specific point in time, providing a snapshot of the population being studied.
• Longitudinal survey: A type of survey research that gathers data from the same sample of individuals over an extended period of time, allowing researchers to track changes or trends in the population being studied.
• Panel survey: A type of longitudinal survey research that tracks the same sample of individuals over time, typically collecting data at multiple points in time.
• Epidemiological survey: A type of survey research that studies the distribution and determinants of health and disease in a population, often used to identify risk factors and inform public health interventions.
• Observational survey: A type of survey research that collects data through direct observation of individuals or groups, often used in behavioral or social research.
• Correlational survey: A type of survey research that measures the degree of association or relationship between two or more variables, often used to identify patterns or trends in data.
• Experimental survey: A type of survey research that involves manipulating one or more variables to observe the effect on an outcome, often used to test causal hypotheses.
• Descriptive survey: A type of survey research that describes the characteristics or attributes of a population or phenomenon, often used in exploratory research or to summarize existing data.
• Diagnostic survey: A type of survey research that assesses the current state or condition of an individual or system, often used in health or organizational research.
• Explanatory survey: A type of survey research that seeks to explain or understand the causes or mechanisms behind a phenomenon, often used in social or psychological research.
• Process evaluation survey: A type of survey research that measures the implementation and outcomes of a program or intervention, often used in program evaluation or quality improvement.
• Impact evaluation survey: A type of survey research that assesses the effectiveness or impact of a program or intervention, often used to inform policy or decision-making.
• Customer satisfaction survey: A type of survey research that measures the satisfaction or dissatisfaction of customers with a product, service, or experience, often used in marketing or customer service research.
• Market research survey: A type of survey research that collects data on consumer preferences, behaviors, or attitudes, often used in market research or product development.
• Public opinion survey: A type of survey research that measures the attitudes, beliefs, or opinions of a population on a specific issue or topic, often used in political or social research.
• Behavioral survey: A type of survey research that measures actual behavior or actions of individuals, often used in health or social research.
• Attitude survey: A type of survey research that measures the attitudes, beliefs, or opinions of individuals, often used in social or psychological research.
• Opinion poll: A type of survey research that measures the opinions or preferences of a population on a specific issue or topic, often used in political or media research.
• Ad hoc survey: A type of survey research that is conducted for a specific purpose or research question, often used in exploratory research or to answer a specific research question.

Types Based on Methodology

Based on Methodology Survey are divided into two Types:

Quantitative Survey Research

Qualitative survey research.

Quantitative survey research is a method of collecting numerical data from a sample of participants through the use of standardized surveys or questionnaires. The purpose of quantitative survey research is to gather empirical evidence that can be analyzed statistically to draw conclusions about a particular population or phenomenon.

In quantitative survey research, the questions are structured and pre-determined, often utilizing closed-ended questions, where participants are given a limited set of response options to choose from. This approach allows for efficient data collection and analysis, as well as the ability to generalize the findings to a larger population.

Quantitative survey research is often used in market research, social sciences, public health, and other fields where numerical data is needed to make informed decisions and recommendations.

Qualitative survey research is a method of collecting non-numerical data from a sample of participants through the use of open-ended questions or semi-structured interviews. The purpose of qualitative survey research is to gain a deeper understanding of the experiences, perceptions, and attitudes of participants towards a particular phenomenon or topic.

In qualitative survey research, the questions are open-ended, allowing participants to share their thoughts and experiences in their own words. This approach allows for a rich and nuanced understanding of the topic being studied, and can provide insights that are difficult to capture through quantitative methods alone.

Qualitative survey research is often used in social sciences, education, psychology, and other fields where a deeper understanding of human experiences and perceptions is needed to inform policy, practice, or theory.

Data Analysis Methods

There are several Survey Research Data Analysis Methods that researchers may use, including:

• Descriptive statistics: This method is used to summarize and describe the basic features of the survey data, such as the mean, median, mode, and standard deviation. These statistics can help researchers understand the distribution of responses and identify any trends or patterns.
• Inferential statistics: This method is used to make inferences about the larger population based on the data collected in the survey. Common inferential statistical methods include hypothesis testing, regression analysis, and correlation analysis.
• Factor analysis: This method is used to identify underlying factors or dimensions in the survey data. This can help researchers simplify the data and identify patterns and relationships that may not be immediately apparent.
• Cluster analysis: This method is used to group similar respondents together based on their survey responses. This can help researchers identify subgroups within the larger population and understand how different groups may differ in their attitudes, behaviors, or preferences.
• Structural equation modeling: This method is used to test complex relationships between variables in the survey data. It can help researchers understand how different variables may be related to one another and how they may influence one another.
• Content analysis: This method is used to analyze open-ended responses in the survey data. Researchers may use software to identify themes or categories in the responses, or they may manually review and code the responses.
• Text mining: This method is used to analyze text-based survey data, such as responses to open-ended questions. Researchers may use software to identify patterns and themes in the text, or they may manually review and code the text.

Applications of Survey Research

Here are some common applications of survey research:

• Market Research: Companies use survey research to gather insights about customer needs, preferences, and behavior. These insights are used to create marketing strategies and develop new products.
• Public Opinion Research: Governments and political parties use survey research to understand public opinion on various issues. This information is used to develop policies and make decisions.
• Social Research: Survey research is used in social research to study social trends, attitudes, and behavior. Researchers use survey data to explore topics such as education, health, and social inequality.
• Academic Research: Survey research is used in academic research to study various phenomena. Researchers use survey data to test theories, explore relationships between variables, and draw conclusions.
• Customer Satisfaction Research: Companies use survey research to gather information about customer satisfaction with their products and services. This information is used to improve customer experience and retention.
• Employee Surveys: Employers use survey research to gather feedback from employees about their job satisfaction, working conditions, and organizational culture. This information is used to improve employee retention and productivity.
• Health Research: Survey research is used in health research to study topics such as disease prevalence, health behaviors, and healthcare access. Researchers use survey data to develop interventions and improve healthcare outcomes.

Examples of Survey Research

Here are some real-time examples of survey research:

• COVID-19 Pandemic Surveys: Since the outbreak of the COVID-19 pandemic, surveys have been conducted to gather information about public attitudes, behaviors, and perceptions related to the pandemic. Governments and healthcare organizations have used this data to develop public health strategies and messaging.
• Political Polls During Elections: During election seasons, surveys are used to measure public opinion on political candidates, policies, and issues in real-time. This information is used by political parties to develop campaign strategies and make decisions.
• Customer Feedback Surveys: Companies often use real-time customer feedback surveys to gather insights about customer experience and satisfaction. This information is used to improve products and services quickly.
• Event Surveys: Organizers of events such as conferences and trade shows often use surveys to gather feedback from attendees in real-time. This information can be used to improve future events and make adjustments during the current event.
• Website and App Surveys: Website and app owners use surveys to gather real-time feedback from users about the functionality, user experience, and overall satisfaction with their platforms. This feedback can be used to improve the user experience and retain customers.
• Employee Pulse Surveys: Employers use real-time pulse surveys to gather feedback from employees about their work experience and overall job satisfaction. This feedback is used to make changes in real-time to improve employee retention and productivity.

Survey Sample

Purpose of survey research.

The purpose of survey research is to gather data and insights from a representative sample of individuals. Survey research allows researchers to collect data quickly and efficiently from a large number of people, making it a valuable tool for understanding attitudes, behaviors, and preferences.

Here are some common purposes of survey research:

• Descriptive Research: Survey research is often used to describe characteristics of a population or a phenomenon. For example, a survey could be used to describe the characteristics of a particular demographic group, such as age, gender, or income.
• Exploratory Research: Survey research can be used to explore new topics or areas of research. Exploratory surveys are often used to generate hypotheses or identify potential relationships between variables.
• Explanatory Research: Survey research can be used to explain relationships between variables. For example, a survey could be used to determine whether there is a relationship between educational attainment and income.
• Evaluation Research: Survey research can be used to evaluate the effectiveness of a program or intervention. For example, a survey could be used to evaluate the impact of a health education program on behavior change.
• Monitoring Research: Survey research can be used to monitor trends or changes over time. For example, a survey could be used to monitor changes in attitudes towards climate change or political candidates over time.

When to use Survey Research

there are certain circumstances where survey research is particularly appropriate. Here are some situations where survey research may be useful:

• When the research question involves attitudes, beliefs, or opinions: Survey research is particularly useful for understanding attitudes, beliefs, and opinions on a particular topic. For example, a survey could be used to understand public opinion on a political issue.
• When the research question involves behaviors or experiences: Survey research can also be useful for understanding behaviors and experiences. For example, a survey could be used to understand the prevalence of a particular health behavior.
• When a large sample size is needed: Survey research allows researchers to collect data from a large number of people quickly and efficiently. This makes it a useful method when a large sample size is needed to ensure statistical validity.
• When the research question is time-sensitive: Survey research can be conducted quickly, which makes it a useful method when the research question is time-sensitive. For example, a survey could be used to understand public opinion on a breaking news story.
• When the research question involves a geographically dispersed population: Survey research can be conducted online, which makes it a useful method when the population of interest is geographically dispersed.

How to Conduct Survey Research

Conducting survey research involves several steps that need to be carefully planned and executed. Here is a general overview of the process:

• Define the research question: The first step in conducting survey research is to clearly define the research question. The research question should be specific, measurable, and relevant to the population of interest.
• Develop a survey instrument : The next step is to develop a survey instrument. This can be done using various methods, such as online survey tools or paper surveys. The survey instrument should be designed to elicit the information needed to answer the research question, and should be pre-tested with a small sample of individuals.
• Select a sample : The sample is the group of individuals who will be invited to participate in the survey. The sample should be representative of the population of interest, and the size of the sample should be sufficient to ensure statistical validity.
• Administer the survey: The survey can be administered in various ways, such as online, by mail, or in person. The method of administration should be chosen based on the population of interest and the research question.
• Analyze the data: Once the survey data is collected, it needs to be analyzed. This involves summarizing the data using statistical methods, such as frequency distributions or regression analysis.
• Draw conclusions: The final step is to draw conclusions based on the data analysis. This involves interpreting the results and answering the research question.

Advantages of Survey Research

There are several advantages to using survey research, including:

• Efficient data collection: Survey research allows researchers to collect data quickly and efficiently from a large number of people. This makes it a useful method for gathering information on a wide range of topics.
• Standardized data collection: Surveys are typically standardized, which means that all participants receive the same questions in the same order. This ensures that the data collected is consistent and reliable.
• Cost-effective: Surveys can be conducted online, by mail, or in person, which makes them a cost-effective method of data collection.
• Anonymity: Participants can remain anonymous when responding to a survey. This can encourage participants to be more honest and open in their responses.
• Easy comparison: Surveys allow for easy comparison of data between different groups or over time. This makes it possible to identify trends and patterns in the data.
• Versatility: Surveys can be used to collect data on a wide range of topics, including attitudes, beliefs, behaviors, and preferences.

Limitations of Survey Research

Here are some of the main limitations of survey research:

• Limited depth: Surveys are typically designed to collect quantitative data, which means that they do not provide much depth or detail about people’s experiences or opinions. This can limit the insights that can be gained from the data.
• Potential for bias: Surveys can be affected by various biases, including selection bias, response bias, and social desirability bias. These biases can distort the results and make them less accurate.
• L imited validity: Surveys are only as valid as the questions they ask. If the questions are poorly designed or ambiguous, the results may not accurately reflect the respondents’ attitudes or behaviors.
• Limited generalizability : Survey results are only generalizable to the population from which the sample was drawn. If the sample is not representative of the population, the results may not be generalizable to the larger population.
• Limited ability to capture context: Surveys typically do not capture the context in which attitudes or behaviors occur. This can make it difficult to understand the reasons behind the responses.
• Limited ability to capture complex phenomena: Surveys are not well-suited to capture complex phenomena, such as emotions or the dynamics of interpersonal relationships.

Following is an example of a Survey Sample:

Welcome to our Survey Research Page! We value your opinions and appreciate your participation in this survey. Please answer the questions below as honestly and thoroughly as possible.

1. What is your age?

• A) Under 18
• G) 65 or older

2. What is your highest level of education completed?

• A) Less than high school
• B) High school or equivalent
• C) Some college or technical school
• D) Bachelor’s degree
• E) Graduate or professional degree

3. What is your current employment status?

• A) Employed full-time
• B) Employed part-time
• C) Self-employed
• D) Unemployed

4. How often do you use the internet per day?

•  A) Less than 1 hour
• B) 1-3 hours
• C) 3-5 hours
• D) 5-7 hours
• E) More than 7 hours

5. How often do you engage in social media per day?

6. Have you ever participated in a survey research study before?

7. If you have participated in a survey research study before, how was your experience?

• A) Excellent
• E) Very poor

8. What are some of the topics that you would be interested in participating in a survey research study about?

……………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………….

9. How often would you be willing to participate in survey research studies?

• A) Once a week
• B) Once a month
• C) Once every 6 months
• D) Once a year

10. Any additional comments or suggestions?

Thank you for taking the time to complete this survey. Your feedback is important to us and will help us improve our survey research efforts.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Article Contents

What is survey research, advantages and disadvantages of survey research, essential steps in survey research, research methods, designing the research tool, sample and sampling, data collection, data analysis.

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Good practice in the conduct and reporting of survey research

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KATE KELLEY, BELINDA CLARK, VIVIENNE BROWN, JOHN SITZIA, Good practice in the conduct and reporting of survey research, International Journal for Quality in Health Care , Volume 15, Issue 3, May 2003, Pages 261–266, https://doi.org/10.1093/intqhc/mzg031

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Survey research is sometimes regarded as an easy research approach. However, as with any other research approach and method, it is easy to conduct a survey of poor quality rather than one of high quality and real value. This paper provides a checklist of good practice in the conduct and reporting of survey research. Its purpose is to assist the novice researcher to produce survey work to a high standard, meaning a standard at which the results will be regarded as credible. The paper first provides an overview of the approach and then guides the reader step-by-step through the processes of data collection, data analysis, and reporting. It is not intended to provide a manual of how to conduct a survey, but rather to identify common pitfalls and oversights to be avoided by researchers if their work is to be valid and credible.

Survey research is common in studies of health and health services, although its roots lie in the social surveys conducted in Victorian Britain by social reformers to collect information on poverty and working class life (e.g. Charles Booth [ 1 ] and Joseph Rowntree [ 2 ]), and indeed survey research remains most used in applied social research. The term ‘survey’ is used in a variety of ways, but generally refers to the selection of a relatively large sample of people from a pre-determined population (the ‘population of interest’; this is the wider group of people in whom the researcher is interested in a particular study), followed by the collection of a relatively small amount of data from those individuals. The researcher therefore uses information from a sample of individuals to make some inference about the wider population.

Data are collected in a standardized form. This is usually, but not necessarily, done by means of a questionnaire or interview. Surveys are designed to provide a ‘snapshot of how things are at a specific time’ [ 3 ]. There is no attempt to control conditions or manipulate variables; surveys do not allocate participants into groups or vary the treatment they receive. Surveys are well suited to descriptive studies, but can also be used to explore aspects of a situation, or to seek explanation and provide data for testing hypotheses. It is important to recognize that ‘the survey approach is a research strategy, not a research method’ [ 3 ]. As with any research approach, a choice of methods is available and the one most appropriate to the individual project should be used. This paper will discuss the most popular methods employed in survey research, with an emphasis upon difficulties commonly encountered when using these methods.

Descriptive research

Descriptive research is a most basic type of enquiry that aims to observe (gather information on) certain phenomena, typically at a single point in time: the ‘cross-sectional’ survey. The aim is to examine a situation by describing important factors associated with that situation, such as demographic, socio-economic, and health characteristics, events, behaviours, attitudes, experiences, and knowledge. Descriptive studies are used to estimate specific parameters in a population (e.g. the prevalence of infant breast feeding) and to describe associations (e.g. the association between infant breast feeding and maternal age).

Analytical studies

Analytical studies go beyond simple description; their intention is to illuminate a specific problem through focused data analysis, typically by looking at the effect of one set of variables upon another set. These are longitudinal studies, in which data are collected at more than one point in time with the aim of illuminating the direction of observed associations. Data may be collected from the same sample on each occasion (cohort or panel studies) or from a different sample at each point in time (trend studies).

Evaluation research

This form of research collects data to ascertain the effects of a planned change.

Advantages:

The research produces data based on real-world observations (empirical data).

The breadth of coverage of many people or events means that it is more likely than some other approaches to obtain data based on a representative sample, and can therefore be generalizable to a population.

Surveys can produce a large amount of data in a short time for a fairly low cost. Researchers can therefore set a finite time-span for a project, which can assist in planning and delivering end results.

Disadvantages:

The significance of the data can become neglected if the researcher focuses too much on the range of coverage to the exclusion of an adequate account of the implications of those data for relevant issues, problems, or theories.

The data that are produced are likely to lack details or depth on the topic being investigated.

Securing a high response rate to a survey can be hard to control, particularly when it is carried out by post, but is also difficult when the survey is carried out face-to-face or over the telephone.

Research question

Good research has the characteristic that its purpose is to address a single clear and explicit research question; conversely, the end product of a study that aims to answer a number of diverse questions is often weak. Weakest of all, however, are those studies that have no research question at all and whose design simply is to collect a wide range of data and then to ‘trawl’ the data looking for ‘interesting’ or ‘significant’ associations. This is a trap novice researchers in particular fall into. Therefore, in developing a research question, the following aspects should be considered [ 4 ]:

Be knowledgeable about the area you wish to research.

Widen the base of your experience, explore related areas, and talk to other researchers and practitioners in the field you are surveying.

Consider using techniques for enhancing creativity, for example brainstorming ideas.

Avoid the pitfalls of: allowing a decision regarding methods to decide the questions to be asked; posing research questions that cannot be answered; asking questions that have already been answered satisfactorily.

The survey approach can employ a range of methods to answer the research question. Common survey methods include postal questionnaires, face-to-face interviews, and telephone interviews.

Postal questionnaires

This method involves sending questionnaires to a large sample of people covering a wide geographical area. Postal questionnaires are usually received ‘cold’, without any previous contact between researcher and respondent. The response rate for this type of method is usually low, ∼20%, depending on the content and length of the questionnaire. As response rates are low, a large sample is required when using postal questionnaires, for two main reasons: first, to ensure that the demographic profile of survey respondents reflects that of the survey population; and secondly, to provide a sufficiently large data set for analysis.

Face-to-face interviews

Face-to-face interviews involve the researcher approaching respondents personally, either in the street or by calling at people’s homes. The researcher then asks the respondent a series of questions and notes their responses. The response rate is often higher than that of postal questionnaires as the researcher has the opportunity to sell the research to a potential respondent. Face-to-face interviewing is a more costly and time-consuming method than the postal survey, however the researcher can select the sample of respondents in order to balance the demographic profile of the sample.

Telephone interviews

Telephone surveys, like face-to-face interviews, allow a two-way interaction between researcher and respondent. Telephone surveys are quicker and cheaper than face-to-face interviewing. Whilst resulting in a higher response rate than postal surveys, telephone surveys often attract a higher level of refusals than face-to-face interviews as people feel less inhibited about refusing to take part when approached over the telephone.

Whether using a postal questionnaire or interview method, the questions asked have to be carefully planned and piloted. The design, wording, form, and order of questions can affect the type of responses obtained, and careful design is needed to minimize bias in results. When designing a questionnaire or question route for interviewing, the following issues should be considered: (1) planning the content of a research tool; (2) questionnaire layout; (3) interview questions; (4) piloting; and (5) covering letter.

Planning the content of a research tool

The topics of interest should be carefully planned and relate clearly to the research question. It is often useful to involve experts in the field, colleagues, and members of the target population in question design in order to ensure the validity of the coverage of questions included in the tool (content validity).

Researchers should conduct a literature search to identify existing, psychometrically tested questionnaires. A well designed research tool is simple, appropriate for the intended use, acceptable to respondents, and should include a clear and interpretable scoring system. A research tool must also demonstrate the psychometric properties of reliability (consistency from one measurement to the next), validity (accurate measurement of the concept), and, if a longitudinal study, responsiveness to change [ 5 ]. The development of research tools, such as attitude scales, is a lengthy and costly process. It is important that researchers recognize that the development of the research tool is equal in importance—and deserves equal attention—to data collection. If a research instrument has not undergone a robust process of development and testing, the credibility of the research findings themselves may legitimately be called into question and may even be completely disregarded. Surveys of patient satisfaction and similar are commonly weak in this respect; one review found that only 6% of patient satisfaction studies used an instrument that had undergone even rudimentary testing [ 6 ]. Researchers who are unable or unwilling to undertake this process are strongly advised to consider adopting an existing, robust research tool.

Questionnaire layout

Questionnaires used in survey research should be clear and well presented. The use of capital (upper case) letters only should be avoided, as this format is hard to read. Questions should be numbered and clearly grouped by subject. Clear instructions should be given and headings included to make the questionnaire easier to follow.

The researcher must think about the form of the questions, avoiding ‘double-barrelled’ questions (two or more questions in one, e.g. ‘How satisfied were you with your personal nurse and the nurses in general?’), questions containing double negatives, and leading or ambiguous questions. Questions may be open (where the respondent composes the reply) or closed (where pre-coded response options are available, e.g. multiple-choice questions). Closed questions with pre-coded response options are most suitable for topics where the possible responses are known. Closed questions are quick to administer and can be easily coded and analysed. Open questions should be used where possible replies are unknown or too numerous to pre-code. Open questions are more demanding for respondents but if well answered can provide useful insight into a topic. Open questions, however, can be time consuming to administer and difficult to analyse. Whether using open or closed questions, researchers should plan clearly how answers will be analysed.

Interview questions

Open questions are used more frequently in unstructured interviews, whereas closed questions typically appear in structured interview schedules. A structured interview is like a questionnaire that is administered face to face with the respondent. When designing the questions for a structured interview, the researcher should consider the points highlighted above regarding questionnaires. The interviewer should have a standardized list of questions, each respondent being asked the same questions in the same order. If closed questions are used the interviewer should also have a range of pre-coded responses available.

If carrying out a semi-structured interview, the researcher should have a clear, well thought out set of questions; however, the questions may take an open form and the researcher may vary the order in which topics are considered.

A research tool should be tested on a pilot sample of members of the target population. This process will allow the researcher to identify whether respondents understand the questions and instructions, and whether the meaning of questions is the same for all respondents. Where closed questions are used, piloting will highlight whether sufficient response categories are available, and whether any questions are systematically missed by respondents.

When conducting a pilot, the same procedure as as that to be used in the main survey should be followed; this will highlight potential problems such as poor response.

Covering letter

All participants should be given a covering letter including information such as the organization behind the study, including the contact name and address of the researcher, details of how and why the respondent was selected, the aims of the study, any potential benefits or harm resulting from the study, and what will happen to the information provided. The covering letter should both encourage the respondent to participate in the study and also meet the requirements of informed consent (see below).

The concept of sample is intrinsic to survey research. Usually, it is impractical and uneconomical to collect data from every single person in a given population; a sample of the population has to be selected [ 7 ]. This is illustrated in the following hypothetical example. A hospital wants to conduct a satisfaction survey of the 1000 patients discharged in the previous month; however, as it is too costly to survey each patient, a sample has to be selected. In this example, the researcher will have a list of the population members to be surveyed (sampling frame). It is important to ensure that this list is both up-to date and has been obtained from a reliable source.

The method by which the sample is selected from a sampling frame is integral to the external validity of a survey: the sample has to be representative of the larger population to obtain a composite profile of that population [ 8 ].

There are methodological factors to consider when deciding who will be in a sample: How will the sample be selected? What is the optimal sample size to minimize sampling error? How can response rates be maximized?

The survey methods discussed below influence how a sample is selected and the size of the sample. There are two categories of sampling: random and non-random sampling, with a number of sampling selection techniques contained within the two categories. The principal techniques are described here [ 9 ].

Random sampling

Generally, random sampling is employed when quantitative methods are used to collect data (e.g. questionnaires). Random sampling allows the results to be generalized to the larger population and statistical analysis performed if appropriate. The most stringent technique is simple random sampling. Using this technique, each individual within the chosen population is selected by chance and is equally as likely to be picked as anyone else. Referring back to the hypothetical example, each patient is given a serial identifier and then an appropriate number of the 1000 population members are randomly selected. This is best done using a random number table, which can be generated using computer software (a free on-line randomizer can be found at http://www.randomizer.org/index.htm ).

Alternative random sampling techniques are briefly described. In systematic sampling, individuals to be included in the sample are chosen at equal intervals from the population; using the earlier example, every fifth patient discharged from hospital would be included in the survey. Stratified sampling selects a specific group and then a random sample is selected. Using our example, the hospital may decide only to survey older surgical patients. Bigger surveys may employ cluster sampling, which randomly assigns groups from a large population and then surveys everyone within the groups, a technique often used in national-scale studies.

Non-random sampling

Non-random sampling is commonly applied when qualitative methods (e.g. focus groups and interviews) are used to collect data, and is typically used for exploratory work. Non-random sampling deliberately targets individuals within a population. There are three main techniques. (1) purposive sampling: a specific population is identified and only its members are included in the survey; using our example above, the hospital may decide to survey only patients who had an appendectomy. (2) Convenience sampling: the sample is made up of the individuals who are the easiest to recruit. Finally, (3) snowballing: the sample is identified as the survey progresses; as one individual is surveyed he or she is invited to recommend others to be surveyed.

It is important to use the right method of sampling and to be aware of the limitations and statistical implications of each. The need to ensure that the sample is representative of the larger population was highlighted earlier and, alongside the sampling method, the degree of sampling error should be considered. Sampling error is the probability that any one sample is not completely representative of the population from which it has been drawn [ 9 ]. Although sampling error cannot be eliminated entirely, the sampling technique chosen will influence the extent of the error. Simple random sampling will give a closer estimate of the population than a convenience sample of individuals who just happened to be in the right place at the right time.

Sample size

What sample size is required for a survey? There is no definitive answer to this question: large samples with rigorous selection are more powerful as they will yield more accurate results, but data collection and analysis will be proportionately more time consuming and expensive. Essentially, the target sample size for a survey depends on three main factors: the resources available, the aim of the study, and the statistical quality needed for the survey. For ‘qualitative’ surveys using focus groups or interviews, the sample size needed will be smaller than if quantitative data is collected by questionnaire. If statistical analysis is to be performed on the data then sample size calculations should be conducted. This can be done using computer packages such as G * Power [ 10 ]; however, those with little statistical knowledge should consult a statistician. For practical recommendations on sample size, the set of survey guidelines developed by the UK Department of Health [ 11 ] should be consulted.

Larger samples give a better estimate of the population but it can be difficult to obtain an adequate number of responses. It is rare that everyone asked to participate in the survey will reply. To ensure a sufficient number of responses, include an estimated non-response rate in the sample size calculations.

Response rates are a potential source of bias. The results from a survey with a large non-response rate could be misleading and only representative of those who replied. French [ 12 ] reported that non-responders to patient satisfaction surveys are less likely to be satisfied than people who reply. It is unwise to define a level above which a response rate is acceptable, as this depends on many local factors; however, an achievable and acceptable rate is ∼75% for interviews and 65% for self-completion postal questionnaires [ 9 , 13 ]. In any study, the final response rate should be reported with the results; potential differences between the respondents and non-respondents should be explicitly explored and their implications discussed.

There are techniques to increase response rates. A questionnaire must be concise and easy to understand, reminders should be sent out, and method of recruitment should be carefully considered. Sitzia and Wood [ 13 ] found that participants recruited by mail or who had to respond by mail had a lower mean response rate (67%) than participants who were recruited personally (mean response 76.7%). A most useful review of methods to maximize response rates in postal surveys has recently been published [ 14 ].

Researchers should approach data collection in a rigorous and ethical manner. The following information must be clearly recorded:

How, where, how many times, and by whom potential respondents were contacted.

How many people were approached and how many of those agreed to participate.

How did those who agreed to participate differ from those who refused with regard to characteristics of interest in the study, for example how were they identified, where were they approached, and what was their gender, age, and features of their illness or health care.

How was the survey administered (e.g. telephone interview).

What was the response rate (i.e. the number of usable data sets as a proportion of the number of people approached).

The purpose of all analyses is to summarize data so that it is easily understood and provides the answers to our original questions: ‘In order to do this researchers must carefully examine their data; they should become friends with their data’ [ 15 ]. Researchers must prepare to spend substantial time on the data analysis phase of a survey (and this should be built into the project plan). When analysis is rushed, often important aspects of the data are missed and sometimes the wrong analyses are conducted, leading to both inaccurate results and misleading conclusions [ 16 ]. However, and this point cannot be stressed strongly enough, researchers must not engage in data dredging, a practice that can arise especially in studies in which large numbers of dependent variables can be related to large numbers of independent variables (outcomes). When large numbers of possible associations in a dataset are reviewed at P < 0.05, one in 20 of the associations by chance will appear ‘statistically significant’; in datasets where only a few real associations exist, testing at this significance level will result in the large majority of findings still being false positives [ 17 ].

The method of data analysis will depend on the design of the survey and should have been carefully considered in the planning stages of the survey. Data collected by qualitative methods should be analysed using established methods such as content analysis [ 18 ], and where quantitative methods have been used appropriate statistical tests can be applied. Describing methods of analysis here would be unproductive as a multitude of introductory textbooks and on-line resources are available to help with simple analyses of data (e.g. [ 19 , 20 ]). For advanced analysis a statistician should be consulted.

When reporting survey research, it is essential that a number of key points are covered (though the length and depth of reporting will be dependent upon journal style). These key points are presented as a ‘checklist’ below:

Explain the purpose or aim of the research, with the explicit identification of the research question.

Explain why the research was necessary and place the study in context, drawing upon previous work in relevant fields (the literature review).

State the chosen research method or methods, and justify why this method was chosen.

Describe the research tool. If an existing tool is used, briefly state its psychometric properties and provide references to the original development work. If a new tool is used, you should include an entire section describing the steps undertaken to develop and test the tool, including results of psychometric testing.

Describe how the sample was selected and how data were collected, including:

How were potential subjects identified?

How many and what type of attempts were made to contact subjects?

Who approached potential subjects?

Where were potential subjects approached?

How was informed consent obtained?

How many agreed to participate?

How did those who agreed differ from those who did not agree?

What was the response rate?

Describe and justify the methods and tests used for data analysis.

Present the results of the research. The results section should be clear, factual, and concise.

Interpret and discuss the findings. This ‘discussion’ section should not simply reiterate results; it should provide the author’s critical reflection upon both the results and the processes of data collection. The discussion should assess how well the study met the research question, should describe the problems encountered in the research, and should honestly judge the limitations of the work.

Present conclusions and recommendations.

The researcher needs to tailor the research report to meet:

The expectations of the specific audience for whom the work is being written.

The conventions that operate at a general level with respect to the production of reports on research in the social sciences.

Anyone involved in collecting data from patients has an ethical duty to respect each individual participant’s autonomy. Any survey should be conducted in an ethical manner and one that accords with best research practice. Two important ethical issues to adhere to when conducting a survey are confidentiality and informed consent.

The respondent’s right to confidentiality should always be respected and any legal requirements on data protection adhered to. In the majority of surveys, the patient should be fully informed about the aims of the survey, and the patient’s consent to participate in the survey must be obtained and recorded.

The professional bodies listed below, among many others, provide guidance on the ethical conduct of research and surveys.

American Psychological Association: http://www.apa.org

British Psychological Society: http://www.bps.org.uk

British Medical Association: http://www.bma.org.uk .

UK General Medical Council: http://www.gmc-uk.org

American Medical Association: http://www.ama-assn.org

UK Royal College of Nursing: http://www.rcn.org.uk

UK Department of Health: http://www.doh.gov

Survey research demands the same standards in research practice as any other research approach, and journal editors and the broader research community will judge a report of survey research with the same level of rigour as any other research report. This is not to say that survey research need be particularly difficult or complex; the point to emphasize is that researchers should be aware of the steps required in survey research, and should be systematic and thoughtful in the planning, execution, and reporting of the project. Above all, survey research should not be seen as an easy, ‘quick and dirty’ option; such work may adequately fulfil local needs (e.g. a quick survey of hospital staff satisfaction), but will not stand up to academic scrutiny and will not be regarded as having much value as a contribution to knowledge.

Address reprint requests to John Sitzia, Research Department, Worthing Hospital, Lyndhurst Road, Worthing BN11 2DH, West Sussex, UK. E-mail: [email protected]

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Denscombe M. The Good Research Guide: For Small-scale Social Research Projects . Buckingham: Open University Press, 1998 .

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Sitzia J. How valid and reliable are patient satisfaction data? An analysis of 195 studies. Int J Qual Health Care 1999 ; 11: 319 –328.

Bowling A. Research Methods in Health. Investigating Health and Health Services . Buckingham: Open University Press, 2002 .

American Statistical Association, USA. Http://www.amstat.org (accessed 9 December 2002 ).

Arber S. Designing samples. In: Gilbert N, ed. Researching Social Life . London: SAGE Publications, 2001 .

Heinrich Heine University, Dusseldorf, Germany. Http://www.psycho.uni-duesseldorf.de/aap/projects/gpower/index.html (accessed 12 December 2002 ).

Department of Health, England. Http://www.doh.gov.uk/acutesurvey/index.htm (accessed 12 December 2002 ).

French K. Methodological considerations in hospital patient opinion surveys. Int J Nurs Stud 1981 ; 18: 7 –32.

Sitzia J, Wood N. Response rate in patient satisfaction research: an analysis of 210 published studies. Int J Qual Health Care 1998 ; 10: 311 –317.

Edwards P, Roberts I, Clarke M et al. Increasing response rates to postal questionnaires: systematic review. Br Med J 2002 ; 324: 1183 .

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Wright DB, Kelley K. Analysing and reporting data. In: Michie S, Abraham C, eds. Health Psychology in Practice . London: SAGE Publications, 2003 ; in press.

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Study Population

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Study population is a subset of the target population from which the sample is actually selected. It is broader than the concept sample frame . It may be appropriate to say that sample frame is an operationalized form of study population. For example, suppose that a study is going to conduct a survey of high school students on their social well-being . High school students all over the world might be considered as the target population. Because of practicalities, researchers decide to only recruit high school students studying in China who are the study population in this example. Suppose there is a list of high school students of China, this list is used as the sample frame .

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Study population is the operational definition of target population (Henry, 1990 ; Bickman & Rog, 1998 ). Researchers are seldom in a position to study the entire target population, which is not always readily accessible. Instead, only part of it—respondents who are both eligible for the study...

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Babbie, E. R. (2010). The practice of social research . Belmont, CA: Wadsworth Publishing Company.

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Bickman, L., & Rog, D. J. (1998). Handbook of applied social research methods . Thousand Oaks, CA: Sage Publications.

Friedman, L. M., Furberg, C. D., & DeMets, D. L. (2010). Fundamentals of clinical trials . New York: Springer.

Gerrish, K., & Lacey, A. (2010). The research process in nursing . West Sussex: Wiley-Blackwell.

Henry, G. T. (1990). Practical sampling . Newbury Park, CA: Sage Publications.

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Riegelman, R. K. (2005). Studying a study and testing a test: How to read the medical evidence . Philadelphia: Lippincott Williams & Wilkins.

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Hu, S. (2014). Study Population. In: Michalos, A.C. (eds) Encyclopedia of Quality of Life and Well-Being Research. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0753-5_2893

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• Doing Survey Research | A Step-by-Step Guide & Examples

Doing Survey Research | A Step-by-Step Guide & Examples

Published on 6 May 2022 by Shona McCombes . Revised on 10 October 2022.

Survey research means collecting information about a group of people by asking them questions and analysing the results. To conduct an effective survey, follow these six steps:

• Determine who will participate in the survey
• Decide the type of survey (mail, online, or in-person)
• Design the survey questions and layout
• Distribute the survey
• Analyse the responses
• Write up the results

Surveys are a flexible method of data collection that can be used in many different types of research .

Table of contents

What are surveys used for, step 1: define the population and sample, step 2: decide on the type of survey, step 3: design the survey questions, step 4: distribute the survey and collect responses, step 5: analyse the survey results, step 6: write up the survey results, frequently asked questions about surveys.

Surveys are used as a method of gathering data in many different fields. They are a good choice when you want to find out about the characteristics, preferences, opinions, or beliefs of a group of people.

Common uses of survey research include:

• Social research: Investigating the experiences and characteristics of different social groups
• Market research: Finding out what customers think about products, services, and companies
• Health research: Collecting data from patients about symptoms and treatments
• Politics: Measuring public opinion about parties and policies
• Psychology: Researching personality traits, preferences, and behaviours

Surveys can be used in both cross-sectional studies , where you collect data just once, and longitudinal studies , where you survey the same sample several times over an extended period.

Prevent plagiarism, run a free check.

Before you start conducting survey research, you should already have a clear research question that defines what you want to find out. Based on this question, you need to determine exactly who you will target to participate in the survey.

Populations

The target population is the specific group of people that you want to find out about. This group can be very broad or relatively narrow. For example:

• The population of Brazil
• University students in the UK
• Second-generation immigrants in the Netherlands
• Customers of a specific company aged 18 to 24
• British transgender women over the age of 50

Your survey should aim to produce results that can be generalised to the whole population. That means you need to carefully define exactly who you want to draw conclusions about.

It’s rarely possible to survey the entire population of your research – it would be very difficult to get a response from every person in Brazil or every university student in the UK. Instead, you will usually survey a sample from the population.

The sample size depends on how big the population is. You can use an online sample calculator to work out how many responses you need.

There are many sampling methods that allow you to generalise to broad populations. In general, though, the sample should aim to be representative of the population as a whole. The larger and more representative your sample, the more valid your conclusions.

There are two main types of survey:

• A questionnaire , where a list of questions is distributed by post, online, or in person, and respondents fill it out themselves
• An interview , where the researcher asks a set of questions by phone or in person and records the responses

Which type you choose depends on the sample size and location, as well as the focus of the research.

Questionnaires

Sending out a paper survey by post is a common method of gathering demographic information (for example, in a government census of the population).

• You can easily access a large sample.
• You have some control over who is included in the sample (e.g., residents of a specific region).
• The response rate is often low.

Online surveys are a popular choice for students doing dissertation research , due to the low cost and flexibility of this method. There are many online tools available for constructing surveys, such as SurveyMonkey and Google Forms .

• You can quickly access a large sample without constraints on time or location.
• The data is easy to process and analyse.
• The anonymity and accessibility of online surveys mean you have less control over who responds.

If your research focuses on a specific location, you can distribute a written questionnaire to be completed by respondents on the spot. For example, you could approach the customers of a shopping centre or ask all students to complete a questionnaire at the end of a class.

• You can screen respondents to make sure only people in the target population are included in the sample.
• You can collect time- and location-specific data (e.g., the opinions of a shop’s weekday customers).
• The sample size will be smaller, so this method is less suitable for collecting data on broad populations.

Oral interviews are a useful method for smaller sample sizes. They allow you to gather more in-depth information on people’s opinions and preferences. You can conduct interviews by phone or in person.

• You have personal contact with respondents, so you know exactly who will be included in the sample in advance.
• You can clarify questions and ask for follow-up information when necessary.
• The lack of anonymity may cause respondents to answer less honestly, and there is more risk of researcher bias.

Like questionnaires, interviews can be used to collect quantitative data : the researcher records each response as a category or rating and statistically analyses the results. But they are more commonly used to collect qualitative data : the interviewees’ full responses are transcribed and analysed individually to gain a richer understanding of their opinions and feelings.

Next, you need to decide which questions you will ask and how you will ask them. It’s important to consider:

• The type of questions
• The content of the questions
• The phrasing of the questions
• The ordering and layout of the survey

Open-ended vs closed-ended questions

There are two main forms of survey questions: open-ended and closed-ended. Many surveys use a combination of both.

Closed-ended questions give the respondent a predetermined set of answers to choose from. A closed-ended question can include:

• A binary answer (e.g., yes/no or agree/disagree )
• A scale (e.g., a Likert scale with five points ranging from strongly agree to strongly disagree )
• A list of options with a single answer possible (e.g., age categories)
• A list of options with multiple answers possible (e.g., leisure interests)

Closed-ended questions are best for quantitative research . They provide you with numerical data that can be statistically analysed to find patterns, trends, and correlations .

Open-ended questions are best for qualitative research. This type of question has no predetermined answers to choose from. Instead, the respondent answers in their own words.

Open questions are most common in interviews, but you can also use them in questionnaires. They are often useful as follow-up questions to ask for more detailed explanations of responses to the closed questions.

The content of the survey questions

To ensure the validity and reliability of your results, you need to carefully consider each question in the survey. All questions should be narrowly focused with enough context for the respondent to answer accurately. Avoid questions that are not directly relevant to the survey’s purpose.

When constructing closed-ended questions, ensure that the options cover all possibilities. If you include a list of options that isn’t exhaustive, you can add an ‘other’ field.

Phrasing the survey questions

In terms of language, the survey questions should be as clear and precise as possible. Tailor the questions to your target population, keeping in mind their level of knowledge of the topic.

Use language that respondents will easily understand, and avoid words with vague or ambiguous meanings. Make sure your questions are phrased neutrally, with no bias towards one answer or another.

Ordering the survey questions

The questions should be arranged in a logical order. Start with easy, non-sensitive, closed-ended questions that will encourage the respondent to continue.

If the survey covers several different topics or themes, group together related questions. You can divide a questionnaire into sections to help respondents understand what is being asked in each part.

If a question refers back to or depends on the answer to a previous question, they should be placed directly next to one another.

Before you start, create a clear plan for where, when, how, and with whom you will conduct the survey. Determine in advance how many responses you require and how you will gain access to the sample.

When you are satisfied that you have created a strong research design suitable for answering your research questions, you can conduct the survey through your method of choice – by post, online, or in person.

There are many methods of analysing the results of your survey. First you have to process the data, usually with the help of a computer program to sort all the responses. You should also cleanse the data by removing incomplete or incorrectly completed responses.

If you asked open-ended questions, you will have to code the responses by assigning labels to each response and organising them into categories or themes. You can also use more qualitative methods, such as thematic analysis , which is especially suitable for analysing interviews.

Statistical analysis is usually conducted using programs like SPSS or Stata. The same set of survey data can be subject to many analyses.

Finally, when you have collected and analysed all the necessary data, you will write it up as part of your thesis, dissertation , or research paper .

In the methodology section, you describe exactly how you conducted the survey. You should explain the types of questions you used, the sampling method, when and where the survey took place, and the response rate. You can include the full questionnaire as an appendix and refer to it in the text if relevant.

Then introduce the analysis by describing how you prepared the data and the statistical methods you used to analyse it. In the results section, you summarise the key results from your analysis.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviours. It is made up of four or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with five or seven possible responses, to capture their degree of agreement.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyse your data.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analysing data from people using questionnaires.

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Statistics without tears: Populations and samples

Amitav banerjee.

Department of Community Medicine, D Y Patil Medical College, Pune, India

Suprakash Chaudhury

1 Department of Psychiatry, RINPAS, Kanke, Ranchi, India

Research studies are usually carried out on sample of subjects rather than whole populations. The most challenging aspect of fieldwork is drawing a random sample from the target population to which the results of the study would be generalized. In actual practice, the task is so difficult that some sampling bias occurs in almost all studies to a lesser or greater degree. In order to assess the degree of this bias, the informed reader of medical literature should have some understanding of the population from which the sample was drawn. The ultimate decision on whether the results of a particular study can be generalized to a larger population depends on this understanding. The subsequent deliberations dwell on sampling strategies for different types of research and also a brief description of different sampling methods.

Research workers in the early 19th century endeavored to survey entire populations. This feat was tedious, and the research work suffered accordingly. Current researchers work only with a small portion of the whole population (a sample) from which they draw inferences about the population from which the sample was drawn.

This inferential leap or generalization from samples to population, a feature of inductive or empirical research, can be full of pitfalls. In clinical medicine, it is not sufficient merely to describe a patient without assessing the underlying condition by a detailed history and clinical examination. The signs and symptoms are then interpreted against the total background of the patient's history and clinical examination including mental state examination. Similarly, in inferential statistics, it is not enough to just describe the results in the sample. One has to critically appraise the real worth or representativeness of that particular sample. The following discussion endeavors to explain the inputs required for making a correct inference from a sample to the target population.

TARGET POPULATION

Any inferences from a sample refer only to the defined population from which the sample has been properly selected. We may call this the target population. For example, if in a sample of lawyers from Delhi High Court it is found that 5% are having alcohol dependence syndrome, can we say that 5% of all lawyers all over the world are alcoholics? Obviously not, as the lawyers of Delhi High Court may be an institution by themselves and may not represent the global lawyers′ community. The findings of this study, therefore, apply only to Delhi High Court lawyers from which a representative sample was taken. Of course, this finding may nevertheless be interesting, but only as a pointer to further research. The data on lawyers in a particular city tell us nothing about lawyers in other cities or countries.

POPULATIONS IN INFERENTIAL STATISTICS

In statistics, a population is an entire group about which some information is required to be ascertained. A statistical population need not consist only of people. We can have population of heights, weights, BMIs, hemoglobin levels, events, outcomes, so long as the population is well defined with explicit inclusion and exclusion criteria. In selecting a population for study, the research question or purpose of the study will suggest a suitable definition of the population to be studied, in terms of location and restriction to a particular age group, sex or occupation. The population must be fully defined so that those to be included and excluded are clearly spelt out (inclusion and exclusion criteria). For example, if we say that our study populations are all lawyers in Delhi, we should state whether those lawyers are included who have retired, are working part-time, or non-practicing, or those who have left the city but still registered at Delhi.

Use of the word population in epidemiological research does not correspond always with its demographic meaning of an entire group of people living within certain geographic or political boundaries. A population for a research study may comprise groups of people defined in many different ways, for example, coal mine workers in Dhanbad, children exposed to German measles during intrauterine life, or pilgrims traveling to Kumbh Mela at Allahabad.

GENERALIZATION (INFERENCES) FROM A POPULATION

When generalizing from observations made on a sample to a larger population, certain issues will dictate judgment. For example, generalizing from observations made on the mental health status of a sample of lawyers in Delhi to the mental health status of all lawyers in Delhi is a formalized procedure, in so far as the errors (sampling or random) which this may hazard can, to some extent, be calculated in advance. However, if we attempt to generalize further, for instance, about the mental statuses of all lawyers in the country as a whole, we hazard further pitfalls which cannot be specified in advance. We do not know to what extent the study sample and population of Delhi is typical of the larger population – that of the whole country – to which it belongs.

The dilemmas in defining populations differ for descriptive and analytic studies.

POPULATION IN DESCRIPTIVE STUDIES

In descriptive studies, it is customary to define a study population and then make observations on a sample taken from it. Study populations may be defined by geographic location, age, sex, with additional definitions of attributes and variables such as occupation, religion and ethnic group.[ 1 ]

Geographic location

In field studies, it may be desirable to use a population defined by an administrative boundary such as a district or a state. This may facilitate the co-operation of the local administrative authorities and the study participants. Moreover, basic demographic data on the population such as population size, age, gender distribution (needed for calculating age- and sex-specific rates) available from census data or voters’ list are easier to obtain from administrative headquarters. However, administrative boundaries do not always consist of homogenous group of people. Since it is desirable that a modest descriptive study does not cover a number of different groups of people, with widely differing ways of life or customs, it may be necessary to restrict the study to a particular ethnic group, and thus ensure better genetic or cultural homogeneity. Alternatively, a population may be defined in relation to a prominent geographic feature, such as a river, or mountain, which imposes a certain uniformity of ways of life, attitudes, and behavior upon the people who live in the vicinity.

If cases of a disease are being ascertained through their attendance at a hospital outpatient department (OPD), rather than by field surveys in the community, it will be necessary to define the population according to the so-called catchment area of the hospital OPD. For administrative purposes, a dispensary, health center or hospital is usually considered to serve a population within a defined geographic area. But these catchment areas may only represent in a crude manner with the actual use of medical facilities by the local people. For example, in OPD study of psychiatric illnesses in a particular hospital with a defined catchment area, many people with psychiatric illnesses may not visit the particular OPD and may seek treatment from traditional healers or religious leaders.

Catchment areas depend on the demography of the area and the accessibility of the health center or hospital. Accessibility has three dimensions – physical, economic and social.[ 2 ] Physical accessibility is the time required to travel to the health center or medical facility. It depends on the topography of the area (e.g. hill and tribal areas with poor roads have problems of physical accessibility). Economic accessibility is the paying capacity of the people for services. Poverty may limit health seeking behavior if the person cannot afford the bus fare to the health center even if the health services may be free of charge. It may also involve absence from work which, for daily wage earners, is a major economic disincentive. Social factors such as caste, culture, language, etc. may adversely affect accessibility to health facility if the treating physician is not conversant with the local language and customs. In such situations, the patient may feel more comfortable with traditional healers.

Ascertainment of a particular disease within a particular area may be incomplete either because some patient may seek treatment elsewhere or some patients do not seek treatment at all. Focus group discussions (qualitative study) with local people, especially those residing away from the health center, may give an indication whether serious underreporting is occurring.

When it is impossible to relate cases of a disease to a population, perhaps because the cases were ascertained through a hospital with an undefined catchment area, proportional morbidity rates may be used. These rates have been widely used in cancer epidemiology where the number of cases of one form of cancer is expressed as a proportion of the number of cases of all forms of cancer among patients attending the same hospital during the same period.

POPULATIONS IN ANALYTIC STUDIES

Case control studies.

As opposed to descriptive studies where a study population is defined and then observations are made on a representative sample from it, in case control studies observations are made on a group of patients. This is known as the study group , which usually is not selected by sampling of a defined larger group. For instance, a study on patients of bipolar disorder may include every patient with this disorder attending the psychiatry OPD during the study period. One should not forget, however, that in this situation also, there is a hypothetical population consisting of all patients with bipolar disorder in the universe (which may be a certain region, a country or globally depending on the extent of the generalization intended from the findings of the study). Case control studies are often carried out in hospital settings because this is more convenient and accessible group than cases in the community at large. However, the two groups of cases may differ in many respects. At the outset of the study, it should be deliberated whether these differences would affect the external validity (generalization) of the study. Usually, analytic studies are not carried out in groups containing atypical cases of the disorder, unless there is a special indication to do so.

Populations in cohort studies

Basically, cohort studies compare two groups of people (cohorts) and demonstrate whether or not there are more cases of the disease among the cohort exposed to the suspected cause than among the cohort not exposed. To determine whether an association exists between positive family history of schizophrenia and subsequent schizophrenia in persons having such a history, two cohorts would be required: first, the exposed group, that is, people with a family history of mental disorders (the suspected cause) and second, the unexposed group, that is, people without a family history of mental disorders. These two cohorts would need to be followed up for a number of years and cases of schizophrenia in either group would be recorded. If a positive family history is associated with development of schizophrenia, then more cases would occur in the first group than in the second group.

The crucial challenges in a cohort study are that it should include participants exposed to a particular cause being investigated and that it should consist of persons who can be followed up for the period of time between exposure (cause) and development of the disorder. It is vital that the follow-up of a cohort should be complete as far as possible. If more than a small proportion of persons in the cohort cannot be traced (loss to follow-up or attrition), the findings will be biased , in case these persons differ significantly from those remaining in the study.

Depending on the type of exposure being studied, there may or may not be a range of choice of cohort populations exposed to it who may form a larger population from which one has to select a study sample. For instance, if one is exploring association between occupational hazard such as job stress in health care workers in intensive care units (ICUs) and subsequent development of drug addiction, one has to, by the very nature of the research question, select health care workers working in ICUs. On the other hand, cause effect study for association between head injury and epilepsy offers a much wider range of possible cohorts.

Difficulties in making repeated observations on cohorts depend on the length of time of the study. In correlating maternal factors (pregnancy cohort) with birth weight, the period of observation is limited to 9 months. However, if in a study it is tried to find the association between maternal nutrition during pregnancy and subsequent school performance of the child, the study will extend to years. For such long duration investigations, it is wise to select study cohorts that are firstly, not likely to migrate, cooperative and likely to be so throughout the duration of the study, and most importantly, easily accessible to the investigator so that the expense and efforts are kept within reasonable limits. Occupational groups such as the armed forces, railways, police, and industrial workers are ideal for cohort studies. Future developments facilitating record linkage such as the Unique Identification Number Scheme may give a boost to cohort studies in the wider community.

A sample is any part of the fully defined population. A syringe full of blood drawn from the vein of a patient is a sample of all the blood in the patient's circulation at the moment. Similarly, 100 patients of schizophrenia in a clinical study is a sample of the population of schizophrenics, provided the sample is properly chosen and the inclusion and exclusion criteria are well defined.

To make accurate inferences, the sample has to be representative. A representative sample is one in which each and every member of the population has an equal and mutually exclusive chance of being selected.

Sample size

Inputs required for sample size calculation have been dealt from a clinical researcher's perspective avoiding the use of intimidating formulae and statistical jargon in an earlier issue of the journal.[ 1 ]

Target population, study population and study sample

A population is a complete set of people with a specialized set of characteristics, and a sample is a subset of the population. The usual criteria we use in defining population are geographic, for example, “the population of Uttar Pradesh”. In medical research, the criteria for population may be clinical, demographic and time related.

• Clinical and demographic characteristics define the target population, the large set of people in the world to which the results of the study will be generalized (e.g. all schizophrenics).
• The study population is the subset of the target population available for study (e.g. schizophrenics in the researcher's town).
• The study sample is the sample chosen from the study population.

METHODS OF SAMPLING

Purposive (non-random samples).

• Volunteers who agree to participate
• Snowball sample, where one case identifies others of his kind (e.g. intravenous drug users)
• Convenient sample such as captive medical students or other readily available groups
• Quota sampling, at will selection of a fixed number from each group
• Referred cases who may be under pressure to participate
• Haphazard with combination of the above methods

Non-random samples have certain limitations. The larger group (target population) is difficult to identify. This may not be a limitation when generalization of results is not intended. The results would be valid for the sample itself (internal validity). They can, nevertheless, provide important clues for further studies based on random samples. Another limitation of non-random samples is that statistical inferences such as confidence intervals and tests of significance cannot be estimated from non-random samples. However, in some situations, the investigator has to make crucial judgments. One should remember that random samples are the means but representativeness is the goal. When non-random samples are representative (compare the socio-demographic characteristics of the sample subjects with the target population), generalization may be possible.

Random sampling methods

Simple random sampling.

A sample may be defined as random if every individual in the population being sampled has an equal likelihood of being included. Random sampling is the basis of all good sampling techniques and disallows any method of selection based on volunteering or the choice of groups of people known to be cooperative.[ 3 ]

In order to select a simple random sample from a population, it is first necessary to identify all individuals from whom the selection will be made. This is the sampling frame. In developing countries, listings of all persons living in an area are not usually available. Census may not catch nomadic population groups. Voters’ and taxpayers’ lists may be incomplete. Whether or not such deficiencies are major barriers in random sampling depends on the particular research question being investigated. To undertake a separate exercise of listing the population for the study may be time consuming and tedious. Two-stage sampling may make the task feasible.

The usual method of selecting a simple random sample from a listing of individuals is to assign a number to each individual and then select certain numbers by reference to random number tables which are published in standard statistical textbooks. Random number can also be generated by statistical software such as EPI INFO developed by WHO and CDC Atlanta.

Systematic sampling

A simple method of random sampling is to select a systematic sample in which every n th person is selected from a list or from other ordering. A systematic sample can be drawn from a queue of people or from patients ordered according to the time of their attendance at a clinic. Thus, a sample can be drawn without an initial listing of all the subjects. Because of this feasibility, a systematic sample may have some advantage over a simple random sample.

To fulfill the statistical criteria for a random sample, a systematic sample should be drawn from subjects who are randomly ordered. The starting point for selection should be randomly chosen. If every fifth person from a register is being chosen, then a random procedure must be used to determine whether the first, second, third, fourth, or fifth person should be chosen as the first member of the sample.

Multistage sampling

Sometimes, a strictly random sample may be difficult to obtain and it may be more feasible to draw the required number of subjects in a series of stages. For example, suppose we wish to estimate the number of CATSCAN examinations made of all patients entering a hospital in a given month in the state of Maharashtra. It would be quite tedious to devise a scheme which would allow the total population of patients to be directly sampled. However, it would be easier to list the districts of the state of Maharashtra and randomly draw a sample of these districts. Within this sample of districts, all the hospitals would then be listed by name, and a random sample of these can be drawn. Within each of these hospitals, a sample of the patients entering in the given month could be chosen randomly for observation and recording. Thus, by stages, we draw the required sample. If indicated, we can introduce some element of stratification at some stage (urban/rural, gender, age).

It should be cautioned that multistage sampling should only be resorted to when difficulties in simple random sampling are insurmountable. Those who take a simple random sample of 12 hospitals, and within each of these hospitals select a random sample of 10 patients, may believe they have selected 120 patients randomly from all the 12 hospitals. In statistical sense, they have in fact selected a sample of 12 rather than 120.[ 4 ]

Stratified sampling

If a condition is unevenly distributed in a population with respect to age, gender, or some other variable, it may be prudent to choose a stratified random sampling method. For example, to obtain a stratified random sample according to age, the study population can be divided into age groups such as 0–5, 6–10, 11–14, 15–20, 21–25, and so on, depending on the requirement. A different proportion of each group can then be selected as a subsample either by simple random sampling or systematic sampling. If the condition decreases with advancing age, then to include adequate number in the older age groups, one may select more numbers in older subsamples.

Cluster sampling

In many surveys, studies may be carried out on large populations which may be geographically quite dispersed. To obtain the required number of subjects for the study by a simple random sample method will require large costs and will be cumbersome. In such cases, clusters may be identified (e.g. households) and random samples of clusters will be included in the study; then, every member of the cluster will also be part of the study. This introduces two types of variations in the data – between clusters and within clusters – and this will have to be taken into account when analyzing data.

Cluster sampling may produce misleading results when the disease under study itself is distributed in a clustered fashion in an area. For example, suppose we are studying malaria in a population. Malaria incidence may be clustered in villages having stagnant water collections which may serve as a source of mosquito breeding. In villages without such water stagnation, there will be lesser malaria cases. The choice of few villages in cluster sampling may give erroneous results. The selection of villages as a cluster may be quite unrepresentative of the whole population by chance.[ 5 ]

Lot quality assurance sampling

Lot quality assurance sampling (LQAS), which originated in the manufacturing industry for quality control purposes, was used in the nineties to assess immunization coverage, estimate disease prevalence, and evaluate control measures and service coverage in different health programs.[ 6 ] Using only a small sample size, LQAS can effectively differentiate between areas that have or have not met the performance targets. Thus, this method is used not only to estimate the coverage of quality care but also to identify the exact subdivisions where it is deficient so that appropriate remedial measures can be implemented.

The choice of sampling methods is usually dictated by feasibility in terms of time and resources. Field research is quite messy and difficult like actual battle. It may be sometimes difficult to get a sample which is truly random. Most samples therefore tend to get biased. To estimate the magnitude of this bias, the researcher should have some idea about the population from which the sample is drawn. In conclusion, the following quote cited by Bradford Hill[ 4 ] elegantly sums up the benefit of random sampling:

…The actual practice of medicine is virtually confined to those members of the population who either are ill, or think they are ill, or are thought by somebody to be ill, and these so amply fill up the working day that in the course of time one comes unconsciously to believe that they are typical of the whole. This is not the case. The use of a random sample brings to light the individuals who are ill and know they are ill but have no intention of doing anything about it, as well as those who have never been ill, and probably never will be until their final illness. These would have been inaccessible to any other method of approach but that of the random sample… . J. H. Sheldon

Source of Support: Nil.

Conflict of Interest: None declared.

A Comprehensive Guide to Survey Research Methodologies

For decades, researchers and businesses have used survey research to produce statistical data and explore ideas. The survey process is simple, ask questions and analyze the responses to make decisions. Data is what makes the difference between a valid and invalid statement and as the American statistician, W. Edwards Deming said:

“Without data, you’re just another person with an opinion.” - W. Edwards Deming

In this article, we will discuss what survey research is, its brief history, types, common uses, benefits, and the step-by-step process of designing a survey.

What is Survey Research

A survey is a research method that is used to collect data from a group of respondents in order to gain insights and information regarding a particular subject. It’s an excellent method to gather opinions and understand how and why people feel a certain way about different situations and contexts.

Brief History of Survey Research

Survey research may have its roots in the American and English “social surveys” conducted around the turn of the 20th century. The surveys were mainly conducted by researchers and reformers to document the extent of social issues such as poverty. ( 1 ) Despite being a relatively young field to many scientific domains, survey research has experienced three stages of development ( 2 ):

-       First Era (1930-1960)

-       Second Era (1960-1990)

-       Third Era (1990 onwards)

Over the years, survey research adapted to the changing times and technologies. By exploiting the latest technologies, researchers can gain access to the right population from anywhere in the world, analyze the data like never before, and extract useful information.

Survey Research Methods & Types

Survey research can be classified into seven categories based on objective, data sources, methodology, deployment method, and frequency of deployment.

Surveys based on Objective

Exploratory survey research.

Exploratory survey research is aimed at diving deeper into research subjects and finding out more about their context. It’s important for marketing or business strategy and the focus is to discover ideas and insights instead of gathering statistical data.

Generally, exploratory survey research is composed of open-ended questions that allow respondents to express their thoughts and perspectives. The final responses present information from various sources that can lead to fresh initiatives.

Predictive Survey Research

Predictive survey research is also called causal survey research. It’s preplanned, structured, and quantitative in nature. It’s often referred to as conclusive research as it tries to explain the cause-and-effect relationship between different variables. The objective is to understand which variables are causes and which are effects and the nature of the relationship between both variables.

Descriptive Survey Research

Descriptive survey research is largely observational and is ideal for gathering numeric data. Due to its quantitative nature, it’s often compared to exploratory survey research. The difference between the two is that descriptive research is structured and pre-planned.

 The idea behind descriptive research is to describe the mindset and opinion of a particular group of people on a given subject. The questions are every day multiple choices and users must choose from predefined categories. With predefined choices, you don’t get unique insights, rather, statistically inferable data.

Survey Research Types based on Concept Testing

Monadic concept testing.

Monadic testing is a survey research methodology in which the respondents are split into multiple groups and ask each group questions about a separate concept in isolation. Generally, monadic surveys are hyper-focused on a particular concept and shorter in duration. The important thing in monadic surveys is to avoid getting off-topic or exhausting the respondents with too many questions.

Sequential Monadic Concept Testing

Another approach to monadic testing is sequential monadic testing. In sequential monadic surveys, groups of respondents are surveyed in isolation. However, instead of surveying three groups on three different concepts, the researchers survey the same groups of people on three distinct concepts one after another. In a sequential monadic survey, at least two topics are included (in random order), and the same questions are asked for each concept to eliminate bias.

Based on Data Source

Primary data.

Data obtained directly from the source or target population is referred to as primary survey data. When it comes to primary data collection, researchers usually devise a set of questions and invite people with knowledge of the subject to respond. The main sources of primary data are interviews, questionnaires, surveys, and observation methods.

 Compared to secondary data, primary data is gathered from first-hand sources and is more reliable. However, the process of primary data collection is both costly and time-consuming.

Secondary Data

Survey research is generally used to collect first-hand information from a respondent. However, surveys can also be designed to collect and process secondary data. It’s collected from third-party sources or primary sources in the past.

 This type of data is usually generic, readily available, and cheaper than primary data collection. Some common sources of secondary data are books, data collected from older surveys, online data, and data from government archives. Beware that you might compromise the validity of your findings if you end up with irrelevant or inflated data.

Based on Research Method

Quantitative research.

Quantitative research is a popular research methodology that is used to collect numeric data in a systematic investigation. It’s frequently used in research contexts where statistical data is required, such as sciences or social sciences. Quantitative research methods include polls, systematic observations, and face-to-face interviews.

Qualitative Research

Qualitative research is a research methodology where you collect non-numeric data from research participants. In this context, the participants are not restricted to a specific system and provide open-ended information. Some common qualitative research methods include focus groups, one-on-one interviews, observations, and case studies.

Based on Deployment Method

Online surveys.

With technology advancing rapidly, the most popular method of survey research is an online survey. With the internet, you can not only reach a broader audience but also design and customize a survey and deploy it from anywhere. Online surveys have outperformed offline survey methods as they are less expensive and allow researchers to easily collect and analyze data from a large sample.

Paper or Print Surveys

As the name suggests, paper or print surveys use the traditional paper and pencil approach to collect data. Before the invention of computers, paper surveys were the survey method of choice.

Though many would assume that surveys are no longer conducted on paper, it's still a reliable method of collecting information during field research and data collection. However, unlike online surveys, paper surveys are expensive and require extra human resources.

Telephonic Surveys

Telephonic surveys are conducted over telephones where a researcher asks a series of questions to the respondent on the other end. Contacting respondents over a telephone requires less effort, human resources, and is less expensive.

What makes telephonic surveys debatable is that people are often reluctant in giving information over a phone call. Additionally, the success of such surveys depends largely on whether people are willing to invest their time on a phone call answering questions.

One-on-one Surveys

One-on-one surveys also known as face-to-face surveys are interviews where the researcher and respondent. Interacting directly with the respondent introduces the human factor into the survey.

Face-to-face interviews are useful when the researcher wants to discuss something personal with the respondent. The response rates in such surveys are always higher as the interview is being conducted in person. However, these surveys are quite expensive and the success of these depends on the knowledge and experience of the researcher.

Based on Distribution

The easiest and most common way of conducting online surveys is sending out an email. Sending out surveys via emails has a higher response rate as your target audience already knows about your brand and is likely to engage.

Buy Survey Responses

Purchasing survey responses also yields higher responses as the responders signed up for the survey. Businesses often purchase survey samples to conduct extensive research. Here, the target audience is often pre-screened to check if they're qualified to take part in the research.

Embedding Survey on a Website

Embedding surveys on a website is another excellent way to collect information. It allows your website visitors to take part in a survey without ever leaving the website and can be done while a person is entering or exiting the website.

Post the Survey on Social Media

Social media is an excellent medium to reach abroad range of audiences. You can publish your survey as a link on social media and people who are following the brand can take part and answer questions.

Based on Frequency of Deployment

Cross-sectional studies.

Cross-sectional studies are administered to a small sample from a large population within a short period of time. This provides researchers a peek into what the respondents are thinking at a given time. The surveys are usually short, precise, and specific to a particular situation.

Longitudinal Surveys

Longitudinal surveys are an extension of cross-sectional studies where researchers make an observation and collect data over extended periods of time. This type of survey can be further divided into three types:

-       Trend surveys are employed to allow researchers to understand the change in the thought process of the respondents over some time.

-       Panel surveys are administered to the same group of people over multiple years. These are usually expensive and researchers must stick to their panel to gather unbiased opinions.

-       In cohort surveys, researchers identify a specific category of people and regularly survey them. Unlike panel surveys, the same people do not need to take part over the years, but each individual must fall into the researcher’s primary interest category.

Retrospective Survey

Retrospective surveys allow researchers to ask questions to gather data about past events and beliefs of the respondents. Since retrospective surveys also require years of data, they are similar to the longitudinal survey, except retrospective surveys are shorter and less expensive.

Why Should You Conduct Research Surveys?

“In God we trust. All others must bring data” - W. Edwards Deming

 In the information age, survey research is of utmost importance and essential for understanding the opinion of your target population. Whether you’re launching a new product or conducting a social survey, the tool can be used to collect specific information from a defined set of respondents. The data collected via surveys can be further used by organizations to make informed decisions.

Furthermore, compared to other research methods, surveys are relatively inexpensive even if you’re giving out incentives. Compared to the older methods such as telephonic or paper surveys, online surveys have a smaller cost and the number of responses is higher.

 What makes surveys useful is that they describe the characteristics of a large population. With a larger sample size , you can rely on getting more accurate results. However, you also need honest and open answers for accurate results. Since surveys are also anonymous and the responses remain confidential, respondents provide candid and accurate answers.

Common Uses of a Survey

Surveys are widely used in many sectors, but the most common uses of the survey research include:

-       Market research : surveying a potential market to understand customer needs, preferences, and market demand.

-       Customer Satisfaction: finding out your customer’s opinions about your services, products, or companies .

-       Social research: investigating the characteristics and experiences of various social groups.

-       Health research: collecting data about patients’ symptoms and treatments.

-       Politics: evaluating public opinion regarding policies and political parties.

-       Psychology: exploring personality traits, behaviors, and preferences.

6 Steps to Conduct Survey Research

An organization, person, or company conducts a survey when they need the information to make a decision but have insufficient data on hand. Following are six simple steps that can help you design a great survey.

Step 1: Objective of the Survey

The first step in survey research is defining an objective. The objective helps you define your target population and samples. The target population is the specific group of people you want to collect data from and since it’s rarely possible to survey the entire population, we target a specific sample from it. Defining a survey objective also benefits your respondents by helping them understand the reason behind the survey.

Step 2: Number of Questions

The number of questions or the size of the survey depends on the survey objective. However, it’s important to ensure that there are no redundant queries and the questions are in a logical order. Rephrased and repeated questions in a survey are almost as frustrating as in real life. For a higher completion rate, keep the questionnaire small so that the respondents stay engaged to the very end. The ideal length of an interview is less than 15 minutes. ( 2 )

Step 3: Language and Voice of Questions

While designing a survey, you may feel compelled to use fancy language. However, remember that difficult language is associated with higher survey dropout rates. You need to speak to the respondent in a clear, concise, and neutral manner, and ask simple questions. If your survey respondents are bilingual, then adding an option to translate your questions into another language can also prove beneficial.

Step 4: Type of Questions

In a survey, you can include any type of questions and even both closed-ended or open-ended questions. However, opt for the question types that are the easiest to understand for the respondents, and offer the most value. For example, compared to open-ended questions, people prefer to answer close-ended questions such as MCQs (multiple choice questions)and NPS (net promoter score) questions.

Step 5: User Experience

Designing a great survey is about more than just questions. A lot of researchers underestimate the importance of user experience and how it affects their response and completion rates. An inconsistent, difficult-to-navigate survey with technical errors and poor color choice is unappealing for the respondents. Make sure that your survey is easy to navigate for everyone and if you’re using rating scales, they remain consistent throughout the research study.

Additionally, don’t forget to design a good survey experience for both mobile and desktop users. According to Pew Research Center, nearly half of the smartphone users access the internet mainly from their mobile phones and 14 percent of American adults are smartphone-only internet users. ( 3 )

Step 6: Survey Logic

Last but not least, logic is another critical aspect of the survey design. If the survey logic is flawed, respondents may not continue in the right direction. Make sure to test the logic to ensure that selecting one answer leads to the next logical question instead of a series of unrelated queries.

How to Effectively Use Survey Research with Starlight Analytics

Designing and conducting a survey is almost as much science as it is an art. To craft great survey research, you need technical skills, consider the psychological elements, and have a broad understanding of marketing.

The ultimate goal of the survey is to ask the right questions in the right manner to acquire the right results.

Bringing a new product to the market is a long process and requires a lot of research and analysis. In your journey to gather information or ideas for your business, Starlight Analytics can be an excellent guide. Starlight Analytics' product concept testing helps you measure your product's market demand and refine product features and benefits so you can launch with confidence. The process starts with custom research to design the survey according to your needs, execute the survey, and deliver the key insights on time.

• Survey research in the United States: roots and emergence, 1890-1960 https://searchworks.stanford.edu/view/10733873    
• How to create a survey questionnaire that gets great responses https://luc.id/knowledgehub/how-to-create-a-survey-questionnaire-that-gets-great-responses/    
• Internet/broadband fact sheet https://www.pewresearch.org/internet/fact-sheet/internet-broadband/    

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Study Population: Characteristics & Sampling Techniques

How do you define a study population?  Research studies require specific groups to draw conclusions and make decisions based on their results. This group of interest is known as a sample. The method used to select respondents is known as sampling.

What is a Study Population?

A study population is a group considered for a study or statistical reasoning. The study population is not limited to the human population only. It is a set of aspects that have something in common. They can be objects, animals, measurements, etc., with many characteristics within a group.

For example, suppose you are interested in the average time a person between the ages of 30 and 35 takes to recover from a particular condition after consuming a specific type of medication. In that case, the study population will be all people between the ages of 30 and 35.

A medical study examines the spread of a specific disease in stray dogs in a city. Here, the stray dogs belonging to that city are the study population. This population or sample represents the entire population you want to conclude about.

How to establish a study population?

Sampling is a powerful technique for collecting opinions from a wide range of people, chosen from a particular group, to learn more about the whole group in general.

For any research study to be effective, it is necessary to select the study population that truly represents the entire population. Before starting your study, the target population must be identified and agreed upon. By appointing and knowing your sample well in advance, any feedback deemed useless to the study will be largely eliminated.

If your survey aims to understand a product’s or service’s effectiveness, then the study population should be the customers who have used it or are best suited to their needs and who will use the product/service.

It would be costly and time-consuming to collect data from the entire population of your target market. By accurately sampling your study population, it is possible to build a true picture of the target market using the trends in the results.

LEARN ABOUT: Survey Sampling

Choosing an accurate sample from the study population

The decision on an appropriate sample depends on several key factors.

• First, you decide which population parameters you want to estimate.
• Don’t expect estimates from a sample to be exact. Always expect a margin of error when making assumptions based on the results of a sample.
• Understanding the cost of sampling helps us determine how precise our estimates need to be.
• Know how variable the population you want to measure is. It is not necessary to assume that a large sample is required if the study population is large.
• Take into account the response rate of your population. A 20% response rate is considered “good” for an online research study.

Sampling characteristics in the study population

• Sampling is a mechanism to collect data without surveying the entire target population.
• The study population is the entire unit of people you consider for your research. A sample is a subset of this group that represents the population.
• Sampling reduces survey fatigue as it is used to prevent pollsters from conducting too many surveys, thereby increasing response rates.
• Also, it is much cheaper and saves more time than measuring the entire group.
• Tracking the response rate patterns of different groups will help determine how many respondents to select.
• The study is not only limited to the selected part, but is applied to the entire target population.

Sampling techniques for your study population

Now that you understand that you cannot survey the entire study population due to various factors, you should adopt one of the sample selection methodologies that best suits your research study.

In general terms, two methodologies can be applied: probability sampling and non-probability sampling .

Sampling Techniques: Probability Sampling

This method is used to select sample objects from a population based on probability theory. Everyone is included in the sample and has an equal chance of being selected. There is no bias in this type of sample. Every person in the population has the opportunity to be part of the research.

Probability sampling can be categorized into four types:

• Simple Random Sampling : Simple random sampling is the easiest way to select a sample. Here, each member has an equal chance of being part of the sample. The objects in this sample are chosen at random, and each member has exactly the same probability of being selected.
• Cluster sampling : Cluster sampling is a method in which respondents are grouped into clusters. These groups can be defined based on age, gender, location, and demographic parameters.
• Systematic Sampling : In systematic sampling, individuals are chosen at equal intervals from the population. A starting point is selected, and then respondents are chosen at predefined sample intervals.
• Stratified Sampling: S tratified random sampling is a process of dividing respondents into distinct but predefined parameters. In this method, respondents do not overlap but collectively represent the entire population.

Sampling techniques: Non-probabilistic sampling

The non-probability sampling method uses the researcher’s preference regarding sample selection bias . This sampling method derives primarily from the researcher’s ability to access this sample. Here the population members do not have the same opportunities to be part of the sample.

Non-probability sampling can be further classified into four distinct types:

• Convenience Sampling: As the name implies, convenience sampling represents the convenience with which the researcher can reach the respondent. The researchers do not have the authority to select the samples and they are done solely for reasons of proximity and not representativeness.
• Deliberate, critical, or judgmental sampling: In this type of sampling the researcher judges and develops his sample on the nature of the study and the understanding of his target audience. Only people who meet the research criteria and the final objective are selected.
• Snowball Sampling: As a snowball speeds up, it accumulates more snow around itself. Similarly, with snowball sampling, respondents are tasked with providing references or recruiting samples for the study once their participation ends.
• Quota Sampling: Quota sampling is a method where the researcher has the privilege to select a sample based on its strata. In this method, two people cannot exist under two different conditions.

LEARN ABOUT: Theoretical Research

Advantages and disadvantages of sampling in a study population

In most cases, of the total study population, perceptions can only be obtained from predefined samples. This comes with its own advantages and disadvantages. Some of them are listed below.

• Highly accurate – low probability of sampling errors (if sampled well)
• Economically feasible by nature, highly reliable
• High fitness ratio to different surveys Takes less time compared to surveying the entire population Reduced resource deployment
• Data-intensive and comprehensive Properties are applied to a larger population wideIdeal when the study population is vast.

Disadvantages

• Insufficient samples
• Possibility of bias
• Precision problems (if sampling is poor)
• Difficulty obtaining the typical sample
• Lack of quality sources
• Possibility of making mistakes.

At QuestionPro we can help you carry out your study with your study population. Learn about all the features of our online survey software and start conducting your research today!

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International Surveys

Pew Research Center regularly conducts public opinion surveys in countries outside the United States as part of its ongoing exploration of attitudes, values and behaviors around the globe. To date, the Center has conducted more than 800,000 interviews in over 110 countries, mainly in conjunction with the longstanding Global Attitudes and Religion & Public Life projects but including others such as a 20-country international science study and another on digital connectivity in 11 emerging economies.

Country Specific Methodology

View detailed information such as mode of interview, sampling design, margin of error, and design effect, for each country we survey.

Cross-national studies constitute the bulk of Pew Research Center’s international survey research. Such studies pose special challenges when it comes to ensuring the comparability of data across multiple languages, cultures and contexts. To learn more about the challenges and best practices of polling in foreign countries and in multiple languages, see here .

Pew Research Center staff are responsible for the overall design and execution of each cross-national survey project, including topical focus, questionnaire development, countries to be surveyed and sample design. The Center’s staff frequently contract with a coordinating vendor to identify local, reputable research organizations, which are hired to collaborate on all aspects of sample and questionnaire design, survey administration and data processing. Both coordinating vendors and local research organizations are consulted on matters of sampling, fieldwork logistics, data quality and weighting. In addition, Pew Research Center often seeks the advice of subject matter experts and experienced survey researchers regarding the design and content of its cross-national studies.

Field periods for cross-national studies vary by country, study complexity and mode of administration, typically ranging from four to eight weeks. To the degree possible, Pew Research Center attempts to synchronize fieldwork across countries in order to minimize the chance that major events or developments might interfere with the comparability of results.

Pew Research Center’s cross-national studies are designed to be nationally representative using probability-based methods and target the non-institutional adult population (18 and older) in each country. The Center strives for samples that cover as much of the adult population as possible, given logistical, security and other constraints. Coverage limitations are noted in the detailed methods for each country.

Sample sizes for Pew Research Center’s cross-national studies are usually designed to yield at least 1,000 interviews, though larger samples may be required for more robust within-country comparisons. Reported margins of error account for design effects due to clustering, stratification and weighting.

In the case of both telephone and face-to-face surveys, weighting procedures correct for unequal selection probabilities as well as adjust to key sociodemographic distributions – such as gender, age and education – to align as closely as possible with reliable, official population statistics. To learn about the weighting approach used for U.S. surveys using the nationally representative online American Trends Panel, visit here .

Pew Research Center is a charter member of the American Association of Public Opinion Research (AAPOR) Transparency Initiative .

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• Volume 81, Issue 7
• Sex and race disparities in the association between work characteristics and vitamin D deficiency: findings from the National Health and Nutrition Examination Survey, 2005–2010
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• http://orcid.org/0000-0001-5744-4866 Raquel Velazquez-Kronen 1 ,
• Leslie A MacDonald 1 ,
• Amy E Millen 2
• 1 Field Research Branch, Division of Field Studies and Engineering , National Institute for Occupational Safety and Health , Cincinnati , Ohio , USA
• 2 Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, The State University of New York , Buffalo , New York , USA
• Correspondence to Dr Raquel Velazquez-Kronen, Field Research Branch, Division of Field Studies and Engineering, National Institute for Occupational Safety and Health, Cincinnati OH, 45226, Ohio, USA; ohc0{at}cdc.gov

Objectives Vitamin D deficiency is highly prevalent worldwide; however, few large population-based studies have examined occupational risk factors. We examined associations between shift work, work schedule, hours worked, outdoor work, occupation and serum 25-hydroxyvitamin D (25(OH)D) levels in the US working population.

Methods This cross-sectional study included 8601 workers from the 2005–2010 National Health and Nutrition Examination Survey (NHANES) cycles. NHANES occupational data were supplemented with measures of outdoor work from the Occupational Information Network. Serum 25(OH)D concentration in nanomoles per litre (nmol/L) was categorised as sufficient (≥75), insufficient (50–<75), moderately deficient (30–<50) and severely deficient (<30). Age-adjusted weighted multinomial and binary logistic regression were used to examine associations between work-related factors and vitamin D status with sex-race/ethnicity stratification.

Results Shift workers had higher odds of severe vitamin D deficiency compared with day workers (OR: 1.64, 95% CI 1.22 to 2.19). Compared with those in white-collar occupations, those in natural resources were less likely to be deficient (OR: 0.31, 95% CI 0.19 to 0.52), while those in production were more likely to be deficient (OR: 2.25, 95% CI 1.48 to 3.43). Women working ≥40 hours/week compared with <40 hours/week were more likely to be moderately deficient (OR: 1.30, 95% CI 1.06 to 1.59). Black women working in sales were more likely to be deficient than those in management (OR: 1.53, 95% CI 1.03 to 2.27). Mexican American men working nights had the highest odds of deficiency (OR: 2.64, 95% CI 1.38 to 5.06).

Conclusions Work-related factors were associated with vitamin D status and there were race/ethnicity and sex differences. Targeted vitamin D screening and supplementation interventions may reduce these disparities.

• Shift Work Schedule
• Epidemiology
• Occupational Health
• Cross-Sectional Studies
• Environment

Data availability statement

Data are available in a public, open access repository. This study uses data publicly accessible data from the National Health and Nutrition Examination Survey (NHANES) and can be downloaded from https://www.cdc.gov/nchs/nhanes/index.htm .

https://doi.org/10.1136/oemed-2024-109473

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Contributors RV-K conceived and designed the study and produced an analytical plan. RV-K conducted the data analysis and drafted the manuscript. All authors interpreted the results, reviewed the manuscript and provided intellectual input. RV-K is the guarantor of the study.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Disclaimer The findings and conclusions in this article are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention or the National Institute for Occupational Safety and Health.

Competing interests None declared.

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Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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A mixed method study exploring similarities and differences in general and social services-specific barriers to treatment-seeking among individuals with a problematic use of alcohol, cannabis, or gambling

• Greta Schettini 1 , 2 ,
• Philip Lindner 1 , 2 ,
• Veronica Ekström 3 &
• Magnus Johansson 1 , 2  

BMC Health Services Research volume  24 , Article number:  970 ( 2024 ) Cite this article

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Introduction

The treatment gap for addictive disorders is one of the largest in health care. Several studies have investigated barriers to treatment for different addictive disorders, but very few studies conducted have explored whether the barriers differ depending on substance or behavior or if they are common among all addictive disorders. In Sweden, addiction care is provided both by the healthcare and social services, where the latter is common, but also less popular. To our knowledge, there are no studies exploring whether the barriers are different depending on where the treatment is given.

The aim was to thoroughly explore both which general and social services-specific barriers to treatment that are common, which barriers that differs, and how the barriers are described among individuals with a problematic use of alcohol, cannabis and/or gambling.

A mixed method convergent parallel design was conducted. For the quantitative measures, surveys including the validated Barriers to Treatment Inventory as well as questions regarding barriers in the Swedish multi-provider landscape, were collected from individuals with a problematic use of alcohol ( n  = 207), cannabis ( n  = 51), and gambling ( n  = 37). In parallel, 17 semi-structured interviews from the same population were conducted and analyzed with thematic analysis. Thereafter, the quantitative and qualitative data was compared, contrasted, and at last, interpreted.

The quantitative data showed that the largest general barriers in all groups were privacy concern and poor availability, and the largest barriers for seeking help from the social services was stigma, unawareness of what is offered, and fear of consequences for all groups. The qualitative data resulted in five general barriers: stigma, ambivalence, accessibility, fear of consequences, and lack of knowledge about addiction and its’ treatments, and three barriers specifically towards social services: social services reputation, fear of meeting acquaintances, and lack of knowledge. The themes were developed from data from all groups, but different aspects of the themes were mentioned by different groups.

There are details and aspects that differentiates both the general and social service-specific barriers to treatment between individuals with a problematic use of alcohol, cannabis, and gambling, but in large they perceive similar barriers.

Peer Review reports

Addictive disorders are a multi-level problem that contribute to as much as 5% of the global disease burden [ 19 ]. This not only by affecting the individual with mental and physical suffering [ 44 ], but also his or her relatives [ 37 ], as well as the society, both economically [ 4 ], and with increased criminal behaviors [ 5 , 24 , 34 , 35 ]. It is highly prevalent: in Sweden, 4% of the adult population fulfill the criteria for an addictive disorder [ 1 ] and up to 15–20% have a harmful use of alcohol, cannabis, or gambling [ 36 ]. This reflects patterns in other countries [ 20 , 21 , 22 ]. With indications that help-seeking per se can increase the likelihood of recovery [ 14 ], it is concerning that only approximately 10% of individuals with addictive disorders seek and receive help for it [ 9 , 17 , 18 ]. This constitutes one of the largest treatment gaps among all mental disorders [ 31 ] and can be understood from various perspectives.

One perspective is structural, i.e. what is offered, and the circumstances around it, such as how long the waiting times is or how/if it is journalized. For instance, in Sweden, where addiction care is a shared responsibility between regional health care and municipal social service, it was found in a study from 2013 that only 5% of individuals with problematic drinking preferred seeking treatment from the social services as compared to e.g. psychiatric or addiction specialist treatment, which more than 50% would prefer (Andréasson et al.). The authors of the same study speculate that this could be explained by labelling addiction as a social problem, with associations to poverty or homelessness, and that this could increase stigma. The low preference for treatment at social services is particularly concerning since in many parts of Sweden, there is little region-run addiction healthcare beyond immediate detoxification.

The treatment gap for addictive disorders can also be understood by internal barriers. For the most common dependence, alcohol [ 19 ], the belief that “one should be able to make it by themselves” is a well-documented barrier [ 16 , 46 ], similar to the desire for self-reliance [ 29 , 49 ] reported by individuals with the most common substance use, cannabis [ 19 ]. Further, the fear of consequences is common among individuals with alcohol use disorder in the Danish population [ 16 ], and the view that seeking treatment requires total abstinence is a prevalent barrier [ 50 ]. Gambling disorder was included in DSM-5 2014, as the first, and most prevalent, diagnostic behavioral addiction [ 7 ]. One study conducted in Poland investigated if the barriers to treatment were similar for gambling disorder and other addictive disorders, showing that overall, the barriers overlapped, but it also identified specific barriers which were linked to a pervasive lack of recognition regarding the classification of gambling as a legitimate illness [ 12 ]. That unawareness and uncertainty is large and common barriers were later replicated in a study that only looked at barriers among individuals with problematic gambling [ 30 ]. Further, independent of specific addiction, stigma has consistently been reported as a major barrier [ 13 , 47 , 50 ].

There are large overlaps in both the psychological and neurological [ 39 ] mechanisms behind addiction of different substances, and different addictions are typically treated at the same clinics, with similar methods [ 15 , 38 , 42 ]. There are however also obvious differences between them, such as legal status, and the consequences the consumption can result in.

There are to our knowledge few published studies investigating common barriers across various addictions, and there are no studies focusing on the contextual perspective nor which attempts to also uncover the mechanisms behind the barriers. Therefore, research questions for the current study were:

Are the general and social service-specific barriers to treatment and preferences on where to seek help different among individuals with a problematic use of cannabis, alcohol, and gambling (quantitative inquiry)?

How do individuals with a problematic use of cannabis, alcohol, or gambling describe the barriers that prevents them from seeking help (qualitative inquiry)?

Study design

A mixed method convergent parallel design was conducted, in order to both compare the three groups (alcohol, cannabis, and gambling) statistically, and gain an in-depth understanding of any differences and similarities. This entails that qualitative and quantitative data were collected and analyzed concurrently, and then interpreted together [ 10 , 11 ]. An overview of the procedure is presented in Fig.  1 . The process of both designing the surveys and the interview guide was done in collaboration with the patient representative from Stockholm Center for Dependency Disorders, a region-run healthcare provider.

Analytic pipeline that illustrates the process from the separate data collections and analysis to interpretation

Quantitative data collection and sample

The intention was to recruit participants with a problematic use of alcohol, cannabis or gambling that were considering seeking help but had not necessary sought it at time of recruitment. The main recruitment channels were therefore alkoholhjälpen.se , cannabishjälpen.se , droghjälpen.se , and stödlinjen.se (for gambling issues), which are all Swedish anonymous support-sites for addictive disorders. Recruitment was also conducted through spreading information about the study in social media. Three separate surveys with identical questions were developed, with separate links for each of the three participant groups.

For the quantitative inquiry, a cross-sectional design was used. All participants were anonymous, and the data was collected using REDCap [ 26 , 27 ], a secure survey tool hosted by Karolinska Institutet, that can offer anonymity according to the GDPR definition. Links in advertisements (separate for each group, i.e. alcohol, cannabis, and gambling) directed potential participants to a landing page describing the study. Before proceeding, participants provided digital informed consent. To see all items in the survey, see Appendix 1.

The participants were asked about how many days they had consumed alcohol, cannabis, or gambling during a regular week the last year, whether they had tried to lower their consumption but failed, and if they had needed inpatient care the last year because of this consumption. All participants that consumed > 0/week, and reported problem concerning their consumption, were included. To collect the participant’s care provider preferences, they were both asked where they could see themselves seek help (where it was possible to choose multiple provider options) and their primary preference in where to seek help (only one answer).

Barriers to treatment inventory

The surveys included the validated questionnaire Barriers to Treatment Inventory (BTI) [ 41 ], which measures self-reported barriers to seek help. BTI consists of 25 items, divided into 7 latent constructs: absence of problem, negative social support, fear of treatment, privacy concerns, time conflict, poor treatment availability, and admission difficulty. Because the constructs consist of different number of items, the mean score of each construct is used in the analyses. In this study, Cronbach’s Alpha among the different sub scales ranged from 0.633–0.829, which is considered as acceptable internal consistency reliability measures [ 23 ]. The reliability measures are presented in Table  1 .

Barriers specifically toward social services

Due to the previous study in a similar setting that showed the low preferences for seeking help at social service [ 2 ], the survey in the current study also included five questions regarding barriers toward social services. These barriers were based on findings on the past study, as well as clinical experience within the research group and conversations with the patient representative, and were answered on a Likert scale from 0–4, where 0 represented “do not agree at all” and 4 represented “completely agree”. One of these barriers was concerns over consequences, and if one answered > 1, representing agree to some degree, an open question popped up asking “which consequences are you worried about?” where participants could write their answers.. The answers to this open-ended question were initially planned to be analyzed as qualitative data, but the answers were short and not rich enough for this, so it was therefore instead decided to group them into categories, and quantify them according to Sandelowski et al., [ 45 ].

Quantitative data analysis

The statistical analyses were done using R (Version 2023.09.1 + 494) [ 48 ] and Jamovi (Version 2.3.28.0) [ 28 ]. To identify differences in the constructs of BTI as well as the barriers specifically towards the social services within the groups (alcohol, cannabis, and gambling), repeated measure ANOVAs were conducted on the two forms separately. Post hoc one-way ANOVAs comparing the difference substance groups were also conducted.

Qualitative semi-structured interviews & data collection

The semi-structured interview guide was based on previous research in a similar setting [ 3 ], conversations with the patient representative, and with minor revisions during the interview process, based on the findings that came up. The full interviews investigated several research questions, where the semi-structured questions for the present study can be found in Appendix 1.

The participants for the interviews were recruited through a nested process, where the participants that had answered the survey also could sign up to be interviewed. Initially, the aim purpose was to conduct 10 interviews per group, starting with the alcohol group and after analysis continuing with the cannabis and gambling groups. However, thematic saturation [ 40 ] was achieved (i.e. no new information between the codes emerged) after 17 interviews, where n  = 8 had a problematic use of alcohol, n  = 5 of cannabis, and n  = 4 of gambling. The interviews were done over telephone between February 2023-September 2023 by first-author and clinical psychologist GS, recorded with a dictaphone, transcribed by first using Microsoft Word’s transcription feature and then re-transcribed again by GS.

Qualitative data analysis

The subsequent process of coding, thematizing, and categorizing was done according to Braun and Clark [ 6 ]. More specifically, the transcriptions were coded by GS, with author MJ double-coding one interview and then GS and MJ having discussions about similarities and differences in the codes conducted. Initial themes were developed from the codes, with no consideration taken to participant groups. In the themes, the groups were then marked, to both observe if the themes were found in all groups and see if there were specific patterns within the groups. The codes, themes and subthemes were then discussed between authors GS, MJ, and VE. It should be noted that the process was not linear, and that the authors transitioned between coding, merging codes, thematizing and merging themes, and revising the results several times. The software used was NVivo 14.

Quantitative results: preferences seeking help

In total, 294 participants completed the survey, and their basic demographics are presented in Table  2 . A total of n  = 198 (67%) reported that they had not yet sought help; of these, n  = 135 (68%) reported that they could see themselves seeking help. Among the participants in this study that had not sought help, 54% could see themselves seeking help from regional health care, while only 17% could see themselves seeking municipal help, i.e. social services.

Preferences for first-hand preferred help did not differ between the groups. Significantly more with a problematic cannabis- and gambling use could see themselves seek help at social service specifically, compared to the participants with a problematic alcohol use; there were no other significant differences in help-seeking with specific care providers. The preferences of addiction care for those who have not sought help are presented in Table  3 .

Quantitative results: barriers to treatment in general and specifically towards social services

The Barriers to Treatment Inventory (BTI) revealed similar patterns for all groups. As shown in Fig.  2 , repeated measure ANOVAs revealed that negative social support (BTI2) was the weakest reported barrier. Privacy concerns (BTI4), such as being uncomfortable opening up about private topics with other people, along with poor availability (BTI6) were perceived to be significantly stronger barriers than the others, with one exception; that BTI4 was not significantly greater than admission difficulties (BTI7). For the full analysis, see appendix 2. Further, as shown in Table  4 , there were no significant differences in any BTI scores between the groups in six of the seven subscales.

The results from the Barriers to Treatment Inventory for all three groups

Regarding Barriers toward social service (BTSS), the patterns were similar. Here, worry about the secrecy was significantly lower than all other barriers, while stigma, unawareness what was offered and worry about the consequences, were reported as large barriers. This is presented in Fig.  3 , which is based on repeated measure ANOVAs, and for all analysis, see appendix 3. As visualized Fig.  3 , worry of consequences was a larger barrier than stigma, the individuals with cannabis problems, which it was not for the other two groups. This is also shown significant in Table  4 , where worry about consequences is reported as significantly larger for the cannabis than alcohol participants.

The results from the Barriers to towards social service for all three groups

All those who answered positive to the question on fear of consequences were asked which consequences they were worried about. In total, 148 answers were collected and categorized. For all groups, fear of consequences regarding family, work, fear of who will find out, and fear of how one would be treated was prevalent. The fear of legal issues was not present in the gambling group, a minority in the alcohol group, while a barrier that more of half of the participants in the cannabis group reported. The results are presented in Table  5 .

Qualitative results

When analyzing the interviews, the barriers to treatment was explored in two main categories: a) in general and b) specifically towards the social service. From these perspectives it was both explored which barriers that were overlapping for all three groups (alcohol, cannabis, and gambling), and how the groups differed from each other. The A, C, and G in the citations below refer to which group the participant belong to, and the F and M refers to the gender.

General barriers to treatment

Five themes were identified when exploring general barriers to treatment: stigma, ambivalence, accessibility, fear of consequences, and lack of knowledge about addiction and its’ treatments. When describing why they did not seek help earlier or speculating in why others with a harmful use do not seek help, some individuals described lack of motivation, and not seeing the use as harmful. However, since none of the participants described experiencing this as a present barrier, this was not included as a theme.

Stigma is a well-known barrier to treatment [ 13 , 47 , 50 ], which was confirmed in these interviews for all the groups. These interviews did however reveal that what one was ashamed of depended on which group one belong to. For those with a problematic alcohol use, the stigma was related to feeling weak and having a bad character and appear to be “someone that cannot handle life on their own” (A13F), where the stigma in the group of cannabis users instead was related to that buying and using is a criminal act, and the social view on this, for instance described like “if someone is an alcoholic, you feel sorry for them, but if someone is a junkie, they are felonious” (C159M). For those with gambling issues the stigma is rather directed to one’s relatives “all with gambling addiction that I have talked to say that their biggest fear is the guilt, knowing that one has done something wrong and expose one’s loved ones to stuff you do not want to expose them to…” (G10M).

Ambivalence

This theme reflects the ambivalence participants in all groups described on giving up the function that the substance serves. In all groups the substance filled the function of escaping anxiety and loneliness, described such as “I am not feeling good, I never have, but when I am high, time disappears” (C159M), “I gamble because I am lonely” (G17M), and “alcohol is the best way to suppress anxiety” (A11F), and quitting using means one must deal with this in another way. It was also described how the substances fills other, different functions depending on group. Here, alcohol was described as filling a social function, cannabis was used for both creativity and pain relief, and continuing gambling gave the hope of winning back all money that one has lost. Another factor in this theme is that seeking help will imply setting goals, and the ambivalence on committing to this with the risk of failing. One participant described that “I feel that I can’t bear to disappoint myself … and when one is trying to get clean that is what happens, because there will be relapses … and for every relapse, it hurts even more” (C156F).

Accessibility

Accessibility was a barrier described in all groups. A few participants mentioned that when one seeks help, the waiting list is too long, and one described that “to have to describe the situation and tell the story for like three different people before one receives the right help” (G19M) was a barrier. One alcohol participant said that “When one finally found something… well, then the opening hours are only a few days a week … and many that drinks do it every day but especially during the weekends, and then you can’t find almost any place to reach out for help” (A18F), and also in the gambling group it was also described how one often sits and gambles in the middle of the night and the help offered is available between a small range of time during the day when one is at work. Here, both groups describe a barrier that the help is not offered during the time where it is the most risk that they will relapse. A pattern that was only described in the gambling group was that “there are small gaps where one feel like “I need help!” and if help would be as visible as the gambling advertisement with banners one would click at it right away … but when help lines are closed or there is a long waiting line you just shut your computer or hang on the phone and maybe try again in six months” (G19M).

Fear of consequences

Seeking help will include admitting that one has problems, and the fear that this will not only imply receiving help, but also other, negative consequences was mentioned as a large barrier for all groups. Among the alcohol and cannabis groups, the fear of losing one’s driver’s license was such a consequence, as well as among parents, the fear of receiving a report of concern regarding one’s child(ren). In turn, this comes with the fear that “one can actually loose custody of the children if it is really bad” (A52M) or that “what if the social service would come knock at the door? When you live in a smaller village and there is an unknown car coming up with two ladies, the neighbors will start wondering and the rumors will begin spreading” (A2F).Further mentioned consequences were not receiving other psychiatric care when the caregivers find out about the use, and that one’s boss will find out about the use and that it will affect one’s working situation. However, neither of these consequences were mentioned in the group of gamblers. They did however describe the fear of the consequences when relatives find out: “especially if one has a family, then you are so afraid they will leave you, which you absolutely think that they will” (G10M). Among those with a problematic use of alcohol and cannabis it was also a fear for the consequences when relatives find out, but more that they will look at you in a different way, with examples from interviews like “my children will not need to feel that they have a mom that is an alcoholic” (A6F) and “if my wife’s parents would find out that I use cannabis… I mean, they already hate me so…” (C159M).

Lack of knowledge about addiction and its treatments

In the gambling group, one mentioned barrier is the unawareness that gambling addiction is stated as a real psychiatric problem that one can receive help for. This is not mentioned in the other groups, but instead the unawareness that it is possible to receive help to lowering, but not necessary 100% quit, one’s consumption. One participant from the cannabis group stated that: “there is a fear when I seek help, that it implies that I should be sober for the rest of my life… and also include everything (like taking a beer)” (C150M), and a participant from the alcohol group said that “I am not ready to fully quit, so I am trying to lowering the consumption on my own” (A55F).

The barriers to treatment specifically from the social service

Three themes were identified when exploring barriers to treatment specifically from social services. These themes were social services reputation, fear of meeting acquaintances, and lack of knowledge.

Social services reputation

When asked about barriers to seek help from social service specifically, it was described that if there is a stigma in general seeking help for addiction, it is even more stigmatizing seeking it from the social services: “it feels like the social service is somehow related to extreme misery” (C150M) and “the social services, is related to… well maybe that you feel like a loser if you end up there” (A52M). Participants from the alcohol and cannabis groups also stated that they had heard or read about negative narratives related to social services and having close one’s with negative experiences. This theme was not as clearly appearing in the gambling group, since the social services can help with debt settlements and financial aid, which some of the gambling participants have received.

Fear of meeting acquaintances

For all groups, one barrier to seek help from the social service was that these are more local, and in smaller villages it is a worry to meet someone that one knows: “I know every social worker in this municipality, and I will not go to them with my problems, I just won’t… and that barrier needs to go away in order for the addiction care to work, because there will always be a risk in smaller municipalities that you will run in to your therapist” (C93M). Another participant described that “the rumors that “she is an alcoholic” can become a tragedy for a family or an individual” (A55F).

Lack of knowledge about social services and the prerequisites

When asking the participants of barriers to seek help for their use at social services, many answered with both surprise that help was offered from the social service; “Why do they not make themselves heard that there is help there to get?” (G87M) and with skepticism on what this help implied “seeking at the social service? Never! You never know what they are up to… will they force you to compulsory care? Will they steal my driver’s license? What happens?” (A13F). This was clearly described in relation to cannabis: “when using a substance that is also illegal, one really needs to get information on what can happen (in order to seek help)” (C150M).

The present study revealed that individuals with a problematic use of alcohol, cannabis, and gambling perceived similar barriers to seeking treatment, both in general and with social services specifically. When asked to describe these barriers, themes were superficially similar; however, key between-group differences also emerged in terms of group-dependent subthemes.

Similarities between groups

We were not able to find any significant differences between groups regarding preferences on where they would firsthand seek help, although it should be noted that some of the pairwise contrasts were not powered to detect small differences. In all groups, (regional) health care was more popular than (municipal) social services. One prominent theme that emerged from the qualitative interviews was the reputation of social services, with individuals from all – and especially in the alcohol and cannabis groups – describing it as more stigmatizing seeking help from social services. Another theme was the lack of knowledge about social services, both regarding care offered as well as the prerequisites for receiving treatment. Further, in the quantitative questions regarding barriers toward social services (BTSS), fear of consequences was reported as a large barrier among all groups. In the open question about which consequences one feared if seeking help at social service, fears regarding family members, colleagues, and similar finding out, and worry about how one would be treated, were frequent in all three groups.

Both the qualitative themes, and the complementary open questions, help explain the quantitative preference ratings. Notably, the earlier study by Andréasson, et al. (2013), that also investigated preferences, found that as few as 5% of their participants (individuals in Sweden with a problematic alcohol use) preferred seeking help from the social services. The present study replicates these results and extends them by including individuals with a problematic use of gambling or cannabis. The present study also offers deeper insight into why so few prefer seeking treatment from social services, with both the interviews and the open-ended answers from the surveys indicating that the fear of meeting acquaintances as well as the worry of receiving a report of concern, or loosing custody of one’s children, is what keeps people from seeking help from social service. In Sweden, social services have a broad set of responsibilities that include exercising public authority such as rehoming children, which (regional) health care cannot. The present study’s results indicate that this dual role explains why so few would choose to seek addiction help from social services.

Of note, there were one significant difference between groups in general barriers to treatment-seeking (BTI), where poor availability being top-ranked barriers among the gambling and cannabis groups, but not the alcohol group. This could reflect that there actually are more help available for the alcohol group. Regarding rated barriers to treatment seeking at social services specifically (BTSS), no significant differences were found in the questions concerning distrust that the treatment would work, stigma, or unawareness, the latter two being rated high. These results are not only congruent with uncovered qualitative themes around stigma and accessibility, but also the themes around lack of knowledge about addiction and its treatments, as well as lack of knowledge about social services and the prerequisites. Our findings support the need for future initiatives to lower stigma, make treatment more available, and as well as reaching out with information about both addiction and its treatments, to attempt to lower the well-known addiction treatment gap.

Differences between groups

In the present study, we also uncovered important differences between the groups. The alcohol group rated themselves significantly less likely to seek help at social service compared to the other groups. Further, the cannabis group was the only one that rated fear of consequences to be a larger barrier than stigma. In the open question about which consequences that one feared if seeking help at social services, the fear of legal consequences was the most reported factor in the cannabis group; this fear was only mentioned a few times in the alcohol group and not mentioned at all in the gambling group. In the qualitative data, the fact that cannabis use is illegal in Sweden was mentioned both in conjunction with the theme of stigma, in the fear of consequences, and in lack of knowledge about social services and the prerequisites. This would appear to suggest that decriminalization cannabis could lower barriers to seek treatment, but according to a recent systematic review, there seem to be no indications of such trends in jurisdictions where cannabis has been legalized in the last decade [ 8 ].

There were also differences between the groups in the themes uncovered in the qualitative data. Other than the ambivalence on losing an escape from anxiety or loneliness participants, all groups also described ambivalence on giving up other perceived functions of their consumption, but the different groups described different types of functions. Similarly, participants from all groups talked about stigma as a major barrier, but in different ways. In many of the themes, the alcohol and cannabis groups were similar, while the gambling group differed more. For instance, regarding accessibility, only gamblers described short, intense, and transient moments when motivation to seeking help was high – if help is not easily available then, motivation to seeking help would be lost. They also differed in describing fear of consequences, which were more oriented towards how it would affect their relationships with close relatives rather than the fear of being labeled as an “addict” in their social community. In difference to the other groups, they did not either mention the fear that their gambling would result in an exercise of public authority.

This pattern, with the gambling group deviating from the others, likely stems from it being a behavioral addiction instead of the use of a substance. Practically, a behavioral addiction does not automatically result in e.g. being unsuitable for driving vehicles or taking care of children. This could explain that worry about receiving a report of concern or losing custody regarding one’s child(ren) was not described as a fear of consequence in the interviews among gamblers, to the degree that it was in the other groups. Further, even though all groups described worry about their relatives being disappointed, the gambling group was the only one expressing worry about their relatives also leaving them when finding out about the use. This could be explained by the fact that the cessation process differs between gambling and other addictions: someone quitting gambling has typically not only lost (large amounts of) money, but also have large debts that requires numerous years to pay back [ 33 ], in turn affecting the everyday life for both oneself and most probably also one’s relatives. The gamblers were also the only ones describing their windows of motivation to seek help to be short, intense, and then closed if help is not available right away. One difference between gambling disorder and other addictions is that quitting predicts higher suicidality [ 32 ], which is in turn related to giving up the hope of winning back money lost. This severe struggle to quit is unique to gambling addiction, which in turn could be one possible explanation for the short, and intense motivation gaps closing so quick.

In the study conducted in Poland [ 12 ], that also compared barriers to treatment-seeking among gamblers to other addictions, it was found that the largest difference from the other addictions was the lack of knowledge that gambling addiction is defined as a psychiatric diagnosis, and that there was a lack of adequate help offered. The results from the present study can be seen as a replication of this, although these findings were not as prominent. One possible explanation for this could be that gambling addiction has seen a better integration into the health care landscape in the last few years, at least in Sweden [ 25 ]. However, gambling is the most recently added addiction in the Diagnostic and Statistical Manual of Mental Disorders [ 43 ], which could explain it being perceived as difficult to find help for, since fewer professionals are focused on it. This might also contribute to the fear of relatives finding out, with it still being less established as a psychiatric problem in society.

Future research and limitations

Considering the quantitative and qualitative results jointly, one driver behind many of the barriers appears to be a lack of information and lack of transparency on how treatment provision works. This not only applies to the quantitative results such as unawareness ratings, but also to the privacy concerns, as well as barriers such as fear of treatment and fear of consequences. Exploring the effects of transparency initiatives appears to be promising avenue for future research. Also, because there were few differences in perceived barriers to treatment between the groups, this could imply that successful initiatives to lower the treatment gap in one group ( e.g. alcohol users) could be tested, with small alterations, also among other groups. Further, this study focused on barriers between groups with different primary use. It encouraged to continue exploring both barriers to treatment being moderated by other factors than which problematic use one has, such as gender, age, or other social factors. Also, our open questions regarding barriers revealed themes that the validated BTI instrument [ 41 ] does not cover, such as the fear of consequences or self-stigma. Findings from the current study indicate the need for a new or revised version of this and similar instruments, to cover a broader spectrum of perceived barriers.

A strength of the study is the amount of both quantitative and qualitative data, giving a strong foundation for interpretation. There are also several limitations of the current study, one being the between-group difference in sample sizes. Since statistical power of a given contrast is primarily driven by the size of the smaller group, we cannot rule out that more differences would have been revealed with larger groups, in particular problem gamblers. The groups were however recruited from similar contexts and were overall similar, and population-level difference in prevalence rates of the different addiction groups entail that differing sample sizes are not unexpected. To minimize the demography items, to increase response rates as well as for anonymity reasons, no information about co-morbidity was collected. This makes it possible in theory for participants with a problematic use of more than one of the substances to answer the survey twice or even three times, visiting it from different sites, which would render reported values statistically dependent. However, open-ended questions did not in any way signal this to be the case, nor were participants reimbursed for their participation, entailing no incentive to participate multiple times.

Other limitations include that we only investigated barriers in general, and specifically towards social service, but not barriers specific to other care providers such as the health care. Including this would have increased insights into broader, provider-specific barriers. Lastly, the included participants were recruited from help-seeking sites. Even though it could be considered a strength to include individuals that wanted help but who had not yet sought it, the sample still only represent a minority of the broader group that we aimed to reach.

This study uncovered similarities and differences in perceived barriers to treatment-seeking among individuals with a problematic use of alcohol, cannabis, or gambling, and offers some in-depth insight into what may be needed to overcome them. There are details and aspects that differentiates both the general and social service-specific barriers to treatment between individuals with a problematic use of alcohol, cannabis, and gambling, but in large they perceive similar barriers. This suggests that initiatives proven effective in reducing treatment barriers to in one addiction group may be used as guidance for other groups, albeit with some tailoring.

Availability of data and materials

Data will be made available upon reasonable request to the corresponding author.

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Acknowledgements

We extend our gratitude to the patient representative for giving her perspective, which gave several important insights when designing this study.

Open access funding provided by Karolinska Institute. This research has been supported by the Swedish Research Council for Health, Working Life and Welfare (FORTE), Applied welfare research: 2021–02066. Open access funding provided by Karolinska Institute.

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Greta Schettini, Philip Lindner & Magnus Johansson

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The Author Contributions according to CRediT: GS: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data Curation, Writing - Original Draft, Writing - Review & Editing, Visualization, Project administration PL: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data Curation, Writing - Review & Editing, Visualization, Supervision, Funding acquisition. VE: Methodology, Formal analysis, Investigation, Resources, Data Curation, Writing - Review & Editing, Supervision. MJ: Conceptualization, Methodology, Formal analysis, Investigation, Resources, Data Curation, Writing - Review & Editing, Supervision, Funding acquisition.

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Schettini, G., Lindner, P., Ekström, V. et al. A mixed method study exploring similarities and differences in general and social services-specific barriers to treatment-seeking among individuals with a problematic use of alcohol, cannabis, or gambling. BMC Health Serv Res 24 , 970 (2024). https://doi.org/10.1186/s12913-024-11304-5

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A National Survey of Marijuana Use Among U.S. Adults According to Obesity Status, 2016-2022

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• 1 Department of Public Health, College of Life Sciences, Brigham Young University, Provo, Utah, USA.
• PMID: 39158998
• DOI: 10.1089/can.2024.0069

Background and Objective: Research has linked marijuana use with lower body mass index (BMI). The current study explores the correlation between marijuana use on BMI in the general U.S. population. It reports the prevalence of marijuana in adults in relation to BMI, overall and across the levels of important variables. Materials and Methods: This study used a probability sample of U.S. adults 18 years of age and older from the 2016 through 2022 Behavioral Risk Factor Surveillance System, a telephone-administered survey. The survey collects data from a representative sample regarding health-related risk behaviors, chronic health conditions, and use of preventive services. The primary outcome variables are current (at least once in the last 30 days) and daily (at least 20 of the last 30 days) marijuana use. Results: The study sample consists of 735,921 participants in the surveys that completed the optional module on marijuana use. Prevalence of marijuana use in adults doubled during the study period (7.48% to 14.91%). The increase directly corresponds with a shift toward legalization of medical and recreational marijuana. On average, the prevalence of use is 9% higher when medical marijuana is legal and 81% higher when recreational marijuana is legal (vs. not legal). For obese individuals, prevalence of current marijuana use is 35% lower than for nonobese individuals on average. Lower prevalence of marijuana use in obese individuals is consistently observed across the levels of certain demographic variables, employment status, tobacco smoking history, marijuana legalization status, and certain medical conditions (asthma, arthritis, and depression). In 2022, the adjusted odds of current or daily marijuana use are significantly lower and similar among obese (vs. non-obese) (0.68, 0.69, respectively), such that reduced obesity does not require daily use. Similarly, the adjusted odds of current marijuana use decrease in similar fashion to daily marijuana use with higher BMI weight classification. Conclusion: Marijuana use is correlated with lower BMI. As legalization and prevalence of the drug in the U.S. increases, the prevalence of obesity may decline. However, clinicians should view this outcome along with the known health risks associated with marijuana use.

Keywords: appetite; body weight; legalization; medical marijuana; prevalence.

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Wednesday, August 21, 2024

Russian offensive campaign assessment, august 21, 2024.

  Angelica Evans, Karolina Hird, Nicole Wolkov, Kateryna Stepanenko, and George Barros

Ukrainian forces continued offensive operations throughout the Kursk Oblast salient on August 21 and have made additional marginal advances. Ukraine's Special Operations Forces posted footage on August 21 showing Ukrainian strikes on several pontoon bridges and staging areas along the Seim River in Glushkovsky Raion, west of the current Ukrainian salient in Kursk Oblast. [13] The Ukrainian Special Operations Forces suggested that Ukrainian forces may have used HIMARS in some of the strikes against pontoon bridges, while milbloggers speculated that Ukrainian forces used air-launched small-diameter glide bombs. [14] Geolocated footage published on August 21 shows drone operators of Russia's 155th Naval Infantry Brigade (Pacific Fleet, Eastern Military District [EMD]) striking Ukrainian forces in and around Vishnevka (south of Koreveno and 14km from the international border), confirming that Ukrainian forces have likely advanced into and beyond the settlement. [15] A Russian milblogger claimed that a reinforced platoon-sized Ukrainian element unsuccessfully attacked from Vishnevka towards Komarovka (southwest of Koreveno and 12km from the international border), but that Russian drone strikes and anti-tank guided missile (ATGM) fire stopped Ukrainian forces from establishing positions within Komarovka. [16] Another Russian milblogger claimed that Ukrainian forces conducted a platoon-sized mechanized attack towards Korenevo but were unsuccessful. [17] Additional geolocated footage published on August 21 indicates that Ukrainian forces hold positions in forest areas east of Aleksandrovka (northeast of Koreveno and 33km from the international border). [18] Geolocated footage published on August 21 also shows that elements of the Russian 200th Guards Motorized Rifle Brigade (14th Army Corps, Leningrad Military District [LMD]) hold positions along the 38H-564 road east of Zhuravli (east of Koreveno and 21km from the international border), indicating that Russian forces either recently retook these positions or that Ukrainian forces have not yet closed the small salient along the 38H-564 road near Zhuravli. [19] Russian milbloggers continued to claim that Ukrainian forces are advancing north of Sudzha near Malaya Loknya and are encircling Russian forces in Martynovka (northeast of Sudzha and 19km from the international border). [20] Elements of the Russian 810th Naval Infantry Brigade (Black Sea Fleet) are reportedly facing encirclement in Martynovka, and Russian milbloggers lauded a soldier from the Russian 11th Airborne (VDV) Brigade for allegedly leading conscripts out of an encirclement in an unspecified area in Kursk Oblast, potentially in reference to the Martynovka pocket. [21] Geolocated footage published on August 21 indicates that Ukrainian forces have advanced into southern Russkaya Konopelka (east of Sudzha and 12km from the international border). [22] The Russian 810th Naval Infantry Brigade appears to be deployed particularly sporadically throughout the Kursk Oblast salient — various Russian sources have reported that its elements are operating as far north as the Kauchuk area (30km from the international border) and between Martynovka and Spalnoye (southeast of Sudzha and 45km away from Kauchuk). [23]

  Key Takeaways:

• The Kremlin appears to have launched an intricate messaging campaign aimed at justifying to its domestic audience why Russia is prioritizing maintaining the initiative in eastern Ukraine over immediately expelling Ukrainian forces from Kursk Oblast.
• Putin notably appears to be demanding that Russia defeat Ukraine’s incursion into Kursk Oblast without sacrificing the stability of his regime, deprioritizing the offensive in eastern Ukraine, or firing his incompetent but loyal lieutenants. The results of such a strategy are too early to forecast.
• Ukrainian forces continued offensive operations throughout the Kursk Oblast salient on August 21 and have made additional marginal advances.
• Russian President Vladimir Putin visited the Republic of Chechnya for the first time in 13 years on August 20, likely in an effort to shift domestic focus away from the Ukrainian incursion into Kursk Oblast and posture normalcy and stability.
• Recent US intelligence assessments highlight Ukraine's efforts to develop alternative and asymmetric capabilities in the face of Russian manpower and materiel advantages, as well as Ukraine's continued dependence on Western security assistance.
• Ukraine continues efforts to attrit Russia's air defense and aviation capabilities.
• Russian authorities may have attempted to block Telegram and other non-Russian internet communications services on August 21.
• The Russian government is reportedly supporting a bill that would allow Russian authorities to draw up administrative protocols against Russian citizens who violate Russian law while living aboard.
• Russian President Vladimir Putin and People's Republic of China (PRC) Premier Li Qiang discussed deepening bilateral economic and trade relations in Moscow on August 21.
• Russian forces recently advanced southeast of Pokrovsk, southwest of Donetsk City, and northeast of Robotyne.
• Russian occupation authorities continue to create Cossack organizations in occupied Ukraine, likely to build out Russia's military reserves and law enforcement bodies in occupied Ukraine.

• Russian Main Effort – Eastern Ukraine (comprised of three subordinate main efforts)
• Russian Subordinate Main Effort #1 – Push Ukrainian forces back from the international border with Belgorod Oblast and approach to within tube artillery range of Kharkiv City
• Russian Subordinate Main Effort #2 – Capture the remainder of Luhansk Oblast and push westward into eastern Kharkiv Oblast and encircle northern Donetsk Oblast
• Russian Subordinate Main Effort #3 – Capture the entirety of Donetsk Oblast
• Russian Supporting Effort – Southern Axis
• Russian Air, Missile, and Drone Campaign
• Russian Mobilization and Force Generation Efforts
• Russian Technological Adaptations
• Activities in Russian-occupied areas
• Ukrainian Defense Industrial Base Efforts
• Russian Information Operations and Narratives
• Significant Activity in Belarus

Limited positional engagements continued in northern Kharkiv Oblast on August 21, but there were no confirmed changes to the frontline. Ukrainian Kharkiv Group of Forces Spokesperson Colonel Vitaly Sarantsev noted that Russian forces have decreased their use of tactical aviation targeting Kharkiv Oblast in order to prioritize airstrikes in other areas of the theater and in Kursk Oblast. [57] Sarantsev also reported that a contingent of Russian forces remains blocked in the Vovchansk Aggregate Plant in Vovchansk (northeast of Kharkiv City), but that they are unable to attack their way out of the plant because Ukrainian forces control all logistics routes into and out of the plant. [58] Fighting continued north of Kharkiv City near Lyptsi and Hlyboke and in and around Vovchansk. [59] Elements of the Russian 11th Tank Regiment (18th Motorized Rifle Division, 11th Army Corps [AC], Leningrad Military District [LMD]) and 7th Motorized Rifle Regiment (11th AC, LMD) reportedly continue operating near Hlyboke and Lukyantsi (north of Kharkiv City and east of Hlyboke), respectively. [60]

Limited positional engagements continued along the Kupyansk-Svatove-Kreminna line on August 21, but there were no confirmed changes to the frontline. The Ukrainian General Staff reported that Russian forces attacked northeast of Kupyansk near Synkivka; east of Kupyansk near Petropavlivka; southeast of Kupyansk near Stepova Novoselivka and Berestove; northwest of Svatove near Stelmakhivka; west of Svatove near Andriivka; southwest of Svatove near Serhiivka and Novoserhiivka; northwest of Kreminna near Novosadove, Makiivka, Hrekivka, and Torske; and southwest of Kreminna near Dibrova on August 20 and 21. [61] Elements of the Russian 144th Motorized Rifle Division (20th Combined Arms Army [CAA], Moscow Military District [MMD]) reportedly continue operating in the Lyman direction (west of Kreminna). [62]

Russian forces did not make confirmed advances in the Chasiv Yar direction amid ongoing offensive operations on August 20 and 21. The Ukrainian General Staff reported that Russian forces continued offensive operations near Chasiv Yar; north of Chasiv Yar near Hryhorivka; southeast of Chasiv Yar near Ivanivske and Klishchiivka; and south of Chasiv Yar near Predtechyne on August 20 and 21. [64] A Russian milblogger claimed that Russian forces consolidated their positions in the forest south of Hryhorivka (north of Chasiv Yar) in an area up to 2.36 kilometers wide and that elements of the Russian Sever-V Brigade (Russian Volunteer Corps) reportedly continued operating near Hryhorivka. [65] A Russian milblogger claimed that Russian forces advanced southeast of Orikhovo-Vasylivka (northeast of Chasiv Yar), but ISW has not observed confirmation of this claim. [66]

Russian Defense Minister Andrei Belousov claimed on August 21 that Russian forces seized Niu York (south of Toretsk), but Ukrainian military sources continue to report that Ukrainian forces still control 20 percent of the settlement. [67] Belousov credited elements of the Russian 9th Motorized Rifle Brigade (1st Donetsk People’s Republic Army Corps [DNR AC]) for the seizure of Niu York. [68] A source from a Ukrainian brigade operating in the Toretsk direction told Ukrainian outlet Suspilne that the situation in Niu York is very difficult because Russian forces are constantly attacking the settlement in small assault groups but that Ukrainian forces still control about 20 percent of Niu York. [69] Russian milbloggers claimed that Russian forces advanced in the fields northeast of Druzhba (east of Toretsk); in northwestern Pivnichne (east of Toretsk); and in eastern Toretsk. [70] The Ukrainian General Staff reported that Russian forces launched assaults near Toretsk; southeast of Toretsk near Zalizne; south of Toretsk near Nelipivka and Niu York; and southwest of Toretsk near Panteleymonivka on August 20 and 21. [71] Belousov claimed on August 20 that elements of the Russian 132nd Motorized Rifle Brigade and 1st Slavic Brigade (both 1st DNR AC) seized Zalizne. [72]

Russian forces recently advanced southeast of Pokrovsk and continued offensive operations in this direction on August 20 and 21. Geolocated footage published on August 20 shows that Russian forces advanced in northwestern Zhuravka (southeast of Pokrovsk), and some Russian milbloggers claimed that elements of the Russian 27th Motorized Rifle Division (2nd Combined Arms Army [CAA], Central Military District [CMD]) seized Zhuravka. [73] ISW had not observed visual evidence confirming Russian advances in the northwesternmost part of Zhuravka, nor to suggest that Russian forces control the entire settlement. Russian milbloggers claimed that Russian forces advanced northeast of Hrodivka (east of Porkrovsk) and northeast of Novohrodivka, south of Mykolaivka, and north of Ptyche (all southeast of Pokrovsk). [74] Russian milbloggers also claimed that Russian forces seized Komyshivka (southeast of Pokrovsk) but ISW has not yet observed visual evidence confirming these milblogger claims. [75] The Russian Ministry of Defense (MoD) retroactively announced on August 21 that the Russian Central Grouping of Forces (GoF) seized Zhelanne, and ISW assessed that Russian forces likely seized Zhelanne around August 18. [76] The Ukrainian General Staff reported that Russian forces continued offensive operations east of Pokrovsk near Vozdvyzhenka, Zelene Pole, Myrolyubivka, Hrodivka, and Kalynove; and southeast of Pokrovsk near Mykolaivka, Novohrodivka, Mykhailivka, Ptyche, and Skuchne. [77] Elements of the Russian BARS-15 unit (Russian Combat Army Reserve) are reportedly operating in the Avdiivka direction, and a drone company of the ”Volga” Brigade is reportedly operating near Novohrodivka. [78]

Russian forces continued offensive operations west of Donetsk City near Krasnohorivka and Heorhiivka on August 20 and 21, but there were no confirmed changes to the frontline in the area. [79]

Geolocated footage published on August 20 indicates that Russian forces have recently advanced in western Zaporizhia Oblast to the southern outskirts of Luhivske (northeast of Robotyne). [85] Some Russian milbloggers claimed that Ukrainian forces are preparing for offensive operations in Zaporizhia Oblast, with one milblogger claiming on the evening of August 20 that Ukrainian forces launched an offensive operation near Polohy (in central Zaporizhia Oblast east of Robotyne). [86] Former Roscosmos (Russian space agency) head and Zaporizhia Oblast occupation senator Dmitry Rogozin and several milbloggers denied claims of Ukrainian activation anywhere in Zaporizhia Oblast, however, and accused other Russian commentators of overhyping false information and creating panic in the information space. [87] The Ukrainian General Staff reported that Russian forces attacked north of Robotyne near Novodanylivka and northeast of Robotyne near Mala Tokmachka on August 20 and 21. [88] Elements of BARS-32 (Russian Combat Army Reserve) are reportedly operating near Enerhodar (west of Robotyne), while elements of the 503rd Motorized Rifle Regiment (19th Motorized Rifle Division, 58th Combined Arms Army [CAA], Southern Military District [SMD]) and BARS-3 are operating in the general Zaporizhia Oblast direction. [89]

Russian forces continued unsuccessful ground attacks in east (left) bank Kherson Oblast on August 20 and 21 but did not make any confirmed advances. [90] Russian forces conducted drone, artillery, and air strikes against settlements and civilian infrastructure in west bank Kherson Oblast. [91]

Russian Mobilization and Force Generation Efforts (Russian objective: Expand combat power without conducting general mobilization)

Russia continues to find ways to incorporate US-produced electronic components into its weapons and censorship, and surveillance systems. Russian investigative outlet The Insider published a story on August 20 detailing how Russia is importing programmable logic devices (PLDs) for use in missile and drone navigation systems, internet content filtration, and facial recognition, many of which come from US companies. [101] The Insider noted that Russia is using Xilex and Altera integrated circuits, both products of American PLD manufacturers, in the navigation systems for Iskander and Kalibr missiles. The Insider also emphasized that Russia is able to import such PLDs despite extensive international sanctions, largely because many export companies are ignoring international embargoes.

Note: ISW does not receive any classified material from any source, uses only publicly available information, and draws extensively on Russian, Ukrainian, and Western reporting and social media as well as commercially available satellite imagery and other geospatial data as the basis for these reports. References to all sources used are provided in the endnotes of each update.

[1] https://meduza dot io/feature/2024/08/21/v-kremle-schitayut-chto-boi-v-kurskoy-oblasti-prodlyatsya-neskolko-mesyatsev-i-hotyat-ubedit-rossiyan-chto-eto-novaya-normalnost

[2] https://www.pravda dot com.ua/eng/news/2024/08/21/7471267/

[3] https://www.pravda dot com.ua/eng/news/2024/08/21/7471267/; https://t.me/cikrossii/3944

[4] https://meduza dot io/paragraph/2024/08/21/kursk-ne-slomit-kak-v-geroicheskom-1943-m-my-vse-ot-hipsterov-do-vatnikov-dolzhny-ob-edinitsya

[5] https://t.me/warhistoryalconafter/180564

[6] https://meduza dot io/feature/2024/08/21/v-kremle-schitayut-chto-boi-v-kurskoy-oblasti-prodlyatsya-neskolko-mesyatsev-i-hotyat-ubedit-rossiyan-chto-eto-novaya-normalnost

[7] https://www.understandingwar.org/backgrounder/assessing-significance-current-russian-and-ukrainian-operations-course-war

[8] https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-june-7-2024

[9] https://meduza dot io/feature/2024/08/21/v-kremle-schitayut-chto-boi-v-kurskoy-oblasti-prodlyatsya-neskolko-mesyatsev-i-hotyat-ubedit-rossiyan-chto-eto-novaya-normalnost

[10] https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-february-26-2023 ; https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-march-12-2023 ; https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-august-10-2024

[11] https://www.understandingwar.org/backgrounder/putin-vulnerable-western-policy-masks-russian-weakness

[12] https://understandingwar.org/backgrounder/russian-offensive-campaign-assessment-april-30-2023 ; https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-may-12-2024 ; https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-june-24-2023

[13] https://t.me/ukr_sof/1183; https://t.me/kiber_boroshno/9623 ; https://t.me/WarArchive_ua/19046 ; https://t.me/warhistoryalconafter/180535 ; https://x.com/666_mancer/status/1826175406610829766

[14] https://t.me/ukr_sof/1183; https://t.me/motopatriot/26477

[15] https://t.me/VARYAGI_155/105 ; https://x.com/AudaxonX/status/1826198757483896954 ; https://x.com/AudaxonX/status/1826222767802351867 ; https://x.com/AudaxonX/status/1826225646197952718 ; https://x.com/AudaxonX/status/1826233665426456948 ; https://x.com/AudaxonX/status/1826236574507679841; https://x.com/giK1893/status/1826214202534437305; https://t.me/creamy_caprice/6464

[16] https://t.me/rusich_army/16657

[17] https://t.me/dva_majors/50276

[18] https://t.me/morpexiMO/6306 ; https://x.com/franfran2424/status/1826200674851344588 ; https://x.com/GNovosibir79446/status/1826192342170189845; https://x.com/moklasen/status/1826202988295127510; https://x.com/moklasen/status/1826202992103539130; https://t.me/morpexiMO/6306; https://t.me/creamy_caprice/6462

[19] https://x.com/moklasen/status/1826296192013304096; https://t.me/khornegroup/2556

[20] https://t.me/DnevnikDesantnika/14331; https://t.me/motopatriot/26459; https://t.me/motopatriot/26447; https://t.me/voenkorKotenok/58401; https://t.me/philologist_zov/1260

[21] https://t.me/dva_majors/50306 ; https://t.me/RVvoenkor/75284 ; https://t.me/s/NgP_raZVedka ; https://t.me/boris_rozhin/134360 ; https://t.me/mobilizationnews/19905

[22] https://x.com/EjShahid/status/1826295667318280343; https://t.me/rusich_army/16669

[23] https://t.me/astrapress/62346 ; https://t.me/dva_majors/50306; https://x.com/AudaxonX/status/1825990685901615544 ; https://t.me/dva_majors/50231; https://t.me/control_sigma/33709 ; https://t.me/DnevnikDesantnika/14319

[24] http://kremlin dot ru/events/president/news/74906

[25] http://kremlin dot ru/events/president/news/74904

[26] https://isw.pub/UkrWar103023 ; https://isw.pub/UkrWar122023 ; https://isw.pub/UkrWar033024

[27] http://kremlin dot ru/events/president/news/74904

[28] https://isw.pub/UkrWar081624 ; https://isw.pub/UkrWar081924 ; https://isw.pub/UkrWar081424

[29] https://media.defense.gov/2024/Aug/16/2003527561/-1/-1/1/OAR_Q3_JUN2024_FINAL_508.PDF

[30] https://media.defense.gov/2024/Aug/16/2003527561/-1/-1/1/OAR_Q3_JUN2024_FINAL_508.PDF

[31] https://isw.pub/UkrWar061524 ; https://isw.pub/UkrWar042024 ; https://isw.pub/UkrWar041924 ; https://isw.pub/UkrWar020924 ; https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-may-2-2024 ; https://isw.pub/UkrWar041924 ; https://www.ft.com/content/daa1a6ad-9ada-42ba-bfb2-2c199118e904 ; https://archive.ph/7LGbR

[32] https://isw.pub/UkrWar060724

[33] https://isw.pub/UkrWar072524 ; https://isw.pub/UkrWar073024 ; https://isw.pub/UkrWar052124 ; https://isw.pub/UkrWar031224 ; https://isw.pub/UkrWar121623 ;

[34] https://isw.pub/UkrWar071624 ; https://isw.pub/UkrWar071424 ; https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-june-9-2024 ; https://isw.pub/UkrWar042324 ; https://isw.pub/UkrWar040824 ; https://isw.pub/UkrWar032024 ; https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-january-29-2024 ; https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-january-21-2024

[35] https://media.defense.gov/2024/Aug/16/2003527561/-1/-1/1/OAR_Q3_JUN2024_FINAL_508.PDF

[36] https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-august-17-2024

[37] https://isw.pub/UkrWar071224

[38] https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-august-17-2024

[39] https://isw.pub/UkrWar072424 ; https://isw.pub/UkrWar072024

[40] https://www.facebook.com/GeneralStaff.ua/posts/pfbid0xJoc2FgD5hUcErqdGSmku86d2jBJSLAp22DNCGhaDHtvNVkjW5hVySfgxy9PRMbJl

[41] h ttps://t.me/andriyshTime/26252 ; https://t.me/vrogov/17127 ; https://x.com/666_mancer/status/1826251220308430880 ; https://vk.com/wall-90644414_39836 ; https://t.me/golubev_vu/1494 ; https://t.me/golubev_vu/1495 ; https://t.me/etorostov/62864 ; https://t.me/vchkogpu/50118

[42] https://t.me/rybar/62951

[43] https://t.me/sotaproject/85660 ; https://t.me/astrapress/62407; https://t.me/milinfolive/128915

[44] https://t.me/VGrudina/2794 ; https://x.com/666_mancer/status/1826270072307855362 https://x.com/666_mancer/status/1826292246460711184 ; https://x.com/Grimm_Intel/status/1826270127722996150; https://t.me/voenkorKotenok/58419 ; https://t.me/voenkorKotenok/58417 ; https://t.me/milinfolive/128912%20;%20https:/t.me/milinfolive/128915

[45] https://t.me/tass_agency/267622 ; https://t.me/sotaproject/85664

[46] https://t.me/milinfolive/128915 ; https://t.me/vchkogpu/50145 ; https://meduza dot io/news/2024/08/21/glava-murmanskoy-oblasti-zayavil-ob-ugroze-bespilotnikov-v-regione-mestnyy-aeroport-vremenno-zakryli

[47] https://suspilne dot media/800159-droni-gur-atakuvali-npz-vijskovi-aerodromi-rf-i-poskodili-nadzvukovij-bombarduvalnik-dzerela/ ; https://armyinform.com dot ua/2024/07/27/chorni-dni-rosijskoyi-aviacziyi-detali-atak-na-obyekty-v-tylu-rf/

[48] https://t.me/tass_agency/267588 ; https://t.me/tass_agency/267597 ; https://meduza dot io/feature/2024/08/21/v-rossii-perestali-otkryvatsya-telegram-whatsapp-steam-discord-i-mnogie-drugie-servisy-chto-proishodit-neponyatno

[49] https://t.me/tass_agency/267601

[50] https://t.me/agentstvonews/7046

[51] https://meduza dot io/short/2020/06/18/dva-goda-popytok-blokirovki-telegram-v-rossii-kak-eto-bylo

[52] https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-july-7-2023

[53] https://www.kommersant dot ru/doc/6905653 ; https://www.vesti dot ru/article/4103600; https://meduza dot io/feature/2024/08/21/vlasti-smogut-privlekat-uehavshih-rossiyan-po-politicheskim-statyam-koap-a-razve-ranshe-ne-privlekali-ili-teper-oni-budut-delat-eto-chasche

[54] http://kremlin dot ru/events/president/news/74910

[55] https://t.me/tass_agency/267576

[56] https://t.me/tass_agency/267528

[57] https://armyinform dot com.ua/2024/08/21/na-harkivskomu-napryamku-rosiyany-zmenshyly-zastosuvannya-taktychnoyi-aviacziyi/

[58] https://suspilne dot media/817997-zsu-kontroluut-93-naseleni-punkti-v-kurskij-oblasti-trivae-evakuacia-z-pokrovska-910-den-vijni-onlajn/?anchor=live_1724252042&utm_source=copylink&utm_medium=ps; https://armyinform.com dot ua/2024/08/21/v-otu-harkiv-rozpovily-pro-stanovyshhe-okupantiv-na-agregatnomu-zavodu-u-vovchansku/

[59] https://www.facebook.com/GeneralStaff.ua/posts/pfbid02mbcfj2CsF4Zev5JUw6tqFFzHABZsE9uVBhz9tSKev7YcfT8NvQUYajTna6B7vAUQl ; https://www.facebook.com/GeneralStaff.ua/posts/pfbid02F1pqeXSUWGYRQ1xdb5t2uwAyf9GudeWCy3oNHFZvP775hNmxQpgwqVTfYVPgbnD6l; https://t.me/wargonzo/21670

[60] https://t.me/otukharkiv/841

[61] https://www.facebook.com/GeneralStaff.ua/posts/pfbid02mbcfj2CsF4Zev5JUw6tqFFzHABZsE9uVBhz9tSKev7YcfT8NvQUYajTna6B7vAUQl ; https://www.facebook.com/GeneralStaff.ua/posts/pfbid02F1pqeXSUWGYRQ1xdb5t2uwAyf9GudeWCy3oNHFZvP775hNmxQpgwqVTfYVPgbnD6l

[62] https://t.me/vysokygovorit/17012

[63] https://www.facebook.com/GeneralStaff.ua/posts/pfbid02F1pqeXSUWGYRQ1xdb5t2uwAyf9GudeWCy3oNHFZvP775hNmxQpgwqVTfYVPgbnD6l; https://www.facebook.com/GeneralStaff.ua/posts/pfbid02KwX54hpu7qM9kdUS9qLQhEGJavR3XHNSBMNzsBeoh6Q6oH644FBij6cnaZVo2Cssl; https://www.facebook.com/GeneralStaff.ua/posts/pfbid02mbcfj2CsF4Zev5JUw6tqFFzHABZsE9uVBhz9tSKev7YcfT8NvQUYajTna6B7vAUQl

[64] https://www.facebook.com/GeneralStaff.ua/posts/pfbid02mbcfj2CsF4Zev5JUw6tqFFzHABZsE9uVBhz9tSKev7YcfT8NvQUYajTna6B7vAUQl ; https://www.facebook.com/GeneralStaff.ua/posts/pfbid02F1pqeXSUWGYRQ1xdb5t2uwAyf9GudeWCy3oNHFZvP775hNmxQpgwqVTfYVPgbnD6l; https://www.facebook.com/GeneralStaff.ua/posts/pfbid02KwX54hpu7qM9kdUS9qLQhEGJavR3XHNSBMNzsBeoh6Q6oH644FBij6cnaZVo2Cssl

[65] https://t.me/RVvoenkor/75309; https://t.me/voin_dv/10389

[66] https://t [dot] me/motopatriot/26441

[67] https://t.me/mod_russia/42426 ; https://suspilne dot media/817997-zsu-kontroluut-93-naseleni-punkti-v-kurskij-oblasti-trivae-evakuacia-z-pokrovska-910-den-vijni-onlajn/?anchor=live_1724242209&utm_source=copylink&utm_medium=ps

[68] https://t.me/mod_russia/42426

[69] https://suspilne dot media/817997-zsu-kontroluut-93-naseleni-punkti-v-kurskij-oblasti-trivae-evakuacia-z-pokrovska-910-den-vijni-onlajn/?anchor=live_1724242209&utm_source=copylink&utm_medium=ps

[70] https://t [dot] me/z_arhiv/27719; https://t.me/rybar/62934 ; https://t.me/RVvoenkor/75309; https://t.me/dva_majors/50276; https://t.me/boris_rozhin/134415

[71] https://www.facebook.com/GeneralStaff.ua/posts/pfbid02mbcfj2CsF4Zev5JUw6tqFFzHABZsE9uVBhz9tSKev7YcfT8NvQUYajTna6B7vAUQl ; https://www.facebook.com/GeneralStaff.ua/posts/pfbid02F1pqeXSUWGYRQ1xdb5t2uwAyf9GudeWCy3oNHFZvP775hNmxQpgwqVTfYVPgbnD6l; https://www.facebook.com/GeneralStaff.ua/posts/pfbid02KwX54hpu7qM9kdUS9qLQhEGJavR3XHNSBMNzsBeoh6Q6oH644FBij6cnaZVo2Cssl

[72] https://t.me/mod_russia/42396

[73] https://t.me/dva_majors/50276 ; https://t.me/motopatriot/26417; https://t.me/RVvoenkor/75278

[74] https://t [dot] me/z_arhiv/27717; https://t [dot] me/boris_rozhin/134415; https://t [dot] me/z_arhiv/27715; https://t [dot] me/motopatriot/26443; https://t [dot] me/boris_rozhin/134415

[75] https://t.me/RVvoenkor/75281 ; https://t.me/DnevnikDesantnika/14318; https://t.me/boris_rozhin/134415

[76] https://t.me/mod_russia/42412 ; https://understandingwar.org/backgrounder/russian-offensive-campaign-assessment-august-19-2024

[77] https://www.facebook.com/GeneralStaff.ua/posts/pfbid02mbcfj2CsF4Zev5JUw6tqFFzHABZsE9uVBhz9tSKev7YcfT8NvQUYajTna6B7vAUQl ; https://www.facebook.com/GeneralStaff.ua/posts/pfbid02F1pqeXSUWGYRQ1xdb5t2uwAyf9GudeWCy3oNHFZvP775hNmxQpgwqVTfYVPgbnD6l; https://www.facebook.com/GeneralStaff.ua/posts/pfbid02KwX54hpu7qM9kdUS9qLQhEGJavR3XHNSBMNzsBeoh6Q6oH644FBij6cnaZVo2Cssl

[78] https://t.me/ButusovPlus/12923 ; https://t.me/voenkorKotenok/58405 ; https://t.me/motopatriot/26423

[79] https://www.facebook.com/GeneralStaff.ua/posts/pfbid02mbcfj2CsF4Zev5JUw6tqFFzHABZsE9uVBhz9tSKev7YcfT8NvQUYajTna6B7vAUQl ; https://www.facebook.com/GeneralStaff.ua/posts/pfbid02F1pqeXSUWGYRQ1xdb5t2uwAyf9GudeWCy3oNHFZvP775hNmxQpgwqVTfYVPgbnD6l ; https://www.facebook.com/GeneralStaff.ua/posts/pfbid02KwX54hpu7qM9kdUS9qLQhEGJavR3XHNSBMNzsBeoh6Q6oH644FBij6cnaZVo2Cssl ; https://t.me/wargonzo/21670 ; https://t.me/voenkorKotenok/58402

[80] https://x.com/PuenteUribarri/status/1826153970517029174; https://x.com/klinger66/status/1826174132343517470; https://x.com/klinger66/status/1826188895937020218; https://t.me/creamy_caprice/6461 ; https://t.me/odshbr79/304; https://t.me/creamy_caprice/6452

[81] https://t.me/DnevnikDesantnika/14324 ; https://t.me/boris_rozhin/134415 ; https://t.me/z_arhiv/27713

[82] https://www.facebook.com/GeneralStaff.ua/posts/pfbid02mbcfj2CsF4Zev5JUw6tqFFzHABZsE9uVBhz9tSKev7YcfT8NvQUYajTna6B7vAUQl

[83] https://t.me/motopatriot/26456

[84] https://understandingwar.org/backgrounder/russian-offensive-campaign-assessment-august-2-2024

[85] https://t.me/operativnoZSU/154906; https://t.me/creamy_caprice/6459

[86] https://t.me/romanov_92/45062; https://t.me/DnevnikDesantnika/14310; https://t.me/sashakots/48517; https://t.me/sashakots/48517

[87] https://t.me/rogozin_do/6326; https://t.me/NgP_raZVedka/18763 ; https://t.me/NgP_raZVedka/18762; https://t.me/NgP_raZVedka/18765; https://t.me/dva_majors/50271 ; https://t.me/romanov_92/45062

[88] https://www.facebook.com/GeneralStaff.ua/posts/pfbid02KwX54hpu7qM9kdUS9qLQhEGJavR3XHNSBMNzsBeoh6Q6oH644FBij6cnaZVo2Cssl; https://www.facebook.com/GeneralStaff.ua/posts/pfbid02F1pqeXSUWGYRQ1xdb5t2uwAyf9GudeWCy3oNHFZvP775hNmxQpgwqVTfYVPgbnD6l ; https://t.me/SJTF_Odes/11036

[89] https://t.me/orly_rs/5549;

[90] https://www.facebook.com/GeneralStaff.ua/posts/pfbid02KwX54hpu7qM9kdUS9qLQhEGJavR3XHNSBMNzsBeoh6Q6oH644FBij6cnaZVo2Cssl ; https://www.facebook.com/GeneralStaff.ua/posts/pfbid02F1pqeXSUWGYRQ1xdb5t2uwAyf9GudeWCy3oNHFZvP775hNmxQpgwqVTfYVPgbnD6l; https://t.me/SJTF_Odes/11036

[91] https://t.me/khersonskaODA/23489; https://t.me/olexandrprokudin/4265; https://t.me/khersonskaODA/23488; https://t.me/khersonskaODA/23482; https://t.me/khersonskaODA/23468; https://t.me/khersonskaODA/23467; https://t.me/khersonskaODA/23459; https://t.me/olexandrprokudin/4268

[92] https://t.me/ComAFUA/393

[93] https://t.me/VA_Kyiv/7492

[94] https://t.me/BalitskyEV/3765

[95] https://t.me/SALDO_VGA/4063

[96] https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-june-23-2024 ; https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-march-24-2024

[97] https://t.me/voenkom_on_line/410 ; https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-august-13-2024

[98] https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-august-13-2024

[99] https://t.me/svobodnieslova/5479 ; https://verstka dot media/chistki-v-minoborony-50-uvoleno

[100] https://www.interfax dot ru/russia/977489

[101] https://theins dot ru/obshestvo/273811

[102] https://t.me/DIUkraine/4274; https://armyinform dot com.ua/2024/08/21/operacziyi-proty-ukrayinskyh-bizhencziv-u-yevropi-gotuye-rf-gur/

[103] https://t.me/tass_agency/267509 ; https://t.me/dva_majors/50281 ; https://t.me/medvedev_telegram/523 ; https://t.me/medvedev_telegramE/19

[104] https://t.me/tass_agency/267567

[105] https://isw.pub/UkrWar081324 ; https://isw.pub/UkrWar072524 ; https://isw.pub/UkrWar072424 ; https://isw.pub/UkrWar071924

[106] https://t.me/modmilby/41217

[107] https://t.me/pul_1/13409