Chris Drew (PhD)
Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]
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There are 13 different types of hypothesis. These include simple, complex, null, alternative, composite, directional, non-directional, logical, empirical, statistical, associative, exact, and inexact.
A hypothesis can be categorized into one or more of these types. However, some are mutually exclusive and opposites. Simple and complex hypotheses are mutually exclusive, as are direction and non-direction, and null and alternative hypotheses.
Below I explain each hypothesis in simple terms for absolute beginners. These definitions may be too simple for some, but they’re designed to be clear introductions to the terms to help people wrap their heads around the concepts early on in their education about research methods .
Before you Proceed: Dependent vs Independent Variables
A research study and its hypotheses generally examine the relationships between independent and dependent variables – so you need to know these two concepts:
Read my full article on dependent vs independent variables for more examples.
Example: Eating carrots (independent variable) improves eyesight (dependent variable).
A simple hypothesis is a hypothesis that predicts a correlation between two test variables: an independent and a dependent variable.
This is the easiest and most straightforward type of hypothesis. You simply need to state an expected correlation between the dependant variable and the independent variable.
You do not need to predict causation (see: directional hypothesis). All you would need to do is prove that the two variables are linked.
Question | Simple Hypothesis |
---|---|
Do people over 50 like Coca-Cola more than people under 50? | On average, people over 50 like Coca-Cola more than people under 50. |
According to national registries of car accident data, are Canadians better drivers than Americans? | Canadians are better drivers than Americans. |
Are carpenters more liberal than plumbers? | Carpenters are more liberal than plumbers. |
Do guitarists live longer than pianists? | Guitarists do live longer than pianists. |
Do dogs eat more in summer than winter? | Dogs do eat more in summer than winter. |
A complex hypothesis is a hypothesis that contains multiple variables, making the hypothesis more specific but also harder to prove.
You can have multiple independent and dependant variables in this hypothesis.
Question | Complex Hypothesis |
---|---|
Do (1) age and (2) weight affect chances of getting (3) diabetes and (4) heart disease? | (1) Age and (2) weight increase your chances of getting (3) diabetes and (4) heart disease. |
In the above example, we have multiple independent and dependent variables:
Because there are multiple variables, this study is a lot more complex than a simple hypothesis. It quickly gets much more difficult to prove these hypotheses. This is why undergraduate and first-time researchers are usually encouraged to use simple hypotheses.
A null hypothesis will predict that there will be no significant relationship between the two test variables.
For example, you can say that “The study will show that there is no correlation between marriage and happiness.”
A good way to think about a null hypothesis is to think of it in the same way as “innocent until proven guilty”[1]. Unless you can come up with evidence otherwise, your null hypothesis will stand.
A null hypothesis may also highlight that a correlation will be inconclusive . This means that you can predict that the study will not be able to confirm your results one way or the other. For example, you can say “It is predicted that the study will be unable to confirm a correlation between the two variables due to foreseeable interference by a third variable .”
Beware that an inconclusive null hypothesis may be questioned by your teacher. Why would you conduct a test that you predict will not provide a clear result? Perhaps you should take a closer look at your methodology and re-examine it. Nevertheless, inconclusive null hypotheses can sometimes have merit.
Question | Null Hypothesis (H ) |
---|---|
Do people over 50 like Coca-Cola more than people under 50? | Age has no effect on preference for Coca-Cola. |
Are Canadians better drivers than Americans? | Nationality has no effect on driving ability. |
Are carpenters more liberal than plumbers? | There is no statistically significant difference in political views between carpenters and plumbers. |
Do guitarists live longer than pianists? | There is no statistically significant difference in life expectancy between guitarists and pianists. |
Do dogs eat more in summer than winter? | Time of year has no effect on dogs’ appetites. |
An alternative hypothesis is a hypothesis that is anything other than the null hypothesis. It will disprove the null hypothesis.
We use the symbol H A or H 1 to denote an alternative hypothesis.
The null and alternative hypotheses are usually used together. We will say the null hypothesis is the case where a relationship between two variables is non-existent. The alternative hypothesis is the case where there is a relationship between those two variables.
The following statement is always true: H 0 ≠ H A .
Let’s take the example of the hypothesis: “Does eating oatmeal before an exam impact test scores?”
We can have two hypotheses here:
For the alternative hypothesis to be true, all we have to do is disprove the null hypothesis for the alternative hypothesis to be true. We do not need an exact prediction of how much oatmeal will impact the test scores or even if the impact is positive or negative. So long as the null hypothesis is proven to be false, then the alternative hypothesis is proven to be true.
A composite hypothesis is a hypothesis that does not predict the exact parameters, distribution, or range of the dependent variable.
Often, we would predict an exact outcome. For example: “23 year old men are on average 189cm tall.” Here, we are giving an exact parameter. So, the hypothesis is not composite.
But, often, we cannot exactly hypothesize something. We assume that something will happen, but we’re not exactly sure what. In these cases, we might say: “23 year old men are not on average 189cm tall.”
We haven’t set a distribution range or exact parameters of the average height of 23 year old men. So, we’ve introduced a composite hypothesis as opposed to an exact hypothesis.
Generally, an alternative hypothesis (discussed above) is composite because it is defined as anything except the null hypothesis. This ‘anything except’ does not define parameters or distribution, and therefore it’s an example of a composite hypothesis.
A directional hypothesis makes a prediction about the positivity or negativity of the effect of an intervention prior to the test being conducted.
Instead of being agnostic about whether the effect will be positive or negative, it nominates the effect’s directionality.
We often call this a one-tailed hypothesis (in contrast to a two-tailed or non-directional hypothesis) because, looking at a distribution graph, we’re hypothesizing that the results will lean toward one particular tail on the graph – either the positive or negative.
Question | Directional Hypothesis |
---|---|
Does adding a 10c charge to plastic bags at grocery stores lead to changes in uptake of reusable bags? | Adding a 10c charge to plastic bags in grocery stores will lead to an in uptake of reusable bags. |
Does a Universal Basic Income influence retail worker wages? | Universal Basic Income retail worker wages. |
Does rainy weather impact the amount of moderate to high intensity exercise people do per week in the city of Vancouver? | Rainy weather the amount of moderate to high intensity exercise people do per week in the city of Vancouver. |
Does introducing fluoride to the water system in the city of Austin impact number of dental visits per capita per year? | Introducing fluoride to the water system in the city of Austin the number of dental visits per capita per year? |
Does giving children chocolate rewards during study time for positive answers impact standardized test scores? | Giving children chocolate rewards during study time for positive answers standardized test scores. |
A non-directional hypothesis does not specify the predicted direction (e.g. positivity or negativity) of the effect of the independent variable on the dependent variable.
These hypotheses predict an effect, but stop short of saying what that effect will be.
A non-directional hypothesis is similar to composite and alternative hypotheses. All three types of hypothesis tend to make predictions without defining a direction. In a composite hypothesis, a specific prediction is not made (although a general direction may be indicated, so the overlap is not complete). For an alternative hypothesis, you often predict that the even will be anything but the null hypothesis, which means it could be more or less than H 0 (or in other words, non-directional).
Let’s turn the above directional hypotheses into non-directional hypotheses.
Question | Non-Directional Hypothesis |
---|---|
Does adding a 10c charge to plastic bags at grocery stores lead to changes in uptake of reusable bags? | Adding a 10c charge to plastic bags in grocery stores will lead to a in uptake of reusable bags. |
Does a Universal Basic Income influence retail worker wages? | Universal Basic Income retail worker wages. |
Does rainy weather impact the amount of moderate to high intensity exercise people do per week in the city of Vancouver? | Rainy weather the amount of moderate to high intensity exercise people do per week in the city of Vancouver. |
Does introducing fluoride to the water system in the city of Austin impact number of dental visits per capita per year? | Introducing fluoride to the water system in the city of Austin the number of dental visits per capita per year? |
Does giving children chocolate rewards during study time for positive answers impact standardized test scores? | Giving children chocolate rewards during study time for positive answers standardized test scores. |
A logical hypothesis is a hypothesis that cannot be tested, but has some logical basis underpinning our assumptions.
These are most commonly used in philosophy because philosophical questions are often untestable and therefore we must rely on our logic to formulate logical theories.
Usually, we would want to turn a logical hypothesis into an empirical one through testing if we got the chance. Unfortunately, we don’t always have this opportunity because the test is too complex, expensive, or simply unrealistic.
Here are some examples:
An empirical hypothesis is the opposite of a logical hypothesis. It is a hypothesis that is currently being tested using scientific analysis. We can also call this a ‘working hypothesis’.
We can to separate research into two types: theoretical and empirical. Theoretical research relies on logic and thought experiments. Empirical research relies on tests that can be verified by observation and measurement.
So, an empirical hypothesis is a hypothesis that can and will be tested.
Each of the above hypotheses can be tested, making them empirical rather than just logical (aka theoretical).
A statistical hypothesis utilizes representative statistical models to draw conclusions about broader populations.
It requires the use of datasets or carefully selected representative samples so that statistical inference can be drawn across a larger dataset.
This type of research is necessary when it is impossible to assess every single possible case. Imagine, for example, if you wanted to determine if men are taller than women. You would be unable to measure the height of every man and woman on the planet. But, by conducting sufficient random samples, you would be able to predict with high probability that the results of your study would remain stable across the whole population.
You would be right in guessing that almost all quantitative research studies conducted in academic settings today involve statistical hypotheses.
An associative hypothesis predicts that two variables are linked but does not explore whether one variable directly impacts upon the other variable.
We commonly refer to this as “ correlation does not mean causation ”. Just because there are a lot of sick people in a hospital, it doesn’t mean that the hospital made the people sick. There is something going on there that’s causing the issue (sick people are flocking to the hospital).
So, in an associative hypothesis, you note correlation between an independent and dependent variable but do not make a prediction about how the two interact. You stop short of saying one thing causes another thing.
A causal hypothesis predicts that two variables are not only associated, but that changes in one variable will cause changes in another.
A causal hypothesis is harder to prove than an associative hypothesis because the cause needs to be definitively proven. This will often require repeating tests in controlled environments with the researchers making manipulations to the independent variable, or the use of control groups and placebo effects .
If we were to take the above example of lice in the hair of sick people, researchers would have to put lice in sick people’s hair and see if it made those people healthier. Researchers would likely observe that the lice would flee the hair, but the sickness would remain, leading to a finding of association but not causation.
Question | Causation Hypothesis | Correlation Hypothesis |
---|---|---|
Does marriage cause baldness among men? | Marriage causes stress which leads to hair loss. | Marriage occurs at an age when men naturally start balding. |
What is the relationship between recreational drugs and psychosis? | Recreational drugs cause psychosis. | People with psychosis take drugs to self-medicate. |
Do ice cream sales lead to increase drownings? | Ice cream sales cause increased drownings. | Ice cream sales peak during summer, when more people are swimming and therefore more drownings are occurring. |
For brevity’s sake, I have paired these two hypotheses into the one point. The reality is that we’ve already seen both of these types of hypotheses at play already.
An exact hypothesis (also known as a point hypothesis) specifies a specific prediction whereas an inexact hypothesis assumes a range of possible values without giving an exact outcome. As Helwig [2] argues:
“An “exact” hypothesis specifies the exact value(s) of the parameter(s) of interest, whereas an “inexact” hypothesis specifies a range of possible values for the parameter(s) of interest.”
Generally, a null hypothesis is an exact hypothesis whereas alternative, composite, directional, and non-directional hypotheses are all inexact.
See Next: 15 Hypothesis Examples
This is introductory information that is basic and indeed quite simplified for absolute beginners. It’s worth doing further independent research to get deeper knowledge of research methods and how to conduct an effective research study. And if you’re in education studies, don’t miss out on my list of the best education studies dissertation ideas .
[1] https://jnnp.bmj.com/content/91/6/571.abstract
[2] http://users.stat.umn.edu/~helwig/notes/SignificanceTesting.pdf
Wow! This introductionary materials are very helpful. I teach the begginers in research for the first time in my career. The given tips and materials are very helpful. Chris, thank you so much! Excellent materials!
You’re more than welcome! If you want a pdf version of this article to provide for your students to use as a weekly reading on in-class discussion prompt for seminars, just drop me an email in the Contact form and I’ll get one sent out to you.
When I’ve taught this seminar, I’ve put my students into groups, cut these definitions into strips, and handed them out to the groups. Then I get them to try to come up with hypotheses that fit into each ‘type’. You can either just rotate hypothesis types so they get a chance at creating a hypothesis of each type, or get them to “teach” their hypothesis type and examples to the class at the end of the seminar.
Cheers, Chris
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We have heard of many hypotheses which have led to great inventions in science. Assumptions that are made on the basis of some evidence are known as hypotheses. In this article, let us learn in detail about the hypothesis and the type of hypothesis with examples.
A hypothesis is an assumption that is made based on some evidence. This is the initial point of any investigation that translates the research questions into predictions. It includes components like variables, population and the relation between the variables. A research hypothesis is a hypothesis that is used to test the relationship between two or more variables.
Following are the characteristics of the hypothesis:
Following are the sources of hypothesis:
There are six forms of hypothesis and they are:
It shows a relationship between one dependent variable and a single independent variable. For example – If you eat more vegetables, you will lose weight faster. Here, eating more vegetables is an independent variable, while losing weight is the dependent variable.
It shows the relationship between two or more dependent variables and two or more independent variables. Eating more vegetables and fruits leads to weight loss, glowing skin, and reduces the risk of many diseases such as heart disease.
It shows how a researcher is intellectual and committed to a particular outcome. The relationship between the variables can also predict its nature. For example- children aged four years eating proper food over a five-year period are having higher IQ levels than children not having a proper meal. This shows the effect and direction of the effect.
It is used when there is no theory involved. It is a statement that a relationship exists between two variables, without predicting the exact nature (direction) of the relationship.
It provides a statement which is contrary to the hypothesis. It’s a negative statement, and there is no relationship between independent and dependent variables. The symbol is denoted by “H O ”.
Associative hypothesis occurs when there is a change in one variable resulting in a change in the other variable. Whereas, the causal hypothesis proposes a cause and effect interaction between two or more variables.
Following are the examples of hypotheses based on their types:
Following are the functions performed by the hypothesis:
Researchers use hypotheses to put down their thoughts directing how the experiment would take place. Following are the steps that are involved in the scientific method:
What is hypothesis.
A hypothesis is an assumption made based on some evidence.
What are the types of hypothesis.
Types of hypothesis are:
Define complex hypothesis..
A complex hypothesis shows the relationship between two or more dependent variables and two or more independent variables.
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Hypothesis is a hypothesis is fundamental concept in the world of research and statistics. It is a testable statement that explains what is happening or observed. It proposes the relation between the various participating variables.
Hypothesis is also called Theory, Thesis, Guess, Assumption, or Suggestion . Hypothesis creates a structure that guides the search for knowledge.
In this article, we will learn what hypothesis is, its characteristics, types, and examples. We will also learn how hypothesis helps in scientific research.
Table of Content
Characteristics of hypothesis, sources of hypothesis, types of hypothesis, functions of hypothesis, how hypothesis help in scientific research.
Hypothesis is a suggested idea or an educated guess or a proposed explanation made based on limited evidence, serving as a starting point for further study. They are meant to lead to more investigation.
It’s mainly a smart guess or suggested answer to a problem that can be checked through study and trial. In science work, we make guesses called hypotheses to try and figure out what will happen in tests or watching. These are not sure things but rather ideas that can be proved or disproved based on real-life proofs. A good theory is clear and can be tested and found wrong if the proof doesn’t support it.
A hypothesis is a proposed statement that is testable and is given for something that happens or observed.
Here are some key characteristics of a hypothesis:
Hypotheses can come from different places based on what you’re studying and the kind of research. Here are some common sources from which hypotheses may originate:
Here are some common types of hypotheses:
Complex hypothesis, directional hypothesis.
Alternative hypothesis (h1 or ha), statistical hypothesis, research hypothesis, associative hypothesis, causal hypothesis.
Simple Hypothesis guesses a connection between two things. It says that there is a connection or difference between variables, but it doesn’t tell us which way the relationship goes. Example: Studying more can help you do better on tests. Getting more sun makes people have higher amounts of vitamin D.
Complex Hypothesis tells us what will happen when more than two things are connected. It looks at how different things interact and may be linked together. Example: How rich you are, how easy it is to get education and healthcare greatly affects the number of years people live. A new medicine’s success relies on the amount used, how old a person is who takes it and their genes.
Directional Hypothesis says how one thing is related to another. For example, it guesses that one thing will help or hurt another thing. Example: Drinking more sweet drinks is linked to a higher body weight score. Too much stress makes people less productive at work.
Non-Directional Hypothesis are the one that don’t say how the relationship between things will be. They just say that there is a connection, without telling which way it goes. Example: Drinking caffeine can affect how well you sleep. People often like different kinds of music based on their gender.
Null hypothesis is a statement that says there’s no connection or difference between different things. It implies that any seen impacts are because of luck or random changes in the information. Example: The average test scores of Group A and Group B are not much different. There is no connection between using a certain fertilizer and how much it helps crops grow.
Alternative Hypothesis is different from the null hypothesis and shows that there’s a big connection or gap between variables. Scientists want to say no to the null hypothesis and choose the alternative one. Example: Patients on Diet A have much different cholesterol levels than those following Diet B. Exposure to a certain type of light can change how plants grow compared to normal sunlight.
Statistical Hypothesis are used in math testing and include making ideas about what groups or bits of them look like. You aim to get information or test certain things using these top-level, common words only. Example: The average smarts score of kids in a certain school area is 100. The usual time it takes to finish a job using Method A is the same as with Method B.
Research Hypothesis comes from the research question and tells what link is expected between things or factors. It leads the study and chooses where to look more closely. Example: Having more kids go to early learning classes helps them do better in school when they get older. Using specific ways of talking affects how much customers get involved in marketing activities.
Associative Hypothesis guesses that there is a link or connection between things without really saying it caused them. It means that when one thing changes, it is connected to another thing changing. Example: Regular exercise helps to lower the chances of heart disease. Going to school more can help people make more money.
Causal Hypothesis are different from other ideas because they say that one thing causes another. This means there’s a cause and effect relationship between variables involved in the situation. They say that when one thing changes, it directly makes another thing change. Example: Playing violent video games makes teens more likely to act aggressively. Less clean air directly impacts breathing health in city populations.
Hypotheses have many important jobs in the process of scientific research. Here are the key functions of hypotheses:
Researchers use hypotheses to put down their thoughts directing how the experiment would take place. Following are the steps that are involved in the scientific method:
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Hypothesis is a testable statement serving as an initial explanation for phenomena, based on observations, theories, or existing knowledge . It acts as a guiding light for scientific research, proposing potential relationships between variables that can be empirically tested through experiments and observations.
The hypothesis must be specific, testable, falsifiable, and grounded in prior research or observation, laying out a predictive, if-then scenario that details a cause-and-effect relationship. It originates from various sources including existing theories, observations, previous research, and even personal curiosity, leading to different types, such as simple, complex, directional, non-directional, null, and alternative hypotheses, each serving distinct roles in research methodology .
The hypothesis not only guides the research process by shaping objectives and designing experiments but also facilitates objective analysis and interpretation of data , ultimately driving scientific progress through a cycle of testing, validation, and refinement.
What is a hypothesis.
A guess is a possible explanation or forecast that can be checked by doing research and experiments.
The components of a Hypothesis are Independent Variable, Dependent Variable, Relationship between Variables, Directionality etc.
Testability, Falsifiability, Clarity and Precision, Relevance are some parameters that makes a Good Hypothesis
You cannot prove conclusively that most hypotheses are true because it’s generally impossible to examine all possible cases for exceptions that would disprove them.
Hypothesis testing is used to assess the plausibility of a hypothesis by using sample data
Yes, you can change or improve your ideas based on new information discovered during the research process.
Hypotheses are used to support scientific research and bring about advancements in knowledge.
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What is a hypothesis.
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I got requests for commentating and revising research proposals all the time, mostly from trainees and junior PIs. However, I found a serious problem: many of them did not know how to write a hypothesis. They did not understand scientific method and, especially, the meaning of falsibility.
The beginner's level of mistake is to regard hypothesis as a re-statement of a study aim. For example, "we hypothesize that analysis of gene expression can reveal therapeutic targets." A hypothesis needs to be a theory-deduced prediction that can be tested experimentally. This is a key part of scientific method, which is frequently explained as the following procedure: (1) defining a "why" or "what" question; (2) constructing a theory to provide an answer to the question; (3) deducing a prediction from the theory; and (4) testing the prediction by an experimental study.
Therefore, a hypothesis should be generated from a theory and predict an unknown but testable phenomenon. In other words, the hypothesis needs to be sufficient to instruct what kind of test needs to be performed. For example, a theory can be as simple as "DNA is the genetic material of cells.", to answer the question "what is genetic material composed of?". We can deduce a hypothesis from this theory: transferring the DNA from bacterial strain A into strain B will make the later to get the phenotype of the former. This was tested by British bacteriologist Frederick Griffith in 1928, validating the cellular function of DNA.
The renowned philosopher Karl Popper defined the "testable" feature as "falsibility" of a scientific theory. That is, a theory is scientific only when it provide the possibility to be proved wrong. Therefore, if a theory is claimed always right and provide no approach to test if it's wrong, it is not a scientific theory. For example, astrological theory drives people to find facts to match its prediction, so it is always right and therefore not scientific.
Popper invented the concept of falsibility for the purpose to distinguish science from pseudoscience, and better theory from worse theory (e.g. Copernican model vs. Ptolemaic model). However, many people mistake that falsibility is the ONLY feature of scientific method. In such idea, as long as the hypothesis is testable in the format, it is a legit one. We can often find in a research proposal, the hypothesis is generated without the base theory and the process of deduction. For example, I've seen many like "compound X can kill cancer cells, so X can be used as a anti-cancer therapy". I am pretty sure adding salt into culture dish can kill many different kinds of cells, including cancer cells. Unfortunately, salt is never used to treat cancer patients. Again, the problem here is the lack of theory and deduction of prediction. In this example, the question could be "what kind of compound can be used to treat cancer?" The theory could be "compounds specifically killing cancer cells instead of normal cells"; so the theory needs to be further developed to instruct how to find such compounds, allowing to deduce the hypothesis. Though looked way over-simplified, this example is actually the "magic bullet" concept developed by Paul Ehrlich in 1907, and eventually evolved to the concept of targeted therapy.
In the history of scientific research, there are abundant examples of well-defined questions, development of theories, generation of hypotheses, and instruction of experimental design to test the hypothesis. We can learn scientific method from them, write a well-formed hypothesis in our research proposal, and make a good study design to test it. Unfortunately, I hardly find the history of (biomedical) research in any curriculum of undergraduate or graduate programs. I sincerely hope that the agencies of scientific education consider it.
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Recommended content, is ai a hype, circle of connections, career transition in my 50s, in memory of a young postdoc, supernova, wow signal, and reproducibility.
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by Antony W
August 1, 2024
You’ll need to come up with a research question or a hypothesis to guide your next research project. But what is a hypothesis in the first place? What is the perfect definition for a research question? And, what’s the difference between the two?
In this guide to research questions vs hypothesis, we’ll look at the definition of each component and the difference between the two.
We’ll also look at when a research question and a hypothesis may be useful and provide you with some tips that you can use to come up with hypothesis and research questions that will suit your research topic .
Let’s get to it.
We define a research question as the exact question you want to answer on a given topic or research project. Good research questions should be clear and easy to understand, allow for the collection of necessary data, and be specific and relevant to your field of study.
Research questions are part of heuristic research methods, where researchers use personal experiences and observations to understand a research subject. By using such approaches to explore the question, you should be able to provide an analytical justification of why and how you should respond to the question.
While it’s common for researchers to focus on one question at a time, more complex topics may require two or more questions to cover in-depth.
A research question may be useful when and if:
Perhaps the biggest drawback with research questions is that they tend to researchers in a position to “fish expectations” or excessively manipulate their findings.
Again, research questions sometimes tend to be less specific, and the reason is that there often no sufficient previous research on the questions.
A hypothesis is a statement you can approve or disapprove. You develop a hypothesis from a research question by changing the question into a statement.
Primarily applied in deductive research, it involves the use of scientific, mathematical, and sociological findings to agree to or write off an assumption.
Researchers use the null approach for statements they can disapprove. They take a hypothesis and add a “not” to it to make it a working null hypothesis.
A null hypothesis is quite common in scientific methods. In this case, you have to formulate a hypothesis, and then conduct an investigation to disapprove the statement.
If you can disapprove the statement, you develop another hypothesis and then repeat the process until you can’t disapprove the statement.
In other words, if a hypothesis is true, then it must have been repeatedly tested and verified.
The consensus among researchers is that, like research questions, a hypothesis should not only be clear and easy to understand but also have a definite focus, answerable, and relevant to your field of study.
A hypothesis may be useful when or if:
The drawback to hypothesis as a scientific method is that it can hinder flexibility, or possibly blind a researcher not to see unanticipated results.
Researchers use scientific methods to hone on different theories. So if the purpose of the research project were to analyze a concept, a scientific method would be necessary.
Such a case requires coming up with a research question first, followed by a scientific method.
Since a hypothesis is part of a research method, it will come after the research question.
The following are the differences between a research question and a hypothesis.
We look at the differences in purpose and structure, writing, as well as conclusion.
As much as there are differences between hypothesis and research questions, you have to state either one in the introduction and then repeat the same in the conclusion of your research paper.
Whichever element you opt to use, you should clearly demonstrate that you understand your topic, have achieved the goal of your research project, and not swayed a bit in your research process.
If it helps, start and conclude every chapter of your research project by providing additional information on how you’ve or will address the hypothesis or research question.
You should also include the aims and objectives of coming up with the research question or formulating the hypothesis. Doing so will go a long way to demonstrate that you have a strong focus on the research issue at hand.
If you need help with coming up with research questions, formulating a hypothesis, and completing your research paper writing , feel free to talk to us.
About the author
Antony W is a professional writer and coach at Help for Assessment. He spends countless hours every day researching and writing great content filled with expert advice on how to write engaging essays, research papers, and assignments.
Hypothesis testing is the act of testing a hypothesis or a supposition in relation to a statistical parameter. Analysts implement hypothesis testing in order to test if a hypothesis is plausible or not.
In data science and statistics , hypothesis testing is an important step as it involves the verification of an assumption that could help develop a statistical parameter. For instance, a researcher establishes a hypothesis assuming that the average of all odd numbers is an even number.
In order to find the plausibility of this hypothesis, the researcher will have to test the hypothesis using hypothesis testing methods. Unlike a hypothesis that is ‘supposed’ to stand true on the basis of little or no evidence, hypothesis testing is required to have plausible evidence in order to establish that a statistical hypothesis is true.
Perhaps this is where statistics play an important role. A number of components are involved in this process. But before understanding the process involved in hypothesis testing in research methodology, we shall first understand the types of hypotheses that are involved in the process. Let us get started!
In data sampling, different types of hypothesis are involved in finding whether the tested samples test positive for a hypothesis or not. In this segment, we shall discover the different types of hypotheses and understand the role they play in hypothesis testing.
Alternative Hypothesis (H1) or the research hypothesis states that there is a relationship between two variables (where one variable affects the other). The alternative hypothesis is the main driving force for hypothesis testing.
It implies that the two variables are related to each other and the relationship that exists between them is not due to chance or coincidence.
When the process of hypothesis testing is carried out, the alternative hypothesis is the main subject of the testing process. The analyst intends to test the alternative hypothesis and verifies its plausibility.
The Null Hypothesis (H0) aims to nullify the alternative hypothesis by implying that there exists no relation between two variables in statistics. It states that the effect of one variable on the other is solely due to chance and no empirical cause lies behind it.
The null hypothesis is established alongside the alternative hypothesis and is recognized as important as the latter. In hypothesis testing, the null hypothesis has a major role to play as it influences the testing against the alternative hypothesis.
(Must read: What is ANOVA test? )
The Non-directional hypothesis states that the relation between two variables has no direction.
Simply put, it asserts that there exists a relation between two variables, but does not recognize the direction of effect, whether variable A affects variable B or vice versa.
The Directional hypothesis, on the other hand, asserts the direction of effect of the relationship that exists between two variables.
Herein, the hypothesis clearly states that variable A affects variable B, or vice versa.
A statistical hypothesis is a hypothesis that can be verified to be plausible on the basis of statistics.
By using data sampling and statistical knowledge, one can determine the plausibility of a statistical hypothesis and find out if it stands true or not.
(Related blog: z-test vs t-test )
Now that we have understood the types of hypotheses and the role they play in hypothesis testing, let us now move on to understand the process in a better manner.
In hypothesis testing, a researcher is first required to establish two hypotheses - alternative hypothesis and null hypothesis in order to begin with the procedure.
To establish these two hypotheses, one is required to study data samples, find a plausible pattern among the samples, and pen down a statistical hypothesis that they wish to test.
A random population of samples can be drawn, to begin with hypothesis testing. Among the two hypotheses, alternative and null, only one can be verified to be true. Perhaps the presence of both hypotheses is required to make the process successful.
At the end of the hypothesis testing procedure, either of the hypotheses will be rejected and the other one will be supported. Even though one of the two hypotheses turns out to be true, no hypothesis can ever be verified 100%.
(Read also: Types of data sampling techniques )
Therefore, a hypothesis can only be supported based on the statistical samples and verified data. Here is a step-by-step guide for hypothesis testing.
First things first, one is required to establish two hypotheses - alternative and null, that will set the foundation for hypothesis testing.
These hypotheses initiate the testing process that involves the researcher working on data samples in order to either support the alternative hypothesis or the null hypothesis.
Once the hypotheses have been formulated, it is now time to generate a testing plan. A testing plan or an analysis plan involves the accumulation of data samples, determining which statistic is to be considered and laying out the sample size.
All these factors are very important while one is working on hypothesis testing.
As soon as a testing plan is ready, it is time to move on to the analysis part. Analysis of data samples involves configuring statistical values of samples, drawing them together, and deriving a pattern out of these samples.
While analyzing the data samples, a researcher needs to determine a set of things -
Significance Level - The level of significance in hypothesis testing indicates if a statistical result could have significance if the null hypothesis stands to be true.
Testing Method - The testing method involves a type of sampling-distribution and a test statistic that leads to hypothesis testing. There are a number of testing methods that can assist in the analysis of data samples.
Test statistic - Test statistic is a numerical summary of a data set that can be used to perform hypothesis testing.
P-value - The P-value interpretation is the probability of finding a sample statistic to be as extreme as the test statistic, indicating the plausibility of the null hypothesis.
The analysis of data samples leads to the inference of results that establishes whether the alternative hypothesis stands true or not. When the P-value is less than the significance level, the null hypothesis is rejected and the alternative hypothesis turns out to be plausible.
As we have already looked into different aspects of hypothesis testing, we shall now look into the different methods of hypothesis testing. All in all, there are 2 most common types of hypothesis testing methods. They are as follows -
The frequentist hypothesis or the traditional approach to hypothesis testing is a hypothesis testing method that aims on making assumptions by considering current data.
The supposed truths and assumptions are based on the current data and a set of 2 hypotheses are formulated. A very popular subtype of the frequentist approach is the Null Hypothesis Significance Testing (NHST).
The NHST approach (involving the null and alternative hypothesis) has been one of the most sought-after methods of hypothesis testing in the field of statistics ever since its inception in the mid-1950s.
A much unconventional and modern method of hypothesis testing, the Bayesian Hypothesis Testing claims to test a particular hypothesis in accordance with the past data samples, known as prior probability, and current data that lead to the plausibility of a hypothesis.
The result obtained indicates the posterior probability of the hypothesis. In this method, the researcher relies on ‘prior probability and posterior probability’ to conduct hypothesis testing on hand.
On the basis of this prior probability, the Bayesian approach tests a hypothesis to be true or false. The Bayes factor, a major component of this method, indicates the likelihood ratio among the null hypothesis and the alternative hypothesis.
The Bayes factor is the indicator of the plausibility of either of the two hypotheses that are established for hypothesis testing.
(Also read - Introduction to Bayesian Statistics )
To conclude, hypothesis testing, a way to verify the plausibility of a supposed assumption can be done through different methods - the Bayesian approach or the Frequentist approach.
Although the Bayesian approach relies on the prior probability of data samples, the frequentist approach assumes without a probability. A number of elements involved in hypothesis testing are - significance level, p-level, test statistic, and method of hypothesis testing.
(Also read: Introduction to probability distributions )
A significant way to determine whether a hypothesis stands true or not is to verify the data samples and identify the plausible hypothesis among the null hypothesis and alternative hypothesis.
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A title page is required for all APA Style papers. There are both student and professional versions of the title page. Students should use the student version of the title page unless their instructor or institution has requested they use the professional version. APA provides a student title page guide (PDF, 199KB) to assist students in creating their title pages.
The student title page includes the paper title, author names (the byline), author affiliation, course number and name for which the paper is being submitted, instructor name, assignment due date, and page number, as shown in this example.
Title page setup is covered in the seventh edition APA Style manuals in the Publication Manual Section 2.3 and the Concise Guide Section 1.6
Student papers do not include a running head unless requested by the instructor or institution.
Follow the guidelines described next to format each element of the student title page.
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Paper title | Place the title three to four lines down from the top of the title page. Center it and type it in bold font. Capitalize of the title. Place the main title and any subtitle on separate double-spaced lines if desired. There is no maximum length for titles; however, keep titles focused and include key terms. |
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Author names | Place one double-spaced blank line between the paper title and the author names. Center author names on their own line. If there are two authors, use the word “and” between authors; if there are three or more authors, place a comma between author names and use the word “and” before the final author name. | Cecily J. Sinclair and Adam Gonzaga |
Author affiliation | For a student paper, the affiliation is the institution where the student attends school. Include both the name of any department and the name of the college, university, or other institution, separated by a comma. Center the affiliation on the next double-spaced line after the author name(s). | Department of Psychology, University of Georgia |
Course number and name | Provide the course number as shown on instructional materials, followed by a colon and the course name. Center the course number and name on the next double-spaced line after the author affiliation. | PSY 201: Introduction to Psychology |
Instructor name | Provide the name of the instructor for the course using the format shown on instructional materials. Center the instructor name on the next double-spaced line after the course number and name. | Dr. Rowan J. Estes |
Assignment due date | Provide the due date for the assignment. Center the due date on the next double-spaced line after the instructor name. Use the date format commonly used in your country. | October 18, 2020 |
| Use the page number 1 on the title page. Use the automatic page-numbering function of your word processing program to insert page numbers in the top right corner of the page header. | 1 |
The professional title page includes the paper title, author names (the byline), author affiliation(s), author note, running head, and page number, as shown in the following example.
Follow the guidelines described next to format each element of the professional title page.
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Paper title | Place the title three to four lines down from the top of the title page. Center it and type it in bold font. Capitalize of the title. Place the main title and any subtitle on separate double-spaced lines if desired. There is no maximum length for titles; however, keep titles focused and include key terms. |
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Author names
| Place one double-spaced blank line between the paper title and the author names. Center author names on their own line. If there are two authors, use the word “and” between authors; if there are three or more authors, place a comma between author names and use the word “and” before the final author name. | Francesca Humboldt |
When different authors have different affiliations, use superscript numerals after author names to connect the names to the appropriate affiliation(s). If all authors have the same affiliation, superscript numerals are not used (see Section 2.3 of the for more on how to set up bylines and affiliations). | Tracy Reuter , Arielle Borovsky , and Casey Lew-Williams | |
Author affiliation
| For a professional paper, the affiliation is the institution at which the research was conducted. Include both the name of any department and the name of the college, university, or other institution, separated by a comma. Center the affiliation on the next double-spaced line after the author names; when there are multiple affiliations, center each affiliation on its own line.
| Department of Nursing, Morrigan University |
When different authors have different affiliations, use superscript numerals before affiliations to connect the affiliations to the appropriate author(s). Do not use superscript numerals if all authors share the same affiliations (see Section 2.3 of the for more). | Department of Psychology, Princeton University | |
Author note | Place the author note in the bottom half of the title page. Center and bold the label “Author Note.” Align the paragraphs of the author note to the left. For further information on the contents of the author note, see Section 2.7 of the . | n/a |
| The running head appears in all-capital letters in the page header of all pages, including the title page. Align the running head to the left margin. Do not use the label “Running head:” before the running head. | Prediction errors support children’s word learning |
| Use the page number 1 on the title page. Use the automatic page-numbering function of your word processing program to insert page numbers in the top right corner of the page header. | 1 |
Anatomy of articles in the arts and humanities, why does this matter.
Most original research articles or empirical studies in the sciences and social sciences are made up of the same basic parts. Understanding each of these parts will help you be a better reader of these kinds of articles.
The abstract provides a summary of the entire article. It will provide the research question, hypothesis or thesis, methods, and conclusion. Key words may also be included by the abstract. Abstracts are usually written by the author(s) of the article, but not always.
The introduction will provide context for the research question, state the purpose of the article, and explain why the question is important. Importantly, the introduction will also state the hypothesis or thesis of the article.
Not all scholarly articles will contain a formal literature review. In this section the author(s) will discuss and contextualize related studies and scholarly literature.
The methodology section contains the "how" of the research, by what means was the research accomplished. In a scientific article, this section should provide enough information for the study to be repeated and the results verified.
The results section explains what happened in the study. This section will often contain tables, charts, and graphs.
The discussion section contains an analysis of the study. Here the author(s) explain the meaning or importance of the results. Note that in some cases the discussion and results sections are combined.
The conclusion section contains the final thoughts of the author(s) on the study. This may include an additional summary and evaluation of the study such as strengths and weaknesses of the methods or data.
This section lists complete information about the the scholarly literature the author(s) utilized throughout the study.
Articles in the arts and humanities are often less formulaic than articles in the sciences and social sciences. However, the following parts can be usefully distinguished.
The discussion section is the main body of the article in the arts and humanities. In this section the author will make an argument in support of their thesis by drawing on primary sources, careful argumentation, and engagement with other scholars. The discussion section is subdivided according to the internal logic of the article.
The conclusion section contains the final thoughts of the author(s) on the study. This may include an additional summary and evaluation of the study such as strengths and weaknesses of the methods or data. Additionally, the conclusion may suggest avenues for further research.
This section lists complete information about the the sources utilized throughout the study. Often, this section will be omitted because the relevant information is contained in the footnotes.
Scholarly articles are structured to make them predictable and therefore easier to read. It is not always necessary to read an article from start to finish. Instead, you may find it more useful to focus on an articles results or methodology depending on your needs as a researcher. In any event, understanding the anatomy of a scholarly article will allow you to make the most if it according to your own purposes.
BMC Medical Education volume 24 , Article number: 882 ( 2024 ) Cite this article
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Metrics details
Despite the central role of mixed methods in health research, studies evaluating online methods training in the health sciences are nonexistent. The focused goal was to evaluate online training by comparing the self-rated skills of scholars who experienced an in-person retreat to scholars in an online retreat in specific domains of mixed methods research for the health sciences from 2015–2023.
The authors administered a scholar Mixed Methods Skills Self-Assessment instrument based on an educational competency scale that included domains on: “research questions,” “design/approach,” “sampling,” “analysis,” and “dissemination” to participants of the Mixed Methods Research Training Program for the Health Sciences (MMRTP). Self-ratings on confidence on domains were compared before and after retreat participation within cohorts who attended in person ( n = 73) or online ( n = 57) as well as comparing across in-person to online cohorts. Responses to open-ended questions about experiences with the retreat were analyzed.
Scholars in an interactive program to improve mixed methods skills reported significantly increased confidence in ability to define or explain concepts and in ability to apply the concepts to practical problems, whether the program was attended in-person or synchronously online. Scholars in the online retreat had self-rated skill improvements as good or better than scholars who participated in person. With the possible exception of networking, scholars found the online format was associated with advantages such as accessibility and reduced burden of travel and finding childcare. No differences in difficulty of learning concepts was described.
Keeping in mind that the retreat is only one component of the MMRTP, this study provides evidence that mixed methods training online was associated with the same increases in self-rated skills as persons attending online and can be a key component to increasing the capacity for mixed methods research in the health sciences.
Peer Review reports
The coronavirus pandemic accelerated interest in distance or remote learning. While the acute nature of the pandemic has abated, changes in the way people work have largely remained, with hybrid conferences and trainings more commonly implemented now than during the pre-pandemic period. Studies of health-related online teaching have focused on medical students [ 1 , 2 , 3 ], health professionals [ 4 , 5 ], and medical conferences [ 6 , 7 , 8 ] and have touted the advantages of virtual training and conferences in health education, but few studies have assessed relative growth in skills and competencies in health research methods for synchronous online vs. in-person training.
The National Institutes of Health (NIH)-funded Mixed Methods Research Training Program (MMRTP) for the Health Sciences provided training to faculty-level investigators across health disciplines from 2015–2023. The NIH is a major funder of health-related research in the United States. Its institutes span diseases and conditions (e.g., mental health, environmental health) in addition to focus areas (e.g., minority health and health disparities, nursing) and developing research capacity. Scholars in the MMRTP seek to develop skills in mixed methods research through participation in a summer retreat followed by ongoing mentorship for one year from a mixed methods expert matched to the scholar to support their development of a research proposal. Webinars leading up to the retreat include didactic sessions taught by the same faculty each year, and the retreat itself contains multiple interactive small group sessions in which each scholar presents their project and receives feedback on their grant proposal. Due to pandemic restrictions on gatherings and travel, in 2020 the MMRTP retained all components of the program but transitioned the in-person retreat to a synchronous online retreat.
The number of NIH agencies funding mixed methods research increased from 23 in 1997–2008 to 36 in 2009–2014 [ 9 ]. The usefulness of mixed methods research aligns with several Institutes’ strategic priories, including improving health equity, enhancing feasibility, acceptability, and sustainability of interventions, and addressing patient-centeredness. However, there is a tension between growing interest in mixed methods for health sciences research and a lack of training for investigators to acquire mixed methods research skills. Mixed methods research is not routinely taught in doctoral programs, institutional grant-writing programs, nor research training that academic physicians receive. The relative lack of researchers trained in mixed methods research necessitates ongoing research capacity building and mentorship [ 10 ]. Online teaching has the potential to meet growing demand for training and mentoring in mixed methods, as evidenced by the growth of online offerings by the Mixed Methods International Research Association [ 11 ]. Yet, the nature of skills and attitudes required for doing mixed methods research, such as integration of quantitative and qualitative data collection, analysis, and epistemologies, may make this type of training difficult to adapt to an online format without compromising its effectiveness.
Few studies have attempted to evaluate mixed methods training [ 12 , 13 , 14 , 15 ] and none appear to have evaluated online trainings in mixed methods research. Our goal was to evaluate our online MMRTP by comparing the self-rated skills of scholars who experienced an in-person retreat to an online retreat across specific domains. While the MMRTP retreat is only one component of the program, assessment before and after the retreat among persons who experienced the synchronous retreat online compared to in-person provides an indication of the effectiveness of online instruction in mixed methods for specific domains critical to the design of research in health services. We hypothesized that scholars who attended the retreat online would exhibit improvements in self-rated skills comparable to scholars who attended in person.
Five cohorts with a total of 73 scholars participated in the MMRTP in person (2015–2019), while four cohorts with a total of 57 scholars participated online (2020–2023). Scholars are faculty-level researchers in the health sciences in the United States. The scholars are from a variety of disciplines in the health sciences; namely, pediatrics, psychiatry, general medicine, oncology, nursing, human development, music therapy, nutrition, psychology, and social work.
Formal program activities include two webinars leading up to a retreat followed by ongoing mentorship support. The mixed methods content taught in webinars and the retreat is informed by a widely used textbook by Creswell and Plano Clark [ 18 ] in addition to readings on methodological topics and the practice of mixed methods. The webinars introduce mixed methods research and integration concepts, with the goal of imparting foundational knowledge and ensuring a common language. Specifically, the first webinar introduces mixed methods concepts, research designs, scientific rigor, and becoming a resource at one’s institution, while the second focuses on strategies for the integration of qualitative and quantitative research. Retreats provide an active workshop blending lectures, one-on-one meetings, and interactive faculty-led small workgroups. In addition to scholars, core program faculty who serve as investigators and mentors for the MMRTP, supplemented with consultants and former scholars, lead the retreat. The retreat has covered the state-of-the-art topics within the context of mixed methods research: rationale for use of mixed methods, procedural diagrams, study aims, use of theory, integration strategies, sampling strategies, implementation science, randomized trials, ethics, manuscript and proposal writing, and becoming a resource at one’s home institution. In addition to lectures, the retreat includes multiple interactive small group sessions in which each scholar presents their project and receives feedback on their grant proposal and is expected to make revisions based on feedback and lectures.
Scholars are matched for one year with a mentor based on the Scholar’s needs, career level, and area of health research from a national list of affiliated experienced mixed methods investigators with demonstrated success in obtaining independent funding for research related to the health sciences and a track record and commitment to mentoring. The purpose of this arrangement is to provide different perspectives on mixed methods design while also providing specific feedback on the scholar's research proposal, reviewing new ideas, and together developing a strategy and timeline for submission.
From 2015–2019 (in-person cohorts) the retreat was held over 3 days at the Johns Hopkins University Bloomberg School of Public Health (in 2016 Harvard Catalyst, the Harvard Clinical and Translational Science Center, hosted the retreat at Harvard Medical School). Due to pandemic restrictions, from 2020–2023 the retreat activities were conducted via Zoom with the same number of lecture sessions (over 3 days in 2020 and 4 days thereafter). We made adaptations for the online retreat based on continuous feedback from attendees. We had to rapidly transition to online in 2020 with the same structure as in person, but feedback from scholars led us to extend the retreat to 4 days online from 2021–2023. The extra day allowed for more breaks from Zoom sessions with time for scholars to consider feedback from small groups and to have one-on-one meetings with mentors. Discussion during interactive presentations was encouraged and facilitated by using breakout rooms at breaks mid-presentation. Online resources were available to participants through CoursePlus, the teaching and learning platform used for courses at the Johns Hopkins Bloomberg School of Public Health, hosting publications, presentation materials, recordings of lectures, sharing proposals, email, and discussion boards that scholars have access to before, during, and after the retreat.
Before and after the retreat in each year, we distributed a self-administered scholar Mixed Methods Skills Self-Assessment instrument (Supplement 1) to all participating scholars [ 15 ]; we have reported results from this pre-post assessment for the first two cohorts [ 14 ]. The Mixed Methods Skills Self-Assessment instrument has been previously used and has established reliability for the total items (α = 0.95) and evidence of criterion-related validity between experiences and ability ratings [ 15 ]. In each year, the pre-assessment is completed upon entry to the program, approximately four months prior to the retreat, and the post-assessment is administered two weeks after the retreat. The instrument consists of three sections: 1) professional experiences with mixed methods, including background, software, and resource familiarity; 2) a quantitative, qualitative, and mixed methods skills self-assessment; and 3) open-ended questions focused on learning goals for the MMRTP. The skills assessment contains items for each of the following domains: “research questions,” “design/approach,” “sampling,” “analysis,” and “dissemination.” Each skill was assessed via three items drawn from an educational competency ratings scale that ask scholars to rate: [ 16 ] “My ability to define/explain,” “My ability to apply to practical problems,” and “Extent to which I need to improve my skill.” Response options were on a five-point Likert-type scale that ranged from “Not at all” (coded ‘1’) to “To a great extent” (coded ‘5’), including a mid-point [ 17 ]. We took the mean of the scholar’s item ratings over all component items within each domain (namely, “research questions,” “design/approach,” “sampling,” “analysis,” and “dissemination”).
The baseline survey included two open-ended prompts: 1) What skills and goals are most important to you?, and 2) What would you like to learn? The post-assessment survey also included two additional open-ended questions about the retreat: 1) What aspects of the retreat were helpful?, and 2) What would you like to change about the retreat? In addition, for the online cohorts (2020–2023), we wanted to understand reactions to the online training and added three questions for this purpose: (1) In general, what did you think of the online format for the MMRTP retreat?, 2) What mixed methods concepts are easier or harder to learn virtually?, and 3) What do you think was missing from having the retreat online rather than in person?
Our evaluation employed a convergent mixed methods design [ 18 ], integrating an analysis of ratings pre- and post-retreat with analysis of open-ended responses provided by scholars after the retreat. Our quantitative analysis proceeded in 3 steps. First, we analyzed item-by-item baseline ratings of the extent to which scholars thought they “need to improve skills,” stratified into two groups (5 cohorts who attended in-person and 4 cohorts who attended online). The purpose of comparing the two groups at baseline on learning needs was to assess how similar the scholars in the in-person or online groups were in self-assessment of learning needs before attending the program. Second, to examine the change in scholar ratings of ability to “define or explain a concept” and in their ability to “apply to practical problems,” from before to after the retreat, we conducted paired t-tests. The goal was to compare the ratings before and after the retreat among scholars who attended the program in person to scholars who attended online. Third, we compared post-retreat ratings among in-person cohorts to online cohorts to gauge the effectiveness of the online training. We set statistical significance at α < 0.05 as a guide to inference. We calculated Cohen’s d as a guide to the magnitude of differences [ 19 ]. SPSS Version 28 was employed for all analyses.
We analyzed qualitative data using a thematic analysis approach that consisted of reviewing all open-ended responses, conducting open coding based on the data, developing and refining a codebook, and identifying major themes [ 20 ]. We then compared the qualitative results for the in-person versus online cohorts to understand any thematic differences concerning retreat experiences and reactions.
Scholars in the in-person ( n = 59, 81%) and online ( n = 52, 91%) cohorts reported their primary training was quantitative rather than qualitative or mixed methods, and scholars across cohorts commonly reported at least some exposure to mixed methods research (Table 1 ). However, most scholars did not have previous mixed methods training with 17 (23%) and 16 (28%) of the in-person and online cohorts, respectively, having previously completed a mixed methods course. While experiences were similar across in-person vs. online cohorts, there were two areas in which the scholars reported a statistically significant difference: a larger portion of the online cohorts reported writing a mixed methods application that received funding ( n = 35, 48% in person; n = 46, 81% online), and a smaller proportion of the online cohorts had given a local or institutional mixed methods presentation ( n = 32, 44% in person; n = 15, 26% online).
At baseline, scholars rated the extent to which they needed to improve specific mixed methods skills (Table 2 ). Overall, scholars endorsed a strong need to improve all mixed methods skills. The ratings between the in-person and online cohorts were not statistically significant for any item.
Within cohorts.
For all domains, the differences in pre-post assessment scores were statistically significant for both the in-person and online cohorts in ability to define or explain concepts and to apply concepts to practical problems (left side of Table 3 ). In other words, on average scholars improved in both in-person and online cohorts.
Online cohorts had significantly better self-ratings after the retreat than did in-person cohorts in ability to define or explain concepts and to apply concepts to practical problems (in sampling, data collection, analysis, and dissemination) but no significant differences in research questions and design / approach (rightmost column of Table 3 ).
Goals of training.
In comparing in-person to online cohorts, discussions of the skills that scholars wanted to improve had no discernable differences. Scholars mentioned wanting to develop skills in the foundations of mixed methods research, how to write competitive proposals for funding, the use of the terminology of mixed methods research, and integrative analysis. In addition, some scholars expressed wanting to become a resource at their own institutions and providing training and mentoring to others.
Scholars consistently reported appreciating being able to talk through their project and gaining feedback from experts in small group sessions. Some scholars expressed a preference for afternoon small group sessions, “The small group sessions felt the most helpful, but only because we can apply what we were learning from the morning lecture sessions” (online cohort 9). How participants discussed the benefits of the small group sessions or how they used the sessions did not depend on whether they had experienced the session in person or online.
Online participants described a tradeoff between the accessibility of a virtual retreat versus advantages of in-person training. One participant explained, “I liked the online format, as I do not have reliable childcare” (online cohort 8). Many of the scholars felt that there was an aspect of networking missing when the retreat was held fully online. As one scholar described, when learning online they, “miss getting to know the other fellows and forming lasting connections” (online cohort 9). However, an equal number of others reported that having a virtual retreat meant less hassle; for instance, they were able to join from their preferred location and did not have to travel. Some individuals specifically described the tradeoff of fewer networking opportunities for ease of attendance. One scholar wrote, being online “certainly loses some of the perks of in person connection building but made it equitable to attend” (online cohort 8).
No clear difference in ease of learning concepts was described. A scholar explained: “Learning most concepts is essentially the same virtually versus in person” (online cohort 8). However, scholars described some concepts as easier to learn in one modality versus the other, for example, simpler concepts being more suited to learning virtually while complex concepts were better suited to in-person learning. There was notable variation though in the topics which scholars considered to be simple versus complex. For instance, one scholar noted that “I suppose developing the joint displays were a bit tougher virtually since you were not literally elbow to elbow” (online cohort 7) while another explained, “joint displays lend themselves to the zoom format” (online cohort 8).
In-person and online cohorts were comparable in professional experiences and ratings of the need to improve skills before attending the retreat, sharpening the focus on differences in self-rated skills associated with attendance online compared to in person. If anything, online attendees rated skills as good or better than in-person attendees. Open-ended questions revealed that, for the most part, scholar reflections on learning were similar across in-person and online cohorts. Whether learning the concept of “mixed methods integration” was more difficult online was a source of disagreement. Online attendance was associated with numerous advantages, and small group sessions were valued, regardless of format. Taken together, the evidence from nine cohorts shows that the online retreat was acceptable and as effective in improving self-rated skills as meeting in person.
Mixed methods have become indispensable to health services research from intervention development and testing [ 21 ] to implementation science [ 22 , 23 , 24 ]. We found that scholars participating in an interactive program to improve mixed methods skills reported significantly increased confidence in their ability to define or explain concepts and in their ability to apply the concepts to practical problems, whether the program was attended in-person or synchronously online. Scholars who participated in the online retreat had self-rated skill improvements as good or better than scholars who participated in person, and these improvements were relatively large as indicated by the Cohen’s d estimates. The online retreat appeared to be effective in increasing confidence in the use of mixed methods research in the health sciences and was acceptable to scholars. Our study deserves attention because the national need is so great for investigators with training in mixed methods to address complex behavioral health problems, community- and patient-centered research, and implementation research. No program has been evaluated as we have done here.
Aside from having written a funded mixed methods proposal, the online compared to earlier in person cohorts were comparable in experiences and need to improve specific skills. Within each cohort, scholars reported significant gains in self-rated skills on their ability to “define or explain” a concept and on their ability to “apply to practical problems” in domains essential to mixed methods research. However, consistent with our hypothesis that online training would be as effective as in person we found that online scholars reported better improvement in self-ratings in ability to define or explain concepts and to apply concepts to practical problems in sampling, data collection, analysis, and dissemination but no significant differences in research questions and design / approach. Better ratings in online cohorts could reflect differences in experience with mixed methods, secular changes in knowledge and availability of resources in mixed methods, and maturation of the program facilitated by continued modifications based on feedback from scholars and participating faculty [ 13 , 14 , 15 ].
Ratings related to the “analysis” domain, which includes the central concept of mixed methods integration, deserve notice since scholars rated this skill well below other domains at baseline. While both in-person and online cohorts improved after the retreat, and online cohorts improved substantially more than in-person cohorts, ratings for analysis after the retreat remained lower than for other domains. Scholars consistently have mentioned integration as a difficult concept, and our analysis here is limited to the retreat alone. Continued mentoring one year after the retreat and work on their proposal is built in to the MMRTP to enhance understanding of integration.
Several reviews point out the advantages of online training including savings in time, money, and greenhouse emissions [ 1 , 7 , 8 ]. Online conferences may increase the reach of training to international audiences, improve the diversity of speakers and attendees, facilitate attendance of persons with disabilities, and ease the burden of finding childcare [ 1 , 8 , 25 ]. Online training in health also appears to be effective [ 2 , 4 , 5 , 25 ], though studies are limited because often no skills were evaluated, no comparison groups were used, the response rate was low, or the sample size was small [ 1 , 6 ]. With the possible exception of networking, scholars found the online format was associated with advantages, including saving travel, maintaining work-family balance, and learning effectively. As scholars did discuss perceived increase in difficulty networking, deliberate effort needs to be directed at enhancing collaborations and mentorship [ 8 ]. The MMRTP was designed with components to facilitate networking during and beyond the retreat (e.g., small group sessions, one-on-one meetings, working with a consultant on a specific proposal).
Limitations of our study should be considered. First, the retreat was only one of several components of a mentoring program for faculty in the health sciences. Second, in-person and online cohorts represent different time periods spanning 9 years during which mixed methods applications to NIH and other funders have been increasing [ 9 ]. Third, the pre- and post-evaluations of ability to explain or define concepts, or to apply the concepts to practical problems, were based on self-report. Nevertheless, the pre-post retreat survey on self-rated skills uses a skills self-assessment form we developed [ 15 ], drawing from educational theory related to the epistemology of knowledge [ 26 , 27 ].
Despite the central role of mixed methods in health research, studies evaluating online methods training in the health sciences are nonexistent. Our study provides evidence that mixed methods training online was associated with the same increases in self-rated skills as persons attending online and can be a key component to increasing the capacity for mixed methods research in the health sciences.
The datasets used and analysed during the current study are available from the corresponding author on reasonable request.
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The Mixed Methods Research Training Program is supported by the Office of Behavioral and Social Sciences Research under Grant R25MH104660. Participating institutes are the National Institute of Mental Health, National Heart, Lung, and Blood Institute, National Institute of Nursing Research, and the National Institute on Aging.
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Joseph J. Gallo & Sarah M. Murray
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All authors conceptualized the design of this study. TG analyzed the scholar data in evaluation of the program. TG and JG interpreted results and were major contributors in writing the manuscript. All authors read and approved the final manuscript.
Correspondence to Timothy C. Guetterman .
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The program was reviewed by the Johns Hopkins Institutional Review Board and was deemed exempt as educational research under United States 45 CFR 46.101(b), Category (2). Data were collected through an anonymous survey. Consent to participate was waived.
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Gallo, J.J., Murray, S.M., Creswell, J.W. et al. Going virtual: mixed methods evaluation of online versus in-person learning in the NIH mixed methods research training program retreat. BMC Med Educ 24 , 882 (2024). https://doi.org/10.1186/s12909-024-05877-2
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Homoploid hybrid speciation (HHS) is an enigmatic evolutionary process where new species arise through hybridization of divergent lineages without changes in chromosome number. Although increasingly documented in various taxa and ecosystems, convincing cases of HHS in marine fishes have been lacking. This study presents evidence of HHS in Torpedo scad Megalaspis cordyla based on comprehensive genomic, morphological, and ecological analyses. A Principal Component Analysis using thousands of SNPs identified three sympatric clusters in the western Pacific. Genome-wide differentiation between the clusters and the admixed nature of a cluster between the others were evident from population genomic analyses, species tree estimation, mitochondrial DNA divergence, and tests of introgression. Multiple statistical methods for hybrid detection also supported the admixed ancestry of this cluster. Moreover, model-based demographic inference favored a hybrid speciation scenario over introgression. Examination of occurrence data and ecologically relevant morphological characters suggested ecological differences between the clusters, potentially contributing to reproductive isolation and niche partitioning in sympatry. The clusters are morphologically distinguishable and thus can be taxonomically recognized as separate species. The hybrid cluster is restricted to the coasts of Taiwan and Japan, where all three clusters coexist. The parental clusters are additionally found in lower latitudes such as the coasts of the Philippines, Vietnam, Thailand, and Malaysia, where they display non-overlapping distributions. Given the geographical distributions, estimated times of the species formation, and patterns of historical demographic changes, we propose that the Pleistocene glacial cycles were the primary driver of HHS in this system. Based on this argument, we develop an ecogeographic model of HHS in marine coastal ecosystems, including a novel hypothesis to explain the initial stages of HHS.
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A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes - specificity, clarity and testability. Let's take a look at these more closely.
Simple hypothesis. A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, "Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking. 4.
A research hypothesis is a statement that proposes a possible explanation for an observable phenomenon or pattern. It guides the direction of a study and predicts the outcome of the investigation. A research hypothesis is testable, i.e., it can be supported or disproven through experimentation or observation. Characteristics of a good hypothesis
Definition: Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation. Hypothesis is often used in scientific research to guide the design of experiments ...
Developing a hypothesis (with example) Step 1. Ask a question. Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project. Example: Research question.
A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process. Consider a study designed to examine the relationship between sleep deprivation and test ...
A research hypothesis, in its plural form "hypotheses," is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.
A research hypothesis helps test theories. A hypothesis plays a pivotal role in the scientific method by providing a basis for testing existing theories. For example, a hypothesis might test the predictive power of a psychological theory on human behavior. It serves as a great platform for investigation activities.
Essentially, a hypothesis is a tentative statement that predicts the relationship between two or more variables in a research study. It is usually derived from a theoretical framework or previous ...
A hypothesis is a statement that explains the predictions and reasoning of your research—an "educated guess" about how your scientific experiments will end. As a fundamental part of the scientific method, a good hypothesis is carefully written, but even the simplest ones can be difficult to put into words.
The steps to write a research hypothesis are: 1. Stating the problem: Ensure that the hypothesis defines the research problem. 2. Writing a hypothesis as an 'if-then' statement: Include the action and the expected outcome of your study by following a 'if-then' structure.
INTRODUCTION. Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses.1,2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results.3,4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the ...
Types of Research Hypothesis. Y- and X-Centered Research Designs Y-Centered Research Design Hypothesis In a Y-centered research design, the focus is on the dependent variable (DV) which is specified in the research question. Theories are then used to identify independent variables (IV) and explain their causal relationship with the DV.
A hypothesis is a prediction of what will be found at the outcome of a research project and is typically focused on the relationship between two different variables studied in the research. It is usually based on both theoretical expectations about how things work and already existing scientific evidence. Within social science, a hypothesis can ...
Step 5: Present your findings. The results of hypothesis testing will be presented in the results and discussion sections of your research paper, dissertation or thesis.. In the results section you should give a brief summary of the data and a summary of the results of your statistical test (for example, the estimated difference between group means and associated p-value).
Hypothesis is a prediction of the outcome of a study. Hypotheses are drawn from theories and research questions or from direct observations. In fact, a research problem can be formulated as a hypothesis. To test the hypothesis we need to formulate it in terms that can actually be analysed with statistical tools.
Theory vs. Hypothesis: Basics of the Scientific Method. Written by MasterClass. Last updated: Jun 7, 2021 • 2 min read. Though you may hear the terms "theory" and "hypothesis" used interchangeably, these two scientific terms have drastically different meanings in the world of science.
A hypothesis is a specific prediction, based on previous research that can be tested in an experiment. A hypothesis is often called an "educated guess," but this is an oversimplification. An ...
An empirical hypothesis is the opposite of a logical hypothesis. It is a hypothesis that is currently being tested using scientific analysis. We can also call this a 'working hypothesis'. We can to separate research into two types: theoretical and empirical. Theoretical research relies on logic and thought experiments.
What is Hypothesis? A hypothesis is an assumption that is made based on some evidence. This is the initial point of any investigation that translates the research questions into predictions. It includes components like variables, population and the relation between the variables.
Hypothesis is a testable statement that explains what is happening or observed. It proposes the relation between the various participating variables. Hypothesis is also called Theory, Thesis, Guess, Assumption, or Suggestion. Hypothesis creates a structure that guides the search for knowledge. In this article, we will learn what is hypothesis ...
In the history of scientific research, there are abundant examples of well-defined questions, development of theories, generation of hypotheses, and instruction of experimental design to test the hypothesis. We can learn scientific method from them, write a well-formed hypothesis in our research proposal, and make a good study design to test it.
A hypothesis is a statement you can approve or disapprove. You develop a hypothesis from a research question by changing the question into a statement. Primarily applied in deductive research, it involves the use of scientific, mathematical, and sociological findings to agree to or write off an assumption. Researchers use the null approach for ...
Hypothesis Testing is a statistical concept to verify the plausibility of a hypothesis that is based on data samples derived from a given population, using two competing hypotheses. ... Alternative Hypothesis (H1) or the research hypothesis states that there is a relationship between two variables (where one variable affects the other). ...
For a professional paper, the affiliation is the institution at which the research was conducted. Include both the name of any department and the name of the college, university, or other institution, separated by a comma. Center the affiliation on the next double-spaced line after the author names; when there are multiple affiliations, center ...
The introduction will provide context for the research question, state the purpose of the article, and explain why the question is important. Importantly, the introduction will also state the hypothesis or thesis of the article. Discussion. The discussion section is the main body of the article in the arts and humanities.
Despite the central role of mixed methods in health research, studies evaluating online methods training in the health sciences are nonexistent. The focused goal was to evaluate online training by comparing the self-rated skills of scholars who experienced an in-person retreat to scholars in an online retreat in specific domains of mixed methods research for the health sciences from 2015-2023.
Theory plays an important role in academic work. It is what differentiates academic work from consultancy work. In this article, I discuss the definition of theory, the purpose of theory in academic research, characteristics of a good theory, the difference between a theory and a hypothesis, and how to make theoretical contribution in quantitative research.
Homoploid hybrid speciation (HHS) is an enigmatic evolutionary process where new species arise through hybridization of divergent lineages without changes in chromosome number. Although increasingly documented in various taxa and ecosystems, convincing cases of HHS in marine fishes have been lacking. This study presents evidence of HHS in Torpedo scad Megalaspis cordyla based on comprehensive ...