hypothesis in survey research

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Research Hypothesis: What It Is, Types + How to Develop?

A research study starts with a question. Researchers worldwide ask questions and create research hypotheses. The effectiveness of research relies on developing a good research hypothesis. Examples of research hypotheses can guide researchers in writing effective ones.

In this blog, we’ll learn what a research hypothesis is, why it’s important in research, and the different types used in science. We’ll also guide you through creating your research hypothesis and discussing ways to test and evaluate it.

What is a Research Hypothesis?

A hypothesis is like a guess or idea that you suggest to check if it’s true. A research hypothesis is a statement that brings up a question and predicts what might happen.

It’s really important in the scientific method and is used in experiments to figure things out. Essentially, it’s an educated guess about how things are connected in the research.

A research hypothesis usually includes pointing out the independent variable (the thing they’re changing or studying) and the dependent variable (the result they’re measuring or watching). It helps plan how to gather and analyze data to see if there’s evidence to support or deny the expected connection between these variables.

Importance of Hypothesis in Research

Hypotheses are really important in research. They help design studies, allow for practical testing, and add to our scientific knowledge. Their main role is to organize research projects, making them purposeful, focused, and valuable to the scientific community. Let’s look at some key reasons why they matter:

• 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.

It serves as a launching pad for investigation activities, which offers researchers a clear starting point. A research hypothesis can explore the relationship between exercise and stress reduction.

• Hypothesis guides the research work or study.

A well-formulated hypothesis guides the entire research process. It ensures that the study remains focused and purposeful. For instance, a hypothesis about the impact of social media on interpersonal relationships provides clear guidance for a study.

• Hypothesis sometimes suggests theories.

In some cases, a hypothesis can suggest new theories or modifications to existing ones. For example, a hypothesis testing the effectiveness of a new drug might prompt a reconsideration of current medical theories.

• It helps in knowing the data needs.

A hypothesis clarifies the data requirements for a study, ensuring that researchers collect the necessary information—a hypothesis guiding the collection of demographic data to analyze the influence of age on a particular phenomenon.

• The hypothesis explains social phenomena.

Hypotheses are instrumental in explaining complex social phenomena. For instance, a hypothesis might explore the relationship between economic factors and crime rates in a given community.

• Hypothesis provides a relationship between phenomena for empirical Testing.

Hypotheses establish clear relationships between phenomena, paving the way for empirical testing. An example could be a hypothesis exploring the correlation between sleep patterns and academic performance.

• It helps in knowing the most suitable analysis technique.

A hypothesis guides researchers in selecting the most appropriate analysis techniques for their data. For example, a hypothesis focusing on the effectiveness of a teaching method may lead to the choice of statistical analyses best suited for educational research.

Characteristics of a Good Research Hypothesis

A hypothesis is a specific idea that you can test in a study. It often comes from looking at past research and theories. A good hypothesis usually starts with a research question that you can explore through background research. For it to be effective, consider these key characteristics:

• Clear and Focused Language: A good hypothesis uses clear and focused language to avoid confusion and ensure everyone understands it.
• Related to the Research Topic: The hypothesis should directly relate to the research topic, acting as a bridge between the specific question and the broader study.
• Testable: An effective hypothesis can be tested, meaning its prediction can be checked with real data to support or challenge the proposed relationship.
• Potential for Exploration: A good hypothesis often comes from a research question that invites further exploration. Doing background research helps find gaps and potential areas to investigate.
• Includes Variables: The hypothesis should clearly state both the independent and dependent variables, specifying the factors being studied and the expected outcomes.
• Ethical Considerations: Check if variables can be manipulated without breaking ethical standards. It’s crucial to maintain ethical research practices.
• Predicts Outcomes: The hypothesis should predict the expected relationship and outcome, acting as a roadmap for the study and guiding data collection and analysis.
• Simple and Concise: A good hypothesis avoids unnecessary complexity and is simple and concise, expressing the essence of the proposed relationship clearly.
• Clear and Assumption-Free: The hypothesis should be clear and free from assumptions about the reader’s prior knowledge, ensuring universal understanding.
• Observable and Testable Results: A strong hypothesis implies research that produces observable and testable results, making sure the study’s outcomes can be effectively measured and analyzed.

When you use these characteristics as a checklist, it can help you create a good research hypothesis. It’ll guide improving and strengthening the hypothesis, identifying any weaknesses, and making necessary changes. Crafting a hypothesis with these features helps you conduct a thorough and insightful research study.

Types of Research Hypotheses

The research hypothesis comes in various types, each serving a specific purpose in guiding the scientific investigation. Knowing the differences will make it easier for you to create your own hypothesis. Here’s an overview of the common types:

01. Null Hypothesis

The null hypothesis states that there is no connection between two considered variables or that two groups are unrelated. As discussed earlier, a hypothesis is an unproven assumption lacking sufficient supporting data. It serves as the statement researchers aim to disprove. It is testable, verifiable, and can be rejected.

For example, if you’re studying the relationship between Project A and Project B, assuming both projects are of equal standard is your null hypothesis. It needs to be specific for your study.

02. Alternative Hypothesis

The alternative hypothesis is basically another option to the null hypothesis. It involves looking for a significant change or alternative that could lead you to reject the null hypothesis. It’s a different idea compared to the null hypothesis.

When you create a null hypothesis, you’re making an educated guess about whether something is true or if there’s a connection between that thing and another variable. If the null view suggests something is correct, the alternative hypothesis says it’s incorrect. 

For instance, if your null hypothesis is “I’m going to be $1000 richer,” the alternative hypothesis would be “I’m not going to get $1000 or be richer.”

03. Directional Hypothesis

The directional hypothesis predicts the direction of the relationship between independent and dependent variables. They specify whether the effect will be positive or negative.

If you increase your study hours, you will experience a positive association with your exam scores. This hypothesis suggests that as you increase the independent variable (study hours), there will also be an increase in the dependent variable (exam scores).

04. Non-directional Hypothesis

The non-directional hypothesis predicts the existence of a relationship between variables but does not specify the direction of the effect. It suggests that there will be a significant difference or relationship, but it does not predict the nature of that difference.

For example, you will find no notable difference in test scores between students who receive the educational intervention and those who do not. However, once you compare the test scores of the two groups, you will notice an important difference.

05. Simple Hypothesis

A simple hypothesis predicts a relationship between one dependent variable and one independent variable without specifying the nature of that relationship. It’s simple and usually used when we don’t know much about how the two things are connected.

For example, if you adopt effective study habits, you will achieve higher exam scores than those with poor study habits.

06. Complex Hypothesis

A complex hypothesis is an idea that specifies a relationship between multiple independent and dependent variables. It is a more detailed idea than a simple hypothesis.

While a simple view suggests a straightforward cause-and-effect relationship between two things, a complex hypothesis involves many factors and how they’re connected to each other.

For example, when you increase your study time, you tend to achieve higher exam scores. The connection between your study time and exam performance is affected by various factors, including the quality of your sleep, your motivation levels, and the effectiveness of your study techniques.

If you sleep well, stay highly motivated, and use effective study strategies, you may observe a more robust positive correlation between the time you spend studying and your exam scores, unlike those who may lack these factors.

07. Associative Hypothesis

An associative hypothesis proposes a connection between two things without saying that one causes the other. Basically, it suggests that when one thing changes, the other changes too, but it doesn’t claim that one thing is causing the change in the other.

For example, you will likely notice higher exam scores when you increase your study time. You can recognize an association between your study time and exam scores in this scenario.

Your hypothesis acknowledges a relationship between the two variables—your study time and exam scores—without asserting that increased study time directly causes higher exam scores. You need to consider that other factors, like motivation or learning style, could affect the observed association.

08. Causal Hypothesis

A causal hypothesis proposes a cause-and-effect relationship between two variables. It suggests that changes in one variable directly cause changes in another variable.

For example, when you increase your study time, you experience higher exam scores. This hypothesis suggests a direct cause-and-effect relationship, indicating that the more time you spend studying, the higher your exam scores. It assumes that changes in your study time directly influence changes in your exam performance.

09. Empirical Hypothesis

An empirical hypothesis is a statement based on things we can see and measure. It comes from direct observation or experiments and can be tested with real-world evidence. If an experiment proves a theory, it supports the idea and shows it’s not just a guess. This makes the statement more reliable than a wild guess.

For example, if you increase the dosage of a certain medication, you might observe a quicker recovery time for patients. Imagine you’re in charge of a clinical trial. In this trial, patients are given varying dosages of the medication, and you measure and compare their recovery times. This allows you to directly see the effects of different dosages on how fast patients recover.

This way, you can create a research hypothesis: “Increasing the dosage of a certain medication will lead to a faster recovery time for patients.”

10. Statistical Hypothesis

A statistical hypothesis is a statement or assumption about a population parameter that is the subject of an investigation. It serves as the basis for statistical analysis and testing. It is often tested using statistical methods to draw inferences about the larger population.

In a hypothesis test, statistical evidence is collected to either reject the null hypothesis in favor of the alternative hypothesis or fail to reject the null hypothesis due to insufficient evidence.

For example, let’s say you’re testing a new medicine. Your hypothesis could be that the medicine doesn’t really help patients get better. So, you collect data and use statistics to see if your guess is right or if the medicine actually makes a difference.

If the data strongly shows that the medicine does help, you say your guess was wrong, and the medicine does make a difference. But if the proof isn’t strong enough, you can stick with your original guess because you didn’t get enough evidence to change your mind.

How to Develop a Research Hypotheses?

Step 1: identify your research problem or topic..

Define the area of interest or the problem you want to investigate. Make sure it’s clear and well-defined.

Start by asking a question about your chosen topic. Consider the limitations of your research and create a straightforward problem related to your topic. Once you’ve done that, you can develop and test a hypothesis with evidence.

Step 2: Conduct a literature review

Review existing literature related to your research problem. This will help you understand the current state of knowledge in the field, identify gaps, and build a foundation for your hypothesis. Consider the following questions:

• What existing research has been conducted on your chosen topic?
• Are there any gaps or unanswered questions in the current literature?
• How will the existing literature contribute to the foundation of your research?

Step 3: Formulate your research question

Based on your literature review, create a specific and concise research question that addresses your identified problem. Your research question should be clear, focused, and relevant to your field of study.

Step 4: Identify variables

Determine the key variables involved in your research question. Variables are the factors or phenomena that you will study and manipulate to test your hypothesis.

• Independent Variable: The variable you manipulate or control.
• Dependent Variable: The variable you measure to observe the effect of the independent variable.

Step 5: State the Null hypothesis

The null hypothesis is a statement that there is no significant difference or effect. It serves as a baseline for comparison with the alternative hypothesis.

Step 6: Select appropriate methods for testing the hypothesis

Choose research methods that align with your study objectives, such as experiments, surveys, or observational studies. The selected methods enable you to test your research hypothesis effectively.

Creating a research hypothesis usually takes more than one try. Expect to make changes as you collect data. It’s normal to test and say no to a few hypotheses before you find the right answer to your research question.

Testing and Evaluating Hypotheses

Testing hypotheses is a really important part of research. It’s like the practical side of things. Here, real-world evidence will help you determine how different things are connected. Let’s explore the main steps in hypothesis testing:

• State your research hypothesis.

Before testing, clearly articulate your research hypothesis. This involves framing both a null hypothesis, suggesting no significant effect or relationship, and an alternative hypothesis, proposing the expected outcome.

• Collect data strategically.

Plan how you will gather information in a way that fits your study. Make sure your data collection method matches the things you’re studying.

Whether through surveys, observations, or experiments, this step demands precision and adherence to the established methodology. The quality of data collected directly influences the credibility of study outcomes.

• Perform an appropriate statistical test.

Choose a statistical test that aligns with the nature of your data and the hypotheses being tested. Whether it’s a t-test, chi-square test, ANOVA, or regression analysis, selecting the right statistical tool is paramount for accurate and reliable results.

• Decide if your idea was right or wrong.

Following the statistical analysis, evaluate the results in the context of your null hypothesis. You need to decide if you should reject your null hypothesis or not.

• Share what you found.

When discussing what you found in your research, be clear and organized. Say whether your idea was supported or not, and talk about what your results mean. Also, mention any limits to your study and suggest ideas for future research.

The Role of QuestionPro to Develop a Good Research Hypothesis

QuestionPro is a survey and research platform that provides tools for creating, distributing, and analyzing surveys. It plays a crucial role in the research process, especially when you’re in the initial stages of hypothesis development. Here’s how QuestionPro can help you to develop a good research hypothesis:

• Survey design and data collection: You can use the platform to create targeted questions that help you gather relevant data.
• Exploratory research: Through surveys and feedback mechanisms on QuestionPro, you can conduct exploratory research to understand the landscape of a particular subject.
• Literature review and background research: QuestionPro surveys can collect sample population opinions, experiences, and preferences. This data and a thorough literature evaluation can help you generate a well-grounded hypothesis by improving your research knowledge.
• Identifying variables: Using targeted survey questions, you can identify relevant variables related to their research topic.
• Testing assumptions: You can use surveys to informally test certain assumptions or hypotheses before formalizing a research hypothesis.
• Data analysis tools: QuestionPro provides tools for analyzing survey data. You can use these tools to identify the collected data’s patterns, correlations, or trends.
• Refining your hypotheses: As you collect data through QuestionPro, you can adjust your hypotheses based on the real-world responses you receive.

A research hypothesis is like a guide for researchers in science. It’s a well-thought-out idea that has been thoroughly tested. This idea is crucial as researchers can explore different fields, such as medicine, social sciences, and natural sciences. The research hypothesis links theories to real-world evidence and gives researchers a clear path to explore and make discoveries.

QuestionPro Research Suite is a helpful tool for researchers. It makes creating surveys, collecting data, and analyzing information easily. It supports all kinds of research, from exploring new ideas to forming hypotheses. With a focus on using data, it helps researchers do their best work.

Are you interested in learning more about QuestionPro Research Suite? Take advantage of QuestionPro’s free trial to get an initial look at its capabilities and realize the full potential of your research efforts.

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What is a Research Hypothesis: How to Write it, Types, and Examples

Any research begins with a research question and a research hypothesis . A research question alone may not suffice to design the experiment(s) needed to answer it. A hypothesis is central to the scientific method. But what is a hypothesis ? A hypothesis is a testable statement that proposes a possible explanation to a phenomenon, and it may include a prediction. Next, you may ask what is a research hypothesis ? Simply put, a research hypothesis is a prediction or educated guess about the relationship between the variables that you want to investigate.  

It is important to be thorough when developing your research hypothesis. Shortcomings in the framing of a hypothesis can affect the study design and the results. A better understanding of the research hypothesis definition and characteristics of a good hypothesis will make it easier for you to develop your own hypothesis for your research. Let’s dive in to know more about the types of research hypothesis , how to write a research hypothesis , and some research hypothesis examples .  

Table of Contents

What is a hypothesis ?  

A hypothesis is based on the existing body of knowledge in a study area. Framed before the data are collected, a hypothesis states the tentative relationship between independent and dependent variables, along with a prediction of the outcome.  

What is a research hypothesis ?  

Young researchers starting out their journey are usually brimming with questions like “ What is a hypothesis ?” “ What is a research hypothesis ?” “How can I write a good research hypothesis ?”   

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  

Here are the characteristics of a good hypothesis :  

• Clearly formulated and free of language errors and ambiguity  
• Concise and not unnecessarily verbose  
• Has clearly defined variables  
• Testable and stated in a way that allows for it to be disproven  
• Can be tested using a research design that is feasible, ethical, and practical   
• Specific and relevant to the research problem  
• Rooted in a thorough literature search  
• Can generate new knowledge or understanding.  

How to create an effective research hypothesis  

A study begins with the formulation of a research question. A researcher then performs background research. This background information forms the basis for building a good research hypothesis . The researcher then performs experiments, collects, and analyzes the data, interprets the findings, and ultimately, determines if the findings support or negate the original hypothesis.  

Let’s look at each step for creating an effective, testable, and good research hypothesis :  

• Identify a research problem or question: Start by identifying a specific research problem.   
• Review the literature: Conduct an in-depth review of the existing literature related to the research problem to grasp the current knowledge and gaps in the field.   
• Formulate a clear and testable hypothesis : Based on the research question, use existing knowledge to form a clear and testable hypothesis . The hypothesis should state a predicted relationship between two or more variables that can be measured and manipulated. Improve the original draft till it is clear and meaningful.  
• State the null hypothesis: The null hypothesis is a statement that there is no relationship between the variables you are studying.   
• Define the population and sample: Clearly define the population you are studying and the sample you will be using for your research.  
• Select appropriate methods for testing the hypothesis: Select appropriate research methods, such as experiments, surveys, or observational studies, which will allow you to test your research hypothesis .  

Remember that creating a research hypothesis is an iterative process, i.e., you might have to revise it based on the data you collect. You may need to test and reject several hypotheses before answering the research problem.  

How to write a research hypothesis  

When you start writing a research hypothesis , you use an “if–then” statement format, which states the predicted relationship between two or more variables. Clearly identify the independent variables (the variables being changed) and the dependent variables (the variables being measured), as well as the population you are studying. Review and revise your hypothesis as needed.  

An example of a research hypothesis in this format is as follows:  

“ If [athletes] follow [cold water showers daily], then their [endurance] increases.”  

Population: athletes  

Independent variable: daily cold water showers  

Dependent variable: endurance  

You may have understood the characteristics of a good hypothesis . But note that a research hypothesis is not always confirmed; a researcher should be prepared to accept or reject the hypothesis based on the study findings.  

Research hypothesis checklist  

Following from above, here is a 10-point checklist for a good research hypothesis :  

• Testable: A research hypothesis should be able to be tested via experimentation or observation.  
• Specific: A research hypothesis should clearly state the relationship between the variables being studied.  
• Based on prior research: A research hypothesis should be based on existing knowledge and previous research in the field.  
• Falsifiable: A research hypothesis should be able to be disproven through testing.  
• Clear and concise: A research hypothesis should be stated in a clear and concise manner.  
• Logical: A research hypothesis should be logical and consistent with current understanding of the subject.  
• Relevant: A research hypothesis should be relevant to the research question and objectives.  
• Feasible: A research hypothesis should be feasible to test within the scope of the study.  
• Reflects the population: A research hypothesis should consider the population or sample being studied.  
• Uncomplicated: A good research hypothesis is written in a way that is easy for the target audience to understand.  

By following this research hypothesis checklist , you will be able to create a research hypothesis that is strong, well-constructed, and more likely to yield meaningful results.  

Types of research hypothesis  

Different types of research hypothesis are used in scientific research:  

1. Null hypothesis:

A null hypothesis states that there is no change in the dependent variable due to changes to the independent variable. This means that the results are due to chance and are not significant. A null hypothesis is denoted as H0 and is stated as the opposite of what the alternative hypothesis states.   

Example: “ The newly identified virus is not zoonotic .”  

2. Alternative hypothesis:

This states that there is a significant difference or relationship between the variables being studied. It is denoted as H1 or Ha and is usually accepted or rejected in favor of the null hypothesis.  

Example: “ The newly identified virus is zoonotic .”  

3. Directional hypothesis :

This specifies the direction of the relationship or difference between variables; therefore, it tends to use terms like increase, decrease, positive, negative, more, or less.   

Example: “ The inclusion of intervention X decreases infant mortality compared to the original treatment .”   

4. Non-directional hypothesis:

While it does not predict the exact direction or nature of the relationship between the two variables, a non-directional hypothesis states the existence of a relationship or difference between variables but not the direction, nature, or magnitude of the relationship. A non-directional hypothesis may be used when there is no underlying theory or when findings contradict previous research.  

Example, “ Cats and dogs differ in the amount of affection they express .”  

5. Simple hypothesis :

A simple hypothesis only predicts the relationship between one independent and another independent variable.  

Example: “ Applying sunscreen every day slows skin aging .”  

6 . Complex hypothesis :

A complex hypothesis states the relationship or difference between two or more independent and dependent variables.   

Example: “ Applying sunscreen every day slows skin aging, reduces sun burn, and reduces the chances of skin cancer .” (Here, the three dependent variables are slowing skin aging, reducing sun burn, and reducing the chances of skin cancer.)  

7. Associative hypothesis:  

An associative hypothesis states that a change in one variable results in the change of the other variable. The associative hypothesis defines interdependency between variables.  

Example: “ There is a positive association between physical activity levels and overall health .”  

8 . Causal hypothesis:

A causal hypothesis proposes a cause-and-effect interaction between variables.  

Example: “ Long-term alcohol use causes liver damage .”  

Note that some of the types of research hypothesis mentioned above might overlap. The types of hypothesis chosen will depend on the research question and the objective of the study.  

Research hypothesis examples  

Here are some good research hypothesis examples :  

“The use of a specific type of therapy will lead to a reduction in symptoms of depression in individuals with a history of major depressive disorder.”  

“Providing educational interventions on healthy eating habits will result in weight loss in overweight individuals.”  

“Plants that are exposed to certain types of music will grow taller than those that are not exposed to music.”  

“The use of the plant growth regulator X will lead to an increase in the number of flowers produced by plants.”  

Characteristics that make a research hypothesis weak are unclear variables, unoriginality, being too general or too vague, and being untestable. A weak hypothesis leads to weak research and improper methods.   

Some bad research hypothesis examples (and the reasons why they are “bad”) are as follows:  

“This study will show that treatment X is better than any other treatment . ” (This statement is not testable, too broad, and does not consider other treatments that may be effective.)  

“This study will prove that this type of therapy is effective for all mental disorders . ” (This statement is too broad and not testable as mental disorders are complex and different disorders may respond differently to different types of therapy.)  

“Plants can communicate with each other through telepathy . ” (This statement is not testable and lacks a scientific basis.)  

Importance of testable hypothesis  

If a research hypothesis is not testable, the results will not prove or disprove anything meaningful. The conclusions will be vague at best. A testable hypothesis helps a researcher focus on the study outcome and understand the implication of the question and the different variables involved. A testable hypothesis helps a researcher make precise predictions based on prior research.  

To be considered testable, there must be a way to prove that the hypothesis is true or false; further, the results of the hypothesis must be reproducible.  

Frequently Asked Questions (FAQs) on research hypothesis  

1. What is the difference between research question and research hypothesis ?  

A research question defines the problem and helps outline the study objective(s). It is an open-ended statement that is exploratory or probing in nature. Therefore, it does not make predictions or assumptions. It helps a researcher identify what information to collect. A research hypothesis , however, is a specific, testable prediction about the relationship between variables. Accordingly, it guides the study design and data analysis approach.

2. When to reject null hypothesis ?

A null hypothesis should be rejected when the evidence from a statistical test shows that it is unlikely to be true. This happens when the test statistic (e.g., p -value) is less than the defined significance level (e.g., 0.05). Rejecting the null hypothesis does not necessarily mean that the alternative hypothesis is true; it simply means that the evidence found is not compatible with the null hypothesis.  

3. How can I be sure my hypothesis is testable?  

A testable hypothesis should be specific and measurable, and it should state a clear relationship between variables that can be tested with data. To ensure that your hypothesis is testable, consider the following:  

• Clearly define the key variables in your hypothesis. You should be able to measure and manipulate these variables in a way that allows you to test the hypothesis.  
• The hypothesis should predict a specific outcome or relationship between variables that can be measured or quantified.   
• You should be able to collect the necessary data within the constraints of your study.  
• It should be possible for other researchers to replicate your study, using the same methods and variables.   
• Your hypothesis should be testable by using appropriate statistical analysis techniques, so you can draw conclusions, and make inferences about the population from the sample data.  
• The hypothesis should be able to be disproven or rejected through the collection of data.  

4. How do I revise my research hypothesis if my data does not support it?  

If your data does not support your research hypothesis , you will need to revise it or develop a new one. You should examine your data carefully and identify any patterns or anomalies, re-examine your research question, and/or revisit your theory to look for any alternative explanations for your results. Based on your review of the data, literature, and theories, modify your research hypothesis to better align it with the results you obtained. Use your revised hypothesis to guide your research design and data collection. It is important to remain objective throughout the process.  

5. I am performing exploratory research. Do I need to formulate a research hypothesis?  

As opposed to “confirmatory” research, where a researcher has some idea about the relationship between the variables under investigation, exploratory research (or hypothesis-generating research) looks into a completely new topic about which limited information is available. Therefore, the researcher will not have any prior hypotheses. In such cases, a researcher will need to develop a post-hoc hypothesis. A post-hoc research hypothesis is generated after these results are known.  

6. How is a research hypothesis different from a research question?

A research question is an inquiry about a specific topic or phenomenon, typically expressed as a question. It seeks to explore and understand a particular aspect of the research subject. In contrast, a research hypothesis is a specific statement or prediction that suggests an expected relationship between variables. It is formulated based on existing knowledge or theories and guides the research design and data analysis.

7. Can a research hypothesis change during the research process?

Yes, research hypotheses can change during the research process. As researchers collect and analyze data, new insights and information may emerge that require modification or refinement of the initial hypotheses. This can be due to unexpected findings, limitations in the original hypotheses, or the need to explore additional dimensions of the research topic. Flexibility is crucial in research, allowing for adaptation and adjustment of hypotheses to align with the evolving understanding of the subject matter.

8. How many hypotheses should be included in a research study?

The number of research hypotheses in a research study varies depending on the nature and scope of the research. It is not necessary to have multiple hypotheses in every study. Some studies may have only one primary hypothesis, while others may have several related hypotheses. The number of hypotheses should be determined based on the research objectives, research questions, and the complexity of the research topic. It is important to ensure that the hypotheses are focused, testable, and directly related to the research aims.

9. Can research hypotheses be used in qualitative research?

Yes, research hypotheses can be used in qualitative research, although they are more commonly associated with quantitative research. In qualitative research, hypotheses may be formulated as tentative or exploratory statements that guide the investigation. Instead of testing hypotheses through statistical analysis, qualitative researchers may use the hypotheses to guide data collection and analysis, seeking to uncover patterns, themes, or relationships within the qualitative data. The emphasis in qualitative research is often on generating insights and understanding rather than confirming or rejecting specific research hypotheses through statistical testing.

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The Craft of Writing a Strong Hypothesis

Table of Contents

Writing a hypothesis is one of the essential elements of a scientific research paper. It needs to be to the point, clearly communicating what your research is trying to accomplish. A blurry, drawn-out, or complexly-structured hypothesis can confuse your readers. Or worse, the editor and peer reviewers.

A captivating hypothesis is not too intricate. This blog will take you through the process so that, by the end of it, you have a better idea of how to convey your research paper's intent in just one sentence.

What is a Hypothesis?

The first step in your scientific endeavor, a hypothesis, is a strong, concise statement that forms the basis of your research. It is not the same as a thesis statement , which is a brief summary of your research paper .

The sole purpose of a hypothesis is to predict your paper's findings, data, and conclusion. It comes from a place of curiosity and intuition . When you write a hypothesis, you're essentially making an educated guess based on scientific prejudices and evidence, which is further proven or disproven through the scientific method.

The reason for undertaking research is to observe a specific phenomenon. A hypothesis, therefore, lays out what the said phenomenon is. And it does so through two variables, an independent and dependent variable.

The independent variable is the cause behind the observation, while the dependent variable is the effect of the cause. A good example of this is “mixing red and blue forms purple.” In this hypothesis, mixing red and blue is the independent variable as you're combining the two colors at your own will. The formation of purple is the dependent variable as, in this case, it is conditional to the independent variable.

Different Types of Hypotheses‌

Types of hypotheses

Some would stand by the notion that there are only two types of hypotheses: a Null hypothesis and an Alternative hypothesis. While that may have some truth to it, it would be better to fully distinguish the most common forms as these terms come up so often, which might leave you out of context.

Apart from Null and Alternative, there are Complex, Simple, Directional, Non-Directional, Statistical, and Associative and casual hypotheses. They don't necessarily have to be exclusive, as one hypothesis can tick many boxes, but knowing the distinctions between them will make it easier for you to construct your own.

1. Null hypothesis

A null hypothesis proposes no relationship between two variables. Denoted by H 0 , it is a negative statement like “Attending physiotherapy sessions does not affect athletes' on-field performance.” Here, the author claims physiotherapy sessions have no effect on on-field performances. Even if there is, it's only a coincidence.

2. Alternative hypothesis

Considered to be the opposite of a null hypothesis, an alternative hypothesis is donated as H1 or Ha. It explicitly states that the dependent variable affects the independent variable. A good  alternative hypothesis example is “Attending physiotherapy sessions improves athletes' on-field performance.” or “Water evaporates at 100 °C. ” The alternative hypothesis further branches into directional and non-directional.

• Directional hypothesis: A hypothesis that states the result would be either positive or negative is called directional hypothesis. It accompanies H1 with either the ‘<' or ‘>' sign.
• Non-directional hypothesis: A non-directional hypothesis only claims an effect on the dependent variable. It does not clarify whether the result would be positive or negative. The sign for a non-directional hypothesis is ‘≠.'

3. 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. Complex hypothesis

In contrast to a simple hypothesis, a complex hypothesis implies the relationship between multiple independent and dependent variables. For instance, “Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism.” The independent variable is eating more fruits, while the dependent variables are higher immunity, lesser cholesterol, and high metabolism.

5. Associative and casual hypothesis

Associative and casual hypotheses don't exhibit how many variables there will be. They define the relationship between the variables. In an associative hypothesis, changing any one variable, dependent or independent, affects others. In a casual hypothesis, the independent variable directly affects the dependent.

6. Empirical hypothesis

Also referred to as the working hypothesis, an empirical hypothesis claims a theory's validation via experiments and observation. This way, the statement appears justifiable and different from a wild guess.

Say, the hypothesis is “Women who take iron tablets face a lesser risk of anemia than those who take vitamin B12.” This is an example of an empirical hypothesis where the researcher  the statement after assessing a group of women who take iron tablets and charting the findings.

7. Statistical hypothesis

The point of a statistical hypothesis is to test an already existing hypothesis by studying a population sample. Hypothesis like “44% of the Indian population belong in the age group of 22-27.” leverage evidence to prove or disprove a particular statement.

Characteristics of a Good Hypothesis

Writing a hypothesis is essential as it can make or break your research for you. That includes your chances of getting published in a journal. So when you're designing one, keep an eye out for these pointers:

• A research hypothesis has to be simple yet clear to look justifiable enough.
• It has to be testable — your research would be rendered pointless if too far-fetched into reality or limited by technology.
• It has to be precise about the results —what you are trying to do and achieve through it should come out in your hypothesis.
• A research hypothesis should be self-explanatory, leaving no doubt in the reader's mind.
• If you are developing a relational hypothesis, you need to include the variables and establish an appropriate relationship among them.
• A hypothesis must keep and reflect the scope for further investigations and experiments.

Separating a Hypothesis from a Prediction

Outside of academia, hypothesis and prediction are often used interchangeably. In research writing, this is not only confusing but also incorrect. And although a hypothesis and prediction are guesses at their core, there are many differences between them.

A hypothesis is an educated guess or even a testable prediction validated through research. It aims to analyze the gathered evidence and facts to define a relationship between variables and put forth a logical explanation behind the nature of events.

Predictions are assumptions or expected outcomes made without any backing evidence. They are more fictionally inclined regardless of where they originate from.

For this reason, a hypothesis holds much more weight than a prediction. It sticks to the scientific method rather than pure guesswork. "Planets revolve around the Sun." is an example of a hypothesis as it is previous knowledge and observed trends. Additionally, we can test it through the scientific method.

Whereas "COVID-19 will be eradicated by 2030." is a prediction. Even though it results from past trends, we can't prove or disprove it. So, the only way this gets validated is to wait and watch if COVID-19 cases end by 2030.

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

A hypothesis should instantly address the research question or the problem statement. To do so, you need to ask a question. Understand the constraints of your undertaken research topic and then formulate a simple and topic-centric problem. Only after that can you develop a hypothesis and further test for evidence.

2. Carry out a recce

Once you have your research's foundation laid out, it would be best to conduct preliminary research. Go through previous theories, academic papers, data, and experiments before you start curating your research hypothesis. It will give you an idea of your hypothesis's viability or originality.

Making use of references from relevant research papers helps draft a good research hypothesis. SciSpace Discover offers a repository of over 270 million research papers to browse through and gain a deeper understanding of related studies on a particular topic. Additionally, you can use SciSpace Copilot , your AI research assistant, for reading any lengthy research paper and getting a more summarized context of it. A hypothesis can be formed after evaluating many such summarized research papers. Copilot also offers explanations for theories and equations, explains paper in simplified version, allows you to highlight any text in the paper or clip math equations and tables and provides a deeper, clear understanding of what is being said. This can improve the hypothesis by helping you identify potential research gaps.

3. Create a 3-dimensional hypothesis

Variables are an essential part of any reasonable hypothesis. So, identify your independent and dependent variable(s) and form a correlation between them. The ideal way to do this is to write the hypothetical assumption in the ‘if-then' form. If you use this form, make sure that you state the predefined relationship between the variables.

In another way, you can choose to present your hypothesis as a comparison between two variables. Here, you must specify the difference you expect to observe in the results.

4. Write the first draft

Now that everything is in place, it's time to write your hypothesis. For starters, create the first draft. In this version, write what you expect to find from your research.

Clearly separate your independent and dependent variables and the link between them. Don't fixate on syntax at this stage. The goal is to ensure your hypothesis addresses the issue.

5. Proof your hypothesis

After preparing the first draft of your hypothesis, you need to inspect it thoroughly. It should tick all the boxes, like being concise, straightforward, relevant, and accurate. Your final hypothesis has to be well-structured as well.

Research projects are an exciting and crucial part of being a scholar. And once you have your research question, you need a great hypothesis to begin conducting research. Thus, knowing how to write a hypothesis is very important.

Now that you have a firmer grasp on what a good hypothesis constitutes, the different kinds there are, and what process to follow, you will find it much easier to write your hypothesis, which ultimately helps your research.

Now it's easier than ever to streamline your research workflow with SciSpace Discover . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, write and publish their research and fosters collaboration.

It includes everything you need, including a repository of over 270 million research papers across disciplines, SEO-optimized summaries and public profiles to show your expertise and experience.

If you found these tips on writing a research hypothesis useful, head over to our blog on Statistical Hypothesis Testing to learn about the top researchers, papers, and institutions in this domain.

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

According to the Oxford dictionary, a hypothesis is defined as “An idea or explanation of something that is based on a few known facts, but that has not yet been proved to be true or correct”.

2. What is an example of hypothesis?

The hypothesis is a statement that proposes a relationship between two or more variables. An example: "If we increase the number of new users who join our platform by 25%, then we will see an increase in revenue."

3. What is an example of null hypothesis?

A null hypothesis is a statement that there is no relationship between two variables. The null hypothesis is written as H0. The null hypothesis states that there is no effect. For example, if you're studying whether or not a particular type of exercise increases strength, your null hypothesis will be "there is no difference in strength between people who exercise and people who don't."

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

• Your hypothesis should be able to predict the relationship and outcome.

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

• Null hypotheses are used to test the claim that "there is no difference between two groups of data".

• Alternative hypotheses test the claim that "there is a difference between two data groups".

7. Difference between research question and research hypothesis?

A research question is a broad, open-ended question you will try to answer through your research. A hypothesis is a statement based on prior research or theory that you expect to be true due to your study. Example - Research question: What are the factors that influence the adoption of the new technology? Research hypothesis: There is a positive relationship between age, education and income level with the adoption of the new technology.

8. What is plural for hypothesis?

The plural of hypothesis is hypotheses. Here's an example of how it would be used in a statement, "Numerous well-considered hypotheses are presented in this part, and they are supported by tables and figures that are well-illustrated."

9. What is the red queen hypothesis?

The red queen hypothesis in evolutionary biology states that species must constantly evolve to avoid extinction because if they don't, they will be outcompeted by other species that are evolving. Leigh Van Valen first proposed it in 1973; since then, it has been tested and substantiated many times.

10. Who is known as the father of null hypothesis?

The father of the null hypothesis is Sir Ronald Fisher. He published a paper in 1925 that introduced the concept of null hypothesis testing, and he was also the first to use the term itself.

11. When to reject null hypothesis?

You need to find a significant difference between your two populations to reject the null hypothesis. You can determine that by running statistical tests such as an independent sample t-test or a dependent sample t-test. You should reject the null hypothesis if the p-value is less than 0.05.

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• How to Write a Strong Hypothesis | Guide & Examples

How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more variables . An independent variable is something the researcher changes or controls. A dependent variable is something the researcher observes and measures.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

Prevent plagiarism, run a free check.

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.

Step 2: Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalise more complex constructs.

Step 3: Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

Step 4: Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

Step 5: Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in if … then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

Step 6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis. The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

A hypothesis is not just a guess. It should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

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Understanding and Evaluating Survey Research

A variety of methodologic approaches exist for individuals interested in conducting research. Selection of a research approach depends on a number of factors, including the purpose of the research, the type of research questions to be answered, and the availability of resources. The purpose of this article is to describe survey research as one approach to the conduct of research so that the reader can critically evaluate the appropriateness of the conclusions from studies employing survey research.

SURVEY RESEARCH

Survey research is defined as "the collection of information from a sample of individuals through their responses to questions" ( Check & Schutt, 2012, p. 160 ). This type of research allows for a variety of methods to recruit participants, collect data, and utilize various methods of instrumentation. Survey research can use quantitative research strategies (e.g., using questionnaires with numerically rated items), qualitative research strategies (e.g., using open-ended questions), or both strategies (i.e., mixed methods). As it is often used to describe and explore human behavior, surveys are therefore frequently used in social and psychological research ( Singleton & Straits, 2009 ).

Information has been obtained from individuals and groups through the use of survey research for decades. It can range from asking a few targeted questions of individuals on a street corner to obtain information related to behaviors and preferences, to a more rigorous study using multiple valid and reliable instruments. Common examples of less rigorous surveys include marketing or political surveys of consumer patterns and public opinion polls.

Survey research has historically included large population-based data collection. The primary purpose of this type of survey research was to obtain information describing characteristics of a large sample of individuals of interest relatively quickly. Large census surveys obtaining information reflecting demographic and personal characteristics and consumer feedback surveys are prime examples. These surveys were often provided through the mail and were intended to describe demographic characteristics of individuals or obtain opinions on which to base programs or products for a population or group.

More recently, survey research has developed into a rigorous approach to research, with scientifically tested strategies detailing who to include (representative sample), what and how to distribute (survey method), and when to initiate the survey and follow up with nonresponders (reducing nonresponse error), in order to ensure a high-quality research process and outcome. Currently, the term "survey" can reflect a range of research aims, sampling and recruitment strategies, data collection instruments, and methods of survey administration.

Given this range of options in the conduct of survey research, it is imperative for the consumer/reader of survey research to understand the potential for bias in survey research as well as the tested techniques for reducing bias, in order to draw appropriate conclusions about the information reported in this manner. Common types of error in research, along with the sources of error and strategies for reducing error as described throughout this article, are summarized in the Table .

Sources of Error in Survey Research and Strategies to Reduce Error

The goal of sampling strategies in survey research is to obtain a sufficient sample that is representative of the population of interest. It is often not feasible to collect data from an entire population of interest (e.g., all individuals with lung cancer); therefore, a subset of the population or sample is used to estimate the population responses (e.g., individuals with lung cancer currently receiving treatment). A large random sample increases the likelihood that the responses from the sample will accurately reflect the entire population. In order to accurately draw conclusions about the population, the sample must include individuals with characteristics similar to the population.

It is therefore necessary to correctly identify the population of interest (e.g., individuals with lung cancer currently receiving treatment vs. all individuals with lung cancer). The sample will ideally include individuals who reflect the intended population in terms of all characteristics of the population (e.g., sex, socioeconomic characteristics, symptom experience) and contain a similar distribution of individuals with those characteristics. As discussed by Mady Stovall beginning on page 162, Fujimori et al. ( 2014 ), for example, were interested in the population of oncologists. The authors obtained a sample of oncologists from two hospitals in Japan. These participants may or may not have similar characteristics to all oncologists in Japan.

Participant recruitment strategies can affect the adequacy and representativeness of the sample obtained. Using diverse recruitment strategies can help improve the size of the sample and help ensure adequate coverage of the intended population. For example, if a survey researcher intends to obtain a sample of individuals with breast cancer representative of all individuals with breast cancer in the United States, the researcher would want to use recruitment strategies that would recruit both women and men, individuals from rural and urban settings, individuals receiving and not receiving active treatment, and so on. Because of the difficulty in obtaining samples representative of a large population, researchers may focus the population of interest to a subset of individuals (e.g., women with stage III or IV breast cancer). Large census surveys require extremely large samples to adequately represent the characteristics of the population because they are intended to represent the entire population.

DATA COLLECTION METHODS

Survey research may use a variety of data collection methods with the most common being questionnaires and interviews. Questionnaires may be self-administered or administered by a professional, may be administered individually or in a group, and typically include a series of items reflecting the research aims. Questionnaires may include demographic questions in addition to valid and reliable research instruments ( Costanzo, Stawski, Ryff, Coe, & Almeida, 2012 ; DuBenske et al., 2014 ; Ponto, Ellington, Mellon, & Beck, 2010 ). It is helpful to the reader when authors describe the contents of the survey questionnaire so that the reader can interpret and evaluate the potential for errors of validity (e.g., items or instruments that do not measure what they are intended to measure) and reliability (e.g., items or instruments that do not measure a construct consistently). Helpful examples of articles that describe the survey instruments exist in the literature ( Buerhaus et al., 2012 ).

Questionnaires may be in paper form and mailed to participants, delivered in an electronic format via email or an Internet-based program such as SurveyMonkey, or a combination of both, giving the participant the option to choose which method is preferred ( Ponto et al., 2010 ). Using a combination of methods of survey administration can help to ensure better sample coverage (i.e., all individuals in the population having a chance of inclusion in the sample) therefore reducing coverage error ( Dillman, Smyth, & Christian, 2014 ; Singleton & Straits, 2009 ). For example, if a researcher were to only use an Internet-delivered questionnaire, individuals without access to a computer would be excluded from participation. Self-administered mailed, group, or Internet-based questionnaires are relatively low cost and practical for a large sample ( Check & Schutt, 2012 ).

Dillman et al. ( 2014 ) have described and tested a tailored design method for survey research. Improving the visual appeal and graphics of surveys by using a font size appropriate for the respondents, ordering items logically without creating unintended response bias, and arranging items clearly on each page can increase the response rate to electronic questionnaires. Attending to these and other issues in electronic questionnaires can help reduce measurement error (i.e., lack of validity or reliability) and help ensure a better response rate.

Conducting interviews is another approach to data collection used in survey research. Interviews may be conducted by phone, computer, or in person and have the benefit of visually identifying the nonverbal response(s) of the interviewee and subsequently being able to clarify the intended question. An interviewer can use probing comments to obtain more information about a question or topic and can request clarification of an unclear response ( Singleton & Straits, 2009 ). Interviews can be costly and time intensive, and therefore are relatively impractical for large samples.

Some authors advocate for using mixed methods for survey research when no one method is adequate to address the planned research aims, to reduce the potential for measurement and non-response error, and to better tailor the study methods to the intended sample ( Dillman et al., 2014 ; Singleton & Straits, 2009 ). For example, a mixed methods survey research approach may begin with distributing a questionnaire and following up with telephone interviews to clarify unclear survey responses ( Singleton & Straits, 2009 ). Mixed methods might also be used when visual or auditory deficits preclude an individual from completing a questionnaire or participating in an interview.

FUJIMORI ET AL.: SURVEY RESEARCH

Fujimori et al. ( 2014 ) described the use of survey research in a study of the effect of communication skills training for oncologists on oncologist and patient outcomes (e.g., oncologist’s performance and confidence and patient’s distress, satisfaction, and trust). A sample of 30 oncologists from two hospitals was obtained and though the authors provided a power analysis concluding an adequate number of oncologist participants to detect differences between baseline and follow-up scores, the conclusions of the study may not be generalizable to a broader population of oncologists. Oncologists were randomized to either an intervention group (i.e., communication skills training) or a control group (i.e., no training).

Fujimori et al. ( 2014 ) chose a quantitative approach to collect data from oncologist and patient participants regarding the study outcome variables. Self-report numeric ratings were used to measure oncologist confidence and patient distress, satisfaction, and trust. Oncologist confidence was measured using two instruments each using 10-point Likert rating scales. The Hospital Anxiety and Depression Scale (HADS) was used to measure patient distress and has demonstrated validity and reliability in a number of populations including individuals with cancer ( Bjelland, Dahl, Haug, & Neckelmann, 2002 ). Patient satisfaction and trust were measured using 0 to 10 numeric rating scales. Numeric observer ratings were used to measure oncologist performance of communication skills based on a videotaped interaction with a standardized patient. Participants completed the same questionnaires at baseline and follow-up.

The authors clearly describe what data were collected from all participants. Providing additional information about the manner in which questionnaires were distributed (i.e., electronic, mail), the setting in which data were collected (e.g., home, clinic), and the design of the survey instruments (e.g., visual appeal, format, content, arrangement of items) would assist the reader in drawing conclusions about the potential for measurement and nonresponse error. The authors describe conducting a follow-up phone call or mail inquiry for nonresponders, using the Dillman et al. ( 2014 ) tailored design for survey research follow-up may have reduced nonresponse error.

CONCLUSIONS

Survey research is a useful and legitimate approach to research that has clear benefits in helping to describe and explore variables and constructs of interest. Survey research, like all research, has the potential for a variety of sources of error, but several strategies exist to reduce the potential for error. Advanced practitioners aware of the potential sources of error and strategies to improve survey research can better determine how and whether the conclusions from a survey research study apply to practice.

The author has no potential conflicts of interest to disclose.

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What is and How to Write a Good Hypothesis in Research?

• 4 minute read
• 328.8K views

Table of Contents

One of the most important aspects of conducting research is constructing a strong hypothesis. But what makes a hypothesis in research effective? In this article, we’ll look at the difference between a hypothesis and a research question, as well as the elements of a good hypothesis in research. We’ll also include some examples of effective hypotheses, and what pitfalls to avoid.

What is a Hypothesis in Research?

Simply put, a hypothesis is a research question that also includes the predicted or expected result of the research. Without a hypothesis, there can be no basis for a scientific or research experiment. As such, it is critical that you carefully construct your hypothesis by being deliberate and thorough, even before you set pen to paper. Unless your hypothesis is clearly and carefully constructed, any flaw can have an adverse, and even grave, effect on the quality of your experiment and its subsequent results.

Research Question vs Hypothesis

It’s easy to confuse research questions with hypotheses, and vice versa. While they’re both critical to the Scientific Method, they have very specific differences. Primarily, a research question, just like a hypothesis, is focused and concise. But a hypothesis includes a prediction based on the proposed research, and is designed to forecast the relationship of and between two (or more) variables. Research questions are open-ended, and invite debate and discussion, while hypotheses are closed, e.g. “The relationship between A and B will be C.”

A hypothesis is generally used if your research topic is fairly well established, and you are relatively certain about the relationship between the variables that will be presented in your research. Since a hypothesis is ideally suited for experimental studies, it will, by its very existence, affect the design of your experiment. The research question is typically used for new topics that have not yet been researched extensively. Here, the relationship between different variables is less known. There is no prediction made, but there may be variables explored. The research question can be casual in nature, simply trying to understand if a relationship even exists, descriptive or comparative.

How to Write Hypothesis in Research

Writing an effective hypothesis starts before you even begin to type. Like any task, preparation is key, so you start first by conducting research yourself, and reading all you can about the topic that you plan to research. From there, you’ll gain the knowledge you need to understand where your focus within the topic will lie.

Remember that a hypothesis is a prediction of the relationship that exists between two or more variables. Your job is to write a hypothesis, and design the research, to “prove” whether or not your prediction is correct. A common pitfall is to use judgments that are subjective and inappropriate for the construction of a hypothesis. It’s important to keep the focus and language of your hypothesis objective.

An effective hypothesis in research is clearly and concisely written, and any terms or definitions clarified and defined. Specific language must also be used to avoid any generalities or assumptions.

Use the following points as a checklist to evaluate the effectiveness of your research hypothesis:

• Predicts the relationship and outcome
• Simple and concise – avoid wordiness
• Clear with no ambiguity or assumptions about the readers’ knowledge
• Observable and testable results
• Relevant and specific to the research question or problem

Research Hypothesis Example

Perhaps the best way to evaluate whether or not your hypothesis is effective is to compare it to those of your colleagues in the field. There is no need to reinvent the wheel when it comes to writing a powerful research hypothesis. As you’re reading and preparing your hypothesis, you’ll also read other hypotheses. These can help guide you on what works, and what doesn’t, when it comes to writing a strong research hypothesis.

Here are a few generic examples to get you started.

Eating an apple each day, after the age of 60, will result in a reduction of frequency of physician visits.

Budget airlines are more likely to receive more customer complaints. A budget airline is defined as an airline that offers lower fares and fewer amenities than a traditional full-service airline. (Note that the term “budget airline” is included in the hypothesis.

Workplaces that offer flexible working hours report higher levels of employee job satisfaction than workplaces with fixed hours.

Each of the above examples are specific, observable and measurable, and the statement of prediction can be verified or shown to be false by utilizing standard experimental practices. It should be noted, however, that often your hypothesis will change as your research progresses.

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Elsevier’s Language Editing Plus service can help ensure that your research hypothesis is well-designed, and articulates your research and conclusions. Our most comprehensive editing package, you can count on a thorough language review by native-English speakers who are PhDs or PhD candidates. We’ll check for effective logic and flow of your manuscript, as well as document formatting for your chosen journal, reference checks, and much more.

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What Is A Research (Scientific) Hypothesis? A plain-language explainer + examples

By:  Derek Jansen (MBA)  | Reviewed By: Dr Eunice Rautenbach | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably noticing that the words “research hypothesis” and “scientific hypothesis” are used quite a bit, and you’re wondering what they mean in a research context .

“Hypothesis” is one of those words that people use loosely, thinking they understand what it means. However, it has a very specific meaning within academic research. So, it’s important to understand the exact meaning before you start hypothesizing. 

Research Hypothesis 101

• What is a hypothesis ?
• What is a research hypothesis (scientific hypothesis)?
• Requirements for a research hypothesis
• Definition of a research hypothesis
• The null hypothesis

What is a hypothesis?

Let’s start with the general definition of a hypothesis (not a research hypothesis or scientific hypothesis), according to the Cambridge Dictionary:

Hypothesis: an idea or explanation for something that is based on known facts but has not yet been proved.

In other words, it’s a statement that provides an explanation for why or how something works, based on facts (or some reasonable assumptions), but that has not yet been specifically tested . For example, a hypothesis might look something like this:

Hypothesis: sleep impacts academic performance.

This statement predicts that academic performance will be influenced by the amount and/or quality of sleep a student engages in – sounds reasonable, right? It’s based on reasonable assumptions , underpinned by what we currently know about sleep and health (from the existing literature). So, loosely speaking, we could call it a hypothesis, at least by the dictionary definition.

But that’s not good enough…

Unfortunately, that’s not quite sophisticated enough to describe a research hypothesis (also sometimes called a scientific hypothesis), and it wouldn’t be acceptable in a dissertation, thesis or research paper . In the world of academic research, a statement needs a few more criteria to constitute a true research hypothesis .

What is a research hypothesis?

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.

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Hypothesis Essential #1: Specificity & Clarity

A good research hypothesis needs to be extremely clear and articulate about both what’ s being assessed (who or what variables are involved ) and the expected outcome (for example, a difference between groups, a relationship between variables, etc.).

Let’s stick with our sleepy students example and look at how this statement could be more specific and clear.

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

As you can see, the statement is very specific as it identifies the variables involved (sleep hours and test grades), the parties involved (two groups of students), as well as the predicted relationship type (a positive relationship). There’s no ambiguity or uncertainty about who or what is involved in the statement, and the expected outcome is clear.

Contrast that to the original hypothesis we looked at – “Sleep impacts academic performance” – and you can see the difference. “Sleep” and “academic performance” are both comparatively vague , and there’s no indication of what the expected relationship direction is (more sleep or less sleep). As you can see, specificity and clarity are key.

Hypothesis Essential #2: Testability (Provability)

A statement must be testable to qualify as a research hypothesis. In other words, there needs to be a way to prove (or disprove) the statement. If it’s not testable, it’s not a hypothesis – simple as that.

For example, consider the hypothesis we mentioned earlier:

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.  

We could test this statement by undertaking a quantitative study involving two groups of students, one that gets 8 or more hours of sleep per night for a fixed period, and one that gets less. We could then compare the standardised test results for both groups to see if there’s a statistically significant difference. 

Again, if you compare this to the original hypothesis we looked at – “Sleep impacts academic performance” – you can see that it would be quite difficult to test that statement, primarily because it isn’t specific enough. How much sleep? By who? What type of academic performance?

So, remember the mantra – if you can’t test it, it’s not a hypothesis 🙂

Defining A Research Hypothesis

You’re still with us? Great! Let’s recap and pin down a clear definition of a hypothesis.

A research hypothesis (or scientific hypothesis) is a statement about an expected relationship between variables, or explanation of an occurrence, that is clear, specific and testable.

So, when you write up hypotheses for your dissertation or thesis, make sure that they meet all these criteria. If you do, you’ll not only have rock-solid hypotheses but you’ll also ensure a clear focus for your entire research project.

What about the null hypothesis?

You may have also heard the terms null hypothesis , alternative hypothesis, or H-zero thrown around. At a simple level, the null hypothesis is the counter-proposal to the original hypothesis.

For example, if the hypothesis predicts that there is a relationship between two variables (for example, sleep and academic performance), the null hypothesis would predict that there is no relationship between those variables.

At a more technical level, the null hypothesis proposes that no statistical significance exists in a set of given observations and that any differences are due to chance alone.

And there you have it – hypotheses in a nutshell. 

If you have any questions, be sure to leave a comment below and we’ll do our best to help you. If you need hands-on help developing and testing your hypotheses, consider our private coaching service , where we hold your hand through the research journey.

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16 Comments

Very useful information. I benefit more from getting more information in this regard.

Very great insight,educative and informative. Please give meet deep critics on many research data of public international Law like human rights, environment, natural resources, law of the sea etc

In a book I read a distinction is made between null, research, and alternative hypothesis. As far as I understand, alternative and research hypotheses are the same. Can you please elaborate? Best Afshin

This is a self explanatory, easy going site. I will recommend this to my friends and colleagues.

Very good definition. How can I cite your definition in my thesis? Thank you. Is nul hypothesis compulsory in a research?

It’s a counter-proposal to be proven as a rejection

Please what is the difference between alternate hypothesis and research hypothesis?

It is a very good explanation. However, it limits hypotheses to statistically tasteable ideas. What about for qualitative researches or other researches that involve quantitative data that don’t need statistical tests?

In qualitative research, one typically uses propositions, not hypotheses.

could you please elaborate it more

I’ve benefited greatly from these notes, thank you.

This is very helpful

well articulated ideas are presented here, thank you for being reliable sources of information

Excellent. Thanks for being clear and sound about the research methodology and hypothesis (quantitative research)

I have only a simple question regarding the null hypothesis. – Is the null hypothesis (Ho) known as the reversible hypothesis of the alternative hypothesis (H1? – How to test it in academic research?

this is very important note help me much more

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How to Develop a Good Research Hypothesis

The story of a research study begins by asking a question. Researchers all around the globe are asking curious questions and formulating research hypothesis. However, whether the research study provides an effective conclusion depends on how well one develops a good research hypothesis. Research hypothesis examples could help researchers get an idea as to how to write a good research hypothesis.

This blog will help you understand what is a research hypothesis, its characteristics and, how to formulate a research hypothesis

Table of Contents

What is Hypothesis?

Hypothesis is an assumption or an idea proposed for the sake of argument so that it can be tested. It is a precise, testable statement of what the researchers predict will be outcome of the study.  Hypothesis usually involves proposing a relationship between two variables: the independent variable (what the researchers change) and the dependent variable (what the research measures).

What is a Research Hypothesis?

Research hypothesis is a statement that introduces a research question and proposes an expected result. It is an integral part of the scientific method that forms the basis of scientific experiments. Therefore, you need to be careful and thorough when building your research hypothesis. A minor flaw in the construction of your hypothesis could have an adverse effect on your experiment. In research, there is a convention that the hypothesis is written in two forms, the null hypothesis, and the alternative hypothesis (called the experimental hypothesis when the method of investigation is an experiment).

Characteristics of a Good Research Hypothesis

As the hypothesis is specific, there is a testable prediction about what you expect to happen in a study. You may consider drawing hypothesis from previously published research based on the theory.

A good research hypothesis involves more effort than just a guess. In particular, your hypothesis may begin with a question that could be further explored through background research.

To help you formulate a promising research hypothesis, you should ask yourself the following questions:

• Is the language clear and focused?
• What is the relationship between your hypothesis and your research topic?
• Is your hypothesis testable? If yes, then how?
• What are the possible explanations that you might want to explore?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate your variables without hampering the ethical standards?
• Does your research predict the relationship and outcome?
• Is your research simple and concise (avoids wordiness)?
• Is it clear with no ambiguity or assumptions about the readers’ knowledge
• Is your research observable and testable results?
• Is it relevant and specific to the research question or problem?

The questions listed above can be used as a checklist to make sure your hypothesis is based on a solid foundation. Furthermore, it can help you identify weaknesses in your hypothesis and revise it if necessary.

Source: Educational Hub

How to formulate a research hypothesis.

A testable hypothesis is not a simple statement. It is rather an intricate statement that needs to offer a clear introduction to a scientific experiment, its intentions, and the possible outcomes. However, there are some important things to consider when building a compelling hypothesis.

1. State the problem that you are trying to solve.

Make sure that the hypothesis clearly defines the topic and the focus of the experiment.

2. Try to write the hypothesis as an if-then statement.

Follow this template: If a specific action is taken, then a certain outcome is expected.

3. Define the variables

Independent variables are the ones that are manipulated, controlled, or changed. Independent variables are isolated from other factors of the study.

Dependent variables , as the name suggests are dependent on other factors of the study. They are influenced by the change in independent variable.

4. Scrutinize the hypothesis

Evaluate assumptions, predictions, and evidence rigorously to refine your understanding.

Types of Research Hypothesis

The types of research hypothesis are stated below:

1. Simple Hypothesis

It predicts the relationship between a single dependent variable and a single independent variable.

2. Complex Hypothesis

It predicts the relationship between two or more independent and dependent variables.

3. Directional Hypothesis

It specifies the expected direction to be followed to determine the relationship between variables and is derived from theory. Furthermore, it implies the researcher’s intellectual commitment to a particular outcome.

4. Non-directional Hypothesis

It does not predict the exact direction or nature of the relationship between the two variables. The non-directional hypothesis is used when there is no theory involved or when findings contradict previous research.

5. Associative and Causal Hypothesis

The associative hypothesis defines interdependency between variables. A change in one variable results in the change of the other variable. On the other hand, the causal hypothesis proposes an effect on the dependent due to manipulation of the independent variable.

6. Null Hypothesis

Null hypothesis states a negative statement to support the researcher’s findings that there is no relationship between two variables. There will be no changes in the dependent variable due the manipulation of the independent variable. Furthermore, it states results are due to chance and are not significant in terms of supporting the idea being investigated.

7. Alternative Hypothesis

It states that there is a relationship between the two variables of the study and that the results are significant to the research topic. An experimental hypothesis predicts what changes will take place in the dependent variable when the independent variable is manipulated. Also, it states that the results are not due to chance and that they are significant in terms of supporting the theory being investigated.

Research Hypothesis Examples of Independent and Dependent Variables

Research Hypothesis Example 1 The greater number of coal plants in a region (independent variable) increases water pollution (dependent variable). If you change the independent variable (building more coal factories), it will change the dependent variable (amount of water pollution).

Research Hypothesis Example 2 What is the effect of diet or regular soda (independent variable) on blood sugar levels (dependent variable)? If you change the independent variable (the type of soda you consume), it will change the dependent variable (blood sugar levels)

You should not ignore the importance of the above steps. The validity of your experiment and its results rely on a robust testable hypothesis. Developing a strong testable hypothesis has few advantages, it compels us to think intensely and specifically about the outcomes of a study. Consequently, it enables us to understand the implication of the question and the different variables involved in the study. Furthermore, it helps us to make precise predictions based on prior research. Hence, forming a hypothesis would be of great value to the research. Here are some good examples of testable hypotheses.

More importantly, you need to build a robust testable research hypothesis for your scientific experiments. A testable hypothesis is a hypothesis that can be proved or disproved as a result of experimentation.

Importance of a Testable Hypothesis

To devise and perform an experiment using scientific method, you need to make sure that your hypothesis is testable. To be considered testable, some essential criteria must be met:

• There must be a possibility to prove that the hypothesis is true.
• There must be a possibility to prove that the hypothesis is false.
• The results of the hypothesis must be reproducible.

Without these criteria, the hypothesis and the results will be vague. As a result, the experiment will not prove or disprove anything significant.

What are your experiences with building hypotheses for scientific experiments? What challenges did you face? How did you overcome these challenges? Please share your thoughts with us in the comments section.

Frequently Asked Questions

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. 3. Defining the variables: Define the variables as Dependent or Independent based on their dependency to other factors. 4. Scrutinizing the hypothesis: Identify the type of your hypothesis

Hypothesis testing is a statistical tool which is used to make inferences about a population data to draw conclusions for a particular hypothesis.

Hypothesis in statistics is a formal statement about the nature of a population within a structured framework of a statistical model. It is used to test an existing hypothesis by studying a population.

Research hypothesis is a statement that introduces a research question and proposes an expected result. It forms the basis of scientific experiments.

The different types of hypothesis in research are: • Null hypothesis: Null hypothesis is a negative statement to support the researcher’s findings that there is no relationship between two variables. • Alternate hypothesis: Alternate hypothesis predicts the relationship between the two variables of the study. • Directional hypothesis: Directional hypothesis specifies the expected direction to be followed to determine the relationship between variables. • Non-directional hypothesis: Non-directional hypothesis does not predict the exact direction or nature of the relationship between the two variables. • Simple hypothesis: Simple hypothesis predicts the relationship between a single dependent variable and a single independent variable. • Complex hypothesis: Complex hypothesis predicts the relationship between two or more independent and dependent variables. • Associative and casual hypothesis: Associative and casual hypothesis predicts the relationship between two or more independent and dependent variables. • Empirical hypothesis: Empirical hypothesis can be tested via experiments and observation. • Statistical hypothesis: A statistical hypothesis utilizes statistical models to draw conclusions about broader populations.

Wow! You really simplified your explanation that even dummies would find it easy to comprehend. Thank you so much.

Thanks a lot for your valuable guidance.

I enjoy reading the post. Hypotheses are actually an intrinsic part in a study. It bridges the research question and the methodology of the study.

Useful piece!

This is awesome.Wow.

It very interesting to read the topic, can you guide me any specific example of hypothesis process establish throw the Demand and supply of the specific product in market

Nicely explained

It is really a useful for me Kindly give some examples of hypothesis

It was a well explained content ,can you please give me an example with the null and alternative hypothesis illustrated

clear and concise. thanks.

So Good so Amazing

Good to learn

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Explained well and in simple terms. Quick read! Thank you

It awesome. It has really positioned me in my research project

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• Survey Research | Definition, Examples & Methods

Survey Research | Definition, Examples & Methods

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

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

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

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

Table of contents

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

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

Common uses of survey research include:

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

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

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

Populations

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

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

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

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

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

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

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

There are two main types of survey:

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

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

Questionnaires

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

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

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

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

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

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

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

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

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

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

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

Open-ended vs closed-ended questions

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

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

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

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

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

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

The content of the survey questions

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

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

Phrasing the survey questions

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

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

Ordering the survey questions

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Research bias

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

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

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

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

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

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

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

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

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

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McCombes, S. (2023, June 22). Survey Research | Definition, Examples & Methods. Scribbr. Retrieved June 18, 2024, from https://www.scribbr.com/methodology/survey-research/

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What is a Hypothesis?

A hypothesis can be defined as an assumption statement that is made on the basis of evidence so that this assumption can be tested to see if it might be true. It describes what you expect will happen in your research study before it has taken place and is therefore a prediction that you are trying to explore. Certain research studies may involve several hypotheses in cases where multiple aspects of the research question want to be studied. 

Hypotheses often propose an association between two or more variables: this includes the independent variable (the variable that the researcher controls/manipulates) and the dependent variable (the variable that the researcher observes and measures).

How to Write a Hypothesis

Use the following six steps to effectively create a hypothesis for your research study:

Ask a Question

The first step in writing a hypothesis is asking a question. In this step, you must clearly outline the research question that you want to answer, keeping it specific and focused.

Gather Research

Once you’ve defined your research question, you can start collecting preliminary research. Data collected at this stage can come in the form of existing studies with similar topics, academic journals, and any preliminary primary research conducted such as your own observations and experiments. 

At this stage you can even construct a conceptual framework. This is a visual representation of the expected relationship between the variables being studied.

Formulate an Answer

Once you’ve conducted your preliminary research, you can think about the ways in which you can answer the question. At this stage, your research will have allowed you to develop a stance on what you believe will be the result of the research. You must frame this answer in a clear and concise sentence.

Create a Hypothesis

In this stage, you must formulate your hypothesis. As you already have the answer to your question ready, you can create your hypothesis by including the following in your statement: 

• Relevant Variables
• Specific Group being Studied (Who/What)
• Predicted Outcome of the Experiment

Your hypothesis is a prediction and it should be framed as a statement, not a question.

Refine the Hypothesis

In this step, you must refine your hypothesis to ensure that it is specific and testable. Furthermore, there may be certain cases in which you are studying the difference between more than just one group or are conducting correlational research. In such cases, you must clearly state the relationships or differences that you believe you will find among the variables.

Create a Null Hypothesis

Certain studies may require statistical analysis to be conducted on the data collected. When employing the scientific method to form a hypothesis, you must know the difference between the null hypothesis and the alternative hypothesis. 

• A null hypothesis is a type of hypothesis which suggests that there is no statistical relationship between the given observed variables, whether they be a single set of variables or among two sets of variables. The null hypothesis can be denoted as H 0 .

An alternative hypothesis, often denoted as H 1 , is a statement that contradicts the null hypothesis and can be considered as an alternative to the null hypothesis.

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Types of hypothesis.

We’ve already discussed null and alternative hypotheses and what each entails. Let’s also look at some other types of hypotheses.  Research hypotheses can be categorized into the following groups:

• Null Hypothesis
• Alternative Hypothesis
• Simple Hypothesis : A prediction of the relationship between a single independent variable and a single dependent variable. 
• Complex Hypothesis : A prediction of the relationship between two or more independent and/or dependent variables.
• Directional Hypothesis : A prediction that specifies the direction of the relationship between the variables, and is derived from an existing theory. 
• Non-directional Hypothesis : This is a two-tailed non-directional hypothesis that involves predicting that the independent variable will have an effect on the dependent variable, although the direction of this relationship is not specified. 
• Associative and Causal Hypothesis : An associative hypothesis involves making the assumption that there is a level of interdependency between the variables. It predicts that a change in one variable will result in a change in the other. 

A causal hypothesis predicts that changes in the dependent variable are a result of the manipulation of the independent variable.

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

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 performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
• "Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
• "There is no difference in scores on a memory recall task between children and adults."
• "There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

• "People who take St. John's wort supplements will have less anxiety than those who do not."
• "Adults will perform better on a memory task than children."
• "Children who play first-person shooter games will show higher levels of aggression than children who do not." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when  conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a  correlational study  can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Thompson WH, Skau S. On the scope of scientific hypotheses .  R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:].  Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ?  PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies .  Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Teach yourself statistics

How to Analyze Survey Data for Hypothesis Tests

Traditionally, researchers analyze survey data to estimate population parameters. But very similar analytical techniques can also be applied to test hypotheses.

In this lesson, we describe how to analyze survey data to test statistical hypotheses.

The Logic of the Analysis

In a big-picture sense, the analysis of survey sampling data is easy. When you use sample data to test a hypothesis, the analysis includes the same seven steps:

• Estimate a population parameter.
• Estimate population variance.
• Compute standard error.
• Set the significance level.
• Find the critical value (often a z-score or a t-score).
• Define the upper limit of the region of acceptance.
• Define the lower limit of the region of acceptance.

It doesn't matter whether the sampling method is simple random sampling, stratified sampling, or cluster sampling. And it doesn't matter whether the parameter of interest is a mean score, a proportion, or a total score. The analysis of survey sampling data always includes the same seven steps.

However, formulas used in the first three steps of the analysis can differ, based on the sampling method and the parameter of interest. In the next section, we'll list the formulas to use for each step. By the end of the lesson, you'll know how to test hypotheses about mean scores, proportions, and total scores using data from simple random samples, stratified samples, and cluster samples.

Data Analysis for Hypothesis Testing

Now, let's look in a little more detail at the seven steps required to conduct a hypothesis test, when you are working with data from a survey sample.

Sample mean = x = Σx / n

where x is a sample estimate of the population mean, Σx is the sum of all the sample observations, and n is the number of sample observations.

Population total = t = N * x

where N is the number of observations in the population, and x is the sample mean.

Or, if we know the sample proportion, we can estimate the population total (t) as:

Population total = t = N * p

where t is an estimate of the number of elements in the population that have a specified attribute, N is the number of observations in the population, and p is the sample proportion.

Sample mean = x = Σ( N h / N ) * x h

where N h is the number of observations in stratum h of the population, N is the number of observations in the population, and x h is the mean score from the sample in stratum h .

Sample proportion = p = Σ( N h / N ) * p h

where N h is the number of observations in stratum h of the population, N is the number of observations in the population, and p h is the sample proportion in stratum h .

Population total = t = ΣN h * x h

where N h is the number of observations in the population from stratum h , and x h is the sample mean from stratum h .

Or if we know the population proportion in each stratum, we can use this formula to estimate a population total:

Population total = t = ΣN h * p h

where t is an estimate of the number of observations in the population that have a specified attribute, N h is the number of observations from stratum h in the population, and p h is the sample proportion from stratum h .

x = ( N / ( n * M ) ] * Σ ( M h * x h )

where N is the number of clusters in the population, n is the number of clusters in the sample, M is the number of observations in the population, M h is the number of observations in cluster h , and x h is the mean score from the sample in cluster h .

p = ( N / ( n * M ) ] * Σ ( M h * p h )

where N is the number of clusters in the population, n is the number of clusters in the sample, M is the number of observations in the population, M h is the number of observations in cluster h , and p h is the proportion from the sample in cluster h .

Population total = t = N/n * ΣM h * x h

where N is the number of clusters in the population, n is the number of clusters in the sample, M h is the number of observations in the population from cluster h , and x h is the sample mean from cluster h .

And, if we know the sample proportion for each cluster, we can estimate a population total:

Population total = t = N/n * ΣM h * p h

where t is an estimate of the number of elements in the population that have a specified attribute, N is the number of clusters in the population, n is the number of clusters in the sample, M h is the number of observations from cluster h in the population, and p h is the sample proportion from cluster h .

s 2 = P * (1 - P)

where s 2 is an estimate of population variance, and P is the value of the proportion in the null hypothesis.

s 2 = Σ ( x i - x ) 2 / ( n - 1 )

where s 2 is a sample estimate of population variance, x is the sample mean, x i is the i th element from the sample, and n is the number of elements in the sample.

s 2 h = Σ ( x i h - x h ) 2 / ( n h - 1 )

where s 2 h is a sample estimate of population variance in stratum h , x i h is the value of the i th element from stratum h, x h is the sample mean from stratum h , and n h is the number of sample observations from stratum h .

s 2 h = Σ ( x i h - x h ) 2 / ( m h - 1 )

where s 2 h is a sample estimate of population variance in cluster h , x i h is the value of the i th element from cluster h, x h is the sample mean from cluster h , and m h is the number of observations sampled from cluster h .

s 2 b = Σ ( t h - t/N ) 2 / ( n - 1 )

where s 2 b is a sample estimate of the variance between sampled clusters, t h is the total from cluster h, t is the sample estimate of the population total, N is the number of clusters in the population, and n is the number of clusters in the sample.

You can estimate the population total (t) from the following formula:

where M h is the number of observations in the population from cluster h , and x h is the sample mean from cluster h .

SE = sqrt [ (1 - n/N) * s 2 / n ]

where n is the sample size, N is the population size, and s is a sample estimate of the population standard deviation.

SE = sqrt [ N 2 * (1 - n/N) * s 2 / n ]

where N is the population size, n is the sample size, and s 2 is a sample estimate of the population variance.

SE = (1 / N) * sqrt { Σ [ N 2 h * ( 1 - n h /N h ) * s 2 h / n h ] }

where n h is the number of sample observations from stratum h, N h is the number of elements from stratum h in the population, N is the number of elements in the population, and s 2 h is a sample estimate of the population variance in stratum h.

SE = sqrt { Σ [ N 2 h * ( 1 - n h /N h ) * s 2 h / n h ] }

where N h is the number of elements from stratum h in the population, n h is the number of sample observations from stratum h, and s 2 h is a sample estimate of the population variance in stratum h.

where M is the number of observations in the population, N is the number of clusters in the population, n is the number of clusters in the sample, M h is the number of elements from cluster h in the population, m h is the number of elements from cluster h in the sample, x h is the sample mean from cluster h, s 2 h is a sample estimate of the population variance in stratum h, and t is a sample estimate of the population total. For the equation above, use the following formula to estimate the population total.

t = N/n * Σ M h x h

With one-stage cluster sampling, the formula for the standard error reduces to:

where M is the number of observations in the population, N is the number of clusters in the population, n is the number of clusters in the sample, M h is the number of elements from cluster h in the population, m h is the number of elements from cluster h in the sample, p h is the value of the proportion from cluster h, and t is a sample estimate of the population total. For the equation above, use the following formula to estimate the population total.

t = N/n * Σ M h p h

where N is the number of clusters in the population, n is the number of clusters in the sample, s 2 b is a sample estimate of the variance between clusters, m h is the number of elements from cluster h in the sample, M h is the number of elements from cluster h in the population, and s 2 h is a sample estimate of the population variance in cluster h.

SE = N * sqrt { [ ( 1 - n/N ) / n ] * s 2 b /n }

• Choose a significance level. The significance level (denoted by α) is the probability of committing a Type I error . Researchers often set the significance level equal to 0.05 or 0.01.

When the null hypothesis is two-tailed, the critical value is the z-score or t-score that has a cumulative probability equal to 1 - α/2. When the null hypothesis is one-tailed, the critical value has a cumulative probability equal to 1 - α.

Researchers use a t-score when sample size is small; a z-score when it is large (at least 30). You can use the Normal Distribution Calculator to find the critical z-score, and the t Distribution Calculator to find the critical t-score.

If you use a t-score, you will have to find the degrees of freedom (df). With simple random samples, df is often equal to the sample size minus one.

Note: The critical value for a one-tailed hypothesis does not equal the critical value for a two-tailed hypothesis. The critical value for a one-tailed hypothesis is smaller.

UL = M + SE * CV

• If the null hypothesis is μ > M: The theoretical upper limit of the region of acceptance is plus infinity, unless the parameter in the null hypothesis is a proportion or a percentage. The upper limit is 1 for a proportion, and 100 for a percentage.

LL = M - SE * CV

• If the null hypothesis is μ < M: The theoretical lower limit of the region of acceptance is minus infinity, unless the test statistic is a proportion or a percentage. The lower limit for a proportion or a percentage is zero.

The region of acceptance is the range of values between LL and UL. If the sample estimate of the population parameter falls outside the region of acceptance, the researcher rejects the null hypothesis. If the sample estimate falls within the region of acceptance, the researcher does not reject the null hypothesis.

By following the steps outlined above, you define the region of acceptance in such a way that the chance of making a Type I error is equal to the significance level .

Test Your Understanding

In this section, two hypothesis testing examples illustrate how to define the region of acceptance. The first problem shows a two-tailed test with a mean score; and the second problem, a one-tailed test with a proportion.

Sample Size Calculator

As you probably noticed, defining the region of acceptance can be complex and time-consuming. Stat Trek's Sample Size Calculator can do the same job quickly, easily, and error-free.The calculator is easy to use, and it is free. You can find the Sample Size Calculator in Stat Trek's main menu under the Stat Tools tab. Or you can tap the button below.

An inventor has developed a new, energy-efficient lawn mower engine. He claims that the engine will run continuously for 5 hours (300 minutes) on a single ounce of regular gasoline. Suppose a random sample of 50 engines is tested. The engines run for an average of 295 minutes, with a standard deviation of 20 minutes.

Consider the null hypothesis that the mean run time is 300 minutes against the alternative hypothesis that the mean run time is not 300 minutes. Use a 0.05 level of significance. Find the region of acceptance. Based on the region of acceptance, would you reject the null hypothesis?

Solution: The analysis of survey data to test a hypothesis takes seven steps. We work through those steps below:

However, if we had to compute the sample mean from raw data, we could do it, using the following formula:

where Σx is the sum of all the sample observations, and n is the number of sample observations.

If we hadn't been given the standard deviation, we could have computed it from the raw sample data, using the following formula:

For this problem, we know that the sample size is 50, and the standard deviation is 20. The population size is not stated explicitly; but, in theory, the manufacturer could produce an infinite number of motors. Therefore, the population size is a very large number. For the purpose of the analysis, we'll assume that the population size is 100,000. Plugging those values into the formula, we find that the standard error is:

SE = sqrt [ (1 - 50/100,000) * 20 2 / 50 ]

SE = sqrt(0.9995 * 8) = 2.828

• Choose a significance level. The significance level (α) is chosen for us in the problem. It is 0.05. (Researchers often set the significance level equal to 0.05 or 0.01.)

When the null hypothesis is two-tailed, the critical value has a cumulative probability equal to 1 - α/2. When the null hypothesis is one-tailed, the critical value has a cumulative probability equal to 1 - α.

For this problem, the null hypothesis and the alternative hypothesis can be expressed as:

Since this problem deals with a two-tailed hypothesis, the critical value will be the z-score that has a cumulative probability equal to 1 - α/2. Here, the significance level (α) is 0.05, so the critical value will be the z-score that has a cumulative probability equal to 0.975.

We use the Normal Distribution Calculator to find that the z-score with a cumulative probability of 0.975 is 1.96. Thus, the critical value is 1.96.

where M is the parameter value in the null hypothesis, SE is the standard error, and CV is the critical value. So, for this problem, we compute the lower limit of the region of acceptance as:

LL = 300 - 2.828 * 1.96

LL = 300 - 5.54

LL = 294.46

LL = 300 + 2.828 * 1.96

LL = 300 + 5.54

LL = 305.54

Thus, given a significance level of 0.05, the region of acceptance is range of values between 294.46 and 305.54. In the tests, the engines ran for an average of 295 minutes. That value is within the region of acceptance, so the inventor cannot reject the null hypothesis that the engines run for 300 minutes on an ounce of fuel.

Problem 2 Suppose the CEO of a large software company claims that at least 80 percent of the company's 1,000,000 customers are very satisfied. A survey of 100 randomly sampled customers finds that 73 percent are very satisfied. To test the CEO's hypothesis, find the region of acceptance. Assume a significance level of 0.05.

However, if we had to compute the sample proportion (p) from raw data, we could do it by using the following formula:

where s 2 is the population variance when the true population proportion is P, and P is the value of the proportion in the null hypothesis.

For the purpose of estimating population variance, we assume the null hypothesis is true. In this problem, the null hypothesis states that the true proportion of satisfied customers is 0.8. Therefore, to estimate population variance, we insert that value in the formula:

s 2 = 0.8 * (1 - 0.8)

s 2 = 0.8 * 0.2 = 0.16

For this problem, we know that the sample size is 100, the variance ( s 2 ) is 0.16, and the population size is 1,000,000. Plugging those values into the formula, we find that the standard error is:

SE = sqrt [ (1 - 100/1,000,000) * 0.16 / 100 ]

SE = sqrt(0.9999 * 0.0016) = 0.04

Since this problem deals with a one-tailed hypothesis, the critical value will be the z-score that has a cumulative probability equal to 1 - α. Here, the significance level (α) is 0.05, so the critical value will be the z-score that has a cumulative probability equal to 0.95.

We use the Normal Distribution Calculator to find that the z-score with a cumulative probability of 0.95 is 1.645. Thus, the critical value is 1.645.

LL = 0.8 - 0.04 * 1.645

LL = 0.8 - 0.0658 = 0.7342

• Find the upper limit of the region of acceptance. For this type of one-tailed hypothesis, the theoretical upper limit of the region of acceptance is 1; since any proportion greater than 0.8 is consistent with the null hypothesis, and 1 is the largest value that a proportion can have.

Thus, given a significance level of 0.05, the region of acceptance is the range of values between 0.7342 and 1.0. In the sample survey, the proportion of satisfied customers was 0.73. That value is outside the region of acceptance, so null hypothesis must be rejected.

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Chapter 9: Survey Research

Overview of Survey Research

Learning Objectives

• Define what survey research is, including its two important characteristics.
• Describe several different ways that survey research can be used and give some examples.

What Is Survey Research?

Survey research  is a quantitative and qualitative method with two important characteristics. First, the variables of interest are measured using self-reports. In essence, survey researchers ask their participants (who are often called respondents  in survey research) to report directly on their own thoughts, feelings, and behaviours. Second, considerable attention is paid to the issue of sampling. In particular, survey researchers have a strong preference for large random samples because they provide the most accurate estimates of what is true in the population. In fact, survey research may be the only approach in psychology in which random sampling is routinely used. Beyond these two characteristics, almost anything goes in survey research. Surveys can be long or short. They can be conducted in person, by telephone, through the mail, or over the Internet. They can be about voting intentions, consumer preferences, social attitudes, health, or anything else that it is possible to ask people about and receive meaningful answers.  Although survey data are often analyzed using statistics, there are many questions that lend themselves to more qualitative analysis.

Most survey research is nonexperimental. It is used to describe single variables (e.g., the percentage of voters who prefer one presidential candidate or another, the prevalence of schizophrenia in the general population) and also to assess statistical relationships between variables (e.g., the relationship between income and health). But surveys can also be experimental. The study by Lerner and her colleagues is a good example. Their use of self-report measures and a large national sample identifies their work as survey research. But their manipulation of an independent variable (anger vs. fear) to assess its effect on a dependent variable (risk judgments) also identifies their work as experimental.

History and Uses of Survey Research

Survey research may have its roots in English and American “social surveys” conducted around the turn of the 20th century by researchers and reformers who wanted to document the extent of social problems such as poverty (Converse, 1987) [1] . By the 1930s, the US government was conducting surveys to document economic and social conditions in the country. The need to draw conclusions about the entire population helped spur advances in sampling procedures. At about the same time, several researchers who had already made a name for themselves in market research, studying consumer preferences for American businesses, turned their attention to election polling. A watershed event was the presidential election of 1936 between Alf Landon and Franklin Roosevelt. A magazine called  Literary Digest  conducted a survey by sending ballots (which were also subscription requests) to millions of Americans. Based on this “straw poll,” the editors predicted that Landon would win in a landslide. At the same time, the new pollsters were using scientific methods with much smaller samples to predict just the opposite—that Roosevelt would win in a landslide. In fact, one of them, George Gallup, publicly criticized the methods of Literary Digest  before the election and all but guaranteed that his prediction would be correct. And of course it was. (We will consider the reasons that Gallup was right later in this chapter.) Interest in surveying around election times has led to several long-term projects, notably the Canadian Election Studies which has measured opinions of Canadian voters around federal elections since 1965.  Anyone can access the data and read about the results of the experiments in these studies.

From market research and election polling, survey research made its way into several academic fields, including political science, sociology, and public health—where it continues to be one of the primary approaches to collecting new data. Beginning in the 1930s, psychologists made important advances in questionnaire design, including techniques that are still used today, such as the Likert scale. (See “What Is a Likert Scale?” in  Section 9.2 “Constructing Survey Questionnaires” .) Survey research has a strong historical association with the social psychological study of attitudes, stereotypes, and prejudice. Early attitude researchers were also among the first psychologists to seek larger and more diverse samples than the convenience samples of university students that were routinely used in psychology (and still are).

Survey research continues to be important in psychology today. For example, survey data have been instrumental in estimating the prevalence of various mental disorders and identifying statistical relationships among those disorders and with various other factors. The National Comorbidity Survey is a large-scale mental health survey conducted in the United States . In just one part of this survey, nearly 10,000 adults were given a structured mental health interview in their homes in 2002 and 2003.  Table 9.1  presents results on the lifetime prevalence of some anxiety, mood, and substance use disorders. (Lifetime prevalence is the percentage of the population that develops the problem sometime in their lifetime.) Obviously, this kind of information can be of great use both to basic researchers seeking to understand the causes and correlates of mental disorders as well as to clinicians and policymakers who need to understand exactly how common these disorders are.

And as the opening example makes clear, survey research can even be used to conduct experiments to test specific hypotheses about causal relationships between variables. Such studies, when conducted on large and diverse samples, can be a useful supplement to laboratory studies conducted on university students. Although this approach is not a typical use of survey research, it certainly illustrates the flexibility of this method.

Key Takeaways

• Survey research is a quantitative approach that features the use of self-report measures on carefully selected samples. It is a flexible approach that can be used to study a wide variety of basic and applied research questions.
• Survey research has its roots in applied social research, market research, and election polling. It has since become an important approach in many academic disciplines, including political science, sociology, public health, and, of course, psychology.

Discussion: Think of a question that each of the following professionals might try to answer using survey research.

• a social psychologist
• an educational researcher
• a market researcher who works for a supermarket chain
• the mayor of a large city
• the head of a university police force
• Converse, J. M. (1987). Survey research in the United States: Roots and emergence, 1890–1960 . Berkeley, CA: University of California Press. ↵
• The lifetime prevalence of a disorder is the percentage of people in the population that develop that disorder at any time in their lives. ↵

A quantitative approach in which variables are measured using self-reports from a sample of the population.

Participants of a survey.

Research Methods in Psychology - 2nd Canadian Edition Copyright © 2015 by Paul C. Price, Rajiv Jhangiani, & I-Chant A. Chiang is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Home » Survey Research – Types, Methods, Examples

Survey Research – Types, Methods, Examples

Table of Contents

Survey Research

Definition:

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

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

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

Survey Research Methods

Survey Research Methods are as follows:

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

Types of Survey Research

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

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

Types Based on Methodology

Based on Methodology Survey are divided into two Types:

Quantitative Survey Research

Qualitative survey research.

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

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

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

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

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

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

Data Analysis Methods

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

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

Applications of Survey Research

Here are some common applications of survey research:

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

Examples of Survey Research

Here are some real-time examples of survey research:

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

Survey Sample

Purpose of survey research.

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

Here are some common purposes of survey research:

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

When to use Survey Research

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

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

How to Conduct Survey Research

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

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

Advantages of Survey Research

There are several advantages to using survey research, including:

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

Limitations of Survey Research

Here are some of the main limitations of survey research:

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

Following is an example of a Survey Sample:

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

1. What is your age?

• A) Under 18
• G) 65 or older

2. What is your highest level of education completed?

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

3. What is your current employment status?

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

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

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

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

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

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

• A) Excellent
• E) Very poor

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

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

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

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

10. Any additional comments or suggestions?

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

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Myths About Using Surveys and Questionnaires in Psychology

It might help to listen to people when asking them questions..

Posted June 8, 2024 | Reviewed by Gary Drevitch

• So-called subjective data can be as informative as objective data.
• Some aspects of a person’s experience can only be measured by asking them.

One of the most commonly-used measurement tools in psychology research and practice is what’s called the patient-reported, or self-reported, test. This method involves giving a patient, client, or research study participant a survey or questionnaire to gauge their experience. The survey or questionnaire often asks about how someone feels or what they think. Sometimes, the questions can be about physical symptoms such as pain, fatigue, and poor sleep.

A trend I have noticed is that surveys and questionnaires are often denigrated with pejorative terms like “subjective” or “unreliable." The implications of these criticisms are that asking people to report their feelings, symptoms and thoughts is not worthwhile—or at least is less worthwhile than more “objective” tools like blood tests and brain scans. The definition of subjective is that it is something based on a person’s feelings while something objective is unbiased or based on facts.

This second-class status of self-reported information is highly detrimental to patients and the public in general for several reasons.

A primary reason why the denigration of surveys and questionnaires is harmful is that it is based on a myth. Despite claiming to be objective, the opinion that survey and questionnaire data is not useful is, in fact, not based on facts and is, instead, subjective .

Let’s consider some of the other types of tests that psychologists use in research or that physicians may use in clinical practice. The first is blood tests measuring chemicals in a person’s body such as stress hormones (cortisol), signs of inflammation (c-reactive protein), and metabolism (glucose). Other tests include body scans of the body ranging from X-rays to magnetic resonance imaging. Another common test is to ask the clinician how a patient is doing; for example, asking them to rate a patient’s function in a certain area on a seven-point scale. A common myth is that all these different tools (blood tests, scans, clinician report, surveys, and questionnaires) are measuring the same thing. They are not. What is in someone’s blood or shown on a scan is not the same thing as the emotions they are experiencing, the symptoms they have, or the thoughts running through their mind. Someone’s emotions, thoughts, and symptoms can differ greatly from what their physician or psychologist believes they are experiencing. The myth that all these tests measure the same thing forms the basis for disregarding self-reported data.

Another myth about surveys and questionnaires versus other types of tests is that other types are more reliable. This is not necessarily the case. There is a laundry list of blood tests that are unreliable but still used. Surveys and questionnaires, just like other types of data, have to go through a process of development to ensure that they are reliable—and they sometimes may be more reliable than other forms of data. What is measured by surveys and questionnaires—such as emotions, thoughts, symptoms)—can vary from hour to hour (or even minute to minute) and this natural variability is often mistaken for unreliability. This myth about surveys and questionnaires feeds into the previous myth that self-reported data is not worthwhile.

Despite the logical arguments for self-reported data, there is also a moral argument. Self-reported information from surveys and questionnaires is a way to measure what a person perceives about themselves. To disregard these perceptions in favor of other tests that might not measure the same thing is, to be blunt, paternalistic. It simply says that “I know better than you what you are experiencing based on this blood test/scan/my own opinion.” I’m not saying that these other tests should not be used; only that self-reported data should not be considered as less valuable than other tests.

In case you are wondering why I just spent an entire post waxing poetic about survey and questionnaire myths, there are several reasons. First, disregarding what people say on surveys and questionnaires is disregarding their experience and that is highly invalidating and harmful. Another problem is that time and money is often wasted chasing an “objective” measure of some psychological experience when we could have just asked people. Surveys and questionnaires are often much cheaper both to develop and use than blood tests, scans, and other biologically-based tests. While each type of test has its own place and value, self-reported surveys and questionnaires should definitely be considered as valuable as other measures.

Salene M. W. Jones, Ph.D., is a clinical psychologist in Washington State.

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Perceived Unfairness Moderates the Association Between Relative Deprivation and Subjective Well-Being: Findings from an East Asian Country

• Published: 19 June 2024

Cite this article

• Ahhyun Cho 1 &
• Harris Hyun-soo Kim   ORCID: orcid.org/0000-0003-1311-6507 1  

A large volume of research highlights the adverse effects of relative deprivation on subjective well-being. Across different empirical settings and modelling approaches, a conceptual common denominator exists: the bulk of prior studies assumes that lower social status, by definition, implies higher relative deprivation, resulting in reduced well-being. In the present study, we take issue with this assumption and propose that lower self-ascribed positions on the status hierarchy are necessary but insufficient in and of themselves to undermine well-being. The critical, yet often neglected, factor in the literature is perceived societal unfairness. That is, one must believe that personal predicament as gauged by status disadvantage is, at least partly, due to some exogenous or impersonal forces (e.g., discrimination, limited opportunity). Our central argument is that the magnitude of the focal relationship between relative deprivation and well-being should be more pronounced among those who hold higher perceptions of unfairness. Using three independently collected probability datasets on the South Korean population—Social Science Korea (2017), Seoul Survey (2018), and Korean Social Integration Survey (2018)—we systematically test this hypothesis. Results from multilevel models robustly demonstrate that the connection between lower social status and lower well-being is significantly stronger among individuals who assess their society to be more ‘unfair,’ suggesting that future research should incorporate the level of perceived unfairness as a consequential moderator.

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Data Availability

Data analyzed for this study are available upon reasonable request from the corresponding author.

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A novel approach to anxiety level prediction using small sets of judgment and survey variables

• Sumra Bari 1 ,
• Byoung-Woo Kim 1   na1 ,
• Nicole L. Vike 1   na1 ,
• Shamal Lalvani 2   na1 ,
• Leandros Stefanopoulos 2 , 3   na1 ,
• Nicos Maglaveras 3 ,
• Martin Block 4 ,
• Jeffrey Strawn 5 ,
• Aggelos K. Katsaggelos 2 , 6 , 7   na2 &
• Hans C. Breiter 1 , 8 , 9   na2  

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Anxiety, a condition characterized by intense fear and persistent worry, affects millions each year and, when severe, is distressing and functionally impairing. Numerous machine learning frameworks have been developed and tested to predict features of anxiety and anxiety traits. This study extended these approaches by using a small set of interpretable judgment variables ( n  = 15) and contextual variables (demographics, perceived loneliness, COVID-19 history) to (1) understand the relationships between these variables and (2) develop a framework to predict anxiety levels [derived from the State Trait Anxiety Inventory (STAI)]. This set of 15 judgment variables, including loss aversion and risk aversion, models biases in reward/aversion judgments extracted from an unsupervised, short (2–3 min) picture rating task (using the International Affective Picture System) that can be completed on a smartphone. The study cohort consisted of 3476 de-identified adult participants from across the United States who were recruited using an email survey database. Using a balanced Random Forest approach with these judgment and contextual variables, STAI-derived anxiety levels were predicted with up to 81% accuracy and 0.71 AUC ROC. Normalized Gini scores showed that the most important predictors (age, loneliness, household income, employment status) contributed a total of 29–31% of the cumulative relative importance and up to 61% was contributed by judgment variables. Mediation/moderation statistics revealed that the interactions between judgment and contextual variables appears to be important for accurately predicting anxiety levels. Median shifts in judgment variables described a behavioral profile for individuals with higher anxiety levels that was characterized by less resilience, more avoidance, and more indifference behavior. This study supports the hypothesis that distinct constellations of 15 interpretable judgment variables, along with contextual variables, could yield an efficient and highly scalable system for mental health assessment. These results contribute to our understanding of underlying psychological processes that are necessary to characterize what causes variance in anxiety conditions and its behaviors, which can impact treatment development and efficacy.

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Introduction.

Anxiety disorders affected ~12% of the US population in 2021 1 and affects 4% of the population worldwide 2 , 3 . Anxiety disorders are characterized by intense fear and persistent worry in the absence of a defined threat 4 and are among the most common causes of disability worldwide. Anxiety disorders begin early in life 5 , 6 and increase the risk of subsequent mood disorders, substance misuse, suicidal behavior and economic disadvantage 7 .

The diagnosis of an anxiety disorder involves clinical determination of the severity of symptoms and the presence of specific symptom constellations based on clinical assessment, commonly augmented by surveys and symptom inventories 8 . Recently, automated approaches have been tested for predicting anxiety, as determined by clinical assessment or surveys, with a primary focus on using machine learning (ML) approaches 9 with large variable sets (e.g., >100) 10 , 11 , 12 including clinical data 13 , 14 , 15 , questionnaires 16 , 17 , wearable biosensors 18 , 19 , 20 , social media posts 21 , 22 , 23 , neural measures from MRI 24 , 25 , 26 and cognitive science variables 27 , 28 , 29 , 30 , 31 . These large variable sets add multiple dimensions to the characterization of anxiety across study participants producing higher accuracies and lower unexplained variance. They model complex relationships between the predictors and the outcome, yet can present challenges ranging from significant computational requirements and prohibitive privacy concerns, to lengthy and costly data acquisitions. The current study sought to contribute to current ML-based anxiety level prediction efforts by using a small set of cognitive science variables, that can be acquired in 2–3 min on a small digital device, like a smartphone. Currently, 92% of the US population 32 and 85% of the world population 33 can access such devices.

Cognitive science studies focused on judgment behavior are hypothesized to be relevant to anxiety given the overlap in the neural systems implicated in both 34 , 35 , 36 . Abnormalities in reward/aversion judgment have been linked to dopamine system dysfunction in depression, addiction, suicidality, and chronic stress 37 , 38 , 39 , and individuals with anxiety have shown salient alterations in reward/aversion judgment 40 , 41 , 42 . A number of reward/aversion variables are thought to represent biases in judgment 43 , 44 , such as loss aversion (LA) 45 and risk aversion (RA) 46 . Heightened RA 47 , 48 , 49 and heightened LA 50 , 51 have been reported in those with anxiety using a range of distinct monetary and emotional stimuli that describe reward/aversion judgment.

Reward/aversion judgment has been studied using operant keypress tasks to frame reinforcement reward in humans 52 , 53 , 54 , 55 , 56 , and been used to quantify judgment variables like LA 56 , 57 . These cognitive science studies have compared keypress-based LA to other LA frameworks such as prospect theory (e.g., Lee et al. 58 ), connected keypress methods to imaging of reward/aversion circuitry (e.g., refs. 52 , 59 , 60 , 61 ) and connected LA from operant keypressing to reward/aversion circuitry 62 . The keypress framework allows the modeling of human behavior using variance and entropic variables to produce a set of at least 15 features that characterize an individual’s reward/aversion judgment (e.g., LA, RA, and others, Table 1 ). These features have been linked to brain structure differences in the context of (1) substance use disorder 60 and (2) the characterization of substance use disorder and depression 63 .

These 15 judgment variables can also be computed from a picture rating task that takes 2–3 min 64 . The picture rating task was adapted from the operant keypress task, and is implementable on a smartphone or digital device (Fig. 1 , Table 1 ). Judgment variables from the shorter picture rating task are consistent across multiple data sets 64 , 65 . The 15 judgment variables derived from the picture rating task, when combined with a small set of demographic and survey variables, have been used to predict other mental health and medical health conditions with high accuracy using ML: depression history 65 , suicidality 66 , and vaccine uptake 67 . Based on results from these prior publications, we hypothesized that a small set of 15 judgment variables (see Fig. 1 , Table 1 ), with contextual variables theorized to affect judgment and mental function (in this case: demographics, perceived loneliness, and COVID-19 history), might facilitate the prediction of anxiety levels.

A An example picture from the picture rating task where participants were asked to rate how much they liked or disliked an imagine on a scale of −3 (dislike very much) and +3 (like very much), with 0 being neutral. B Visual representation of the x–y plane for relative preference theory (RPT) value function fitting and resulting features extracted. C Visual representation of the x–y plane for RPT limit function fitting and resulting features extracted. D Visual representation of the x–y plane for RPT tradeoff function fitting and resulting features extracted. E Each of the 15 features and their abbreviated terms.

A short picture rating task (see “Methods”) was administered to 4019 (3476 following data exclusion) de-identified participants in December 2021. Participants rated 48 unique color images from the International Affective Picture System (IAPS) 68 , 69 on a scale of −3 (dislike very much) to +3 (like very much). Anxiety scores were derived from the state component of the State-Trait Anxiety Inventory (STAI) questionnaire 70 , a validated anxiety questionnaire. Random Forest ( RF ) and balanced Random Forest ( bRF ) techniques were used to classify anxiety scores into ‘higher’ versus ‘lower’ classes and to understand the relative importance of the predictors using Gini scores. Post hoc mediation/moderation analyses were conducted to understand the interactions between judgment and contextual variables that may underly anxiety level prediction. Lastly, contextual and judgment variable differences were assessed against anxiety levels.

This study took the perspective that the power of psychological constructs depends on their capacity to make meaningful predictions. The use of mathematical cognitive science to predict survey-based anxiety measures contributes to our understanding of how psychological processes underlie the variance in anxiety conditions and behaviors, which might impact treatment development and efficacy.

This study assessed anxiety levels (derived from STAI questionnaire) in relation to contextual and 15 picture rating-derived judgment variables. Judgment variables include Loss Aversion (LA) 45 , Risk Aversion (RA) 46 , Loss Resilience (LR), Ante, Insurance, Total Reward Risk (Total RR), Total Aversion Risk (Total AR), Peak Positive Risk (Peak PR), Peak Negative Risk (Peak NR), Reward Tipping Point (Reward TP), Aversion Tipping Point (Aversion TP), Reward-Aversion tradeoff (RA tradeoff), Tradeoff range, Reward-Aversion consistency (RA consistency) and Consistency range (see Fig. 1B–D and Table 1 ).

Classification analysis: Random Forest ( RF ) and balanced Random Forest ( bRF )

For the classification of ‘higher’ and ‘lower’ anxiety levels, the bRF performed better than RF in terms of sensitivity, specificity, AUC ROC, and balanced accuracy at all three threshold values with the best performance for the threshold of 35 (Table 2 ). For the bRF classification, the out of bag (OOB) accuracy and accuracy ranged from 72 to 81% and AUC ROC ranged from 0.71 to 0.74 (Table 2 ) which was much higher than the chance levels obtained from the permutation analysis (Supplementary Table 3 ). The sensitivity ranged from 56 to 74% with the lowest values corresponding to a greater class imbalance at a threshold of 55. A greater class imbalance was noted as the threshold values increased as depicted in the ‘Percentage of data’ column of Table 2 . When the threshold value was 55, the percentage of participants with high anxiety was only 12% (418/3476) of the dataset.

For RF classification, the OOB accuracy and test dataset accuracy ranged from 72 to 88% and AUC ROC ranged from 0.52 to 0.72 (Table 2 ). The sensitivity ranged from 3 to 74% with the lowest values corresponding to a greater class imbalance at a threshold of 55. RF had the worst performance at the threshold of 55, with the output metrics close to the chance levels (Supplementary Table 3 ).

Multi-dimensional scaling (MDS) plots demonstrated how ‘higher’ and ‘lower’ clusters were better distinguished at lower STAI-S thresholds (i.e., Supplementary Fig. 2 ) and how bRF always produced better data segregation between the two classes as compared to RF .

Relative importance of features

Age, loneliness, income, and employment were consistently the most important individual features for bRF based on the mean decrease in Gini scores (see Fig. 2B, D, F ). This was also the case for RF with anxiety thresholds of 35 and 45 (Fig. 2A, C ). Together, these four variables contributed between 29 and 33% of the relative importance (Table 3 ). For the RF analysis with a high threshold of 55 (only 12% of the cohort in the ‘higher’ group and 3% sensitivity), age and loneliness remained the top-most contributing variables.

The predictors are arranged according to the mean decrease in Gini scores with the most important predictors on the top. The red box outlines the top contextual variables, and the blue box outlines the 15 judgment variables while the red * in the blue box points to the contextual variables in the cluster of judgment variables. Plots ( A ), ( C ), ( E ) corresponds to RF analyses with thresholds of 35, 45, and 55, respectively, and ( B ), ( D ), ( F ) corresponds to bRF with thresholds of 35, 45, and 55, respectively.

The 15 judgment variables contributed a combined 55–61% of the relative importance (Fig. 2 and Table 3 ). Other contextual variables (education group, education in years, marital status, race/ethnicity, sex, COVID-19 test, and diagnosis) were lower in classification importance, contributing a combined relative importance of 11–21%.

Mediation and moderation analysis

Mediation and moderation analysis were used to define statistical interactions between judgment variables and the most important contextual variables (age, loneliness, income, and employment). These contextual variables were defined as the mediator (Me) or moderator (Mo), judgment variables were defined as the independent variable, and STAI-S scores were defined as the dependent variable. These analyses revealed nine mediation (Table 4A and Supplementary Table 4 ) and seven moderation results (Table 4B ). Age acted as a mediator when loss resilience, Aversion TP, Tradeoff Range, and Consistency range were independent variables. Loneliness appeared as the mediator in four mediation analyses with ante, insurance, Total AR, and Consistency range, whereas employment only mediated Consistency range as the independent variable.

Independent and Me variables were then switched to test if the judgment variables acted as mediators. No significant mediation results were found when contextual variables were independent variables and judgment variables were mediators.

With regard to moderation analyses, age was found to also be involved in four moderations, with ante, insurance, Peak PR, and Peak NR as the independent variables. Loneliness was involved in one moderation with Peak PR, and employment was implicated in two moderations with insurance and Peak NR. There were no mediation or moderation results with the income variable.

Note that age, loneliness, and employment interacted with different judgment variables when acting as a mediators versus when acting as a moderators (see Table 4A and Table 4B ).

Post hoc analysis of contextual variable differences

The seven demographic variables (excluding years of education), perceived loneliness, and COVID-19 history were assessed for differences across anxiety scores using Wilcoxon rank-sum and Kruskal Wallis tests. All contextual variables significantly varied by anxiety score ( p -value < 0.05) (Table 5A ). For the majority of contextual variables, boxplots depicted ascending or descending trends (Supplementary Fig. 3 ). For example, anxiety scores were higher (1) with higher levels of perceived loneliness, (2) among younger individuals, (3) in females, (4) among individuals with lower household income, (5) among individuals with lower education levels, and (6) in individuals reporting a history of COVID-19 infection (test and diagnosis).

Post hoc analysis of judgment variable differences

Judgment variables were analyzed by ‘higher’ and ‘lower’ anxiety scores for the three threshold values (Fig. 3 ). Eleven out of the 15 judgment variables differed using the one-sided Wilcoxon rank sum test (significance α  < 0.05) and 8 out 15 differed after correction for multiple comparisons (significance α  < 0.0083, after Bonferroni correction). The alternative hypothesis, and the respective p -values for each test are reported in Table 5B . The alternative hypothesis was defined as the judgment variable distribution median being greater in the ‘higher’ anxiety group than the ‘lower’ anxiety group, or vice versa. The ‘higher’ anxiety group had higher medians for loss aversion (threshold = 35, p  < 0.05), ante (threshold = 45, 55, p  < 0.05), Peak PR (threshold = 45, p  < 0.0083; 55, p  < 0.05), and Total RR (threshold = 35, p  < 0.05; 45, 55, p  < 0.0083) when compared to the ‘lower’ anxiety group (Table 5B ). The ‘higher’ anxiety group had lower medians for risk aversion (threshold = 45, p  < 0.05), loss resilience (threshold = 35, 45, 55, p  < 0.0083), Peak NR (threshold = 35, p  < 0.0083), Aversion TP (threshold = 35, 45, p  < 0.0083), Total AR (threshold = 35, p  < 0.0083), Tradeoff Range (threshold = 35, 45, 55, p  < 0.0083), and RA consistency (threshold = 35, p  < 0.05; 45, p  < 0.0083) (Table 5B ). Insurance, Reward TP, RA Tradeoff, and Consistency Range showed no significant differences across all threshold values.

The thresholds 35, 45, and 55 roughly corresponds to 50th percentile (median), 75th percentile, and 90th percentile respectively of the anxiety/STAI-S scores. All values below the threshold are considered in ‘lower’ group and values above and equal to threshold are in ‘higher’ group.

This study evaluated how well a small set of judgment and contextual variables (i.e., demographics, perceived loneliness, and COVID-19 infection history), could together predict state anxiety levels. The study produced four major findings. First, prediction accuracy ranged from 72.4 to 81.3% with balanced Random Forest ( bRF) , and sensitivities decreased (74.1% to 56.1%) as the threshold for classifying anxiety was increased. Regardless of the threshold change, all prediction models maintained a relatively high AUC ROC (0.72 to 0.71), comparable with findings in the literature, and distinct from a permutation analysis showing AUC ROC outcomes approximating 0.50. bRF produced uniformly higher sensitivity outcomes than RF approaches. Second, four contextual variables (age, income, employment, and perceived loneliness) had the highest relative importance based on normalized Gini scores across most RF and bRF analyses (5 out of 6 analyses), and contributed a cumulative of 29–33% of relative importance to prediction. Other contextual variables such as race/ethnicity, sex, and COVID-19 infection history showed minimal importance across all analyses. All 15 judgment variables consistently showed similar importance and contributed a cumulative importance ranging from 55 to 61%. Third, nine of the 15 judgment variables were involved in mediation or moderation with contextual variables. Age, employment, and perceived loneliness mediated or moderated relationships with distinct judgment variables to model anxiety scores, consistent with other reports regarding the relationship of cognitive science measures and contextual variables 65 , 66 , 67 , 71 , 72 , 73 , 74 , 75 . Fourth, all contextual variables exhibited significant differences in anxiety scores, and 11 of the 15 judgment variables differed when assessed for median shifts across ‘higher’ and ‘lower’ anxiety groups, indicating that a constellation of judgment alterations are predictive of anxiety levels.

Prediction results from this study were comparable to recent research deploying advanced machine learning algorithms to predict anxiety and other mental health conditions 9 , with a number of limitations and advantages. Over the past decade there have been six general types of data used for predicting anxiety, with considerable heterogeneity in terms of how anxiety is defined, and the machine learning algorithms used. These six general frameworks for prediction included variables from: neuroimaging, physiological signals, survey-based assessments, social media posts, clinical or medical health records and behavioral tasks. Neuroimaging studies targeting anxiety prediction reported high accuracies ranging between 79 and 90% 24 , 25 and low correlation r = 0.28 (using Gaussian Process Regression) 26 but present challenges surrounding the collection of expensive, computationally-intensive, and complex MRI data that requires supervision of trained individuals at specific imaging sites. With similar caveats, studies using bio-signals and physiological signals 18 , 19 , 20 , that involved the use of multiple wearable sensors and collection of data under supervision, reported higher accuracies and model fits of 84.3%, 89.8% and r = 0.81. Survey-based studies have utilized extensive sets of demographic variables and lengthy questionnaires 16 , 17 , to predict anxiety with sensitivities between 62 and 73%. Other studies using demographics, lifestyle, and health surveys 10 , 11 , and transcripts of recorded interviews 12 predicted self-reported anxiety with accuracies between 75–86%. Studies using social media platforms like Reddit predicted anxiety with 75% precision 21 , 78% accuracy 22 ) from posts in mental health discussion groups, and Gruda et al. 23 used tweets to predict anxiety scores based on the level of anxiety assessed by volunteers in those tweets. Some studies 13 , 14 , 15 required access to clinical and medical records of thousands of participants and reported accuracies of 73–89% 11 , 13 , 14 , 18 , 20 , 25 .

A number of studies have utilized cognitive science variables derived from behavioral tasks to study anxiety 27 , 28 , 31 and neuroticism 29 (a general trait that is considered as a vulnerability factor for anxiety 76 ). For instance, Yamamori et al. 27 used approach-avoidance reinforcement learning tasks with hierarchical logistic regression (reporting p  < 0.05) to model task-induced anxiety. Aupperle et al. 28 reported significant correlations ( p  < 0.01) between measures from computer-based approach-avoidance conflict task and self-reported anxiety measures from anxiety sensitivity index and Behavioral Inhibition/Activation Scale. Park et al. 29 reported attenuated processing of gains and losses (using functional MRI responses to a Monetary Incentive Delay task) with higher polygenic risk scores for neuroticism using a general linear model ( p  < 0.001). Forthman et al. 30 predicted repetitive negative thinking (a trait that negatively impacts anxiety) from 20 principal components of behavioral and cognitive variables (derived from detailed neuropsychological and behavioral assessment) and polygenic risk scores using a machine learning ensemble method with R 2 of 0.037 (standard error = 0.002). Richter et al. 31 utilized thorough behavioral testing completed by participants under supervision, and reported a sensitivity of 71.4% and a specificity of 70.8% using RF in individuals with anxiety and/or depression. The current study complements these publications, and supports their findings by using a machine learning-based approach and a short cognitive science task that can be performed without supervision on personal electronic device to predict anxiety with high accuracy and sensitivity.

Four contextual variables (age, loneliness, income, and employment status) were salient for the prediction of anxiety, having a cumulative relative importance of 29–33%. Although the relationship between anxiety levels and demographic measures was consistent with the literature (as described below), most of the other demographic measures did not contribute as much to anxiety level prediction. For instance, sex (gender assigned at birth) consistently contributed less than 1% of relative importance and race/ethnic background contributed 1–1.5% of relative importance across analyses. The 15 judgment variables contributed a cumulative relative importance ranging from 55 to 61%. These variables quantify irrationality, or biases, in judgment (i.e., the bounds to rationality as described by Kahneman 44 ), and support prior publications pointing to the importance of reward/aversion variables for the study anxiety 27 , 28 , 41 , 77 . Gini scores were minimally different across the 15 judgment variables, suggesting further research is needed to assess how these judgment variables interact or cluster together. When the threshold used for segregating ‘higher’ versus ‘lower’ anxiety groups was increased, education and marital status increased in feature importance for prediction, raising a hypothesis that some demographic variables may be more important for predicting severe anxiety. A history of COVID-19 infection was not salient for predicting current anxiety and was consistently one of the least important features. This result contrasts with other literature showing large-scale societal concern about COVID-19 illness, the pandemic and related anxiety 78 , 79 . This study only used the “state” and not the “trait” component from the STAI, to reflect current experience. The study thus does not address any relationship between prior COVID-19 history and long-term trait anxiety, nor address people’s thoughts about infection.

Mediation and moderation models were used to quantify relationships between contextual and judgment variables involved in the prediction of anxiety levels. Three of the four most important contextual variables (age, employment, and loneliness) interacted with judgment variables to predict, or model, anxiety scores in mediation and moderation frameworks. These relationships were not observed with income and no significant mediation were found when contextual variables (age, employment, income, and loneliness) acted as independent variables and judgment variables were the mediator variables. On the other hand, significant mediations with contextual variables as mediators indicated that the contextual variables statistically modulated the relationship between judgment variables and anxiety scores; that is, they sat in the causal pathway between judgment variables and anxiety levels. Seven unique judgment variables were involved in nine significant mediation models with age, employment, and loneliness as mediators. Moderation analyses reflected how an interaction between a contextual variable and judgment variable might predict anxiety scores; these relationships were observed for only four judgment variables across seven significant moderation models. In total, 9 of the 15 judgment variables were thus involved in either mediation or moderation, indicating that contextual variables affect the impact of a majority of the judgment variables on anxiety level prediction. From a psychological perspective, these findings demonstrate how context (e.g., age, employment status, perceived loneliness) modulates or interacts with judgment variables to model anxiety, and how these relationships between judgment and context may aide the assessment of anxiety and ultimately, other mental health conditions. Others have noted that psychological processes occur in a context, and this study supports their work 65 , 66 , 67 , 71 , 72 , 73 , 74 , 75 .

In the current study, anxiety scores significantly varied by the contextual variables used to classify anxiety scores. Anxiety scores increased with increasing levels of perceived loneliness, where participants who often, or always, avoid spending time with others, or spend most of their time alone, had higher levels of anxiety. This is consistent with previous literature where anxiety increased as a function of loneliness 80 , 81 , 82 and higher anxiety was related to avoidant social behavior 83 . Consistent with the literature, anxiety scores were predominately higher in females, as compared to males 84 , 85 , 86 , 87 , and in young adults (aged 18–39 years), as compared to older adults aged (40–70) 87 . As others have published, anxiety scores were higher in participants indicating lower household income levels 88 , 89 and lower education levels 90 . In alignment with other reports, anxiety scores also varied with different levels of employment where retired participants reported the lowest anxiety scores and participants that were unemployed 89 , 91 or had more than one job had the highest levels of anxiety. Anxiety scores also varied with marital status, in alignment with other reports, where participants classifying as ‘single’, ‘separated’, and ‘living with partner’ reported higher anxiety than others (e.g., ‘married’, ‘divorced’, ‘widowed’) 89 . As reported elsewhere our participants reporting mixed race backgrounds had higher anxiety 92 than other racial/ethnic groups (e.g., white, African American, Hispanic, Asian). Lastly, individuals who reported previous COVID-19 experienced more anxiety. This finding is consistent with other studies in adults 93 , 94 , 95 and a longitudinal study of adolescents with anxiety disorders that found SARS-COV-2 infection was associated with a 30% worsening in anxiety severity 96 , regardless of treatment status. Altogether, this concordance with the literature supports the broader set of findings.

Most judgment variables showed significant differences between ‘higher’ and ‘lower’ anxiety groups, suggesting three general constructs. The first being that the ‘higher’ anxiety group had higher loss aversion 50 , 51 which corresponds to an overweighting of bad outcomes relative to good ones 44 . The ‘higher’ anxiety group also had higher Peak Positive Risk and Total Reward Risk, indicating that there was a higher uncertainty that must be overcome to approach stimuli, and that the interactions between reward and the associated risk were higher. Both observations point to difficulties with initiating behavior toward positive things, per Markowitz’s decision utility equation 113 . The same participants had lower Peak Negative Risk as compared to the low anxiety group, indicating there was a lower uncertainty to avoid events. The ‘higher’ anxiety group also had lower Total Aversion Risk suggesting that interactions between aversion and the associated risk were lower. Similarly, they had lower Aversion Tipping Point which corresponds to the intensity of aversion beyond which avoidant choices are made, suggesting lower values in those with higher anxiety scores more readily make avoidant choices. Together, this set of judgment variables quantifies how individuals with high anxiety overweight bad outcomes relative to good ones, have difficulty approaching positive stimuli (i.e., more rewarding and non-aversive items), yet readily seem to avoid negative ones. A second construct suggests that individuals with difficulty approaching positive stimuli seem to be more open to risk-seeking. Participants with high anxiety scores had higher ante and lower risk aversion indicating they would be more willing to play a game with uncertain outcomes, and that they do not prefer actions that lead to certain outcomes (i.e., they prefer two birds in the bush vs. one in the hand). This contrasts with studies that used emotional and monetary stimuli to observe heightened risk aversion in individuals with anxiety 47 , 48 . This difference in observations might depend on how a question is placed in the context of gain or loss (i.e., framing effects) 44 , 97 . The third construct was identified by low loss resilience, tradeoff range, and consistency metrics. Specifically, a lower Tradeoff range in high-anxiety persons is consistent with a restrictive portfolio of positive and negative preferences. Lower Reward Aversion consistency suggests a likelihood of indifference or that a person neither likes nor dislikes a particular stimulus. People with high anxiety scores are less loss resilient, meaning they have a reduced ability to rebound from bad outcomes. The three constructs describe a behavioral profile for high anxiety persons as having less resilience, more avoidance, and more indifference behavior. Together, these three general groupings of judgment variables point to known features of anxiety 49 but provide a lawful, quantitative framework 56 , 57 for framing the condition and support the hypothesis that unique constellations of judgment variables underlie other mental health conditions like depression 65 and suicidality 66 . The current findings support calls for the development of a standard model of mind 98 , albeit based on processes of judgment and agency as opposed to variables focused primarily on cognition.

Several limitations need be considered. First, the participants were recruited from the United States, and region and culture may influence the importance of judgment variables in predicting anxiety as psychiatric symptoms differ across cultures 99 , 100 , 101 , 102 , 103 , 104 . Second, participants with mental health conditions were oversampled to meet criteria for other survey components not discussed here. This oversampling could bias results and more generalized samples are needed to validate and extend our findings. Third, all variables were self-reported and not collected from clinical records or framed as a double-blinded trial with investigator-administered survey instruments. Fourth, the cohort was sampled during the COVID-19 pandemic, in which greater incidents of loneliness and anxiety have been reported 78 , 79 . It will be important to prospectively investigate if similar behavioral patterns predict anxiety in the absence of a pandemic. Fifth, the survey did not request participants to differentiate between white non-Hispanic and non-white; more in-depth questions regarding racial and ethnic backgrounds should be considered in future data collections.

The current study used a computational cognition framework to assess how biases in human judgment might contribute to predicting anxiety levels. Using a small set of judgment and contextual variables (including demographics, perceived loneliness, and COVID-19 history) with a balanced Random Forest framework, this study achieved high accuracies up to 88.51% and AUC ROC values of 0.69–0.74 for predicting state anxiety levels derived from the STAI 70 . Judgment variables were extracted from a short (2–3 min), simple, and unsupervised picture rating task that can be easily completed on a personal electronic device. In these prediction analyses, the four most important variables (age, employment, income, and loneliness) were contextual variables that contributed 29–33% of the relative importance and judgment variables contributed up to 61% of the relative importance for prediction. Furthermore, age, loneliness, and employment status significantly mediated and moderated the relationship between judgment variables and anxiety scores—indicating statistically mechanistic relationships between these variables, and suggesting that both cognitive variables and contextual variables are important for accurately predicting anxiety levels. Judgment variables differed across participants with higher and lower anxiety scores providing a behavioral profile for participants with higher anxiety scores. That is to say, individuals with higher anxiety scores overweighted bad outcomes relative to good ones, had difficulty approaching positive stimuli, yet readily avoided negative ones. Along with this higher avoidance, they also had lower resilience and higher indifference, consistent with prior reports 49 . This study supports the hypothesis that a small set of interpretable judgment and contextual variables can accurately predict psychiatric symptoms and provide a computational cognitive framework to better understand and classify anxiety and other mental health conditions.

Participant recruitment

Gold Research Inc. (San Antonio, Texas) recruited study participants from multiple vendors in December 2021. 4019 de-identified participants (mean age ± std = 51.4 ± 14.9 years) were randomly sampled from the general U.S. population using an email survey database accessed by Gold Research, Inc. and a double opt-in methodology as described in detail in refs. 66 , 67 , 94 , 105 , 106 , and in the Supplemental Material. All participants provided informed consent following oversight by Northwestern University’s and the University of Cincinnati’s Institutional Review Board and in accordance with the Declaration of Helsinki (see “Ethical statement” and refs. 66 , 67 , 94 , 105 , 106 ). Participants were balanced to meet the U.S. Census Bureau’s demographic criteria at the time of the survey (December 2021) and oversampled by 15% of the sample for mental health conditions (see Supplemental Material). The survey was composed of several blocks of questions using questionnaires (detailed below) for depression, anxiety, suicidality, addiction, psychosis, violent ideation, disruptive and destructive behaviors, perceived loneliness, along with demographic, self-reported mental health, and COVID-19 history questionnaires. Participants also completed a 48-item picture rating task (Fig. 1 ) split into two 24 picture blocks.

Ethical statement

Participation was offered with language noting that Gold Research was administering an emotional health questionnaire on behalf of Northwestern University, with the phrasing: “ We will be evaluating how different emotions and experiences are connected and may relate to our emotional health .” All participants provided written informed consent, including their primary participation in the study and the secondary use of their anonymized, de-identified (i.e., all identifying information removed by Gold Research Inc. prior to retrieval by the research group) data in secondary analyses (see Supplemental Material). The study was approved by the Institutional Review Boards for Northwestern University (NU) and University of Cincinnati (UC) in accordance with the Declaration of Helsinki (approval number STU00213665 for NU and 2023-0164 for UC).

Data filtering

Gold Research excluded participants using four criteria: (1) participants selected the same response throughout any section of the questionnaire (e.g., selecting option “1” for all questions), (2) participants indicated they had ten or more clinician-diagnosed illnesses out of a possible 17 (data not described here), (3) if both education level and years of education did not match, and (4) if they completed the questionnaire in less than 800 s. After filtering for these criteria, Gold Research provided the research team data from 4019 participants. These data were further screened using responses from the picture rating task. These procedures have been adapted from Azcona et al. 64 and are detailed in the Supplemental Material under Data filtering based on picture rating task . In short, participants were excluded if there was minimal variance in picture ratings (i.e., all pictures were rated the same or varied only by one point) and the quantitative feature set derived from the picture rating task was incomplete and/or there were extreme outliers (see Judgment variables from picture rating task and Supplemental Material). Using these exclusion criteria, 3476 participants were cleared for statistical analyses.

Contextual variables from survey questionnaires

Participants completed the survey using the online platform provided by Gold Research, Inc. Participants were asked to self-report (a) perceived loneliness in the past month (loneliness), (b) demographics including age, gender assigned at birth (sex), annual household income (income), marital status (marital), employment status (employment), level of education (edu), number of years of education (edu_years), race/ethnicity (race/ethnicity), and (c) two COVID-19 questions: (i) if the participant had ever tested positive for COVID-19 (test) and (ii) if the participant was ever diagnosed by a clinician with COVID-19 (diagnosis). The complete text regarding these questions is listed under the Survey Questions section in Supplemental Material. The response set to (a)–(c) is referred to as ‘contextual variables’ hereafter. Following data filtering as described above and in refs. 94 , 105 , 106 , the 3476 participants were categorized as predominately female (61.5%), married (51.4%), white (85.7%), employed full-time (35.8%) with some college education (29.6%), and on average older (mean age = 51 years), see Supplementary Table 1 for a complete summary.

Anxiety questionnaire

This study assessed anxiety in relation to contextual and picture rating-derived variables (described below). We used the State-Trait Anxiety Inventory (STAI) questionnaire which is commonly used to measure trait and state anxiety 70 . It is used in clinical settings to quantify anxiety. The STAI consists of 20 questions for current state anxiety, and 20 questions for trait anxiety. In this study, only the 20-state anxiety (STAI-S) questions were deployed in the online survey. Participants were instructed to answer each question based on a 4-point Likert scale (1 = Not at all; 2 = Somewhat; 3 = Moderately so; 4 = Very much so) based on how they feel right now , that is, at the time of the survey. The questions were scored following the instructions in the score key for the form Y-1 of STAI ( https://oml.eular.org/sysModules/obxOml/docs/ID_150/State-Trait-Anxiety-Inventory.pdf ). The scored sum of STAI-S ranged from 20 to 80 and is hereafter referred to as ‘STAI-S score’ and/or ‘anxiety score’. STAI-S score distributions are shown in Fig. 3 with three red arrows marking the threshold values used in classification as described under ‘Classification analysis’. The STAI thresholds of 35, 45, and 55 roughly corresponds to 50th percentile (median), 75th percentile and 90th percentile, respectively, of the STAI-S scores (Fig. 3 ).

Picture rating task

Participants were shown 48 unique color images from the International Affective Picture System (IAPS) 68 , 69 . Six picture categories were used: (1) sports, (2) disasters, (3) cute animals, (4) aggressive animals, (5) nature (beach vs. mountains), and (6) adults in bathing suits, with eight pictures per category (48 pictures in total, a sample image is shown in Fig. 1A ), with all pictures in a category having similar published calibration. These images act as mildly emotional stimuli that are employed to assess both positive and negative value (i.e., reward or liking vs. aversion or disliking) and have been broadly used and validated in research of human emotion, attention, and preference 68 , 69 . Images were displayed on participants’ personal devices with a maximum size of 1204 × 768 pixels. Below each picture was a rating scale from −3 (dislike very much) to +3 (like very much), where 0 indicated a neutral point (Fig. 1A ). While there was no time limit for selecting a picture rating, participants were asked in the instructions to rate the images as quickly as possible and to use their first impression; specific instructions can be found in the Supplemental Material. Once a rating was selected, the next image was displayed.

Judgment variables derived from a picture rating task

Data from the picture rating task were analyzed using a computational framework to characterize preference judgments. Referred to as relative preference theory (RPT) 56 , 57 , 62 , this framework has been adapted to derive judgment features from picture ratings 64 , 66 , 67 as opposed to operant keypressing 52 , 56 , 57 , 59 , 60 , 61 , 62 , 107 . For each participant, picture ratings from each of the six image categories were split into two sets—positive and negative. For each of these two sets, and for all six categories, the mean, Shannon entropy 56 , 108 , and variance were calculated, yielding a tuple denoted as \(\left({{\boldsymbol{K}}}^{{\boldsymbol{+}}}{\boldsymbol{,}}{{\boldsymbol{\sigma }}}^{{\boldsymbol{+}}}{\boldsymbol{,}}{{\boldsymbol{H}}}^{{\boldsymbol{+}}}\right)\) for the positive ratings and \(\left({{\boldsymbol{K}}}^{{\boldsymbol{-}}}{\boldsymbol{,}}{{\boldsymbol{\sigma }}}^{{\boldsymbol{-}}}{\boldsymbol{,}}{{\boldsymbol{H}}}^{{\boldsymbol{-}}}\right)\) for the negative ratings. This resulted in a total of 36 \(\left({\boldsymbol{K}}{\boldsymbol{,}}{\boldsymbol{\sigma }}{\boldsymbol{,}}{\boldsymbol{H}}\right)\) variables. Next, for each participant, the mean across the six categories was computed for each \(\left({\boldsymbol{K}}{\boldsymbol{,}}{\boldsymbol{\sigma }}{\boldsymbol{,}}{\boldsymbol{H}}\right)\) variable, resulting in six additional variables: mean \(\left({{\boldsymbol{K}}}^{{\boldsymbol{+}}}{\boldsymbol{,}}{{\boldsymbol{\sigma }}}^{{\boldsymbol{+}}}{\boldsymbol{,}}{{\boldsymbol{H}}}^{{\boldsymbol{+}}}\right)\) representing reward behavior and mean \(\left({{\boldsymbol{K}}}^{{\boldsymbol{-}}}{\boldsymbol{,}}{{\boldsymbol{\sigma }}}^{{\boldsymbol{-}}}{\boldsymbol{,}}{{\boldsymbol{H}}}^{{\boldsymbol{-}}}\right)\) representing aversion behavior. For each participant, three separate curves for value, limit, and tradeoff functions (see Fig. 1B–D were plotted using MATLAB and the library polyfit , following other publications (see details in Supplemental Material) 56 , 57 , 62 , 64 , 107 . Representative curves from 500 randomly selected participants out of the 3476 cohort are shown in Supplementary Fig. 1 .

Goodness of fit for these functions was assessed by computing \({R}^{2}\) values, adjusted \({R}^{2}\) values (accounting for degrees of freedom), and \(F\) -statistics for each participant’s model fit (Supplementary Table 2A ). Individual participants’ \(\left({\boldsymbol{K}}{\boldsymbol{,}}{\boldsymbol{H}}\right)\) value functions were fit by concave logarithmic, or power-law functions (Supplementary Table 2 , Supplementary Fig. 1 ). \({R}^{2}\) values ranged from 0.85 to 0.94 for logarithmic fits of the value function, which was considered very high. Concave quadratic fits across individual participants’ \(\left({\boldsymbol{K}}{\boldsymbol{,}}{\boldsymbol{\sigma }}\right)\) data are displayed in Supplementary Fig. 1 , and goodness of fit assessed using the same metrics as with the \(\left({\boldsymbol{K}}{\boldsymbol{,}}{\boldsymbol{H}}\right)\) data (Supplementary Table 2A ). All \({R}^{2}\) values for the quadratic fits exceeded 0.80 and ranged from 0.84 to 0.96. Lastly, radial functions were fit to test for trade-offs in the distribution of \({{\boldsymbol{H}}}^{{\boldsymbol{-}}}\) and \({{\boldsymbol{H}}}^{{\boldsymbol{+}}}\) values across categories for each individual participant. Supplementary Fig. 1 displays radial fits across individual participants’ \(\left({{\boldsymbol{H}}}^{{\boldsymbol{+}}}{\boldsymbol{,}}{{\boldsymbol{H}}}^{{\boldsymbol{-}}}\right)\) and \(\left({{\boldsymbol{H}}}^{{\boldsymbol{+}}}{\boldsymbol{,}}{{\boldsymbol{H}}}^{{\boldsymbol{-}}}\right)\) data points for a random sample of participants.

The RPT framework fits reward/aversion curves and derives mathematical features from these graphical plots that are psychologically interpretable, scalable, recurrent, and discrete 56 , 57 , 64 . At least 15 variables can be extracted from this framework 64 , including Loss Aversion (LA) 45 , Risk Aversion (RA) 46 , Loss Resilience (LR), Ante, Insurance, Total Reward Risk (Total RR), Total Aversion Risk (Total AR), Peak Positive Risk (Peak PR), Peak Negative Risk (Peak NR), Reward Tipping Point (Reward TP), Aversion Tipping Point (Aversion TP), Reward-Aversion tradeoff (RA tradeoff), Tradeoff range, Reward-Aversion consistency (RA consistency) and Consistency range (Fig. 1B–D , Table 1 ). Each variable describes a quantitative component of the reward/aversion processing involved with judgment behavior (see details about each feature in Supplemental Material). The term ‘judgment variables’ will be used hereafter in reference to these features. Summary statistics for all 15 judgment variables obtained from all participants are summarized in Supplementary Table 2B .

Classification analysis

All analyses were performed in R. Judgment variables and contextual variables, including demographics, perceived loneliness, and COVID-19 questions, were used in the classification analyses. Random Forest ( RF ) and balanced Random Forest ( bRF ) analyses were used to classify anxiety scores into ‘higher’ and ‘lower’ classes (see below). The open access package ‘randomForest’ in R was used to train the RF and bRF models on training dataset.

Random Forest (RF) and balanced Random Forest (bRF) analysis

Anxiety scores were divided into two classes ‘higher’ and ‘lower’ classes based on three threshold values of 35, 45 and 55 as shown in Fig. 3 marked with red arrows. All values below a given threshold were labeled as ‘lower’ and values above and equal to the threshold were labeled as ‘higher’. Data were divided into train and test sets with a 70:30% ratio. RF and bRF approaches were implemented for each of the three thresholds using the command ‘randomForest’ from the package ‘randomForest’ in R. The number of variables randomly sampled as candidates at each split was 5 and the number of trees grown was 1000. The bRF performs random down-sampling of the majority class at each bootstrap sample to match the number of samples in majority and minority classes. The bRF approach was used in addition to the standard RF analysis because of the greater class imbalance when the anxiety score threshold was set to 45 and 55 (i.e., the ‘lower’ class occupied 70% and 88% of the dataset respectively). Note that bRF only performs down-sampling during training of the model. Once the model is trained, it calculates the prediction metrics on the complete, imbalanced train and test sets.

Out of bag (OOB) accuracy was reported for the training set. The model was then tested with the imbalanced test dataset and accuracy, sensitivity, specificity, AUC ROC (area under the receiving operating characteristics curve), and Balanced Accuracy (mean of sensitivity and specificity) were reported. Here, ‘higher’ was considered the positive class. For each threshold, percentages of each class relative to the entire dataset were reported. The entire procedure was repeated for each threshold value (i.e., 35, 45, 55). The multi-dimensional scaling (MDS) scaling coordinates of the proximity matrix for RF and bRF analyses were plotted to see how segregated the two clusters of lower and higher anxiety scores were, using the command ‘MDSplot’ in the package ‘randomForest’. The proximity matrix contains the frequency for each pair of data points. If two data points occupy the same terminal node through one decision tree, their proximity is increased by one. At the end of the run of all decision trees, the proximities are normalized by dividing by the number of trees. The MDS plots display how segregated the clusters were for the classification performed by RF or bRF .

To compare the performance of the classifiers to chance levels, permutation analysis was conducted for RF and bRF for each of the three threshold levels with 100 iterations. For each iteration, the ‘lower’/‘higher’ labels were shuffled randomly and RF and bRF analyses were run with the procedure described above. The above-mentioned output metrics were averaged over the 100 iterations to produce a model of chance effects.

The judgment and contextual variables were sorted based on the mean decrease in Gini scores and were plotted by decreasing feature importance, with the most important features appearing on the top of the plot. Gini score is a fundamental outcome of the random forest algorithms as it shows for each feature how large was the discriminative value for separating the data points into different classes. That is, how important was each variable in the classification, or how much was the uncertainty reduced in the model, leading to accurate predictions. The higher the mean decrease Gini score, the more important the feature was for the classification. The relative importance of the features was analyzed by normalizing the Gini score of each feature to the sum of all Gini scores. This gives the relative proportion of importance for each feature, with the sum for all features being 1. The Gini score plots, and the relative importance of the features were used in this study as an associated sensitivity analysis for the random forest algorithms.

Post hoc analysis: mediation and moderation analysis

Mediation and moderation were used as post hoc analyses to understand how judgment variables and the most important contextual variables, based on Gini scores, interact to model anxiety scores. These statistical mechanisms aide interpretation of the prediction results and follow procedures we have published before 109 , 110 .

Mediation analysis

Mediation was utilized to elucidate statistically causal relationship between judgment variables, contextual variables, and anxiety scores. The mediation model defines the relationship between an independent variable (X) and dependent variable (Y) with the inclusion of a third mediator variable (Me). Two sets of analyses were conducted where Y was the anxiety score and: (i) X were each of the judgment variables and Me were each of the most important contextual variables based on Gini score from RF and bRF analyses (i.e., the top four scores); (ii) X were the most important contextual variables and Me were the judgment variables.

The mediation model proposes that instead of a direct statistical causal relationship between X and Y, X influences Me, which then influences Y. Beta coefficients and their standard error (s) terms from the following linear regression equations, following the four-step process of Baron and Kenny (1986) 111 , 112 , were used to calculate Sobel p -values and mediation effect percentages ( T eff ):

Step 4 : Sobel’s test was then used to test if \({c}^{{\prime} }\) was significantly lower than \(c\) using the following equation:

Using a standard 2-tail z-score table, the Sobel p -value ( \({p}_{{Sobel}})\) was determined from the Sobel z-score and the mediation effect percentage ( T eff ) was calculated using the following equation:

Mediation was considered significant if p -values associated with terms a , b , and c were <0.05 from Step 1–3 and \({p}_{{Sobel}}\)  < 0.05 111 and \({T}_{{eff}}\)  > 50% 109 , 110 .

Secondary mediation analysis was run by switching variables assigned to X and Me to see if the mediation effects were directed. If \({p}_{{Sobel}}\)  > 0.05 and \({T}_{{eff}}\)  < 50% for the secondary mediation analysis, this supported that Me was in the causal pathway between X and Y.

Moderation analysis

The moderation model proposes that the strength and direction of the relationship between an independent variable (X) and dependent variable (Y) is controlled by the moderator variable (Mo). In this study, X were each of the judgment variables, Mo were each of the most important contextual variables based on Gini score from RF and bRF analysis, and Y was the anxiety score. Moderation is characterized by the interaction term between X and Mo in the linear regression equation as given below:

Moderation was considered significant if \({p}_{{\beta }_{3}}\le 0.05\) (the interaction term \({\beta }_{3}\) is significantly different than zero) and \({p}_{{overall}}\le 0.05\) (for the overall model) 109 , 110 . To check if the overall model was significant, we used F-test.

To test if the coefficient of the interaction term ( \({\beta }_{3})\) was significantly different than zero, we built full and restricted models and used partial F-tests to test the null hypothesis.

Full model:

Restricted Model:

Null hypothesis:

Alternative hypothesis:

If \({p}_{{\beta }_{3}}\) , associated with the partial F-test was less than 0.05, we rejected our null hypothesis regarding the interaction term.

Post hoc analysis: variable differences by anxiety score

These post hoc analyses assessed if the contextual variables and judgment variables differed by anxiety score.

Contextual variable differences

Anxiety scores were assessed for differences by the different levels of contextual variables (except years of education) using Wilcoxon rank-sum test for questions with two levels and Kruskal Wallis test for questions with more than two levels. The ten contextual variables tested were loneliness, age, sex, income, marital status, employment status, education level, ethnicity, and COVID-19 test and diagnosis. Boxplots and p -values were reported.

Judgment variable differences

Since judgment variables were continuous, they were divided into corresponding ‘higher’ and ‘lower’ groups following the respective grouping of anxiety scores at each of the three thresholds (35, 45, and 55), and tested using the one-sided Wilcoxon rank-sum test. Alternative hypotheses for each test, and the respective p -values, were reported. Bonferroni correction was done across all six tests (two tests for each of the three thresholds) for each judgment variable. The alternative hypothesis indicated if the judgment variable distributions differed between participants in ‘higher’ and ‘lower’ anxiety classes (for example, if a given judgment variable was higher in the ‘higher’ anxiety class as compared to the ‘lower’ anxiety class, or vice versa).

Data availability

Data were de-identified before being provided to the investigators. Data are available in Microsoft Excel format and include relative preference variables, demographic metrics and survey variables inclusive of anxiety variables. The data may be accessed in Appendix 1 , Supplementary Information.

Code availability

Computational behavior analysis used code published in refs. Azcona et al. 2022 and Kim et al. 2010. ML analyses used parameters as detailed in the Methods and Appendix 2 , Supplementary Information. Mediation/moderation analyses used code sequences as detailed in refs. Bari et al. 2021 and Vike et al. 2022.

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Acknowledgements

We thank Carol Ross, Angela Braggs-Brown, Thomas M. Talavage, Eric Nauman, and Marc Cahay at College of Engineering and Applied Science, University of Cincinnati who significantly impacted the transfer of research funding to University of Cincinnati (UC), allowing this work to be completed. Funding for this work was provided by Office of Naval Research award N00014-21-1-2216 [H.C.B.], Office of Naval Research award N00014-23-1-2396 [H.C.B. (contact), A.K.K.] and Jim Goetz donation to the University of Cincinnati, College of Engineering and Applied Science (H.C.B.).

Author information

These authors contributed equally: Byoung-Woo Kim, Nicole L. Vike, Shamal Lalvani, Leandros Stefanopoulos.

These authors jointly supervised this work: Aggelos K. Katsaggelos, Hans C. Breiter.

Authors and Affiliations

Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA

Sumra Bari, Byoung-Woo Kim, Nicole L. Vike & Hans C. Breiter

Department of Electrical Engineering, Northwestern University, Evanston, IL, USA

Shamal Lalvani, Leandros Stefanopoulos & Aggelos K. Katsaggelos

Laboratory of Medical Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece

Leandros Stefanopoulos & Nicos Maglaveras

Integrated Marketing Communications, Medill School of Journalism, Northwestern University, Evanston, IL, USA

Martin Block

Department of Psychiatry and Behavioral Neuroscience, College of Medicine, University of Cincinnati, Cincinnati, OH, USA

Jeffrey Strawn

Department of Computer Science, Northwestern University, Evanston, IL, USA

Aggelos K. Katsaggelos

Department of Radiology, Northwestern University, Chicago, IL, USA

Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH, USA

Hans C. Breiter

Department of Psychiatry, Massachusetts General Hospital and Harvard School of Medicine, Boston, MA, USA

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Contributions

Study concept/design: H.C.B., A.K.K. and S.B. Acquisition of original data: H.C.B., B.W.K., S.B., N.L.V., S.L., L.S., M.B. and A.K.K. Coding of statistical tools: S.B. and B.W.K. (with guidance from H.C.B. and A.K.K.). Analysis of data: S.B. and B.W.K. (with guidance from H.C.B. and A.K.K.). Interpretation of data: S.B., H.C.B. and A.K.K. (with input from B.W.K., N.L.V., S.L., L.S., B.W.K., M.B. and N.M.). Statistical assessment: S.B. and B.W.K. (with guidance from H.C.B. and A.K.K.). Authored original draft: S.B. and H.C.B. Generated figures: S.B., B.W.K. and H.C.B. Revision of manuscript for content: all authors. All authors approved the final version of the paper for submission.

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Correspondence to Hans C. Breiter .

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Competing interests.

S.B., B.W.K., N.V., S.L., L.S., M.B., A.K. and H.C.B. submitted a provisional patent “Integrating Cognitive Science with Machine Learning for High Accuracy Prediction of Anxiety Disorders”. The provisional application is led by University of Cincinnati (Office of Innovation) in conjunction with Northwestern University, Application # 63/648,898. The other authors declare no competing interests.

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Bari, S., Kim, BW., Vike, N.L. et al. A novel approach to anxiety level prediction using small sets of judgment and survey variables. npj Mental Health Res 3 , 29 (2024). https://doi.org/10.1038/s44184-024-00074-x

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Krumholz believes that this trial demonstrates that a future of clinical research that allows patients to participate at their convenience is not only possible, but more efficient and even cost-effective than standard clinical trials.

For example, April 9 marked Cindy’s* fourth anniversary of living with Long COVID.

It started with severe fatigue that made her feel as if her limbs “weighed a million pounds,” followed by shortness of breath and gastrointestinal issues after she was infected with COVID-19 in 2020. Her symptoms never went away. A single mom in Texas with a 6-year-old son, she says the disease forced her to get creative: She made up a game where he rolled a ball around her while she lay still on their trampoline.

Over the years, her symptoms have ebbed and flowed. She suffered a sharp decline in January 2022 — the height of the Omicron wave — when she was hit with her second acute COVID infection. This triggered the start of a slow decline that eventually took a toll on her cognitive abilities, hindering her ability to perform her job as an aerospace engineer and eventually forcing her to take a leave of absence in the fall of 2023. This was also when she began experiencing another common Long COVID symptom known as post-exertional malaise (PEM), in which her symptoms significantly worsened after physical or mental activity, sometimes leaving her bedbound for days at a time.

“I was known for thinking outside of the box, being able to juggle things, look to the future, and solve problems. I knew how to put big, complicated puzzles together,” she says. “Suddenly, everything seemed so complicated. Living became so hard. I was just trying to survive day-by-day, if not hour-by-hour, managing all of my symptoms.”

Desperate for answers, Cindy stayed on top of ongoing clinical trials. Last year, she applied for a stem cell therapy trial in Houston but was eliminated as a potential participant during the final screening. On the one hand, she was devastated. “I was absolutely convinced that the therapy was going to be the answer for me,” she says.

But enrolling in the trial would have presented its own challenges. She lives 45 minutes away from Houston and would have needed help getting rides. And she wondered if she had the capacity to be able to participate in all that the trial required. “From an accessibility standpoint, most clinical trials require you to be there in person for exams, bloodwork, tests, or whatever else they need,” she says. “That’s just not feasible when you have a limiting condition.”

Then, she came across that the PAX LC Trial. Typically, participating in a clinical trial with a Connecticut-based university would have been impossible. But the YSM team was prepared to bring the study to patients no matter where they lived in the U.S. Participants received the drug or placebo in their mailboxes and filled out electronic diaries. They gave blood and saliva samples at home or at a nearby lab. “The Pax LC trial is a historic contribution to the evolution of a new way of doing trials,” Krumholz says. “I’m really proud of what we’ve accomplished, and I’m hoping it will be a spark for the future.”

Slow enrollment of clinical trials hinders scientific progress

Researchers currently have several hypotheses for the underlying causes of Long COVID, including persistent virus, lingering SARS-CoV-2 viral remnants, autoimmune dysfunction, reactivated latent viruses like Epstein-Barr, and tissue damage. These hypotheses are not mutually exclusive, and it is possible that individuals with Long COVID may experience a combination of these mechanisms. Krumholz, Iwasaki, and their team designed the Pax LC Trial to test the persistent virus hypothesis.

Cindy initiated the prescreening process by filling out a survey about pre-existing conditions and syncing her medical records to an app on her phone. Several months later, she received an email inviting her to move forward. The next steps of the prescreening involved going to a local medical laboratory to give blood samples, as well as adjusting some of her medications.

Once enrolled, Cindy received her course of either Paxlovid or a placebo by mail. Each night, she answered survey questions about her symptoms from her phone. She also gave blood and saliva samples five times. Three of these times, the Pax LC team sent a technician to her home. “To be able to have somebody come to your house — that just made life so much easier,” she says. For the other two sample collection dates, she went to a nearby laboratory.

Despite the fact that our trial is sort of built as a concierge service for each individual patient, it was much less expensive than what we had been doing. Harlan Krumholz, MD

Patients aren’t the only ones who can benefit from decentralized trials, says Krumholz. For over a decade, he has been an advocate for reforming clinical research, arguing that its current structure stymies innovation. His colleagues in the laboratory are discovering potential avenues to treating diseases at a “dizzying pace,” he says. However, because standard clinical trials struggle to recruit and keep patients, they have not been able to keep up with the pace of scientific discovery, which prevents new drugs and medical devices from reaching the market.

“We’re in the midst of a life sciences revolution,” says Krumholz. “The central chokepoint is evidence generation — cycle times are too slow, and trials are so expensive that our level of confidence has to be very high, or no one will make the investment.”

This is because for many, the ability to participate in clinical trials and conform to the schedule they demand is a luxury. Not only does this slow enrollment, but it also contributes to the stark lack of diversity in research. “The current way of doing things is often leaving us in a position where we are having to chase participants and cajole them to stay in the studies that were built for their benefit,” says Krumholz. “Imagine how off-putting clinical trials must be when people who have the most to gain are left in the position where they don’t want to continue.”

The solution, he proposes, is creating clinical trials, modeled after PAX LC, that treat participants as partners and accommodate their constraints. PAX LC not only conformed to participants’ schedules, but also strived to create a sense of community through running virtual town halls for people with ong COVID so they could ask questions and stay updated on results as researchers got them. “People don’t have any obligation to join trials, so we need to make it so they’re something they want to join,” he says.

Bringing clinical trials into patients’ homes

Ezra,* a freelance cartoonist in Minnesota, began experiencing COVID-19 symptoms in September 2022 and never recovered. At the time, he suffered PEM so severe that exertion as small as “microwaving lunch” could put him in bed for days. He saw doctor after doctor at his nearby hospital, and when that failed, he decided to try traveling to the Mayo Clinic. “I feel very fortunate that I was able to get somewhere like Mayo a few times, but it is an hour and a half drive from Minneapolis,” he says. “It cost my partner gas money and lost time at work [to take me there].”

In his search for answers, he learned of the Pax LC Trial and decided to enroll. “The effort to bring the trial into people’s houses is valiant, especially for a population that is chronically ill,” he says. “For disabled people to become a part of clinical trials already requires so much of us—even those of us who have support systems that are able to take care of us. Adding an extra thing to our plates is often just not feasible for most people with chronic illness.”

Other Pax LC Trial participants echo Ezra’s sentiments. “Early on [in my illness], there was no way I could have traveled on my own [to a clinical trial]. It would have taken everything out of me,” says Kyle*, a Vermont filmmaker who has been living with Long COVID since April 2022. While the launch of the ambitious clinical trial was not without its kinks — Kyle and others say they experienced scheduling delays, such as receiving the drug/placebo or payments late, that prolonged the process — he says that PAX LC has opened a window for him that otherwise would have been unavailable. “It was nice the trial tried to make it work for people, understanding that part of our illness is incapacity.”

The PAX LC Trial was largely made possible by the Yale Center for Clinical Investigation team, with Yashira Henriquez, the clinical investigation project manager, who is based in New York, playing a central role. Her daily activities included monitoring participants’ surveys and checking in on anyone who had reported adverse events. “Since there are no study visits, we texted or called each other all the time,” she says. The most common adverse event was dysgeusia, or a metallic taste in the mouth, which has been a common complaint among patients with acute COVID who have received the drug. “In this type of trial where you never meet someone face-to-face, you need to show even more compassion to make them feel at ease.”

In previous roles, Henriquez had worked on studies evaluating treatment for acute COVID-19 infections but wasn’t as familiar with Long COVID. Her experience working for the PAX LC Trial has opened her eyes to the dire need for more research in treatments for the chronic condition.

“I’ve worked on clinical trials in numerous positions, but this is one where patients are especially in desperate need to find answers,” she says. She recalls an hour-long phone conversation she had with a participant who was struggling with depression and becoming a stay-at-home mom after Long COVID forced her to leave her job. “It’s hard to see people who were living a healthy life end up in a state where they’re now debilitated and can’t even get out of bed.”

Most participants she worked with responded well to the design of the trial, she adds. Their cooperation allowed the study to run smoothly despite many of them being states away. “We didn’t have to bother them to do the surveys or get their study visits done,” Henriquez says, “because they all wanted to help.”

New technologies make cost-effective decentralized trials possible

The “record rate” of enrollment speaks to the success of PAX LC Trial’s design. It can take years for clinical trials to finish the enrollment period. PAX LC finished in under a year. Although it is a small trial of 100 participants, given its numerous restrictions around eligibility to participate and requirements for collecting blood and saliva specimens for Iwasaki’s lab, enrollment would have taken significantly longer without its decentralized format, Krumholz says.

Previously, a trial like this was not practical. But now, the evolution of new technologies is making the possibility of decentralized trials more accessible than ever. Recently, for example, Krumholz co-founded Hugo Health, a platform that enables people to transfer their medical records more seamlessly, with their permission, to researchers. “Because of the 21 st century Cures Act [ a 2016 law created to advance medical product innovation], people can get access to their medical records and share them with trusted partners,” he says. “Now we don’t have to go through a Byzantine set of obstacles through a particular health system to access a patient’s medical data – we just need to work in partnership with participants.”

Traveling to each participant’s home would seem to be a pricey endeavor. But given its greater efficiency, the design of the PAX LC Trial has actually cut down on costs, Krumholz argues. If it had been run like a standard clinical trial, his team would have had to set up study sites across the country. The associated costs would have been extremely high; also, the complexities involved in a multitude of institutional review boards (IRBs) whose approval would be needed, would have significantly slowed the study’s progress. Furthermore, many clinical trials need to extend their study periods as they struggle to retain participants, which also adds to the costs.

“Despite the fact that our trial is sort of built as a concierge service for each individual patient, it was much less expensive than what we had been doing,” Krumholz says. “What I hope will happen is that it will be a competitive advantage to do decentralized trials.”

Decentralized clinical trials can be applied to a multitude of fields

About a month into the PAX LC Trial, Ezra noticed that his symptoms had dramatically declined. He still doesn’t know if he had taken the drug or the placebo, but “the brain fog is gone, the memory issues are gone, the cognitive problems seem to have pretty much evaporated,” he says. He can take a 45-minute walk again and was able to return to work. He is not cured, however. He still has symptoms of postural orthostatic tachycardia syndrome (POTS) [a disorder in which standing up triggers symptoms such as rapid heart rate] and gets winded easily.

Unfortunately, for many people with Long COVID, there won’t be a magic pill that restores them to normal. More clinical trials aimed at other possible underlying mechanisms of Long COVID, such as autoimmune dysfunction, will be needed. And these patients, many of whom are struggling to work and provide for their families, don’t have years to wait. Decentralized trials could help bring much-needed answers more quickly.

And this type of trial doesn’t need to be confined to Long COVID. “I’m confident the way we set this up can work with almost any population,” says Krumholz. He is optimistic that PAX LC will inspire other researchers to adopt a more participant-centric format for different kinds of research.

“Our trial is like Kitty Hawk [the Wright Flyer], we just needed to show that it could fly,” he says. “The hope is that soon there will be fleets of planes that adopt this approach.”

Featured in this article

• Harlan Krumholz, MD, SM Harold H. Hines, Jr. Professor of Medicine (Cardiology) and Professor in the Institute for Social and Policy Studies, of Investigative Medicine and of Public Health (Health Policy); Founder, Center for Outcomes Research and Evaluation (CORE)
• Akiko Iwasaki, PhD Sterling Professor of Immunobiology and Professor of Dermatology and of Molecular, Cellular, and Developmental Biology and of Epidemiology (Microbial Diseases); Investigator, Howard Hughes Medical Institute

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What is survey research.

15 min read Find out everything you need to know about survey research, from what it is and how it works to the different methods and tools you can use to ensure you’re successful.

Survey research is the process of  collecting data from a predefined group  (e.g. customers or potential customers) with the ultimate goal of uncovering insights about your products, services, or  brand overall .

As a  quantitative data collection  method, survey research can provide you with a goldmine of information that can inform crucial business and product decisions. But survey research needs careful planning and execution to get the results you want.

So if you’re thinking about using surveys to carry out research, read on.

Get started with our free survey software

Types of survey research

Calling these methods ‘survey research’ slightly underplays the complexity of this type of information gathering. From the expertise required to carry out each activity to the  analysis of the data  and its eventual application, a considerable amount of effort is required.

As for how you can carry out your research, there are several options to choose from — face-to-face interviews, telephone surveys, focus groups (though more interviews than surveys), online surveys, and panel surveys.

Typically, the survey method you choose will largely be guided by who you want to survey,  the size of your sample , your budget, and the type of information you’re hoping to gather.

Here are a few of the most-used survey types:

Face-to-face interviews

Before technology made it possible to conduct research using online surveys, telephone, and mail were the most popular methods for survey research. However face-to-face interviews were considered the gold standard — the only reason they weren’t as popular was due to their highly prohibitive costs.

When it came to face-to-face interviews, organisations would use highly trained researchers who knew when to probe or follow up on vague or problematic answers. They also knew when to offer assistance to respondents when they seemed to be struggling. The result was that these interviewers could get sample members to participate and engage in surveys in the most effective way possible, leading to higher response rates and better quality data.

Telephone surveys

While phone surveys have been popular in the past, particularly for measuring general consumer behaviour or beliefs, response rates have been declining since the 1990s .

Phone surveys are usually conducted using a random dialling system and software that a researcher can use to record responses.

This method is beneficial when you want to survey a large population but don’t have the resources to conduct face-to-face research surveys or run focus groups, or want to ask multiple-choice and  open-ended questions .

The downsides are they can: take a long time to complete depending on the response rate, and you may have to do a lot of cold-calling to get the information you need.

You also run the risk of respondents  not being completely honest . Instead, they’ll answer your survey questions quickly just to get off the phone.

Focus groups (interviews — not surveys)

Focus groups are a separate  qualitative methodology  rather than surveys — even though they’re often bunched together. They’re normally used for  survey pretesting and designing , but they’re also a great way to generate opinions and data from a diverse range of people.

Focus groups involve putting a cohort of  demographically  or socially diverse people in a room with a moderator and engaging them in a discussion on a particular topic, such as your product, brand, or service.

They remain a highly popular  method for market research , but they’re expensive and require a lot of administration to conduct and analyse the data properly.

You also run the risk of more dominant members of the group taking over the discussion and swaying the opinions of other people — potentially providing you with unreliable data.

Online surveys

Online surveys  have become one of the most popular survey methods due to being cost-effective, enabling researchers to accurately survey a large population quickly.

Online surveys can essentially be used by anyone for any research purpose – we’ve all seen the increasing popularity of polls on social media (although these are not scientific).

Using an online survey allows you to ask a series of different question types and collect data instantly that’s easy to analyse with the right software.

There are also several methods for running and distributing online surveys that allow you to get your questionnaire in front of a large population at a fraction of the cost of face-to-face interviews or focus groups.

This is particularly true when it comes to mobile surveys as most people with a smartphone can access them online.

However, you have to be aware of the potential dangers of using online surveys, particularly when it comes to the survey respondents. The biggest risk is because online surveys require access to a computer or mobile device to complete, they could exclude elderly members of the population who don’t have access to the technology — or don’t know how to use it.

It could also exclude those from poorer socio-economic backgrounds who can’t afford a computer or consistent internet access. This could mean the data collected is more biased towards a certain group and can lead to less accurate data when you’re looking for a representative population sample.

When it comes to surveys, every voice matters.

Panel surveys

A panel survey involves recruiting respondents who have specifically signed up to answer questionnaires and who are put on a list by a research company. This could be a workforce of a small company or a major subset of a national population. Usually, these groups are carefully selected so that they represent a sample of your target population — giving you balance across criteria such as age, gender, background, and so on.

Panel surveys give you access to the respondents you need and are usually provided by the research company in question. As a result, it’s much easier to get access to the right audiences as you just need to tell the research company your criteria. They’ll then determine the right panels to use to answer your questionnaire.

However, there are downsides. The main one being that if the research company offers its panels incentives, e.g. discounts, coupons, money — respondents may answer a lot of questionnaires just for the benefits.

This might mean they rush through your survey without providing considered and truthful answers. As a consequence, this can damage the credibility of your data and potentially ruin your analyses.

What are the benefits of using survey research?

Depending on the research method you use, there are lots of benefits to conducting survey research for data collection. Here, we cover a few:

1.   They’re relatively easy to do

Most research surveys are easy to set up, administer and analyse. As long as the planning and survey design is thorough and you target the right audience , the data collection is usually straightforward regardless of which survey type you use.

2.   They can be cost effective

Survey research can be relatively cheap depending on the type of survey you use.

Generally, qualitative research methods that require access to people in person or over the phone are more expensive and require more administration.

Online surveys or mobile surveys are often more cost-effective for market research and can give you access to the global population for a fraction of the cost.

3.   You can collect data from a large sample

Again, depending on the type of survey, you can obtain survey results from an entire population at a relatively low price. You can also administer a large variety of survey types to fit the project you’re running.

4.   You can use survey software to analyse results immediately

Using survey software, you can use advanced statistical analysis techniques to gain insights into your responses immediately.

Analysis can be conducted using a variety of parameters to determine the validity and reliability of your survey data at scale.

5.   Surveys can collect any type of data

While most people view surveys as a quantitative research method, they can just as easily be adapted to gain qualitative information by simply including open-ended questions or conducting interviews face to face.

How to measure concepts with survey questions

While surveys are a great way to obtain data, that data on its own is useless unless it can be analysed and developed into actionable insights.

The easiest, and most effective way to measure survey results, is to use a dedicated research tool that puts all of your survey results into one place.

When it comes to survey measurement, there are four measurement types to be aware of that will determine how you treat your different survey results:

Nominal scale

With a nominal scale , you can only keep track of how many respondents chose each option from a question, and which response generated the most selections.

An example of this would be simply asking a responder to choose a product or brand from a list.

You could find out which brand was chosen the most but have no insight as to why.

Ordinal scale

Ordinal scales are used to judge an order of preference. They do provide some level of quantitative value because you’re asking responders to choose a preference of one option over another.

Ratio scale

Ratio scales can be used to judge the order and difference between responses. For example, asking respondents how much they spend on their weekly shopping on average.

Interval scale

In an interval scale, values are lined up in order with a meaningful difference between the two values — for example, measuring temperature or measuring a credit score between one value and another.

Step by step: How to conduct surveys and collect data

Conducting a survey and collecting data is relatively straightforward, but it does require some careful planning and design to ensure it results in reliable data.

Step 1 – Define your objectives

What do you want to learn from the survey? How is the data going to help you? Having a hypothesis or series of assumptions about survey responses will allow you to create the right questions to test them.

Step 2 – Create your survey questions

Once you’ve got your hypotheses or assumptions, write out the questions you need answering to test your theories or beliefs. Be wary about framing questions that  could lead respondents or inadvertently create biased responses .

Step 3 – Choose your question types

Your survey should include a variety of question types and should aim to obtain quantitative data with some qualitative responses from open-ended questions. Using a mix of questions (simple Yes/ No, multiple-choice, rank in order, etc) not only increases the reliability of your data but also reduces survey fatigue and respondents simply answering questions quickly without thinking.

Step 4 – Test your questions

Before sending your questionnaire out, you should test it (e.g. have a random internal group do the survey) and carry out A/B tests to ensure you’ll gain accurate responses.

Step 5 – Choose your target and send out the survey

Depending on your objectives, you might want to target the general population with your survey or a specific segment of the population. Once you’ve narrowed down who you want to target, it’s time to send out the survey.

After you’ve deployed the survey, keep an eye on the response rate to ensure you’re getting the number you expected. If your response rate is low, you might need to send the survey out to a second group to obtain a large enough sample — or do some troubleshooting to work out why your response rates are so low. This could be down to your questions, delivery method, selected sample, or otherwise.

Step 6 – Analyse results and draw conclusions

Once you’ve got your results back, it’s time for the fun part.

Break down your survey responses using the parameters you’ve set in your objectives and analyse the data to compare to your original assumptions. At this stage, a research tool or software can make the analysis a lot easier — and that’s somewhere Qualtrics can help.

Get reliable insights with survey software from Qualtrics

Gaining feedback from customers  and leads is critical for any business, data gathered from surveys can prove invaluable for understanding your products and your market position, and with survey software from Qualtrics , it couldn’t be easier.

Used by more than 13,000 brands and supporting more than 1 billion surveys a year, Qualtrics empowers everyone in your organisation to gather insights and take action. No coding required — and your data is housed in one system.

Get feedback from more than 125 sources on a single platform and view and measure your data in one place to  create actionable insights  and gain a deeper understanding of your target customers.

Automatically run complex text and statistical analysis to uncover exactly what your survey data is telling you, so you can react in real-time and make smarter decisions.

We can help you with survey management, too. From designing your survey and finding your target respondents to getting your survey in the field and reporting back on the results, we can help you every step of the way.

And for expert market researchers and survey designers, Qualtrics features custom programming to give you total flexibility over question types, survey design, embedded data, and other variables.

No matter what type of survey you want to run, what target audience you want to reach, or what assumptions you want to test or answers you want to uncover, we’ll help you design, deploy and analyse your survey with our team of experts.

Start your survey research today with Qualtrics

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Thematic analysis 11 min read, post event survey questions 10 min read, choosing the best survey tools 16 min read, survey app 11 min read, close-ended questions 7 min read, survey vs questionnaire 12 min read, likert scales 14 min read, request demo.

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