good hypothesis for psychology

2.4 Developing a Hypothesis

Learning objectives.

Distinguish between a theory and a hypothesis.
Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
Understand the characteristics of a good hypothesis.

Theories and Hypotheses

Before describing how to develop a hypothesis it is imporant to distinguish betwee a theory and a hypothesis. A theory is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition. He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.

Outside of science, referring to an idea as a theory often implies that it is untested—perhaps no more than a wild guess. In science, however, the term theory has no such implication. A theory is simply an explanation or interpretation of a set of phenomena. It can be untested, but it can also be extensively tested, well supported, and accepted as an accurate description of the world by the scientific community. The theory of evolution by natural selection, for example, is a theory because it is an explanation of the diversity of life on earth—not because it is untested or unsupported by scientific research. On the contrary, the evidence for this theory is overwhelmingly positive and nearly all scientists accept its basic assumptions as accurate. Similarly, the “germ theory” of disease is a theory because it is an explanation of the origin of various diseases, not because there is any doubt that many diseases are caused by microorganisms that infect the body.

A hypothesis , on the other hand, is a specific prediction about a new phenomenon that should be observed if a particular theory is accurate. It is an explanation that relies on just a few key concepts. Hypotheses are often specific predictions about what will happen in a particular study. They are developed by considering existing evidence and using reasoning to infer what will happen in the specific context of interest. Hypotheses are often but not always derived from theories. So a hypothesis is often a prediction based on a theory but some hypotheses are a-theoretical and only after a set of observations have been made, is a theory developed. This is because theories are broad in nature and they explain larger bodies of data. So if our research question is really original then we may need to collect some data and make some observation before we can develop a broader theory.

Theories and hypotheses always have this if-then relationship. “ If drive theory is correct, then cockroaches should run through a straight runway faster, and a branching runway more slowly, when other cockroaches are present.” Although hypotheses are usually expressed as statements, they can always be rephrased as questions. “Do cockroaches run through a straight runway faster when other cockroaches are present?” Thus deriving hypotheses from theories is an excellent way of generating interesting research questions.

But how do researchers derive hypotheses from theories? One way is to generate a research question using the techniques discussed in this chapter and then ask whether any theory implies an answer to that question. For example, you might wonder whether expressive writing about positive experiences improves health as much as expressive writing about traumatic experiences. Although this question is an interesting one on its own, you might then ask whether the habituation theory—the idea that expressive writing causes people to habituate to negative thoughts and feelings—implies an answer. In this case, it seems clear that if the habituation theory is correct, then expressive writing about positive experiences should not be effective because it would not cause people to habituate to negative thoughts and feelings. A second way to derive hypotheses from theories is to focus on some component of the theory that has not yet been directly observed. For example, a researcher could focus on the process of habituation—perhaps hypothesizing that people should show fewer signs of emotional distress with each new writing session.

Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [1] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the number of examples they bring to mind and the other was that people base their judgments on how easily they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.

Theory Testing

The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method (although this term is much more likely to be used by philosophers of science than by scientists themselves). A researcher begins with a set of phenomena and either constructs a theory to explain or interpret them or chooses an existing theory to work with. He or she then makes a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researcher then conducts an empirical study to test the hypothesis. Finally, he or she reevaluates the theory in light of the new results and revises it if necessary. This process is usually conceptualized as a cycle because the researcher can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As Figure 2.2 shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.

Figure 4.4 Hypothetico-Deductive Method Combined With the General Model of Scientific Research in Psychology Together they form a model of theoretically motivated research.

Figure 2.2 Hypothetico-Deductive Method Combined With the General Model of Scientific Research in Psychology Together they form a model of theoretically motivated research.

As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [2] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans (Zajonc & Sales, 1966) [3] in many other studies afterward).

Incorporating Theory into Your Research

When you write your research report or plan your presentation, be aware that there are two basic ways that researchers usually include theory. The first is to raise a research question, answer that question by conducting a new study, and then offer one or more theories (usually more) to explain or interpret the results. This format works well for applied research questions and for research questions that existing theories do not address. The second way is to describe one or more existing theories, derive a hypothesis from one of those theories, test the hypothesis in a new study, and finally reevaluate the theory. This format works well when there is an existing theory that addresses the research question—especially if the resulting hypothesis is surprising or conflicts with a hypothesis derived from a different theory.

To use theories in your research will not only give you guidance in coming up with experiment ideas and possible projects, but it lends legitimacy to your work. Psychologists have been interested in a variety of human behaviors and have developed many theories along the way. Using established theories will help you break new ground as a researcher, not limit you from developing your own ideas.

Characteristics of a Good Hypothesis

There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use inductive reasoning which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be positive. That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that really it does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.

Key Takeaways

A theory is broad in nature and explains larger bodies of data. A hypothesis is more specific and makes a prediction about the outcome of a particular study.
Working with theories is not “icing on the cake.” It is a basic ingredient of psychological research.
Like other scientists, psychologists use the hypothetico-deductive method. They construct theories to explain or interpret phenomena (or work with existing theories), derive hypotheses from their theories, test the hypotheses, and then reevaluate the theories in light of the new results.
Practice: Find a recent empirical research report in a professional journal. Read the introduction and highlight in different colors descriptions of theories and hypotheses.
Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic. Journal of Personality and Social Psychology, 61 , 195–202. ↵
Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach. Journal of Personality and Social Psychology, 13 , 83–92. ↵
Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168. ↵

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Overview of the Scientific Method

Learning Objectives

Distinguish between a theory and a hypothesis.
Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
Understand the characteristics of a good hypothesis.

Theories and Hypotheses

Before describing how to develop a hypothesis, it is important to distinguish between a theory and a hypothesis. A theory is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition (1965) [1] . He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.

Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [2] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the number of examples they bring to mind and the other was that people base their judgments on how easily they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.

Theory Testing

The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method (although this term is much more likely to be used by philosophers of science than by scientists themselves). Researchers begin with a set of phenomena and either construct a theory to explain or interpret them or choose an existing theory to work with. They then make a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researchers then conduct an empirical study to test the hypothesis. Finally, they reevaluate the theory in light of the new results and revise it if necessary. This process is usually conceptualized as a cycle because the researchers can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As Figure 2.3 shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.

As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [3] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans [Zajonc & Sales, 1966] [4] in many other studies afterward).

Incorporating Theory into Your Research

Characteristics of a Good Hypothesis

There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use inductive reasoning which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be positive. That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that it really does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.

Zajonc, R. B. (1965). Social facilitation. Science, 149 , 269–274 ↵
Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic. Journal of Personality and Social Psychology, 61 , 195–202. ↵
Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach. Journal of Personality and Social Psychology, 13 , 83–92. ↵
Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168. ↵

A coherent explanation or interpretation of one or more phenomena.

A specific prediction about a new phenomenon that should be observed if a particular theory is accurate.

A cyclical process of theory development, starting with an observed phenomenon, then developing or using a theory to make a specific prediction of what should happen if that theory is correct, testing that prediction, refining the theory in light of the findings, and using that refined theory to develop new hypotheses, and so on.

The ability to test the hypothesis using the methods of science and the possibility to gather evidence that will disconfirm the hypothesis if it is indeed false.

Research Methods in Psychology Copyright © 2019 by Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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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.

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 .

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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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Guidelines for Crafting Hypotheses in Psychology

Have you ever wondered what a hypothesis in psychology is and why it is so important? In this article, we will explore the significance of hypotheses in psychology, what makes a good hypothesis, the different types of hypotheses, and how to effectively formulate one.

We will also discuss common mistakes to avoid when crafting hypotheses, so you can ensure your research is clear, testable, and logically sound. Join us as we dive into the world of hypotheses in psychology!

A hypothesis in psychology is a testable and falsifiable statement that predicts the relationship between variables.
Hypotheses are crucial in psychology as they guide research and help to test theories and understand human behavior.
To create a good hypothesis in psychology, it should be based on previous research, specific, logical, and plausible, and include variables, relationship, and specific language.
1 What Is a Hypothesis in Psychology?
2 Why Are Hypotheses Important in Psychology?
3.1 Based on Previous Research
3.2 Testable and Falsifiable
3.3 Specific and Clear
3.4 Logical and Plausible
4.1 Research Hypothesis
4.2 Null Hypothesis
4.3 Directional and Non-Directional Hypotheses
4.4 One-Tailed and Two-Tailed Hypotheses
5.1 Identify the Variables
5.2 Determine the Relationship Between Variables
5.3 Use Specific Language and Format
6.1 Vague or Ambiguous Language
6.2 Biased Hypotheses
6.3 Overgeneralization
6.4 Ignoring Alternative Explanations
7.1 What are the guidelines for crafting hypotheses in psychology?
7.2 Why is it important to have clear and testable hypotheses in psychology?
7.3 How can I ensure that my hypotheses are relevant to my research question?
7.4 Are there specific methods that should be used to test hypotheses in psychology?
7.5 What should I do if my hypothesis is not supported by the data?
7.6 Can I modify my hypothesis during the research process?

What Is a Hypothesis in Psychology?

In psychology, a hypothesis is a proposed explanation based on research and observations that can be tested through experiments or data analysis.

Hypotheses play a crucial role in psychology by guiding researchers in determining the relationship between different variables. By identifying the specific variables under study, hypotheses help establish a framework for designing experiments. They provide a clear direction for gathering data and analyzing results to either support or reject the proposed explanation. Through this process, hypotheses aid in developing scientific theories by contributing to our understanding of human behavior and mental processes.

Why Are Hypotheses Important in Psychology?

Hypotheses play a crucial role in psychology by guiding research efforts, allowing for testable predictions, and contributing to the advancement of scientific knowledge.

Through hypotheses, researchers can formulate educated guesses about the relationships between different variables in a study. This process helps in establishing the foundation for experiments and investigations in the field of psychology.

Hypotheses enable researchers to structure their studies according to existing theory and previous findings, ensuring that their work aligns with the broader scientific knowledge base.

The formulation and testing of hypotheses are essential components of the scientific method in psychology, driving the field forward with empirical evidence and innovative insights.

What Makes a Good Hypothesis in Psychology?

A good hypothesis in psychology is characterized by being testable , specific, logical, and plausible, setting the foundation for meaningful research outcomes.

Testability is essential as it allows researchers to systematically collect data to support or refute the hypothesis.

Specificity ensures clear direction for the study, outlining the relationship between variables.

Logic aids in constructing a rational and coherent argument, guiding the research methodology.

Plausibility contributes to the credibility of the hypothesis, aligning it with existing knowledge in the field.

Incorporating these key criteria not only enhances the quality of the research but also facilitates effective data collection and analysis.

Based on Previous Research

A hypothesis in psychology should be grounded in previous research findings, drawing upon existing theories and employing sound academic research methodologies to advance knowledge in the field.

By basing hypotheses on prior research, psychologists ensure that their investigations are built upon a solid foundation of established knowledge. This helps in developing testable predictions that contribute to the accumulation of evidence supporting or refuting the proposed relationships between variables.

Proper integration of relevant keywords such as independent and dependent variables further enhances the clarity and specificity of the hypothesis, guiding the direction of the research study. It is through this systematic process of hypothesis development that the scientific rigor and credibility of academic research in psychology is upheld.

Testable and Falsifiable

A key characteristic of a good hypothesis is its testability and falsifiability , allowing researchers to make predictions and conduct statistical analyses to either support or reject the null hypothesis.

Testability ensures that a hypothesis can be systematically tested through empirical observations and experimentation. By formulating clear predictions based on the proposed relationship between variables, researchers can design experiments that generate reliable data .

The falsifiability criterion dictates that a hypothesis must be potentially disprovable, leading to robust scientific inquiry. This process involves challenging the hypothesis with empirical evidence and statistical tests to either confirm its validity or refine scientific understanding.

Specific and Clear

A well-formulated hypothesis in psychology should be specific and clear, outlining the expected relationship between variables and enabling direct comparison of effects or outcomes.

When constructing a hypothesis, it is crucial to consider the theory that underpins the research and the methodology used to test it. By clearly defining the relationship between the variables, researchers can determine the impact of one on the other with precision.

Such precision is essential in psychology as it allows for accurate interpretations of experimental results, leading to a deeper understanding of human behavior and mental processes.

Logical and Plausible

A logical and plausible hypothesis considers potential extraneous variables, incorporates control variables where necessary, and offers a rational explanation for the proposed relationship between variables.

When developing a hypothesis, it’s vital to pay attention to the data being utilized for testing. Statistical analysis plays a key role in substantiating the hypothesized relationships between variables. By carefully selecting control variables, researchers can isolate the effect of the main variables under scrutiny. This process not only enhances the credibility of the study but also ensures that the results are robust and reliable. Considering all these aspects in formulating hypotheses adds depth and validity to the research being conducted.

What Are the Different Types of Hypotheses in Psychology?

Psychology encompasses various types of hypotheses, including research hypotheses, directional and non-directional hypotheses, and one-tailed and two-tailed hypotheses, each serving unique purposes in scientific inquiries.

Research hypotheses are formulated to investigate the relationship between variables, aiming to address specific inquiries and predict outcomes based on previous findings. On the other hand, directional hypotheses propose the direction of the expected effect, predicting that changes in one variable will lead to changes in another. Non-directional hypotheses, however, suggest the presence of an effect but do not specify the relationship’s direction.

Regarding hypothesis testing, the distinction between one-tailed and two-tailed hypotheses is crucial. One-tailed hypotheses make predictions about the direction of an effect, while two-tailed hypotheses consider the possibility of an effect in either direction, providing a more comprehensive approach to testing the research question.

Research Hypothesis

A research hypothesis in psychology aims to establish a potential relationship between variables, often focusing on correlation or association to test specific theories or predictions.

These hypotheses serve as the foundation for research studies in psychology, guiding the collection and analysis of data through various methods . The testing of hypotheses provides a framework for examining the proposed relationships between variables, allowing researchers to draw conclusions and make inferences based on empirical evidence. By formulating clear and specific hypotheses, psychologists can investigate complex phenomena and contribute to the understanding of human behavior and mental processes.

Null Hypothesis

The null hypothesis serves as the default position in hypothesis testing, providing a baseline for comparison and addressing the presence of research bias in scientific inquiries.

When designing a research study, psychologists carefully construct null hypotheses as essential components of their methodology. These hypotheses allow researchers to investigate whether the observed results are statistically significant or simply occur by chance. By setting up the null hypothesis, researchers can then use statistical analysis to determine if the alternative hypothesis, the one being tested, offers a more plausible explanation. This meticulous approach ensures that potential biases, whether they be participant-related, experimenter-induced, or measurement errors, are adequately addressed and accounted for.

Directional and Non-Directional Hypotheses

Directional hypotheses predict the specific direction of a relationship between variables, while non-directional hypotheses focus on the presence or absence of effects without specifying a particular direction.

In psychology, when researchers formulate hypotheses , they must decide whether they expect a particular outcome or are exploring general effects. This distinction plays a crucial role in the testing and comparison of theories. Directional hypotheses are used when there is prior evidence or theoretical reasoning suggesting a specific relationship between variables. On the other hand, non-directional hypotheses are employed when researchers want to investigate possible effects without bias towards a particular relationship direction.

One-Tailed and Two-Tailed Hypotheses

One-tailed hypotheses focus on predicting an effect in a single direction, while two-tailed hypotheses consider the possibility of effects in both directions, guiding statistical hypothesis testing approaches.

When researchers formulate a one-tailed hypothesis , they are making a specific prediction about the relationship between variables. For instance, in a study examining the impact of exercise on mood, a one-tailed hypothesis might state that increased exercise leads to improved mood. In comparison, a two-tailed hypothesis acknowledges the potential for different outcomes, suggesting that exercise can either positively or negatively influence mood, or that there may be no significant effect at all.

These distinctions in hypotheses not only shape the directionality of predicted effects but also influence the choice of statistical tests used to analyze the data. In a one-tailed hypothesis, statistical testing focuses on demonstrating whether the effect is present in the predicted direction. Conversely, for a two-tailed hypothesis, the analysis considers the significance of the effect regardless of its direction. This nuanced difference guides researchers in interpreting their findings and drawing conclusions based on the hypotheses they have formulated.

How to Formulate a Hypothesis in Psychology?

Formulating a hypothesis in psychology involves identifying relevant variables, determining the expected relationships between them, and employing specific language and formats to express the proposed hypotheses.

When crafting a hypothesis, psychologists carefully consider the data gathered, existing theories, and observations made. By analyzing these components, psychologists can create meaningful and testable hypotheses that contribute to the understanding of human behavior. The process of hypothesis formulation requires a thorough understanding of the subject matter, an ability to link different variables, and a skill in articulating the expected outcomes. Effective hypotheses in psychology typically stem from a blend of empirical evidence, theoretical frameworks, and keen observations.

Identify the Variables

The first step in formulating a hypothesis is to identify the variables involved, distinguishing between independent and dependent variables that form the core elements of the research question.

Independent variables are the factors that are manipulated or controlled by the researcher to observe their effect on the dependent variable, which is the outcome being studied. These variables play a crucial role in determining the relationship between them. It is essential to establish a clear understanding of the relationship between the independent and dependent variables to formulate a hypothesis that can be tested through the research methodology.

Identifying and considering control variables is important to ensure that any observed effects are actually due to the independent variables and not other external factors. Through a systematic approach to variable identification, researchers can construct hypotheses that are precise and facilitate meaningful research outcomes.

Determine the Relationship Between Variables

After identifying variables, it is essential to determine the nature of the relationship between them, whether it involves correlation, causation, or other forms of association that can be tested through research.

Establishing relationships between variables in hypothesis formulation is a crucial step in ensuring the integrity of the research. Understanding the types of connections, such as correlation or causation, is vital for constructing testable hypotheses. By examining how variables interact, researchers can uncover potential effects and make meaningful comparisons to derive valuable insights from the data collected. This process not only adds depth to the hypothesis but also sets a solid foundation for the subsequent analysis and interpretation of results.

Use Specific Language and Format

Hypotheses in psychology should be articulated using specific language and formats to ensure clarity, precision, and conciseness in conveying the intended research predictions and relationships between variables.

One of the key elements in crafting a hypothesis is to clearly define the theory or concept being tested, followed by a succinct explanation of the methodology used to collect and analyze data. This structured approach not only enhances the overall coherence of the research design but also enables researchers to systematically evaluate and validate their predictions through rigorous testing procedures.

What Are Common Mistakes to Avoid When Crafting Hypotheses in Psychology?

When crafting hypotheses in psychology, it is crucial to avoid common mistakes such as using vague or ambiguous language, introducing bias, overgeneralizing conclusions, and overlooking alternative explanations.

Clarity and precision are key in hypothesis formulation, as clearly defined statements help in setting the foundation for meaningful data collection and analysis. Introducing bias can skew results and lead to inaccurate conclusions, undermining the validity of the research. Overgeneralization can dilute the impact of the hypothesis, making it less specific and testable. Failure to consider alternative explanations may limit the scope of understanding the phenomenon under investigation, which is essential for robust hypothesis testing in psychology.

Vague or Ambiguous Language

The use of vague or ambiguous language in hypotheses can undermine clarity and precision, potentially leading to misinterpretation of the intended relationships or effects being investigated.

When a hypothesis lacks specificity, it becomes challenging to determine the exact nature of the variables being studied, hindering accurate correlation and comparison.

Without clear and concise language in hypotheses, researchers run the risk of drawing erroneous conclusions or misjudging the significance of their findings.

Articulating hypotheses with precision is crucial for ensuring that the intended relationships or effects are accurately captured and analyzed in scientific research.

Biased Hypotheses

Biased hypotheses in psychology introduce partiality and preconceived notions into research inquiries, potentially skewing results and compromising the validity of the scientific association being studied.

One of the critical aspects of psychology research lies in the formulation and testing of various theories, where biased hypotheses pose a significant threat to the integrity of the entire methodology.

Any form of prejudice, whether conscious or subconscious, can lead to the misinterpretation of data and the false validation of predetermined ideas.

It is imperative for researchers to remain vigilant against such influences, as they can obscure the true nature of human behavior and hinder the progress of scientific understanding.

Overgeneralization

Overgeneralization in hypotheses can lead to sweeping conclusions that extend beyond the scope of the study, potentially distorting the actual effects or comparisons being investigated.

When researchers fail to distinguish between correlation and causation, they risk oversimplifying complex relationships between variables. This could misrepresent the true impact of certain factors under investigation, ultimately compromising the integrity of the data collected. Using a flawed methodology or drawing generalized conclusions without sufficient supporting evidence can undermine the credibility of the entire study, leading to inaccurate interpretations and erroneous conclusions.

Ignoring Alternative Explanations

Neglecting alternative explanations when formulating hypotheses can overlook crucial factors that might influence variables or effects under investigation, leading to incomplete or misleading research outcomes.

It is essential in the scientific realm to consider a wide range of possibilities when constructing a hypothesis, ensuring that all potential influencing factors are taken into account. By conducting thorough experiments and analyses, researchers can deepen their knowledge and minimize the risk of overlooking significant variables.

Accounting for alternative explanations not only enhances the credibility of the findings but also enriches the overall scientific process, fostering a culture of critical thinking and meticulous investigation.

This practice contributes to the advancement of understanding and promotes more robust and reliable results in the realm of scientific inquiry.

Frequently Asked Questions

What are the guidelines for crafting hypotheses in psychology.

The guidelines for crafting hypotheses in psychology include identifying the research question, reviewing relevant literature, formulating a clear and testable hypothesis, and selecting appropriate methods for testing the hypothesis.

Why is it important to have clear and testable hypotheses in psychology?

Clear and testable hypotheses in psychology are important because they guide the research process and provide a framework for interpreting results. They also help researchers to avoid bias and ensure the validity and reliability of their findings.

How can I ensure that my hypotheses are relevant to my research question?

To ensure relevance, it is important to thoroughly review relevant literature and theories before formulating a hypothesis. This will help to identify any gaps in knowledge and ensure that the hypothesis is based on established principles and previous research.

Are there specific methods that should be used to test hypotheses in psychology?

Yes, there are various methods that can be used to test hypotheses in psychology, including experiments, surveys, and observational studies. The choice of method will depend on the research question and the type of data that needs to be collected.

What should I do if my hypothesis is not supported by the data?

If your hypothesis is not supported by the data, it is important to acknowledge this and consider alternative explanations for your findings. This can provide valuable insights and lead to further research in the future.

Can I modify my hypothesis during the research process?

Yes, it is common for hypotheses to be modified during the research process as new information is gathered. However, any modifications should be based on sound reasoning and should not deviate too far from the original research question.

Vanessa Patel is an expert in positive psychology, dedicated to studying happiness, resilience, and the factors that contribute to a fulfilling life. Her writing explores techniques for enhancing well-being, overcoming adversity, and building positive relationships and communities. Vanessa’s articles are a resource for anyone looking to find more joy and meaning in their daily lives, backed by the latest research in the field.

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2.4: Developing a Hypothesis

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Learning Objectives

Distinguish between a theory and a hypothesis.
Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
Understand the characteristics of a good hypothesis.

Theories and Hypotheses

Before describing how to develop a hypothesis it is important to distinguish between a theory and a hypothesis. A theory is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition. He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.

Theory Testing

Incorporating Theory into Your Research

Characteristics of a Good Hypothesis

Key Takeaways

A theory is broad in nature and explains larger bodies of data. A hypothesis is more specific and makes a prediction about the outcome of a particular study.
Working with theories is not “icing on the cake.” It is a basic ingredient of psychological research.
Like other scientists, psychologists use the hypothetico-deductive method. They construct theories to explain or interpret phenomena (or work with existing theories), derive hypotheses from their theories, test the hypotheses, and then reevaluate the theories in light of the new results.
Practice: Find a recent empirical research report in a professional journal. Read the introduction and highlight in different colors descriptions of theories and hypotheses.
Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic. Journal of Personality and Social Psychology, 61 , 195–202.
Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach. Journal of Personality and Social Psychology, 13 , 83–92.
Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168.

Research Methods In Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

Research methods in psychology are systematic procedures used to observe, describe, predict, and explain behavior and mental processes. They include experiments, surveys, case studies, and naturalistic observations, ensuring data collection is objective and reliable to understand and explain psychological phenomena.

Hypotheses are statements about the prediction of the results, that can be verified or disproved by some investigation.

There are four types of hypotheses :

Null Hypotheses (H0 ) – these predict that no difference will be found in the results between the conditions. Typically these are written ‘There will be no difference…’
Alternative Hypotheses (Ha or H1) – these predict that there will be a significant difference in the results between the two conditions. This is also known as the experimental hypothesis.
One-tailed (directional) hypotheses – these state the specific direction the researcher expects the results to move in, e.g. higher, lower, more, less. In a correlation study, the predicted direction of the correlation can be either positive or negative.
Two-tailed (non-directional) hypotheses – these state that a difference will be found between the conditions of the independent variable but does not state the direction of a difference or relationship. Typically these are always written ‘There will be a difference ….’

All research has an alternative hypothesis (either a one-tailed or two-tailed) and a corresponding null hypothesis.

Once the research is conducted and results are found, psychologists must accept one hypothesis and reject the other.

So, if a difference is found, the Psychologist would accept the alternative hypothesis and reject the null. The opposite applies if no difference is found.

Sampling techniques

Sampling is the process of selecting a representative group from the population under study.

A sample is the participants you select from a target population (the group you are interested in) to make generalizations about.

Representative means the extent to which a sample mirrors a researcher’s target population and reflects its characteristics.

Generalisability means the extent to which their findings can be applied to the larger population of which their sample was a part.

Volunteer sample : where participants pick themselves through newspaper adverts, noticeboards or online.
Opportunity sampling : also known as convenience sampling , uses people who are available at the time the study is carried out and willing to take part. It is based on convenience.
Random sampling : when every person in the target population has an equal chance of being selected. An example of random sampling would be picking names out of a hat.
Systematic sampling : when a system is used to select participants. Picking every Nth person from all possible participants. N = the number of people in the research population / the number of people needed for the sample.
Stratified sampling : when you identify the subgroups and select participants in proportion to their occurrences.
Snowball sampling : when researchers find a few participants, and then ask them to find participants themselves and so on.
Quota sampling : when researchers will be told to ensure the sample fits certain quotas, for example they might be told to find 90 participants, with 30 of them being unemployed.

Experiments always have an independent and dependent variable .

The independent variable is the one the experimenter manipulates (the thing that changes between the conditions the participants are placed into). It is assumed to have a direct effect on the dependent variable.
The dependent variable is the thing being measured, or the results of the experiment.

Operationalization of variables means making them measurable/quantifiable. We must use operationalization to ensure that variables are in a form that can be easily tested.

For instance, we can’t really measure ‘happiness’, but we can measure how many times a person smiles within a two-hour period.

By operationalizing variables, we make it easy for someone else to replicate our research. Remember, this is important because we can check if our findings are reliable.

Extraneous variables are all variables which are not independent variable but could affect the results of the experiment.

It can be a natural characteristic of the participant, such as intelligence levels, gender, or age for example, or it could be a situational feature of the environment such as lighting or noise.

Demand characteristics are a type of extraneous variable that occurs if the participants work out the aims of the research study, they may begin to behave in a certain way.

For example, in Milgram’s research , critics argued that participants worked out that the shocks were not real and they administered them as they thought this was what was required of them.

Extraneous variables must be controlled so that they do not affect (confound) the results.

Randomly allocating participants to their conditions or using a matched pairs experimental design can help to reduce participant variables.

Situational variables are controlled by using standardized procedures, ensuring every participant in a given condition is treated in the same way

Experimental Design

Experimental design refers to how participants are allocated to each condition of the independent variable, such as a control or experimental group.

Independent design ( between-groups design ): each participant is selected for only one group. With the independent design, the most common way of deciding which participants go into which group is by means of randomization.
Matched participants design : each participant is selected for only one group, but the participants in the two groups are matched for some relevant factor or factors (e.g. ability; sex; age).
Repeated measures design ( within groups) : each participant appears in both groups, so that there are exactly the same participants in each group.
The main problem with the repeated measures design is that there may well be order effects. Their experiences during the experiment may change the participants in various ways.
They may perform better when they appear in the second group because they have gained useful information about the experiment or about the task. On the other hand, they may perform less well on the second occasion because of tiredness or boredom.
Counterbalancing is the best way of preventing order effects from disrupting the findings of an experiment, and involves ensuring that each condition is equally likely to be used first and second by the participants.

If we wish to compare two groups with respect to a given independent variable, it is essential to make sure that the two groups do not differ in any other important way.

Experimental Methods

All experimental methods involve an iv (independent variable) and dv (dependent variable)..

Field experiments are conducted in the everyday (natural) environment of the participants. The experimenter still manipulates the IV, but in a real-life setting. It may be possible to control extraneous variables, though such control is more difficult than in a lab experiment.
Natural experiments are when a naturally occurring IV is investigated that isn’t deliberately manipulated, it exists anyway. Participants are not randomly allocated, and the natural event may only occur rarely.

Case studies are in-depth investigations of a person, group, event, or community. It uses information from a range of sources, such as from the person concerned and also from their family and friends.

Many techniques may be used such as interviews, psychological tests, observations and experiments. Case studies are generally longitudinal: in other words, they follow the individual or group over an extended period of time.

Case studies are widely used in psychology and among the best-known ones carried out were by Sigmund Freud . He conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

Case studies provide rich qualitative data and have high levels of ecological validity. However, it is difficult to generalize from individual cases as each one has unique characteristics.

Correlational Studies

Correlation means association; it is a measure of the extent to which two variables are related. One of the variables can be regarded as the predictor variable with the other one as the outcome variable.

Correlational studies typically involve obtaining two different measures from a group of participants, and then assessing the degree of association between the measures.

The predictor variable can be seen as occurring before the outcome variable in some sense. It is called the predictor variable, because it forms the basis for predicting the value of the outcome variable.

Relationships between variables can be displayed on a graph or as a numerical score called a correlation coefficient.

types of correlation. Scatter plot. Positive negative and no correlation

If an increase in one variable tends to be associated with an increase in the other, then this is known as a positive correlation .
If an increase in one variable tends to be associated with a decrease in the other, then this is known as a negative correlation .
A zero correlation occurs when there is no relationship between variables.

After looking at the scattergraph, if we want to be sure that a significant relationship does exist between the two variables, a statistical test of correlation can be conducted, such as Spearman’s rho.

The test will give us a score, called a correlation coefficient . This is a value between 0 and 1, and the closer to 1 the score is, the stronger the relationship between the variables. This value can be both positive e.g. 0.63, or negative -0.63.

Types of correlation. Strong, weak, and perfect positive correlation, strong, weak, and perfect negative correlation, no correlation. Graphs or charts ...

A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable. A correlation only shows if there is a relationship between variables.

Correlation does not always prove causation, as a third variable may be involved.

Interview Methods

Interviews are commonly divided into two types: structured and unstructured.

A fixed, predetermined set of questions is put to every participant in the same order and in the same way.

Responses are recorded on a questionnaire, and the researcher presets the order and wording of questions, and sometimes the range of alternative answers.

The interviewer stays within their role and maintains social distance from the interviewee.

There are no set questions, and the participant can raise whatever topics he/she feels are relevant and ask them in their own way. Questions are posed about participants’ answers to the subject

Unstructured interviews are most useful in qualitative research to analyze attitudes and values.

Though they rarely provide a valid basis for generalization, their main advantage is that they enable the researcher to probe social actors’ subjective point of view.

Questionnaire Method

Questionnaires can be thought of as a kind of written interview. They can be carried out face to face, by telephone, or post.

The choice of questions is important because of the need to avoid bias or ambiguity in the questions, ‘leading’ the respondent or causing offense.

Open questions are designed to encourage a full, meaningful answer using the subject’s own knowledge and feelings. They provide insights into feelings, opinions, and understanding. Example: “How do you feel about that situation?”
Closed questions can be answered with a simple “yes” or “no” or specific information, limiting the depth of response. They are useful for gathering specific facts or confirming details. Example: “Do you feel anxious in crowds?”

Its other practical advantages are that it is cheaper than face-to-face interviews and can be used to contact many respondents scattered over a wide area relatively quickly.

Observations

There are different types of observation methods :

Covert observation is where the researcher doesn’t tell the participants they are being observed until after the study is complete. There could be ethical problems or deception and consent with this particular observation method.
Overt observation is where a researcher tells the participants they are being observed and what they are being observed for.
Controlled : behavior is observed under controlled laboratory conditions (e.g., Bandura’s Bobo doll study).
Natural : Here, spontaneous behavior is recorded in a natural setting.
Participant : Here, the observer has direct contact with the group of people they are observing. The researcher becomes a member of the group they are researching.
Non-participant (aka “fly on the wall): The researcher does not have direct contact with the people being observed. The observation of participants’ behavior is from a distance

Pilot Study

A pilot study is a small scale preliminary study conducted in order to evaluate the feasibility of the key s teps in a future, full-scale project.

A pilot study is an initial run-through of the procedures to be used in an investigation; it involves selecting a few people and trying out the study on them. It is possible to save time, and in some cases, money, by identifying any flaws in the procedures designed by the researcher.

A pilot study can help the researcher spot any ambiguities (i.e. unusual things) or confusion in the information given to participants or problems with the task devised.

Sometimes the task is too hard, and the researcher may get a floor effect, because none of the participants can score at all or can complete the task – all performances are low.

The opposite effect is a ceiling effect, when the task is so easy that all achieve virtually full marks or top performances and are “hitting the ceiling”.

Research Design

In cross-sectional research , a researcher compares multiple segments of the population at the same time

Sometimes, we want to see how people change over time, as in studies of human development and lifespan. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time.

In cohort studies , the participants must share a common factor or characteristic such as age, demographic, or occupation. A cohort study is a type of longitudinal study in which researchers monitor and observe a chosen population over an extended period.

Triangulation means using more than one research method to improve the study’s validity.

Reliability

Reliability is a measure of consistency, if a particular measurement is repeated and the same result is obtained then it is described as being reliable.

Test-retest reliability : assessing the same person on two different occasions which shows the extent to which the test produces the same answers.
Inter-observer reliability : the extent to which there is an agreement between two or more observers.

Meta-Analysis

A meta-analysis is a systematic review that involves identifying an aim and then searching for research studies that have addressed similar aims/hypotheses.

This is done by looking through various databases, and then decisions are made about what studies are to be included/excluded.

Strengths: Increases the conclusions’ validity as they’re based on a wider range.

Weaknesses: Research designs in studies can vary, so they are not truly comparable.

Peer Review

A researcher submits an article to a journal. The choice of the journal may be determined by the journal’s audience or prestige.

The journal selects two or more appropriate experts (psychologists working in a similar field) to peer review the article without payment. The peer reviewers assess: the methods and designs used, originality of the findings, the validity of the original research findings and its content, structure and language.

Feedback from the reviewer determines whether the article is accepted. The article may be: Accepted as it is, accepted with revisions, sent back to the author to revise and re-submit or rejected without the possibility of submission.

The editor makes the final decision whether to accept or reject the research report based on the reviewers comments/ recommendations.

Peer review is important because it prevent faulty data from entering the public domain, it provides a way of checking the validity of findings and the quality of the methodology and is used to assess the research rating of university departments.

Peer reviews may be an ideal, whereas in practice there are lots of problems. For example, it slows publication down and may prevent unusual, new work being published. Some reviewers might use it as an opportunity to prevent competing researchers from publishing work.

Some people doubt whether peer review can really prevent the publication of fraudulent research.

The advent of the internet means that a lot of research and academic comment is being published without official peer reviews than before, though systems are evolving on the internet where everyone really has a chance to offer their opinions and police the quality of research.

Types of Data

Quantitative data is numerical data e.g. reaction time or number of mistakes. It represents how much or how long, how many there are of something. A tally of behavioral categories and closed questions in a questionnaire collect quantitative data.
Qualitative data is virtually any type of information that can be observed and recorded that is not numerical in nature and can be in the form of written or verbal communication. Open questions in questionnaires and accounts from observational studies collect qualitative data.
Primary data is first-hand data collected for the purpose of the investigation.
Secondary data is information that has been collected by someone other than the person who is conducting the research e.g. taken from journals, books or articles.

Validity means how well a piece of research actually measures what it sets out to, or how well it reflects the reality it claims to represent.

Validity is whether the observed effect is genuine and represents what is actually out there in the world.

Concurrent validity is the extent to which a psychological measure relates to an existing similar measure and obtains close results. For example, a new intelligence test compared to an established test.
Face validity : does the test measure what it’s supposed to measure ‘on the face of it’. This is done by ‘eyeballing’ the measuring or by passing it to an expert to check.
Ecological validit y is the extent to which findings from a research study can be generalized to other settings / real life.
Temporal validity is the extent to which findings from a research study can be generalized to other historical times.

Features of Science

Paradigm – A set of shared assumptions and agreed methods within a scientific discipline.
Paradigm shift – The result of the scientific revolution: a significant change in the dominant unifying theory within a scientific discipline.
Objectivity – When all sources of personal bias are minimised so not to distort or influence the research process.
Empirical method – Scientific approaches that are based on the gathering of evidence through direct observation and experience.
Replicability – The extent to which scientific procedures and findings can be repeated by other researchers.
Falsifiability – The principle that a theory cannot be considered scientific unless it admits the possibility of being proved untrue.

Statistical Testing

A significant result is one where there is a low probability that chance factors were responsible for any observed difference, correlation, or association in the variables tested.

If our test is significant, we can reject our null hypothesis and accept our alternative hypothesis.

If our test is not significant, we can accept our null hypothesis and reject our alternative hypothesis. A null hypothesis is a statement of no effect.

In Psychology, we use p < 0.05 (as it strikes a balance between making a type I and II error) but p < 0.01 is used in tests that could cause harm like introducing a new drug.

A type I error is when the null hypothesis is rejected when it should have been accepted (happens when a lenient significance level is used, an error of optimism).

A type II error is when the null hypothesis is accepted when it should have been rejected (happens when a stringent significance level is used, an error of pessimism).

Ethical Issues

Informed consent is when participants are able to make an informed judgment about whether to take part. It causes them to guess the aims of the study and change their behavior.
To deal with it, we can gain presumptive consent or ask them to formally indicate their agreement to participate but it may invalidate the purpose of the study and it is not guaranteed that the participants would understand.
Deception should only be used when it is approved by an ethics committee, as it involves deliberately misleading or withholding information. Participants should be fully debriefed after the study but debriefing can’t turn the clock back.
All participants should be informed at the beginning that they have the right to withdraw if they ever feel distressed or uncomfortable.
It causes bias as the ones that stayed are obedient and some may not withdraw as they may have been given incentives or feel like they’re spoiling the study. Researchers can offer the right to withdraw data after participation.
Participants should all have protection from harm . The researcher should avoid risks greater than those experienced in everyday life and they should stop the study if any harm is suspected. However, the harm may not be apparent at the time of the study.
Confidentiality concerns the communication of personal information. The researchers should not record any names but use numbers or false names though it may not be possible as it is sometimes possible to work out who the researchers were.

Research Methodology

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Cross-Cultural Research Methodology In Psychology

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What Is Internal Validity In Research?

Research Methodology , Statistics

What Is Face Validity In Research? Importance & How To Measure

Criterion Validity: Definition & Examples

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3 Chapter 3: From Theory to Hypothesis

From theory to hypothesis, 3.1 phenomena and theories.

A phenomenon (plural, phenomena) is a general result that has been observed reliably in systematic empirical research. In essence, it is an established answer to a research question. Some phenomena we have encountered in this book are that expressive writing improves health, women do not talk more than men, and cell phone usage impairs driving ability. Some others are that dissociative identity disorder (formerly called multiple personality disorder) increased greatly in prevalence during the late 20th century, people perform better on easy tasks when they are being watched by others (and worse on difficult tasks), and people recall items presented at the beginning and end of a list better than items presented in the middle.

Some Famous Psychological Phenomena

Phenomena are often given names by their discoverers or other researchers, and these names can catch on and become widely known. The following list is a small sample of famous phenomena in psychology.

· Blindsight. People with damage to their visual cortex are often able to respond to visual stimuli that they do not consciously see.

· Bystander effect. The more people who are present at an emergency situation, the less likely it is that any one of them will help.

· Fundamental attribution error. People tend to explain others’ behavior in terms of their personal characteristics as opposed to the situation they are in.

· McGurk effect. When audio of a basic speech sound is combined with video of a person making mouth movements for a different speech sound, people often perceive a sound that is intermediate between the two.

· Own-race effect. People recognize faces of people of their own race more accurately than faces of people of other races.

· Placebo effect. Placebos (fake psychological or medical treatments) often lead to improvements in people’s symptoms and functioning.

· Mere exposure effect. The more often people have been exposed to a stimulus, the more they like it—even when the stimulus is presented subliminally.

· Serial position effect. Stimuli presented near the beginning and end of a list are remembered better than stimuli presented in the middle.

· Spontaneous recovery. A conditioned response that has been extinguished often returns with no further training after the passage of time.

Although an empirical result might be referred to as a phenomenon after being observed only once, this term is more likely to be used for results that have been replicated. Replication means conducting a study again—either exactly as it was originally conducted or with modifications—to be sure that it produces the same results. Individual researchers usually replicate their own studies before publishing them. Many empirical research reports include an initial study and then one or more follow-up studies that replicate the initial study with minor modifications. Particularly interesting results come to the attention of other researchers who conduct their own replications. The positive effect of expressive writing on health and the negative effect of cell phone usage on driving ability are examples of phenomena that have been replicated many times by many different researchers.

Sometimes a replication of a study produces results that differ from the results of the initial study. This could mean that the results of the initial study or the results of the replication were a fluke—they occurred by chance and do not reflect something that is generally true. In either case, additional replications would be likely to resolve this. A failure to produce the same results could also mean that the replication differed in some important way from the initial study. For example, early studies showed that people performed a variety of tasks better and faster when they were watched by others than when they were alone. Some later replications, however, showed that people performed worse when they were watched by others. Eventually researcher Robert Zajonc identified a key difference between the two types of studies. People seemed to perform better when being watched on highly practiced tasks but worse when being watched on relatively unpracticed tasks (Zajonc, 1965). These two phenomena have now come to be called social facilitation and social inhibition.

What Is a Theory?

A theory is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition. He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.

In addition to theory, researchers in psychology use several related terms to refer to their explanations and interpretations of phenomena. A perspective is a broad approach—more general than a theory—to explaining and interpreting phenomena. For example, researchers who take a biological perspective tend to explain phenomena in terms of genetics or nervous and endocrine system structures and processes, while researchers who take a behavioral perspective tend to explain phenomena in terms of reinforcement, punishment, and other external events. A model is a precise explanation or interpretation of a specific phenomenon—often expressed in terms of equations, computer programs, or biological structures and processes. A hypothesis can be an explanation that relies on just a few key concepts—although this term more commonly refers to a prediction about a new phenomenon based on a theory. Adding to the confusion is the fact that researchers often use these terms interchangeably. It would not be considered wrong to refer to the drive theory as the drive model or even the drive hypothesis. And the biopsychosocial model of health psychology—the general idea that health is determined by an interaction of biological, psychological, and social factors—is really more like a perspective as defined here. Keep in mind, however, that the most important distinction remains that between observations and interpretations.

What Are Theories For?

Of course, scientific theories are meant to provide accurate explanations or interpretations of phenomena. But there must be more to it than this. Consider that a theory can be accurate without being very useful. To say that expressive writing helps people “deal with their emotions” might be accurate as far as it goes, but it seems too vague to be of much use. Consider also that a theory can be useful without being entirely accurate.

3.2 Additional Purposes of Theories

Here we look at three additional purposes of theories: the organization of known phenomena, the prediction of outcomes in new situations, and the generation of new research.

Organization

One important purpose of scientific theories is to organize phenomena in ways that help people think about them clearly and efficiently. The drive theory of social facilitation and social inhibition, for example, helps to organize and make sense of a large number of seemingly contradictory results. The multistore model of human memory efficiently summarizes many important phenomena: the limited capacity and short retention time of information that is attended to but not rehearsed, the importance of rehearsing information for long-term retention, the serial-position effect, and so on.

Thus theories are good or useful to the extent that they organize more phenomena with greater clarity and efficiency. Scientists generally follow the principle of parsimony, which holds that a theory should include only as many concepts as are necessary to explain or interpret the phenomena of interest. Simpler, more parsimonious theories organize phenomena more efficiently than more complex, less parsimonious theories.

A second purpose of theories is to allow researchers and others to make predictions about what will happen in new situations. For example, a gymnastics coach might wonder whether a student’s performance is likely to be better or worse during a competition than when practicing alone. Even if this particular question has never been studied empirically, Zajonc’s drive theory suggests an answer. If the student generally performs with no mistakes, she is likely to perform better during competition. If she generally performs with many mistakes, she is likely to perform worse.

In clinical psychology, treatment decisions are often guided by theories. Consider, for example, dissociative identity disorder (formerly called multiple personality disorder). The prevailing scientific theory of dissociative identity disorder is that people develop multiple personalities (also called alters) because they are familiar with this idea from popular portrayals (e.g., the movie Sybil) and because they are unintentionally encouraged to do so by their clinicians (e.g., by asking to “meet” an alter). This theory implies that rather than encouraging patients to act out multiple personalities, treatment should involve discouraging them from doing this (Lilienfeld & Lynn, 2003).

Generation of New Research

A third purpose of theories is to generate new research by raising new questions. Consider, for example, the theory that people engage in self-injurious behavior such as cutting because it reduces negative emotions such as sadness, anxiety, and anger. This theory immediately suggests several new and interesting questions. Is there, in fact, a statistical relationship between cutting and the amount of negative emotions experienced? Is it causal? If so, what is it about cutting that has this effect? Is it the pain, the sight of the injury, or something else? Does cutting affect all negative emotions equally?

Notice that a theory does not have to be accurate to serve this purpose. Even an inaccurate theory can generate new and interesting research questions. Of course, if the theory is inaccurate, the answers to the new questions will tend to be inconsistent with the theory. This will lead researchers to reevaluate the theory and either revise it or abandon it for a new one. And this is how scientific theories become more detailed and accurate over time.

Multiple Theories

At any point in time, researchers are usually considering multiple theories for any set of phenomena. One reason is that because human behavior is extremely complex, it is always possible to look at it from different perspectives. For example, a biological theory of sexual orientation might focus on the role of sex hormones during critical periods of brain development, while a sociocultural theory might focus on cultural factors that influence how underlying biological tendencies are expressed. A second reason is that—even from the same perspective—there are usually different ways to “go beyond” the phenomena of interest. For example, in addition to the drive theory of social facilitation and social inhibition, there is another theory that explains them in terms of a construct called “evaluation apprehension”—anxiety about being evaluated by the audience. Both theories go beyond the phenomena to be interpreted, but they do so by proposing somewhat different underlying processes.

Different theories of the same set of phenomena can be complementary—with each one supplying one piece of a larger puzzle. A biological theory of sexual orientation and a sociocultural theory of sexual orientation might accurately describe different aspects of the same complex phenomenon. Similarly, social facilitation could be the result of both general physiological arousal and evaluation apprehension. But different theories of the same phenomena can also be competing in the sense that if one is accurate, the other is probably not. For example, an alternative theory of dissociative identity disorder—the posttraumatic theory—holds that alters are created unconsciously by the patient as a means of coping with sexual abuse or some other traumatic experience. Because the sociocognitive theory and the posttraumatic theories attribute dissociative identity disorder to fundamentally different processes, it seems unlikely that both can be accurate.

The fact that there are multiple theories for any set of phenomena does not mean that any theory is as good as any other or that it is impossible to know whether a theory provides an accurate explanation or interpretation. On the contrary, scientists are continually comparing theories in terms of their ability to organize phenomena, predict outcomes in new situations, and generate research. Those that fare poorly are assumed to be less accurate and are abandoned, while those that fare well are assumed to be more accurate and are retained and compared with newer—and hopefully better—theories. Although scientists generally do not believe that their theories ever provide perfectly accurate descriptions of the world, they do assume that this process produces theories that come closer and closer to that ideal.

Key Takeaways

· Scientists distinguish between phenomena, which are their systematic observations, and theories, which are their explanations or interpretations of phenomena.

· In addition to providing accurate explanations or interpretations, scientific theories have three basic purposes. They organize phenomena, allow people to predict what will happen in new situations, and help generate new research.

· Researchers generally consider multiple theories for any set of phenomena. Different theories of the same set of phenomena can be complementary or competing.

3.3 Using Theories in Psychological Research

We have now seen what theories are, what they are for, and the variety of forms that they take in psychological research. In this section we look more closely at how researchers actually use them. We begin with a general description of how researchers test and revise their theories, and we end with some practical advice for beginning researchers who want to incorporate theory into their research.

Theory Testing and Revision

The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method (although this term is much more likely to be used by philosophers of science than by scientists themselves). A researcher begins with a set of phenomena and either constructs a theory to explain or interpret them or chooses an existing theory to work with. He or she then makes a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researcher then conducts an empirical study to test the hypothesis. Finally, he or she reevaluates the theory in light of the new results and revises it if necessary. This process is usually conceptualized as a cycle because the researcher can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. Together they form a model of theoretically motivated research.

As an example, let us return to Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This leads to social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969). The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory.

Constructing or Choosing a Theory

Along with generating research questions, constructing theories is one of the more creative parts of scientific research. But as with all creative activities, success requires preparation and hard work more than anything else. To construct a good theory, a researcher must know in detail about the phenomena of interest and about any existing theories based on a thorough review of the literature. The new theory must provide a coherent explanation or interpretation of the phenomena of interest and have some advantage over existing theories. It could be more formal and therefore more precise, broader in scope, more parsimonious, or it could take a new perspective or theoretical approach. If there is no existing theory, then almost any theory can be a step in the right direction.

As we have seen, formality, scope, and theoretical approach are determined in part by the nature of the phenomena to be interpreted. But the researcher’s interests and abilities play a role too. For example, constructing a theory that specifies the neural structures and processes underlying a set of phenomena requires specialized knowledge and experience in neuroscience (which most professional researchers would acquire in college and then graduate school). But again, many theories in psychology are relatively informal, narrow in scope, and expressed in terms that even a beginning researcher can understand and even use to construct his or her own new theory.

It is probably more common, however, for a researcher to start with a theory that was originally constructed by someone else—giving due credit to the originator of the theory. This is another example of how researchers work collectively to advance scientific knowledge. Once they have identified an existing theory, they might derive a hypothesis from the theory and test it or modify the theory to account for some new phenomenon and then test the modified theory.

Deriving Hypotheses

Again, a hypothesis is a prediction about a new phenomenon that should be observed if a particular theory is accurate. Theories and hypotheses always have this if-then relationship. “If drive theory is correct, then cockroaches should run through a straight runway faster, and a branching runway more slowly, when other cockroaches are present.” Although hypotheses are usually expressed as statements, they can always be rephrased as questions. “Do cockroaches run through a straight runway faster when other cockroaches are present?” Thus deriving hypotheses from theories is an excellent way of generating interesting research questions.

But how do researchers derive hypotheses from theories? One way is to generate a research question using the techniques discussed in Chapter 2 and then ask whether any theory implies an answer to that question. For example, you might wonder whether expressive writing about positive experiences improves health as much as expressive writing about traumatic experiences. Although this is an interesting question on its own, you might then ask whether the habituation theory—the idea that expressive writing causes people to habituate to negative thoughts and feelings—implies an answer. In this case, it seems clear that if the habituation theory is correct, then expressive writing about positive experiences should not be effective because it would not cause people to habituate to negative thoughts and feelings. A second way to derive hypotheses from theories is to focus on some component of the theory that has not yet been directly observed. For example, a researcher could focus on the process of habituation—perhaps hypothesizing that people should show fewer signs of emotional distress with each new writing session.

Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991). Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the number of examples they bring to mind and the other was that people base their judgments on how easily they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.

Evaluating and Revising Theories

If a hypothesis is confirmed in a systematic empirical study, then the theory has been strengthened. Not only did the theory make an accurate prediction, but there is now a new phenomenon that the theory accounts for. If a hypothesis is disconfirmed in a systematic empirical study, then the theory has been weakened. It made an inaccurate prediction, and there is now a new phenomenon that it does not account for.

Although this seems straightforward, there are some complications. First, confirming a hypothesis can strengthen a theory but it can never prove a theory. In fact, scientists tend to avoid the word “prove” when talking and writing about theories. One reason for this is that there may be other plausible theories that imply the same hypothesis, which means that confirming the hypothesis strengthens all those theories equally. A second reason is that it is always possible that another test of the hypothesis or a test of a new hypothesis derived from the theory will be disconfirmed. This is a version of the famous philosophical “problem of induction.” One cannot definitively prove a general principle (e.g., “All swans are white.”) just by observing confirming cases (e.g., white swans)—no matter how many. It is always possible that a disconfirming case (e.g., a black swan) will eventually come along. For these reasons, scientists tend to think of theories—even highly successful ones—as subject to revision based on new and unexpected observations.

A second complication has to do with what it means when a hypothesis is disconfirmed. According to the strictest version of the hypothetico-deductive method, disconfirming a hypothesis disproves the theory it was derived from. In formal logic, the premises “if A then B” and “not B” necessarily lead to the conclusion “not A.” If A is the theory and B is the hypothesis (“if A then B”), then disconfirming the hypothesis (“not B”) must mean that the theory is incorrect (“not A”). In practice, however, scientists do not give up on their theories so easily. One reason is that one disconfirmed hypothesis could be a fluke or it could be the result of a faulty research design. Perhaps the researcher did not successfully manipulate the independent variable or measure the dependent variable. A disconfirmed hypothesis could also mean that some unstated but relatively minor assumption of the theory was not met. For example, if Zajonc had failed to find social facilitation in cockroaches, he could have concluded that drive theory is still correct but it applies only to animals with sufficiently complex nervous systems.

This does not mean that researchers are free to ignore disconfirmations of their theories. If they cannot improve their research designs or modify their theories to account for repeated disconfirmations, then they eventually abandon their theories and replace them with ones that are more successful.

Incorporating Theory Into Your Research

It should be clear from this chapter that theories are not just “icing on the cake” of scientific research; they are a basic ingredient. If you can understand and use them, you will be much more successful at reading and understanding the research literature, generating interesting research questions, and writing and conversing about research. Of course, your ability to understand and use theories will improve with practice. But there are several things that you can do to incorporate theory into your research right from the start.

The first thing is to distinguish the phenomena you are interested in from any theories of those phenomena. Beware especially of the tendency to “fuse” a phenomenon to a commonsense theory of it. For example, it might be tempting to describe the negative effect of cell phone usage on driving ability by saying, “Cell phone usage distracts people from driving.” Or it might be tempting to describe the positive effect of expressive writing on health by saying, “Dealing with your emotions through writing makes you healthier.” In both of these examples, however, a vague commonsense explanation (distraction, “dealing with” emotions) has been fused to the phenomenon itself. The problem is that this gives the impression that the phenomenon has already been adequately explained and closes off further inquiry into precisely why or how it happens.

As another example, researcher Jerry Burger and his colleagues were interested in the phenomenon that people are more willing to comply with a simple request from someone with whom they are familiar (Burger, Soroka, Gonzago, Murphy, & Somervell, 1999). A beginning researcher who is asked to explain why this is the case might be at a complete loss or say something like, “Well, because they are familiar with them.” But digging just a bit deeper, Burger and his colleagues realized that there are several possible explanations. Among them are that complying with people we know creates positive feelings, that we anticipate needing something from them in the future, and that we like them more and follow an automatic rule that says to help people we like.

The next thing to do is turn to the research literature to identify existing theories of the phenomena you are interested in. Remember that there will usually be more than one plausible theory. Existing theories may be complementary or competing, but it is essential to know what they are. If there are no existing theories, you should come up with two or three of your own—even if they are informal and limited in scope. Then get in the habit of describing the phenomena you are interested in, followed by the two or three best theories of it. Do this whether you are speaking or writing about your research. When asked what their research was about, for example, Burger and his colleagues could have said something like the following:

It’s about the fact that we’re more likely to comply with requests from people we know [the phenomenon]. This is interesting because it could be because it makes us feel good [Theory 1], because we think we might get something in return [Theory 2], or because we like them more and have an automatic tendency to comply with people we like [Theory 3].

At this point, you may be able to derive a hypothesis from one of the theories. At the very least, for each research question you generate, you should ask what each plausible theory implies about the answer to that question. If one of them implies a particular answer, then you may have an interesting hypothesis to test. Burger and colleagues, for example, asked what would happen if a request came from a stranger whom participants had sat next to only briefly, did not interact with, and had no expectation of interacting with in the future. They reasoned that if familiarity created liking, and liking increased people’s tendency to comply (Theory 3), then this situation should still result in increased rates of compliance (which it did). If the question is interesting but no theory implies an answer to it, this might suggest that a new theory needs to be constructed or that existing theories need to be modified in some way. These would make excellent points of discussion in the introduction or discussion of an American Psychological Association (APA) style research report or research presentation.

When you do write your research report or plan your presentation, be aware that there are two basic ways that researchers usually include theory. The first is to raise a research question, answer that question by conducting a new study, and then offer one or more theories (usually more) to explain or interpret the results. This format works well for applied research questions and for research questions that existing theories do not address. The second way is to describe one or more existing theories, derive a hypothesis from one of those theories, test the hypothesis in a new study, and finally reevaluate the theory. This format works well when there is an existing theory that addresses the research question—especially if the resulting hypothesis is surprising or conflicts with a hypothesis derived from a different theory.

· Working with theories is not “icing on the cake.” It is a basic ingredient of psychological research.

· Like other scientists, psychologists use the hypothetico-deductive method. They construct theories to explain or interpret phenomena (or work with existing theories), derive hypotheses from their theories, test the hypotheses, and then reevaluate the theories in light of the new results.

· There are several things that even beginning researchers can do to incorporate theory into their research. These include clearly distinguishing phenomena from theories, knowing about existing theories, constructing one’s own simple theories, using theories to make predictions about the answers to research questions, and incorporating theories into one’s writing and speaking.

3.4 Understanding Null Hypothesis Testing

The Purpose of Null Hypothesis Testing

As we have seen, psychological research typically involves measuring one or more variables for a sample and computing descriptive statistics for that sample. In general, however, the researcher’s goal is not to draw conclusions about that sample but to draw conclusions about the population that the sample was selected from. Thus researchers must use sample statistics to draw conclusions about the corresponding values in the population. These corresponding values in the population are called parameters. Imagine, for example, that a researcher measures the number of depressive symptoms exhibited by each of 50 clinically depressed adults and computes the mean number of symptoms. The researcher probably wants to use this sample statistic (the mean number of symptoms for the sample) to draw conclusions about the corresponding population parameter (the mean number of symptoms for clinically depressed adults).

Unfortunately, sample statistics are not perfect estimates of their corresponding population parameters. This is because there is a certain amount of random variability in any statistic from sample to sample. This random variability in a statistic from sample to sample is called sampling error.

One implication of this is that when there is a statistical relationship in a sample, it is not always clear that there is a statistical relationship in the population. A small difference between two group means in a sample might indicate that there is a small difference between the two group means in the population. But it could also be that there is no difference between the means in the population and that the difference in the sample is just a matter of sampling error. Similarly, a Pearson’s r value of −.29 in a sample might mean that there is a negative relationship in the population. But it could also be that there is no relationship in the population and that the relationship in the sample is just a matter of sampling error.

In fact, any statistical relationship in a sample can be interpreted in two ways:

There is a relationship in the population, and the relationship in the sample reflects this.
There is no relationship in the population, and the relationship in the sample reflects only sampling error.

The purpose of null hypothesis testing is simply to help researchers decide between these two interpretations.

The Logic of Null Hypothesis Testing

Null hypothesis testing is a formal approach to deciding between two interpretations of a statistical relationship in a sample. One interpretation is called the null hypothesis (often symbolized H0 and read as “H-naught”). This is the idea that there is no relationship in the population and that the relationship in the sample reflects only sampling error. Informally, the null hypothesis is that the sample relationship “occurred by chance.” The other interpretation is called the alternative hypothesis (often symbolized as H1). This is the idea that there is a relationship in the population and that the relationship in the sample reflects this relationship in the population.

Again, every statistical relationship in a sample can be interpreted in either of these two ways: It might have occurred by chance, or it might reflect a relationship in the population. So researchers need a way to decide between them. Although there are many specific null hypothesis testing techniques, they are all based on the same general logic. The steps are as follows:

Assume for the moment that the null hypothesis is true. There is no relationship between the variables in the population.
Determine how likely the sample relationship would be if the null hypothesis were true.
If the sample relationship would be extremely unlikely, then reject the null hypothesis in favor of the alternative hypothesis. If it would not be extremely unlikely, then retain the null hypothesis.

Following this logic, we can begin to understand why Mehl and his colleagues concluded that there is no difference in talkativeness between women and men in the population. In essence, they asked the following question: “If there were no difference in the population, how likely is it that we would find a small difference of d = 0.06 in our sample?” Their answer to this question was that this sample relationship would be fairly likely if the null hypothesis were true. Therefore, they retained the null hypothesis—concluding that there is no evidence of a sex difference in the population. We can also see why Kanner and his colleagues concluded that there is a correlation between hassles and symptoms in the population. They asked, “If the null hypothesis were true, how likely is it that we would find a strong correlation of +.60 in our sample?” Their answer to this question was that this sample relationship would be fairly unlikely if the null hypothesis were true. Therefore, they rejected the null hypothesis in favor of the alternative hypothesis—concluding that there is a positive correlation between these variables in the population.

A crucial step in null hypothesis testing is finding the likelihood of the sample result if the null hypothesis were true. This probability is called the p value. A low p value means that the sample result would be unlikely if the null hypothesis were true and leads to the rejection of the null hypothesis. A high p value means that the sample result would be likely if the null hypothesis were true and leads to the retention of the null hypothesis. But how low must the p value be before the sample result is considered unlikely enough to reject the null hypothesis? In null hypothesis testing, this criterion is called α (alpha) and is almost always set to .05. If there is less than a 5% chance of a result as extreme as the sample result if the null hypothesis were true, then the null hypothesis is rejected. When this happens, the result is said to be statistically significant. If there is greater than a 5% chance of a result as extreme as the sample result when the null hypothesis is true, then the null hypothesis is retained. This does not necessarily mean that the researcher accepts the null hypothesis as true—only that there is not currently enough evidence to conclude that it is true. Researchers often use the expression “fail to reject the null hypothesis” rather than “retain the null hypothesis,” but they never use the expression “accept the null hypothesis.”

The Misunderstood p Value

The p value is one of the most misunderstood quantities in psychological research (Cohen, 1994). Even professional researchers misinterpret it, and it is not unusual for such misinterpretations to appear in statistics textbooks!

The most common misinterpretation is that the p value is the probability that the null hypothesis is true—that the sample result occurred by chance. For example, a misguided researcher might say that because the p value is .02, there is only a 2% chance that the result is due to chance and a 98% chance that it reflects a real relationship in the population. But this is incorrect. The p value is really the probability of a result at least as extreme as the sample result if the null hypothesis were true. So a p value of .02 means that if the null hypothesis were true, a sample result this extreme would occur only 2% of the time.

You can avoid this misunderstanding by remembering that the p value is not the probability that any particular hypothesis is true or false. Instead, it is the probability of obtaining the sample result if the null hypothesis were true.

Role of Sample Size and Relationship Strength

Recall that null hypothesis testing involves answering the question, “If the null hypothesis were true, what is the probability of a sample result as extreme as this one?” In other words, “What is the p value?” It can be helpful to see that the answer to this question depends on just two considerations: the strength of the relationship and the size of the sample. Specifically, the stronger the sample relationship and the larger the sample, the less likely the result would be if the null hypothesis were true. That is, the lower the p value. This should make sense. Imagine a study in which a sample of 500 women is compared with a sample of 500 men in terms of some psychological characteristic, and Cohen’s d is a strong 0.50. If there were really no sex difference in the population, then a result this strong based on such a large sample should seem highly unlikely. Now imagine a similar study in which a sample of three women is compared with a sample of three men, and Cohen’s d is a weak 0.10. If there were no sex difference in the population, then a relationship this weak based on such a small sample should seem likely. And this is precisely why the null hypothesis would be rejected in the first example and retained in the second.

Of course, sometimes the result can be weak and the sample large, or the result can be strong and the sample small. In these cases, the two considerations trade off against each other so that a weak result can be statistically significant if the sample is large enough and a strong relationship can be statistically significant even if the sample is small. Weak relationships based on medium or small samples are never statistically significant and that strong relationships based on medium or larger samples are always statistically significant. If you keep this in mind, you will often know whether a result is statistically significant based on the descriptive statistics alone. It is extremely useful to be able to develop this kind of intuitive judgment. One reason is that it allows you to develop expectations about how your formal null hypothesis tests are going to come out, which in turn allows you to detect problems in your analyses. For example, if your sample relationship is strong and your sample is medium, then you would expect to reject the null hypothesis. If for some reason your formal null hypothesis test indicates otherwise, then you need to double-check your computations and interpretations. A second reason is that the ability to make this kind of intuitive judgment is an indication that you understand the basic logic of this approach in addition to being able to do the computations.

Statistical Significance Versus Practical Significance

A statistically significant result is not necessarily a strong one. Even a very weak result can be statistically significant if it is based on a large enough sample. This is closely related to Janet Shibley Hyde’s argument about sex differences (Hyde, 2007). The differences between women and men in mathematical problem solving and leadership ability are statistically significant. But the word significant can cause people to interpret these differences as strong and important—perhaps even important enough to influence the college courses they take or even who they vote for. As we have seen, however, these statistically significant differences are actually quite weak—perhaps even “trivial.”

This is why it is important to distinguish between the statistical significance of a result and the practical significance of that result. Practical significance refers to the importance or usefulness of the result in some real-world context. Many sex differences are statistically significant—and may even be interesting for purely scientific reasons—but they are not practically significant. In clinical practice, this same concept is often referred to as “clinical significance.” For example, a study on a new treatment for social phobia might show that it produces a statistically significant positive effect. Yet this effect still might not be strong enough to justify the time, effort, and other costs of putting it into practice—especially if easier and cheaper treatments that work almost as well already exist. Although statistically significant, this result would be said to lack practical or clinical significance.

· Null hypothesis testing is a formal approach to deciding whether a statistical relationship in a sample reflects a real relationship in the population or is just due to chance.

· The logic of null hypothesis testing involves assuming that the null hypothesis is true, finding how likely the sample result would be if this assumption were correct, and then making a decision. If the sample result would be unlikely if the null hypothesis were true, then it is rejected in favor of the alternative hypothesis. If it would not be unlikely, then the null hypothesis is retained.

· The probability of obtaining the sample result if the null hypothesis were true (the p value) is based on two considerations: relationship strength and sample size. Reasonable judgments about whether a sample relationship is statistically significant can often be made by quickly considering these two factors.

· Statistical significance is not the same as relationship strength or importance. Even weak relationships can be statistically significant if the sample size is large enough. It is important to consider relationship strength and the practical significance of a result in addition to its statistical significance.

References from Chapter 3

Burger, J. M., Soroka, S., Gonzago, K., Murphy, E., Somervell, E. (1999). The effect of fleeting attraction on compliance to requests. Personality and Social Psychology Bulletin, 27, 1578–1586.

Cohen, J. (1994). The world is round: p .05. American Psychologist, 49, 997–1003.

Hyde, J. S. (2007). New directions in the study of gender similarities and differences. Current Directions in Psychological Science, 16, 259–263.

Izawa, C. (Ed.) (1999). On human memory: Evolution, progress, and reflections on the 30th anniversary of the Atkinson-Shiffrin model. Mahwah, NJ: Erlbaum.

Lilienfeld, S. O., Lynn, S. J. (2003). Dissociative identity disorder: Multiplepersonalities, multiple controversies. In S. O. Lilienfeld, S. J. Lynn, J. M. Lohr (Eds.), Science and pseudoscience in clinical psychology (pp. 109–142). New York, NY: Guilford Press.

Neisser, U., Boodoo, G., Bouchard, T. J., Boykin, A. W., Brody, N., Ceci,…Urbina, S. (1996). Intelligence: Knowns and unknowns. American Psychologist, 51, 77–101.

Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic. Journal of Personality and Social Psychology, 61, 195–202.

Zajonc, R. B. (1965). Social facilitation. Science, 149, 269–274.

Zajonc, R. B., Heingartner, A., Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach. Journal of Personality and Social Psychology, 13, 83–92.

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6 Hypothesis Examples in Psychology

The hypothesis is one of the most important steps of psychological research. Hypothesis refers to an assumption or the temporary statement made by the researcher before the execution of the experiment, regarding the possible outcome of that experiment. A hypothesis can be tested through various scientific and statistical tools. It is a logical guess based on previous knowledge and investigations related to the problem under investigation. In this article, we’ll learn about the significance of the hypothesis, the sources of the hypothesis, and the various examples of the hypothesis.

Sources of Hypothesis

The formulation of a good hypothesis is not an easy task. One needs to take care of the various crucial steps to get an accurate hypothesis. The hypothesis formulation demands both the creativity of the researcher and his/her years of experience. The researcher needs to use critical thinking to avoid committing any errors such as choosing the wrong hypothesis. Although the hypothesis is considered the first step before further investigations such as data collection for the experiment, the hypothesis formulation also requires some amount of data collection. The data collection for the hypothesis formulation refers to the review of literature related to the concerned topic, and understanding of the previous research on the related topic. Following are some of the main sources of the hypothesis that may help the researcher to formulate a good hypothesis.

Reviewing the similar studies and literature related to a similar problem.
Examining the available data concerned with the problem.
Discussing the problem with the colleagues, or the professional researchers about the problem under investigation.
Thorough research and investigation by conducting field interviews or surveys on the people that are directly concerned with the problem under investigation.
Sometimes ‘institution’ of the well known and experienced researcher is also considered as a good source of the hypothesis formulation.

Real Life Hypothesis Examples

1. null hypothesis and alternative hypothesis examples.

Every research problem-solving procedure begins with the formulation of the null hypothesis and the alternative hypothesis. The alternative hypothesis assumes the existence of the relationship between the variables under study, while the null hypothesis denies the relationship between the variables under study. Following are examples of the null hypothesis and the alternative hypothesis based on the research problem.

Research Problem: What is the benefit of eating an apple daily on your health?

Alternative Hypothesis: Eating an apple daily reduces the chances of visiting the doctor.

Null Hypothesis : Eating an apple daily does not impact the frequency of visiting the doctor.

Research Problem: What is the impact of spending a lot of time on mobiles on the attention span of teenagers.

Alternative Problem: Spending time on the mobiles and attention span have a negative correlation.

Null Hypothesis: There does not exist any correlation between the use of mobile by teenagers on their attention span.

Research Problem: What is the impact of providing flexible working hours to the employees on the job satisfaction level.

Alternative Hypothesis : Employees who get the option of flexible working hours have better job satisfaction than the employees who don’t get the option of flexible working hours.

Null Hypothesis: There is no association between providing flexible working hours and job satisfaction.

2. Simple Hypothesis Examples

The hypothesis that includes only one independent variable (predictor variable) and one dependent variable (outcome variable) is termed the simple hypothesis. For example, the children are more likely to get clinical depression if their parents had also suffered from the clinical depression. Here, the independent variable is the parents suffering from clinical depression and the dependent or the outcome variable is the clinical depression observed in their child/children. Other examples of the simple hypothesis are given below,

If the management provides the official snack breaks to the employees, the employees are less likely to take the off-site breaks. Here, providing snack breaks is the independent variable and the employees are less likely to take the off-site break is the dependent variable.

3. Complex Hypothesis Examples

If the hypothesis includes more than one independent (predictor variable) or more than one dependent variable (outcome variable) it is known as the complex hypothesis. For example, clinical depression in children is associated with a family clinical depression history and a stressful and hectic lifestyle. In this case, there are two independent variables, i.e., family history of clinical depression and hectic and stressful lifestyle, and one dependent variable, i.e., clinical depression. Following are some more examples of the complex hypothesis,

4. Logical Hypothesis Examples

If there are not many pieces of evidence and studies related to the concerned problem, then the researcher can take the help of the general logic to formulate the hypothesis. The logical hypothesis is proved true through various logic. For example, if the researcher wants to prove that the animal needs water for its survival, then this can be logically verified through the logic that ‘living beings can not survive without the water.’ Following are some more examples of logical hypotheses,

Tia is not good at maths, hence she will not choose the accounting sector as her career.
If there is a correlation between skin cancer and ultraviolet rays, then the people who are more exposed to the ultraviolet rays are more prone to skin cancer.
The beings belonging to the different planets can not breathe in the earth’s atmosphere.
The creatures living in the sea use anaerobic respiration as those living outside the sea use aerobic respiration.

5. Empirical Hypothesis Examples

The empirical hypothesis comes into existence when the statement is being tested by conducting various experiments. This hypothesis is not just an idea or notion, instead, it refers to the statement that undergoes various trials and errors, and various extraneous variables can impact the result. The trials and errors provide a set of results that can be testable over time. Following are the examples of the empirical hypothesis,

The hungry cat will quickly reach the endpoint through the maze, if food is placed at the endpoint then the cat is not hungry.
The people who consume vitamin c have more glowing skin than the people who consume vitamin E.
Hair growth is faster after the consumption of Vitamin E than vitamin K.
Plants will grow faster with fertilizer X than with fertilizer Y.

6. Statistical Hypothesis Examples

The statements that can be proven true by using the various statistical tools are considered the statistical hypothesis. The researcher uses statistical data about an area or the group in the analysis of the statistical hypothesis. For example, if you study the IQ level of the women belonging to nation X, it would be practically impossible to measure the IQ level of each woman belonging to nation X. Here, statistical methods come to the rescue. The researcher can choose the sample population, i.e., women belonging to the different states or provinces of the nation X, and conduct the statistical tests on this sample population to get the average IQ of the women belonging to the nation X. Following are the examples of the statistical hypothesis.

30 per cent of the women belonging to the nation X are working.
50 per cent of the people living in the savannah are above the age of 70 years.
45 per cent of the poor people in the United States are uneducated.

Significance of Hypothesis

A hypothesis is very crucial in experimental research as it aims to predict any particular outcome of the experiment. Hypothesis plays an important role in guiding the researchers to focus on the concerned area of research only. However, the hypothesis is not required by all researchers. The type of research that seeks for finding facts, i.e., historical research, does not need the formulation of the hypothesis. In the historical research, the researchers look for the pieces of evidence related to the human life, the history of a particular area, or the occurrence of any event, this means that the researcher does not have a strong basis to make an assumption in these types of researches, hence hypothesis is not needed in this case. As stated by Hillway (1964)

When fact-finding alone is the aim of the study, a hypothesis is not required.”

The hypothesis may not be an important part of the descriptive or historical studies, but it is a crucial part for the experimental researchers. Following are some of the points that show the importance of formulating a hypothesis before conducting the experiment.

Hypothesis provides a tentative statement about the outcome of the experiment that can be validated and tested. It helps the researcher to directly focus on the problem under investigation by collecting the relevant data according to the variables mentioned in the hypothesis.
Hypothesis facilitates a direction to the experimental research. It helps the researcher in analysing what is relevant for the study and what’s not. It prevents the researcher’s time as he does not need to waste time on reviewing the irrelevant research and literature, and also prevents the researcher from collecting the irrelevant data.
Hypothesis helps the researcher in choosing the appropriate sample, statistical tests to conduct, variables to be studied and the research methodology. The hypothesis also helps the study from being generalised as it focuses on the limited and exact problem under investigation.
Hypothesis act as a framework for deducing the outcomes of the experiment. The researcher can easily test the different hypotheses for understanding the interaction among the various variables involved in the study. On this basis of the results obtained from the testing of various hypotheses, the researcher can formulate the final meaningful report.

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

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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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How to Write a Research Hypothesis: Good & Bad Examples

What is a research hypothesis?

A research hypothesis is an attempt at explaining a phenomenon or the relationships between phenomena/variables in the real world. Hypotheses are sometimes called “educated guesses”, but they are in fact (or let’s say they should be) based on previous observations, existing theories, scientific evidence, and logic. A research hypothesis is also not a prediction—rather, predictions are ( should be) based on clearly formulated hypotheses. For example, “We tested the hypothesis that KLF2 knockout mice would show deficiencies in heart development” is an assumption or prediction, not a hypothesis.

The research hypothesis at the basis of this prediction is “the product of the KLF2 gene is involved in the development of the cardiovascular system in mice”—and this hypothesis is probably (hopefully) based on a clear observation, such as that mice with low levels of Kruppel-like factor 2 (which KLF2 codes for) seem to have heart problems. From this hypothesis, you can derive the idea that a mouse in which this particular gene does not function cannot develop a normal cardiovascular system, and then make the prediction that we started with.

What is the difference between a hypothesis and a prediction?

You might think that these are very subtle differences, and you will certainly come across many publications that do not contain an actual hypothesis or do not make these distinctions correctly. But considering that the formulation and testing of hypotheses is an integral part of the scientific method, it is good to be aware of the concepts underlying this approach. The two hallmarks of a scientific hypothesis are falsifiability (an evaluation standard that was introduced by the philosopher of science Karl Popper in 1934) and testability —if you cannot use experiments or data to decide whether an idea is true or false, then it is not a hypothesis (or at least a very bad one).

So, in a nutshell, you (1) look at existing evidence/theories, (2) come up with a hypothesis, (3) make a prediction that allows you to (4) design an experiment or data analysis to test it, and (5) come to a conclusion. Of course, not all studies have hypotheses (there is also exploratory or hypothesis-generating research), and you do not necessarily have to state your hypothesis as such in your paper.

But for the sake of understanding the principles of the scientific method, let’s first take a closer look at the different types of hypotheses that research articles refer to and then give you a step-by-step guide for how to formulate a strong hypothesis for your own paper.

Types of Research Hypotheses

Hypotheses can be simple , which means they describe the relationship between one single independent variable (the one you observe variations in or plan to manipulate) and one single dependent variable (the one you expect to be affected by the variations/manipulation). If there are more variables on either side, you are dealing with a complex hypothesis. You can also distinguish hypotheses according to the kind of relationship between the variables you are interested in (e.g., causal or associative ). But apart from these variations, we are usually interested in what is called the “alternative hypothesis” and, in contrast to that, the “null hypothesis”. If you think these two should be listed the other way round, then you are right, logically speaking—the alternative should surely come second. However, since this is the hypothesis we (as researchers) are usually interested in, let’s start from there.

Alternative Hypothesis

If you predict a relationship between two variables in your study, then the research hypothesis that you formulate to describe that relationship is your alternative hypothesis (usually H1 in statistical terms). The goal of your hypothesis testing is thus to demonstrate that there is sufficient evidence that supports the alternative hypothesis, rather than evidence for the possibility that there is no such relationship. The alternative hypothesis is usually the research hypothesis of a study and is based on the literature, previous observations, and widely known theories.

Null Hypothesis

The hypothesis that describes the other possible outcome, that is, that your variables are not related, is the null hypothesis ( H0 ). Based on your findings, you choose between the two hypotheses—usually that means that if your prediction was correct, you reject the null hypothesis and accept the alternative. Make sure, however, that you are not getting lost at this step of the thinking process: If your prediction is that there will be no difference or change, then you are trying to find support for the null hypothesis and reject H1.

Directional Hypothesis

While the null hypothesis is obviously “static”, the alternative hypothesis can specify a direction for the observed relationship between variables—for example, that mice with higher expression levels of a certain protein are more active than those with lower levels. This is then called a one-tailed hypothesis.

Another example for a directional one-tailed alternative hypothesis would be that

H1: Attending private classes before important exams has a positive effect on performance.

Your null hypothesis would then be that

H0: Attending private classes before important exams has no/a negative effect on performance.

Nondirectional Hypothesis

A nondirectional hypothesis does not specify the direction of the potentially observed effect, only that there is a relationship between the studied variables—this is called a two-tailed hypothesis. For instance, if you are studying a new drug that has shown some effects on pathways involved in a certain condition (e.g., anxiety) in vitro in the lab, but you can’t say for sure whether it will have the same effects in an animal model or maybe induce other/side effects that you can’t predict and potentially increase anxiety levels instead, you could state the two hypotheses like this:

H1: The only lab-tested drug (somehow) affects anxiety levels in an anxiety mouse model.

You then test this nondirectional alternative hypothesis against the null hypothesis:

H0: The only lab-tested drug has no effect on anxiety levels in an anxiety mouse model.

How to Write a Hypothesis for a Research Paper

Now that we understand the important distinctions between different kinds of research hypotheses, let’s look at a simple process of how to write a hypothesis.

Writing a Hypothesis Step:1

Ask a question, based on earlier research. Research always starts with a question, but one that takes into account what is already known about a topic or phenomenon. For example, if you are interested in whether people who have pets are happier than those who don’t, do a literature search and find out what has already been demonstrated. You will probably realize that yes, there is quite a bit of research that shows a relationship between happiness and owning a pet—and even studies that show that owning a dog is more beneficial than owning a cat ! Let’s say you are so intrigued by this finding that you wonder:

What is it that makes dog owners even happier than cat owners?

Let’s move on to Step 2 and find an answer to that question.

Writing a Hypothesis Step 2:

Formulate a strong hypothesis by answering your own question. Again, you don’t want to make things up, take unicorns into account, or repeat/ignore what has already been done. Looking at the dog-vs-cat papers your literature search returned, you see that most studies are based on self-report questionnaires on personality traits, mental health, and life satisfaction. What you don’t find is any data on actual (mental or physical) health measures, and no experiments. You therefore decide to make a bold claim come up with the carefully thought-through hypothesis that it’s maybe the lifestyle of the dog owners, which includes walking their dog several times per day, engaging in fun and healthy activities such as agility competitions, and taking them on trips, that gives them that extra boost in happiness. You could therefore answer your question in the following way:

Dog owners are happier than cat owners because of the dog-related activities they engage in.

Now you have to verify that your hypothesis fulfills the two requirements we introduced at the beginning of this resource article: falsifiability and testability . If it can’t be wrong and can’t be tested, it’s not a hypothesis. We are lucky, however, because yes, we can test whether owning a dog but not engaging in any of those activities leads to lower levels of happiness or well-being than owning a dog and playing and running around with them or taking them on trips.

Writing a Hypothesis Step 3:

Make your predictions and define your variables. We have verified that we can test our hypothesis, but now we have to define all the relevant variables, design our experiment or data analysis, and make precise predictions. You could, for example, decide to study dog owners (not surprising at this point), let them fill in questionnaires about their lifestyle as well as their life satisfaction (as other studies did), and then compare two groups of active and inactive dog owners. Alternatively, if you want to go beyond the data that earlier studies produced and analyzed and directly manipulate the activity level of your dog owners to study the effect of that manipulation, you could invite them to your lab, select groups of participants with similar lifestyles, make them change their lifestyle (e.g., couch potato dog owners start agility classes, very active ones have to refrain from any fun activities for a certain period of time) and assess their happiness levels before and after the intervention. In both cases, your independent variable would be “ level of engagement in fun activities with dog” and your dependent variable would be happiness or well-being .

Examples of a Good and Bad Hypothesis

Let’s look at a few examples of good and bad hypotheses to get you started.

Good Hypothesis Examples

Bad hypothesis examples, tips for writing a research hypothesis.

If you understood the distinction between a hypothesis and a prediction we made at the beginning of this article, then you will have no problem formulating your hypotheses and predictions correctly. To refresh your memory: We have to (1) look at existing evidence, (2) come up with a hypothesis, (3) make a prediction, and (4) design an experiment. For example, you could summarize your dog/happiness study like this:

(1) While research suggests that dog owners are happier than cat owners, there are no reports on what factors drive this difference. (2) We hypothesized that it is the fun activities that many dog owners (but very few cat owners) engage in with their pets that increases their happiness levels. (3) We thus predicted that preventing very active dog owners from engaging in such activities for some time and making very inactive dog owners take up such activities would lead to an increase and decrease in their overall self-ratings of happiness, respectively. (4) To test this, we invited dog owners into our lab, assessed their mental and emotional well-being through questionnaires, and then assigned them to an “active” and an “inactive” group, depending on…

Note that you use “we hypothesize” only for your hypothesis, not for your experimental prediction, and “would” or “if – then” only for your prediction, not your hypothesis. A hypothesis that states that something “would” affect something else sounds as if you don’t have enough confidence to make a clear statement—in which case you can’t expect your readers to believe in your research either. Write in the present tense, don’t use modal verbs that express varying degrees of certainty (such as may, might, or could ), and remember that you are not drawing a conclusion while trying not to exaggerate but making a clear statement that you then, in a way, try to disprove . And if that happens, that is not something to fear but an important part of the scientific process.

Similarly, don’t use “we hypothesize” when you explain the implications of your research or make predictions in the conclusion section of your manuscript, since these are clearly not hypotheses in the true sense of the word. As we said earlier, you will find that many authors of academic articles do not seem to care too much about these rather subtle distinctions, but thinking very clearly about your own research will not only help you write better but also ensure that even that infamous Reviewer 2 will find fewer reasons to nitpick about your manuscript.

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Now that you know how to write a strong research hypothesis for your research paper, you might be interested in our free AI proofreader , Wordvice AI, which finds and fixes errors in grammar, punctuation, and word choice in academic texts. Or if you are interested in human proofreading , check out our English editing services , including research paper editing and manuscript editing .

On the Wordvice academic resources website , you can also find many more articles and other resources that can help you with writing the other parts of your research paper , with making a research paper outline before you put everything together, or with writing an effective cover letter once you are ready to submit.

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Developing a Hypothesis

Rajiv S. Jhangiani; I-Chant A. Chiang; Carrie Cuttler; and Dana C. Leighton

Learning Objectives

Distinguish between a theory and a hypothesis.
Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
Understand the characteristics of a good hypothesis.

Theories and Hypotheses

Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [2] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the number of examples they bring to mind and the other was that people base their judgments on how easily they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.

Theory Testing

The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method (although this term is much more likely to be used by philosophers of science than by scientists themselves). Researchers begin with a set of phenomena and either construct a theory to explain or interpret them or choose an existing theory to work with. They then make a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researchers then conduct an empirical study to test the hypothesis. Finally, they reevaluate the theory in light of the new results and revise it if necessary. This process is usually conceptualized as a cycle because the researchers can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As Figure 2.3 shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.

As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [3] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans [Zajonc & Sales, 1966] [4] in many other studies afterward).

Incorporating Theory into Your Research

Characteristics of a Good Hypothesis

There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use inductive reasoning which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be positive. That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that it really does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.

Zajonc, R. B. (1965). Social facilitation. Science, 149 , 269–274 ↵
Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic. Journal of Personality and Social Psychology, 61 , 195–202. ↵
Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach. Journal of Personality and Social Psychology, 13 , 83–92. ↵
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A coherent explanation or interpretation of one or more phenomena.

A specific prediction about a new phenomenon that should be observed if a particular theory is accurate.

The ability to test the hypothesis using the methods of science and the possibility to gather evidence that will disconfirm the hypothesis if it is indeed false.

Developing a Hypothesis Copyright © by Rajiv S. Jhangiani; I-Chant A. Chiang; Carrie Cuttler; and Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Aims and Hypotheses

Last updated 22 Mar 2021

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Observations of events or behaviour in our surroundings provoke questions as to why they occur. In turn, one or multiple theories might attempt to explain a phenomenon, and investigations are consequently conducted to test them. One observation could be that athletes tend to perform better when they have a training partner, and a theory might propose that this is because athletes are more motivated with peers around them.

The aim of an investigation, driven by a theory to explain a given observation, states the intent of the study in general terms. Continuing the above example, the consequent aim might be “to investigate the effect of having a training partner on athletes’ motivation levels”.

The theory attempting to explain an observation will help to inform hypotheses - predictions of an investigation’s outcome that make specific reference to the independent variables (IVs) manipulated and dependent variables (DVs) measured by the researchers.

There are two types of hypothesis:

- H 1 – Research hypothesis
- H 0 – Null hypothesis

H 1 – The Research Hypothesis

This predicts a statistically significant effect of an IV on a DV (i.e. an experiment), or a significant relationship between variables (i.e. a correlation study), e.g.

In an experiment: “Athletes who have a training partner are likely to score higher on a questionnaire measuring motivation levels than athletes who train alone.”
In a correlation study: ‘There will be a significant positive correlation between athletes’ motivation questionnaire scores and the number of partners athletes train with.”

The research hypothesis will be directional (one-tailed) if theory or existing evidence argues a particular ‘direction’ of the predicted results, as demonstrated in the two hypothesis examples above.

Non-directional (two-tailed) research hypotheses do not predict a direction, so here would simply predict “a significant difference” between questionnaire scores in athletes who train alone and with a training partner (in an experiment), or “a significant relationship” between questionnaire scores and number of training partners (in a correlation study).

H 0 – The Null Hypothesis

This predicts that a statistically significant effect or relationship will not be found, e.g.

In an experiment: “There will be no significant difference in motivation questionnaire scores between athletes who train with and without a training partner.”
In a correlation study: “There will be no significant relationship between motivation questionnaire scores and the number of partners athletes train with.”

When the investigation concludes, analysis of results will suggest that either the research hypothesis or null hypothesis can be retained, with the other rejected. Ultimately this will either provide evidence to support of refute the theory driving a hypothesis, and may lead to further research in the field.

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Aims And Hypotheses, Directional And Non-Directional

March 7, 2021 - paper 2 psychology in context | research methods.

Back to Paper 2 - Research Methods

In Psychology, hypotheses are predictions made by the researcher about the outcome of a study. The research can chose to make a specific prediction about what they feel will happen in their research (a directional hypothesis) or they can make a ‘general,’ ‘less specific’ prediction about the outcome of their research (a non-directional hypothesis). The type of prediction that a researcher makes is usually dependent on whether or not any previous research has also investigated their research aim.

Variables Recap:

The independent variable (IV) is the variable that psychologists manipulate/change to see if changing this variable has an effect on the depen dent variable (DV).

The dependent variable (DV) is the variable that the psychologists measures (to see if the IV has had an effect).

It is important that the only variable that is changed in research is the independent variable (IV), all other variables have to be kept constant across the control condition and the experimental conditions. Only then will researchers be able to observe the true effects of just the independent variable (IV) on the dependent variable (DV).

Research/Experimental Aim(S):

An aim is a clear and precise statement of the purpose of the study. It is a statement of why a research study is taking place. This should include what is being studied and what the study is trying to achieve. (e.g. “This study aims to investigate the effects of alcohol on reaction times”.

It is important that aims created in research are realistic and ethical.

Hypotheses:

This is a testable statement that predicts what the researcher expects to happen in their research. The research study itself is therefore a means of testing whether or not the hypothesis is supported by the findings. If the findings do support the hypothesis then the hypothesis can be retained (i.e., accepted), but if not, then it must be rejected.

Three Different Hypotheses:

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Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

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Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
Education : “Implementing a new teaching method will result in higher student achievement scores.”
Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

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Introduction to Research Methods in Psychology

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

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

There are several different research methods in psychology , each of which can help researchers learn more about the way people think, feel, and behave. If you're a psychology student or just want to know the types of research in psychology, here are the main ones as well as how they work.

Three Main Types of Research in Psychology

stevecoleimages/Getty Images

Psychology research can usually be classified as one of three major types.

1. Causal or Experimental Research

When most people think of scientific experimentation, research on cause and effect is most often brought to mind. Experiments on causal relationships investigate the effect of one or more variables on one or more outcome variables. This type of research also determines if one variable causes another variable to occur or change.

An example of this type of research in psychology would be changing the length of a specific mental health treatment and measuring the effect on study participants.

2. Descriptive Research

Descriptive research seeks to depict what already exists in a group or population. Three types of psychology research utilizing this method are:

Case studies
Observational studies

An example of this psychology research method would be an opinion poll to determine which presidential candidate people plan to vote for in the next election. Descriptive studies don't try to measure the effect of a variable; they seek only to describe it.

3. Relational or Correlational Research

A study that investigates the connection between two or more variables is considered relational research. The variables compared are generally already present in the group or population.

For example, a study that looks at the proportion of males and females that would purchase either a classical CD or a jazz CD would be studying the relationship between gender and music preference.

Theory vs. Hypothesis in Psychology Research

People often confuse the terms theory and hypothesis or are not quite sure of the distinctions between the two concepts. If you're a psychology student, it's essential to understand what each term means, how they differ, and how they're used in psychology research.

A theory is a well-established principle that has been developed to explain some aspect of the natural world. A theory arises from repeated observation and testing and incorporates facts, laws, predictions, and tested hypotheses that are widely accepted.

A hypothesis is a specific, testable prediction about what you expect to happen in your study. For example, an experiment designed to look at the relationship between study habits and test anxiety might have a hypothesis that states, "We predict that students with better study habits will suffer less test anxiety." Unless your study is exploratory in nature, your hypothesis should always explain what you expect to happen during the course of your experiment or research.

While the terms are sometimes used interchangeably in everyday use, the difference between a theory and a hypothesis is important when studying experimental design.

Some other important distinctions to note include:

A theory predicts events in general terms, while a hypothesis makes a specific prediction about a specified set of circumstances.
A theory has been extensively tested and is generally accepted, while a hypothesis is a speculative guess that has yet to be tested.

The Effect of Time on Research Methods in Psychology

There are two types of time dimensions that can be used in designing a research study:

Cross-sectional research takes place at a single point in time. All tests, measures, or variables are administered to participants on one occasion. This type of research seeks to gather data on present conditions instead of looking at the effects of a variable over a period of time.
Longitudinal research is a study that takes place over a period of time. Data is first collected at the beginning of the study, and may then be gathered repeatedly throughout the length of the study. Some longitudinal studies may occur over a short period of time, such as a few days, while others may take place over a period of months, years, or even decades.

The effects of aging are often investigated using longitudinal research.

Causal Relationships Between Psychology Research Variables

What do we mean when we talk about a “relationship” between variables? In psychological research, we're referring to a connection between two or more factors that we can measure or systematically vary.

One of the most important distinctions to make when discussing the relationship between variables is the meaning of causation.

A causal relationship is when one variable causes a change in another variable. These types of relationships are investigated by experimental research to determine if changes in one variable actually result in changes in another variable.

Correlational Relationships Between Psychology Research Variables

A correlation is the measurement of the relationship between two variables. These variables already occur in the group or population and are not controlled by the experimenter.

A positive correlation is a direct relationship where, as the amount of one variable increases, the amount of a second variable also increases.
In a negative correlation , as the amount of one variable goes up, the levels of another variable go down.

In both types of correlation, there is no evidence or proof that changes in one variable cause changes in the other variable. A correlation simply indicates that there is a relationship between the two variables.

The most important concept is that correlation does not equal causation. Many popular media sources make the mistake of assuming that simply because two variables are related, a causal relationship exists.

Psychologists use descriptive, correlational, and experimental research designs to understand behavior . In: Introduction to Psychology . Minneapolis, MN: University of Minnesota Libraries Publishing; 2010.

Caruana EJ, Roman M, Herandez-Sanchez J, Solli P. Longitudinal studies . Journal of Thoracic Disease. 2015;7(11):E537-E540. doi:10.3978/j.issn.2072-1439.2015.10.63

University of Berkeley. Science at multiple levels . Understanding Science 101 . Published 2012.

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

Psychology Hypothesis

Delving into the realm of human behavior and cognition, Psychology Hypothesis Statement Examples illuminate the intricate workings of the mind. These thesis statement examples span various psychological phenomena, offering insights into crafting hypotheses that drive impactful research. From personality traits to cognitive processes, explore the guide to formulate precise and insightful psychology hypothesis statements that shed light on the complexities of human psychology.

What is the Psychology Hypothesis?

In psychology, a good hypothesis is a tentative statement or educated guess that proposes a potential relationship between variables. It serves as a foundation for research, guiding the investigation into specific psychological phenomena or behaviors. A well-constructed psychology hypothesis outlines the expected outcome of the study and provides a framework for data collection and analysis.

Example of a Psychology Hypothesis Statement :

Research Question: Does exposure to nature improve individuals’ mood and well-being?

Hypothesis Statement: “Individuals who spend more time in natural environments will report higher levels of positive mood and overall well-being compared to those who spend less time outdoors.”

In this example, the psychology hypothesis predicts a positive relationship between exposure to nature and improved mood and well-being. The statement sets the direction for the study and provides a clear basis for data collection and analysis.

100 Psychology Hypothesis Statement Examples

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Psychology Hypothesis Statement Examples encompass a diverse range of human behaviors and mental processes. Dive into the complexities of the human mind with Simple hypothesis that explore relationships, patterns, and influences on behavior. From memory recall to social interactions, these examples offer insights into crafting precise and impactful psychology hypotheses that drive meaningful research.

Effect of Color on Mood : Exposure to blue hues elevates mood in individuals.
Social Media and Self-Esteem : Higher social media usage correlates with lower self-esteem levels.
Sleep Quality and Cognitive Performance : Improved sleep quality enhances cognitive performance.
Personality Traits and Leadership : Extroverted individuals are more likely to assume leadership roles.
Parent-Child Attachment and Behavior : Strong parent-child attachment fosters positive behavior in children.
Cognitive Load and Decision Making : Increased cognitive load leads to poorer decision-making abilities.
Mindfulness Meditation and Stress Reduction : Regular mindfulness practice reduces stress levels.
Empathy and Altruistic Behavior : Higher empathy levels predict increased altruistic actions.
Positive Reinforcement and Learning : Positive reinforcement enhances learning outcomes in children.
Attachment Style and Romantic Relationships : Securely attached individuals experience more satisfying romantic relationships.
Body Image and Media Exposure : Greater exposure to idealized body images leads to negative body image perceptions.
Anxiety Levels and Academic Performance : Higher anxiety levels negatively impact academic achievement.
Parenting Style and Aggression : Authoritarian parenting style correlates with higher aggression in children.
Cognitive Aging and Memory Recall : Older adults experience reduced memory recall compared to younger individuals.
Peer Pressure and Risky Behavior : Peer pressure increases engagement in risky behaviors among adolescents.
Emotional Intelligence and Relationship Satisfaction : High emotional intelligence leads to greater relationship satisfaction.
Attachment Style and Coping Mechanisms : Insecure attachment is linked to maladaptive coping strategies.
Perceived Control and Stress Resilience : Higher perceived control buffers against the negative effects of stress.
Social Comparison and Self-Esteem : Frequent social comparison diminishes self-esteem levels.
Gender Stereotypes and Career Aspirations : Gender stereotypes influence career aspirations of young adults.
Technology Usage and Social Isolation : Increased technology usage contributes to feelings of social isolation.
Empathy and Conflict Resolution : Higher empathy levels facilitate effective conflict resolution.
Parental Influence and Academic Motivation : Parental involvement positively impacts student academic motivation.
Attention Deficit Hyperactivity Disorder (ADHD) and Video Games : Children with ADHD show increased hyperactivity after playing video games.
Positive Psychology Interventions and Well-being : Engaging in positive psychology interventions enhances overall well-being.
Social Support and Mental Health : Adequate social support leads to better mental health outcomes.
Parent-Child Communication and Risky Behavior : Open parent-child communication reduces engagement in risky behaviors.
Social Media and Body Dissatisfaction : Extensive social media use is linked to increased body dissatisfaction.
Personality Traits and Coping Strategies : Different personality traits influence varied coping mechanisms.
Peer Influence and Substance Abuse : Peer influence contributes to higher rates of substance abuse among adolescents.
Attentional Bias and Anxiety : Individuals with attentional bias are more prone to experiencing anxiety.
Attachment Style and Romantic Jealousy : Insecure attachment predicts higher levels of romantic jealousy.
Emotion Regulation and Well-being : Effective emotion regulation leads to greater overall well-being.
Parenting Styles and Academic Resilience : Supportive parenting styles enhance academic resilience in children.
Cultural Identity and Self-Esteem : Strong cultural identity is linked to higher self-esteem among minority individuals.
Working Memory and Problem-Solving : Better working memory capacity improves problem-solving abilities.
Fear Conditioning and Phobias : Fear conditioning contributes to the development of specific phobias.
Empathy and Prosocial Behavior : Higher empathy levels result in increased prosocial behaviors.
Social Anxiety and Online Communication : Individuals with social anxiety prefer online communication over face-to-face interactions.
Cognitive Biases and Decision-Making Errors : Cognitive biases lead to errors in judgment and decision-making.
Attachment Style and Romantic Attachment Patterns : Attachment style influences the development of romantic attachment patterns.
Self-Efficacy and Goal Achievement : Higher self-efficacy predicts greater success in achieving personal goals.
Stress Levels and Immune System Functioning : Elevated stress levels impair immune system functioning.
Social Media Use and Loneliness : Excessive social media use is associated with increased feelings of loneliness.
Emotion Recognition and Social Interaction : Improved emotion recognition skills enhance positive social interactions.
Perceived Control and Psychological Resilience : Strong perceived control fosters psychological resilience in adverse situations.
Narcissism and Online Self-Presentation : Narcissistic individuals engage in heightened self-promotion on social media.
Fear of Failure and Performance Anxiety : Fear of failure contributes to performance anxiety in high-pressure situations.
Gratitude Practice and Well-being : Regular gratitude practice leads to improved overall well-being.
Cultural Norms and Communication Styles : Cultural norms shape distinct communication styles among different groups.
Gender Identity and Mental Health : The alignment between gender identity and assigned sex at birth affects mental health outcomes.
Social Influence and Conformity : Social influence leads to increased conformity in group settings.
Parenting Styles and Attachment Security : Parenting styles influence the development of secure or insecure attachment in children.
Perceived Discrimination and Psychological Distress : Perceived discrimination is associated with higher levels of psychological distress.
Emotional Regulation Strategies and Impulse Control : Effective emotional regulation strategies enhance impulse control.
Cognitive Dissonance and Attitude Change : Cognitive dissonance prompts individuals to change attitudes to reduce discomfort.
Prejudice and Stereotype Formation : Exposure to prejudiced attitudes contributes to the formation of stereotypes.
Motivation and Goal Setting : High intrinsic motivation leads to more effective goal setting and achievement.
Coping Mechanisms and Trauma Recovery : Adaptive coping mechanisms facilitate better trauma recovery outcomes.
Personality Traits and Perceived Stress : Certain personality traits influence how individuals perceive and respond to stress.
Cognitive Biases and Decision-Making Strategies : Cognitive biases impact the strategies individuals use in decision-making.
Emotional Intelligence and Interpersonal Relationships : High emotional intelligence fosters healthier and more fulfilling interpersonal relationships.
Sensory Perception and Memory Formation : The accuracy of sensory perception influences the formation of memories.
Parental Influences and Peer Relationships : Parental attitudes shape the quality of adolescents’ peer relationships.
Social Comparison and Body Image : Frequent social comparison contributes to negative body image perceptions.
Attention Deficit Hyperactivity Disorder (ADHD) and Academic Achievement : Children with ADHD face challenges in achieving academic success.
Cultural Identity and Mental Health Stigma : Strong cultural identity buffers against the negative effects of mental health stigma.
Self-Esteem and Risk-Taking Behavior : Individuals with high self-esteem are more likely to engage in risk-taking behaviors.
Resilience and Adversity Coping : High resilience levels enhance individuals’ ability to cope with adversity.
Motivation and Learning Styles : Different types of motivation influence preferred learning styles.
Body Language and Nonverbal Communication : Body language cues play a significant role in nonverbal communication effectiveness.
Social Identity and Intergroup Bias : Strong identification with a social group contributes to intergroup bias.
Mindfulness Practice and Anxiety Reduction : Regular mindfulness practice leads to decreased levels of anxiety.
Attachment Style and Romantic Satisfaction : Attachment style influences satisfaction levels in romantic relationships.
Intrinsic vs. Extrinsic Motivation : Intrinsic motivation yields more sustainable outcomes than extrinsic motivation.
Attention Allocation and Multitasking Performance : Efficient attention allocation enhances multitasking performance.
Neuroplasticity and Skill Acquisition : Neuroplasticity supports the acquisition and refinement of new skills.
Prejudice Reduction Interventions and Attitude Change : Prejudice reduction interventions lead to positive attitude changes.
Parental Support and Adolescent Resilience : Strong parental support enhances resilience in adolescents facing challenges.
Social Media Use and FOMO (Fear of Missing Out) : Extensive social media use contributes to higher levels of FOMO.
Mood and Decision-Making Biases : Different mood states influence cognitive biases in decision-making.
Parental Attachment and Peer Influence : Strong parental attachment moderates the impact of peer influence on adolescents.
Personality Traits and Job Satisfaction : Certain personality traits predict higher job satisfaction levels.
Social Support and Post-Traumatic Growth : Adequate social support fosters post-traumatic growth after adversity.
Cognitive Load and Creativity : High cognitive load impedes creative thinking and problem-solving.
Self-Efficacy and Goal Persistence : Higher self-efficacy leads to increased persistence in achieving goals.
Stress and Physical Health : Chronic stress negatively affects physical health outcomes.
Perceived Control and Psychological Well-being : Strong perceived control is linked to greater psychological well-being.
Parenting Styles and Emotional Regulation in Children : Authoritative parenting styles promote effective emotional regulation.
Cultural Exposure and Empathy Levels : Exposure to diverse cultures enhances empathetic understanding.
Emotional Intelligence and Conflict Resolution : High emotional intelligence leads to more effective conflict resolution strategies.
Personality Traits and Leadership Styles : Different personality traits align with distinct leadership approaches.
Attachment Style and Romantic Relationship Quality : Secure attachment predicts higher quality romantic relationships.
Social Comparison and Self-Perception : Frequent social comparison impacts individuals’ self-perception and self-esteem.
Mindfulness Meditation and Stress Resilience : Regular mindfulness practice enhances resilience in the face of stress.
Cognitive Biases and Prejudice Formation : Cognitive biases contribute to the formation and reinforcement of prejudices.
Parenting Styles and Social Skills Development : Authoritative parenting styles foster positive social skills in children.
Emotion Regulation Strategies and Mental Health : Effective emotion regulation strategies contribute to better mental health outcomes.
Self-Esteem and Academic Achievement : Higher self-esteem correlates with improved academic performance.
Cultural Identity and Intergroup Bias : Strong cultural identity buffers against the effects of intergroup bias.

Psychology Hypothesis Statement Examples for Social Experiments & Studies : Dive into social dynamics with hypotheses that explore human behavior in various contexts. These examples delve into the intricate interplay of psychological factors in social experiments and studies, shedding light on how individuals interact, perceive, and respond within social environments. You may also be interested in our two tailed hypothesis .

Influence of Group Size on Conformity : Larger group sizes lead to higher levels of conformity in social experiments.
Effects of Positive Reinforcement on Prosocial Behavior : Positive reinforcement increases the likelihood of engaging in prosocial actions.
Role of Normative Social Influence in Decision Making : Normative social influence influences decision-making processes in group settings.
Impact of Obedience to Authority on Ethical Decision Making : Obedience to authority influences ethical decision-making tendencies.
Attribution Bias in Social Interactions : Attribution bias leads individuals to attribute their successes to internal factors and failures to external factors.
Social Comparison and Body Dissatisfaction : Frequent social comparison contributes to negative body image perceptions.
Perceived Control and Social Stress Resilience : Strong perceived control mitigates the negative effects of social stress.
Impression Management in Online Social Networks : Individuals engage in impression management to create a favorable online image.
Social Identity and Group Behavior : Strong social identity fosters a sense of belonging and influences group behavior.
Altruistic Behavior and Empathy Levels : Higher empathy levels correlate with increased engagement in altruistic actions.

Social Psychology Hypothesis Statement Examples : Explore the intricacies of human behavior within social contexts through these social psychology hypotheses. These examples delve into the dynamics of social interactions, group dynamics, and the psychological factors that influence how individuals perceive and respond to the social world.

Social Norms and Conformity : Individuals conform to social norms to gain social acceptance and avoid rejection.
Bystander Effect and Helping Behavior : The bystander effect decreases the likelihood of individuals offering help in emergency situations.
In-Group Bias and Intergroup Relations : In-group bias leads to favoritism toward members of one’s own social group.
Social Influence and Decision Making : Social influence impacts decision-making processes in group settings.
Deindividuation and Uninhibited Behavior : Deindividuation leads to reduced self-awareness and increased uninhibited behavior.
Perceived Social Support and Coping Mechanisms : Adequate social support enhances effective coping strategies in challenging situations.
Group Polarization and Risky Decision Making : Group discussions intensify individuals’ pre-existing inclinations, leading to riskier decisions.
Self-Esteem and Social Comparison : Individuals with lower self-esteem are more prone to engaging in negative social comparison.
Cultural Norms and Nonverbal Communication : Cultural norms influence nonverbal communication cues and interpretations.

Alternative Psychology Hypothesis Statement Examples : Explore alternative hypothesis perspectives on psychological phenomena with these hypotheses. These examples challenge conventional wisdom and encourage critical thinking, providing a fresh outlook on various aspects of human behavior, cognition, and emotions.

Nonverbal Communication and Introversion : Nonverbal cues may play a more significant role in communication for introverted individuals.
Perceived Control and External Locus of Control : High perceived control may lead to an external locus of control in certain situations.
Cognitive Dissonance and Reinforcement Theory : Cognitive dissonance can be explained through the lens of reinforcement theory.
Bystander Effect and Social Responsibility : The bystander effect may stem from individuals’ heightened sense of social responsibility.
Emotion Regulation and Emotional Suppression : Emotion regulation strategies like emotional suppression might lead to long-term emotional well-being.
Perceived Social Support and Emotional Independence : Adequate social support may contribute to emotional independence rather than dependence.
Cultural Identity and Interpersonal Conflict : Strong cultural identity might lead to increased interpersonal conflict due to differing values.
Parenting Styles and Personality Development : Parenting styles might have a limited impact on the formation of certain personality traits.
Social Media Use and Positive Self-Presentation : Extensive social media use may lead to a more authentic self-presentation.
Attentional Bias and Cognitive Flexibility : Attentional bias might enhance cognitive flexibility in specific cognitive tasks.

Psychology Hypothesis Statement Examples in Research : Explore the realms of psychological research hypothesis that guide scientific inquiry. These examples span various subfields of psychology, offering insights into human behavior, cognition, and emotions through the lens of empirical investigation.

Effects of Meditation on Mindfulness : Regular meditation practice enhances individuals’ mindfulness levels.
Impact of Parenting Styles on Self-Esteem : Parenting styles significantly influence children’s self-esteem development.
Emotion Regulation Strategies and Anxiety Levels : Effective emotion regulation strategies lead to decreased anxiety levels.
Cultural Identity and Academic Achievement : Strong cultural identity positively impacts academic achievement in multicultural settings.
Influence of Peer Pressure on Risky Behavior : Peer pressure increases engagement in risky behaviors among adolescents.
Effects of Social Support on Depression : Adequate social support leads to decreased depression symptoms in individuals.
Mindfulness Meditation and Attention Span : Regular mindfulness practice improves individuals’ attention span and focus.
Attachment Style and Romantic Satisfaction : Attachment style predicts satisfaction levels in romantic relationships.
Effects of Positive Feedback on Motivation : Positive feedback enhances intrinsic motivation for challenging tasks.
Impact of Sleep Quality on Memory Consolidation : Better sleep quality leads to improved memory consolidation during sleep.

Experimental Research in Psychology Hypothesis Examples : Embark on experimental journeys with hypotheses that guide controlled investigations into psychological phenomena. These examples facilitate the design and execution of experiments, allowing researchers to manipulate variables, observe outcomes, and draw evidence-based conclusions.

Effects of Color on Mood : Exposure to warm colors enhances positive mood, while cool colors evoke calmness.
Impact of Visual Distractions on Concentration : Visual distractions negatively affect individuals’ ability to concentrate on tasks.
Influence of Music Tempo on Heart Rate : Upbeat music tempo leads to increased heart rate and arousal.
Effects of Humor on Stress Reduction : Humor interventions reduce stress levels and increase feelings of relaxation.
Impact of Exercise on Cognitive Function : Regular aerobic exercise improves cognitive function and memory retention.
Influence of Social Norms on Helping Behavior : Observing prosocial behavior in others increases individuals’ likelihood of offering help.
Effects of Sleep Duration on Reaction Time : Longer sleep duration leads to faster reaction times in cognitive tasks.
Impact of Positive Affirmations on Self-Esteem : Repeating positive affirmations boosts self-esteem and self-confidence.
Influence of Noise Levels on Task Performance : High noise levels impair individuals’ performance on cognitive tasks.
Effects of Temperature on Aggressive Behavior : Elevated temperatures lead to an increase in aggressive behavior.

Psychology Hypothesis Tentative Statement Examples : Embark on the journey of exploration and inquiry with these tentative hypotheses. These examples reflect the initial assumptions and predictions that researchers formulate before conducting in-depth investigations, paving the way for further study and empirical examination.

Possible Effects of Mindfulness on Stress Reduction : Mindfulness practices might contribute to reduced stress levels in individuals.
Potential Impact of Social Media Use on Loneliness : Extensive social media use could be linked to increased feelings of loneliness.
Tentative Connection Between Personality Traits and Leadership Styles : Certain personality traits may align with specific leadership approaches.
Potential Relationship Between Parenting Styles and Academic Motivation : Different parenting styles might influence students’ motivation for academics.
Hypothesized Impact of Cognitive Training on Memory Enhancement : Cognitive training interventions may lead to improved memory function.
Preliminary Association Between Emotional Intelligence and Conflict Resolution : Higher emotional intelligence might be related to more effective conflict resolution.
Possible Effects of Music Exposure on Emotional Regulation : Listening to music might impact individuals’ ability to regulate emotions.
Tentative Link Between Self-Esteem and Resilience : Higher self-esteem may contribute to increased resilience in the face of challenges.
Potential Connection Between Cultural Exposure and Empathy Levels : Exposure to diverse cultures might influence individuals’ empathetic understanding.
Tentative Association Between Sleep Quality and Cognitive Performance : Better sleep quality could be linked to improved cognitive function.

Psychology Hypothesis Development Statement Examples : Formulate hypotheses that lay the groundwork for deeper exploration and understanding. These examples illustrate the process of hypothesis development, where researchers craft well-structured statements that guide empirical investigations and contribute to the advancement of psychological knowledge.

Development of a Hypothesis on Emotional Intelligence and Workplace Performance : Emotional intelligence positively influences workplace performance through enhanced interpersonal interactions and adaptive coping mechanisms.
Constructing a Hypothesis on Social Media Use and Well-being : Extensive social media use negatively impacts psychological well-being by fostering social comparison, reducing real-life social interactions, and increasing feelings of inadequacy.
Formulating a Hypothesis on Attachment Styles and Relationship Satisfaction : Secure attachment styles correlate positively with higher relationship satisfaction due to increased trust, effective communication, and emotional support.
Creating a Hypothesis on Parenting Styles and Child Aggression : Authoritative parenting styles lead to reduced child aggression through the cultivation of emotional regulation skills, consistent discipline, and nurturance.
Developing a Hypothesis on Cognitive Biases and Decision Making : Cognitive biases influence decision-making processes by shaping information processing, leading to deviations from rational decision-making models.
Constructing a Hypothesis on Cultural Identity and Psychological Well-being : Strong cultural identity positively impacts psychological well-being by fostering a sense of belonging, social support, and cultural pride.
Formulating a Hypothesis on Attachment Style and Coping Mechanisms : Attachment style influences coping mechanisms in response to stress, with secure attachments leading to adaptive strategies and insecure attachments resulting in maladaptive ones.
Creating a Hypothesis on Self-Efficacy and Academic Performance : High self-efficacy predicts better academic performance due to increased motivation, perseverance, and effective learning strategies.
Developing a Hypothesis on Gender Stereotypes and Career Aspirations : Gender stereotypes negatively impact women’s career aspirations by reinforcing traditional gender roles and limiting their perceived competence in certain fields.
Constructing a Hypothesis on Cultural Exposure and Empathy Levels : Exposure to diverse cultures enhances empathy levels by fostering cross-cultural understanding, reducing ethnocentrism, and promoting perspective-taking.

These psychology hypothesis development statement examples showcase the critical process of crafting hypotheses that guide research investigations and contribute to the depth and breadth of psychological knowledge. In addition, you should review our biology hypothesis .

How Do You Write a Psychology Hypothesis Statement? – Step by Step Guide

Crafting a psychology hypothesis statement is a crucial step in formulating research questions and hypothesis designing empirical investigations. A well-structured hypothesis guides your research, helping you explore, analyze, and understand psychological phenomena. Follow this step-by-step guide to create effective psychology hypothesis statements:

Identify Your Research Question : Start by identifying the specific psychological phenomenon or relationship you want to explore. Your hypothesis should address a clear research question.
Choose the Appropriate Type of Hypothesis : Decide whether your hypothesis will be directional (predicting a specific relationship) or non-directional (predicting a relationship without specifying its direction).
State Your Variables : Clearly identify the independent variable (the factor you’re manipulating or examining) and the dependent variable (the outcome you’re measuring).
Write a Null Hypothesis (If Applicable) : If your research involves comparing groups or conditions, formulate a null hypothesis that states there’s no significant difference or relationship.
Formulate the Hypothesis : Craft a clear and concise statement that predicts the expected relationship between your variables. Use specific language and avoid vague terms.
Use Clear Language : Write your hypothesis in a simple, straightforward manner that is easily understandable by both researchers and readers.
Ensure Testability : Your hypothesis should be testable through empirical research. It should allow you to collect data, analyze results, and draw conclusions.
Consider the Population : Specify the population you’re studying (e.g., adults, adolescents, specific groups) to make your hypothesis more precise.
Be Falsifiable : A good hypothesis can be proven false through empirical evidence. Avoid making statements that cannot be tested or verified.
Revise and Refine : Review your hypothesis for clarity, coherence, and accuracy. Make revisions as needed to ensure it accurately reflects your research question.

Tips for Writing a Psychology Hypothesis

Writing an effective psychology hypothesis statement requires careful consideration and attention to detail. Follow these tips to craft compelling hypotheses:

Be Specific : Clearly define your variables and the expected relationship between them. Avoid vague or ambiguous language.
Avoid Bias : Ensure your hypothesis is objective and unbiased. Avoid making assumptions or including personal opinions.
Use Measurable Terms : Use terms that can be quantified and measured in your research. This makes data collection and analysis more manageable.
Consult Existing Literature : Review relevant literature to ensure your hypothesis aligns with existing research and theories in the field.
Consider Alternative Explanations : Acknowledge other potential explanations for your findings and consider how they might influence your hypothesis.
Stay Consistent : Keep your hypothesis consistent with the overall research question and objectives of your study.
Keep It Concise : Write your hypothesis in a concise manner, avoiding unnecessary complexity or jargon.
Test Your Hypothesis : Consider how you would test your hypothesis using empirical methods. Ensure it’s feasible and practical to gather data to support or refute it.
Seek Feedback : Share your hypothesis with peers, mentors, or advisors to receive constructive feedback and suggestions for improvement.
Refine as Needed : As you gather data and analyze results, be open to revising your hypothesis based on the evidence you uncover.

Crafting a psychology hypothesis statement is a dynamic process that involves careful thought, research, and refinement. A well-constructed hypothesis sets the stage for rigorous and meaningful scientific inquiry in the field of psychology.

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Published: 13 May 2024

The big five factors as differential predictors of self-regulation, achievement emotions, coping and health behavior in undergraduate students

Jesús de la Fuente 1 , 2 ,
Paul Sander 3 ,
Angélica Garzón Umerenkova 4 ,
Begoña Urien 1 ,
Mónica Pachón-Basallo 1 &
Elkin O Luis 1

BMC Psychology volume 12 , Article number: 267 ( 2024 ) Cite this article

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The aim of this research was to analyze whether the personality factors included in the Big Five model differentially predict the self-regulation and affective states of university students and health.

A total of 637 students completed validated self-report questionnaires. Using an ex post facto design, we conducted linear regression and structural prediction analyses.

The findings showed that model factors were differential predictors of both self-regulation and affective states. Self-regulation and affective states, in turn, jointly predict emotional performance while learning and even student health. These results allow us to understand, through a holistic predictive model, the differential predictive relationships of all the factors: conscientiousness and extraversion were predictors regulating positive emotionality and health; the openness to experience factor was non-regulating; nonregulating; and agreeableness and neuroticism were dysregulating, hence precursors of negative emotionality and poorer student health.

Conclusions

These results are important because they allow us to infer implications for guidance and psychological health at university.

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Introduction

The personality characteristics of students have proven to be essential explanatory and predictive factors of learning behavior and performance at universities [ 1 , 2 , 3 , 4 ]. However, our knowledge about such factors does not exhaust further questions, such as which personality factors tend toward the regulation of learning behavior and which do not? Or can personality factors be arranged on a continuum to understand student differences in their emotions when learning? Consequently, the aim of this study was to analyze whether students’ personality traits differentially predict the regulation of behavior and emotionality. These variables align as different motivational-affective profiles of students, through the type of achievement emotions they experience during study, as well as their coping strategies, motivational state, and ultimately health.

Five-factor model

Previous research has shown the value and consistency of the five-factor model for analyzing students’ personality traits. Pervin, Cervone, and John [ 5 ] defined five factors as follows: (1) Conscientiousness includes a sense of duty, persistence, and behavior that is self-disciplined and goal-directed. The descriptors organized, responsible, and efficient are typically used to describe conscientious persons. (2) Extraversion is characterized by the quantity and intensity of interpersonal relationships, as well as sensation seeking. The descriptors sociable, assertive, and energetic are typically used to describe extraverted persons. (3) Openness to experience incorporates autonomous thinking and willingness to examine unfamiliar ideas and try new things. The descriptors inquisitive, philosophical, and innovative are typically used to describe persons open to experience. (4) Agreeableness is quantified along a continuum from social antagonism to compassion in one’s quality of interpersonal interactions. The descriptors inquisitive, kind, considerate, and generous are often used to describe persons characterized by agreeableness. (5) Finally, neuroticism tends to indicate negative emotions . Persons showing neuroticism are often described as moody, nervous, or touchy.

This construct has appeared to consistently predict individual differences between university students. Prior research has documented its essential role in explaining differences in achievement [ 6 , 7 ], motivational states [ 8 ], students’ learning approaches [ 9 ], self-regulated learning [ 10 ].

Five-factor model, self-regulation, achievement emotions and health

The relationship between the Big Five factors and self-regulation has been analyzed historically with much interest [ 11 , 12 , 13 , 14 , 15 ]. The dimensions of the five-factor model describe fundamental ways in which people differ from one another [ 16 , 17 ]. Of the five factors, conscientiousness may be the best reflection of self-regulation capacity. More recent research has shown consistent evidence of the relationship between these two constructs, especially conscientiousness, which has a positive relationship, and neuroticism, which has a negative relationship with self-regulation [ 18 , 19 ]. The Big Five factors are also related to coping strategies [ 20 ].

The evidence on the role of the five-factor model in self-regulation, achievement emotions, and health has been fairly consistent. On the one hand, self-regulation has a confirmed role as a meta-cognitive variable that is present in students’ mental health problems [ 21 ]. Similarly, personality factors and types of perfectionism have been associated with mental health in university students [ 22 ]. In a complementary fashion, one longitudinal study has shown that personality factors have a persistent effect on self-regulation and health. Sirois and Hirsch [ 23 ] confirmed that the Big Five traits affect balance and health behaviors.

Self-regulation, achievement emotions and health

Self-regulation has recently been considered a significant behavioral meta-ability that regulates other skills in the university environment. It has consistently appeared to be a predictor of achievement emotions [ 24 ], coping strategies [ 25 ], and health behavior [ 26 ]. In the context of university learning, the level of self-regulation is a determining factor in learning approaches, motivation and achievement [ 27 ]. Similarly, the self- vs. externally regulated behavior theory [ 27 , 28 ] assumes that the continuum of self-regulation can be divided into three types: (1) self-regulation behavior, which is the meta-behavior or meta-skill of planning and executing control over one’s behavior; (2) nonregulation behavior (deregulation) , where consistent self-regulating behavior is absent; and (3) nonregulation behavior, when regulatory behavior is maladaptive or contrary to what is expected. Some example behaviors are presented below, and these have already been documented (see Table 1 ). Recently, Beaulieu and collaborators [ 29 ] proposed a self-dysregulation latent profile for describing subjects with lower scores on subscales regarding extraversion, agreeableness and conscientiousness and higher scores concerning negative emotional facets.

Table 1 here.

Consequently, the question that we pose - as yet unresolved - is whether the different personality factors predict a determined type of regulation on the continuum of regulatory behavior, nonregulatory (deregulatory) behavior and dysregulatory behavior, based on evidence.

Aims and hypotheses

Based on the existing evidence, the aim of this study was to establish a structural predictive model that would order personality factors along a continuum as predictors of university students’ regulatory behavior. The following hypotheses were proposed for this purpose: (1) personality factors differentially predict students’ regulatory, nonregulatory and dysregulatory behavior during academic learning; they also differentially determine students’ type of emotional states (positive vs. negative affect); (2) the preceding factors differentially predict achievement emotions (positive vs. negative) during learning, coping strategies (problem-focused vs. emotion-focused) and motivational state (engagement vs. burnout); and (3) all these factors ultimately predict student health, either positively or negatively, depending on their regulatory or dysregulatory nature.

Participants

Data were gathered from 2019 to 2022, encompassing a total of 626 undergraduate students enrolled in Psychology, Primary Education, and Educational Psychology programs across two Spanish universities. Within this cohort, 85.5% were female, and 14.5% were male, with ages ranging from 19 to 24 years and a mean age of 21.33 years. The student distribution was equal between the two universities, with 324 attending one and 318 attending the other. The study employed an incidental, nonrandomized design. The guidance departments at both universities extended invitations for teacher participation, and teachers, in turn, invited their students to partake voluntarily, ensuring anonymity. Questionnaires were completed online for each academic subject, corresponding to the specific teaching-learning process.

Instruments

Five personality factors.

The Big Five Questionnaire [ 30 ], based on the version by Barbaranelli et al. [ 31 ], assessed scores for five personality factors. Confirmatory factor analysis (CFA) of the 67 scale items resulted in a five-factor structure aligned with the Big Five Model. The outcomes demonstrated satisfactory psychometric properties and acceptable fit indices. The second-order confirmatory model exhibited a good fit (chi-square = 38.273; degrees of freedom (20–15) = 5; p > 0.10; chi/df = 7.64; RMR = 0.0425; NFI = 0.939; RFI = 0.917; IFI = 0.947; TLI = 0.937; CFI = 0.946; RMSEA = 0.065; HoeLength index = 2453 ( p < 0.05) and 617 ( p < 0.01)). Internal consistency of the total scale was also strong (alpha = 0.956; Part 1 = 0.932 and Part 2 = 0.832; Spearman-Brown = 0.962 and Guttman = 0.932).

Self-Regulation : The Short Self-Regulation Questionnaire (SSRQ) [ 32 ] gauged self-regulation. The Spanish adaptation, previously validated in Spanish samples [ 33 ], encompassed four factors measured by a total of 17 items. Confirmatory factor analysis confirmed a consistent factor structure (chi-square = 845.593; df = 113; chi/df = 7.483; RMSM = 0.0299; CFI = 0.959, GFI = 0.94, AGFI = 0.96, RMSEA = 0.059). Validity and reliability values (Cronbach’s alpha) were deemed acceptable (total (α = 0.86; Omega = 0.843); goal-setting planning (α = 0.79; Omega = 0.784); perseverance (α = 0.78; Omega = 0.779); decision-making (α = 0.72; Omega = 0.718); and learning from mistakes (α = 0.72; Omega = 0.722)), comparable to those of the English version. Example statements include: “I usually keep track of my progress toward my goals,” “In regard to deciding about a change, I feel overwhelmed by the choice,” and “I learn from my mistakes.”

Positive-negative affect

The Positive and Negative Affect Scale (PANAS-N) [ 34 ], validated with university students, assessed positive and negative affect. The PANAS comprises two factors and 20 items, demonstrating a consistent confirmatory factor structure (chi-square = 1111.147; df = 169; chi/df = 6.518; RMSM = 0.0346; CFI = 0.955, GFI = 0.963, AGFI = 0.96, RMSEA = 0.058). Validity and reliability values (Cronbach’s alpha) were acceptable (total (α = 0.891; Omega = 0.857); positive affect (α = 0.8199; Omega = 0.784); and negative affect (α = 0.795; Omega = 0.776), comparable to those of the English version. Sample items include “I am a lively person, I usually get excited; I have bad moods (I get upset or irritated).”

Learning Achievement Emotion : The variable was measured using the Spanish version [ 35 ] of the Achievement Emotions Questionnaire (AEQ-Learning) [ 36 ], encompassing nine emotions (enjoyment, hope, pride, relief, anger, anxiety, hopelessness, shame, and boredom). Emotions were classified based on valence (positive or negative) and activation (activating or deactivating), resulting in four quadrants. Another classification considered the source or trigger: the ongoing activity, prospective outcome, or retrospective outcome. Psychometric properties were adequate, and the confirmatory model displayed a good fit (chi-square = 529.890; degrees of freedom = 79; chi/df = 6.70; SRMR = 0.053; p > 0.08; NFI = 0.964; RFI = 0.957; IFI = 0.973; TLI = 0.978, CFI = 0.971; RMSEA = 0.080; HOELTER = 165 ( p < 0.05) and 178 ( p < 0.01)). Good internal consistency was found for the total scale (Alpha = 0.939; Part 1 = 0.880, Part 2 = 0.864; Spearman-Brown = 0.913 and 884; Guttman = 0.903). Example items include Item 90: “I am angry when I have to study”; Item 113: “My sense of confidence motivates me”; and Item 144: “I am proud of myself”.

Engagement-Burnout : Engagement was assessed using a validated Spanish version of the Utrecht Work Engagement Scale for Students [ 37 ], demonstrating satisfactory psychometric properties for Spanish students. The model displayed good fit indices, with a second-order structure comprising three factors: vigor, dedication, and absorption. Scale unidimensionality and metric invariance were verified in the samples assessed (chi-square = 592.526, p > 0.09; df = 84, chi/df = 7.05; SRMR = 0.034; TLI = 0.976, IFI = 0.954, and CFI = 0.923; RMSEA = 0.083; HOELTER = 153, p < 0.05; 170 p < 0.01). Cronbach’s alpha for this sample was 0.900 (14 items); the two parts of the scale produced values of 0.856 (7 items) and 0.786 (7 items).

Burnout : The Maslach Burnout Inventory (MBI) [ 38 ], in its validated Spanish version, was employed to assess burnout. This version exhibited adequate psychometric properties for Spanish students. Good fit indices were obtained, with a second-order structure comprising three factors: exhaustion or depletion, cynicism, and lack of effectiveness. Scale unidimensionality and metric invariance were confirmed in the samples assessed (chi-square = 567.885, p > 0.010, df = 87, chi/df = 6.52; SRMR = 0.054; CFI = 0.956, IFI = 0.951, TLI = 0.951; RMSEA = 0.071; HOELTER = 224, p < 0.05; 246 p < 0.01). Cronbach’s alpha for this sample was 0.874 (15 items); the two parts of the scale were 0.853 (8 items) and 0.793 (7 items).

Strategies for coping with academic stress : The Coping Strategies Scale (Escala Estrategias de Coping - EEC) [ 39 ] was utilized in its original version. Constructed based on the Lazarus and Folkman questionnaire [ 40 ] using theoretical-rational criteria, the original 90-item instrument resulted in a 64-item first-order structure. The second-order structure comprised 10 factors and two significant dimensions. A satisfactory fit was observed in the second-order structure (chi-square = 478.750; degrees of freedom = 73, p > 0.09; chi/df = 6.55; RMSR = 0.052; NFI = 0.901; RFI = 0.945; IFI = 0.903, TLI = 0.951, CFI = 0.903). Reliability was confirmed with Cronbach’s alpha values of 0.93 (complete scale), 0.93 (first half), and 0.90 (second half); Spearman-Brown coefficient of 0.84; and Guttman coefficient of 0.80. Two dimensions and 11 factors were identified: (1) Dimension: emotion-focused coping—F1. Fantasy distraction; F6. Help for action; F8. Preparing for the worst; F9. Venting and emotional isolation; F11. Resigned acceptance. (2) Dimension: problem-focused coping—F2. Help seeking and family counsel; F10. Self-instructions; F10. Positive reappraisal and firmness; F12. Communicating feelings and social support; F13. Seeking alternative reinforcement.

Student Health Behavior : The Physical and Psychosocial Health Inventory [ 41 ] measured this variable, summarizing the World Health Organization (WHO) definition of health: “Health is a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity.” The inventory focused on the impact of studies, with questions such as “I feel anxious about my studies.” Students responded on a Likert scale from 1 (strongly disagree) to 5 (strongly agree). In the Spanish sample, the model displayed good fit indices (CFI = 0.95, GFI = 0.96, NFI = 0.94; RMSEA = 0.064), with a Cronbach’s alpha of 0.82.

All participants provided informed consent before engaging in the study. The completion of scales was voluntary and conducted through an online platform. Over two academic years, students reported on five distinct teaching-learning processes, each corresponding to a different university subject they were enrolled in during this period. Students took their time to answer the questionnaires gradually throughout the academic year. The assessment for Presage variables took place in September-October of 2018 and 2019, Process variables were assessed in the subsequent February-March, and Product variables were evaluated in May-June. The procedural steps were ethically approved by the Ethics Committee under reference 2018.170, within the broader context of an R&D Project spanning 2018 to 2021.

Data analysis

The ex post facto design [ 42 ] of this cross-sectional study involved bivariate association analyses, multiple regression, and structural predictions (SEMs). Preliminary analyses were executed to ensure the appropriateness of the parameters used in the analyses, including tests for normality (Kolmogorov-Smirnov), skewness, and kurtosis (+-0.05).

Multiple regression

Hypothesis 1 was evaluated using multiple regression analysis through SPSS (v. 26).

Confirmatory factor analysis

To test Hypotheses 2 and 3, a structural equation model (SEM) was employed in this sample. Model fit was assessed by examining the chi-square to degrees of freedom ratio, along with RMSEA (root mean square error of approximation), NFI (normed fit index), CFI (comparative fit index), GFI (goodness-of-fit index), and AGFI (adjusted goodness-of-fit index) [ 43 ]. Ideally, all these values should surpass 0.90. The adequacy of the sample size was confirmed using the Hoelter index [ 44 ]. These analyses were conducted using AMOS (v.22).

Prediction results

The predictive relationships exhibited a continuum along two extremes. On the one hand, conscientiousness, extraversion and openness were significant, graded, and positive predictors of self-regulation. On the other hand, Agreeableness and Neuroticism were negative, graded predictors of self-regulation. A considerable percentage of explained variance was observed ( r 2 = 0.499). The most meaningful finding, however, is that this predictive differential grading is maintained for the rest of the variables analyzed: positive affect ( r 2 = 0.571) and negative affect ( r 2 = 0.524), achievement emotions during study, engagement burnout, problem- and emotion-focused coping strategies, and student health. See Table 2 .

Structural prediction results

Structural prediction model.

Three models were tested. Model 1 proposes the exclusive prediction of personality factors on the rest of the factors, not including self-regulation. Model 2 evaluated the predictive potential of self-regulation on the factors of the Big Five model. Model 3 tested the ability of the Big Five personality traits to predict self-regulation and the other factors. The latter model presented adequate statistical values. These models are shown in Table 3 .

Models of the linear structural results of the variables

Direct effects.

The statistical effects showed a direct, significant, positive predictive effect of the personality factors C (Conscientiousness) and E (Extraversion) on self-regulation. The result for factor O (openness to experience) was not significant. Factors A (agreeableness) and N (neuroticism) were negatively related, especially the latter. In a complementary fashion, factors C and E showed significant, positive predictions of positive affect, while O and A had less strength. Factor N most strongly predicted negative affect.

Moreover, self-regulation positively predicted positive achievement emotions during study and negatively predicted negative achievement emotions. Positive affect predicted positive emotions during study, engagement, and problem-focused coping strategies; negative affect predicted negative emotions during study, burnout, and emotion-focused strategies. Positive emotions during study negatively predict negative emotions and burnout. Engagement positively predicted problem-focused coping and negatively predicted burnout. Finally, problem-focused coping also predicted emotion-focused coping. Emotion-focused coping negatively predicts health and well-being.

Indirect effects

The Big Five factors exhibited consistent directionality. Factors C and E positively predicted positive emotions, engagement, problem-focused coping, and health and negatively predicted negative emotions and burnout. Factor O had low prediction values in both negative and positive cases. Factors A and N were positive predictors of negative emotions during study, burnout, emotion-focused coping and health, while the opposite was true for factors C and E. These factors had positive predictive effects on self-regulation, positive affect, positive emotions during study, engagement, problem-focused strategies and health; in contrast, the other factors had negative effects on negative affect, negative emotions during study, burnout, emotion-focused strategies and health. See Table 4 ; Fig. 1 .

SEM of prediction in the variables Note. C = Conscientiousness; E = Extraversion; O = Openness to experience; A = Agreeableness; N = Neuroticism; SR = Self-Regulation; Pos.A = Positive Affect; Neg.A = Negative Affect; Pe.S = Positive emotions during study; Ne.S = Negative emotions during study; ENG = Engagement; BURN = Burnout; EFCS = Emotion-focused coping strategies; PFCS = Problem-focused coping strategies: HEALTH: Health behavior.

Based on the Self- vs. External-Regulation theory [ 27 , 28 ], the aim of this study was to show, differentially, the regulatory, nonregulatory or dysregulatory power of the Big Five personality factors with respect to study behaviors, associated emotionality during study, motivational states, and ultimately, student health behavior.

Regarding Hypothesis 1 , the results showed a differential, graded prediction of the Big Five personality factors affecting both self-regulation and affective states. The results from the logistic and structural regression analyses showed a clear, graded pattern from the positive predictive relationship of C to the negative predictive relationship of N. On the one hand, they showed the regulatory effect (direct and indirect) of factors C and E, the nonregulatory effect of O, and the dysregulatory effect of factors A and especially N. This evidence offers a differential categorization of the five factors in an integrated manner. On the other hand, their effects on affective tone (direct and indirect) take the same positive direction in C and E, intermediate in the case of O, and negative in A and N. There is plentiful prior evidence that has shown this relationship, though only in part, not in the integrated manner of the model presented here [ 29 , 45 , 46 , 47 ].

Regarding Hypothesis 2 , the evidence shows that self-regulation directly and indirectly predicts affective states in achievement emotions during study. Directionality can be positive or negative according to the influence of C and E and of positive emotionality or of A and N with negative affect. This finding agrees with prior research [ 29 , 48 , 49 , 50 , 51 ].

Regarding Hypothesis 3 , the results have shown clear bidirectionality. Subsequent to the prior influence of personality factors and self-regulation, achievement emotions bring about the resulting motivational states of engagement-burnout and the use of different coping strategies (problem-focused vs. emotion-focused). Positive achievement emotions during study predicted a motivational state of engagement and problem-focused coping strategies and were positive predictors of health; however, negative emotions predicted burnout and emotion-focused coping strategies and were negative predictors of health. These results are in line with prior evidence [ 49 , 52 , 53 ]. Finally, we unequivocally showed a double, sequenced path of emotional variables and affective motivations in a process that ultimately and differentially predicts student health [ 54 , 55 ].

In conclusion, these results allow us to understand the predictive relationships involving these multiple variables in a holistic predictive model, while previous research has addressed this topic only in part [ 56 ]. We believe that these results lend empirical support to the sequence proposed by the SR vs. ER model [ 27 ]: the factors of conscientiousness and extraversion appear to be regulators of positive emotionality, engagement and health; openness to experience is considered to be nonregulating; and agreeableness and neuroticism are dysregulators of the learning process and precursors of negative emotionality and poorer student health [ 57 ]. New levels of detail—in a graded heuristic—have been added to our understanding of the relationships among the five-factor model, self-regulation, achievement emotions and health [ 23 ].

Limitations and research prospects

A primary limitation of this study was that the analysis focused exclusively on the student. The role of the teaching context, therefore, was not considered. Previous research has reported the role of the teaching process, in interaction with student characteristics, in predicting positive or negative emotionality in students [ 49 , 58 ]. However, such results do not undercut the value of the results presented here. Future research should further analyze potential personality types derived from the present categorization according to heuristic values.

Practical implications

The relationships presented may be considered a mental map that orders the constituent factors of the Five-Factor Model on a continuum, from the most adaptive (or regulatory) and deregulatory to the most maladaptive or dysregulatory. This information is very important for carrying out preventive intervention programs for students and for designing programs for those who could benefit from training in self-regulation and positivity. Such intervention could improve how students experience the difficulties inherent in university studies [ 47 , 59 ], another indicator of the need for active Psychology and Counseling Centers at universities.

Data availability

No datasets were generated or analysed during the current study.

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This research was funded by the R&D Project PID2022-136466NB-I00 and the R&D Project PGC2018-094672-B-I00. University of Navarra (Ministry of Science and Education, Spain), R&D Project UAL18-SEJ-DO31-A-FEDER (University of Almería, Spain), and the European Social Fund.

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Conceptualization, J.d.l.F and ELG; formal analysis and methodology, J.d.l.F and ELG.; project administration, J.d.l.F.; writing—original draft, J.d.l.F, PS, AG, BU, MP, and ELG; writing—review & editing, J.d.l.F, PS, AG, BU, MP and ELG. All authors have read and agreed to the published version of the manuscript.

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Fuente, J.d.l., Sander, P., Garzón Umerenkova, A. et al. The big five factors as differential predictors of self-regulation, achievement emotions, coping and health behavior in undergraduate students. BMC Psychol 12 , 267 (2024). https://doi.org/10.1186/s40359-024-01768-9

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Personality

14 Ways to Tell if Your Personality Is Working Against You

New research shows how having a certain personality type can be bad for your heart..

Posted May 14, 2024 | Reviewed by Michelle Quirk

What Is Personality?
Find counselling near me
The Type A personality was found to be an invalid concept based on retractions of the original research.
Type D personality remains a valid concept, and as shown in new research, can help explain heart health.
By tuning into your emotional reactivity, you can keep your stress levels, and your heart, in shape.

When the Type A personality research was revealed to be based on fraudulent research, its retraction caused shock waves to reverberate throughout the behavioral medicine community. After all, doesn’t it make sense that people whose personality leads them to be hard-driving, competitive, and impatient would be heart attacks just waiting to happen? Although they may not be pleasant to be with, these people formerly known as Type A don’t seem to be any worse off than their counterparts, the so-called Type B. What’s more, the Type C personality also proposed by behavioral health researchers is based on just as flawed a set of studies.

The tendency to type people by letters ended in the alphabet with Type D, which remains the only personality style standing amidst all the retractions and controversy. According to a new systematic review of the literature, it still appears to hold up.

What Is Type D, and Why Does It Matter?

The “D” in Type D stands for “distressed.” Baylor University’s Adam O’Riordan and colleagues (2023), who conducted the review, further define people with Type D as exhibiting the two components of negative affectivity, or sadness and anxiety , and social inhibition, the tendency to push aside the emotions they feel when they’re with other people.

Type D was originally identified in cardiac patients, and in prior reviews of the literature, stood up to scientific scrutiny. Cardiac patients with this personality, in at least a majority of studies conducted, had twice the risk of dying, and as a result, European Guidelines for Cardiovascular Disease Prevention, as cited by O’Riordan et al., include Type D as a “psychosocial risk factor to be assessed in clinical practice.”

People with Type D personalities, prior researchers find, have poorer lifestyle habits such as less frequent physical activity and unhealthy eating. However, this would not be enough to predispose them to higher mortality risk. Because they perceive life events to be more stressful than do the non-Type D people, their bodies release more stress hormones . This “cardiovascular reactivity” hypothesis proposes that this chronic overarousal causes such conditions as hypertension.

Another counter-proposal suggests that it’s the opposite, an under-reactivity, which contributes to Type D’s negative effects on the body. This tendency, a blunted way of dealing with stress, shows up in other research on the ways that Type D people respond to experimentally induced stress. Perhaps it is what the Baylor U. researchers call “homeostatic dysregulation” that accounts for Type D’s harmful health effects.

Testing Type D’s Link to Health

Using standard methods of conducting systematic reviews, O’Riordan et al. began with a set of 401 studies, which, after eliminating those that didn’t make the grade, led to a final collection of 14 studies averaging 99 participants each. The authors rated each study on its methodological quality on a 1 to 9 scale; the final studies included in the review received scores of 5.64 for those reporting significant effects, and 5 for those reporting null effects.

The physiological criteria included measures such as systolic blood pressure (SBP), diastolic blood pressure (DBP), heart rate (HR), cardiac output (CO), or total peripheral resistance reactivity (changes in the arteries).

You can test yourself on your own Type D tendencies with these items (0=False, 4=True).

Negative affectivity:

I often make a fuss about unimportant things.
I often feel unhappy.
I am often irritated.
I take a gloomy view of things.
I often find myself worrying about something.
I am often down in the dumps.

Social inhibition:

I make contact easily when I meet people (R).
I often talk to strangers (R).
I often feel inhibited in social interactions.
I find it hard to start a conversation.
I am a closed kind of person.
I would rather keep other people at a distance.
When socializing, I don't find the right things to talk about.

Individuals who obtain a score of 10 or more on both scales are classified as Type D.

The Baylor U. review sought to examine the contributions of sex and the importance of a social stressor to the Type D-cardiac reactivity equation. High social stressors included experimental manipulations requiring direct and reciprocal communication between the participant and another person, such as giving a speech, and/or receiving negative social feedback or evaluation after an experimental task.

Overall, Type D personality was associated with lower blood pressure and HR reactivity across studies, which would support the “blunted” hypothesis about Type D’s effect on cardiovascular functioning. However, consistent with the prior literature, sex and the nature of the social situation did make a difference in this overall effect.

The authors go on to explain how blunting could be just as bad as hyperarousal, depending on the nature of the stress. As they note, this is a “suboptimal” response in the parts of the brain responsible for “motivational and behavioral regulation.” When engaging in stressful tasks, you want these brain regions to be performing, not dampening. Otherwise, you will experience “withdrawal and disengagement.”

There may be, however, a bright side to this form of withdrawal. Might it not be healthy to settle down if there’s no social pressure on you to perform? Now, though, sex might come into play. Women may be more likely to internalize their feelings of stress than men due to differing socialization in confrontations with stress. However, women are also more likely to express their emotions than men, particularly in social situations.

All in all, the differences across study findings led the authors to conclude that future studies should investigate prospectively, rather than through correlational studies, whether being Type D and having blunted responses to social stressors is healthy or pathological, and whether this varies by sex.

Paying Attention to Your Emotions

The strength of this comprehensive review was its attention to nuanced detail and experimental rigor. Rather than make a blanket statement that it’s always good or always bad to be Type D, the Baylor U. researchers show that the answer is “It depends.” Unlike the Type A researchers, who tended to make broad (and unsubstantiated) claims, O’Riordan et al. showed that Type D’s effects depended on which situations and for whom the cardiovascular responses were observed.

There are practical implications of these findings. By identifying your own Type D tendencies, you can use the “data” you collect across your own daily interactions. If someone is evaluating you, are you able to mobilize and rise to the occasion, or do you retreat and let your emotions eat away at you? On the other hand, when there’s no pressure, do you allow yourself to relax and focus on doing your best?

To sum up, personality can affect your health, but not in a direct and necessarily obvious way. Learning to tune into your own reactivity can help you find ways to keep stress at bay and focus instead on your ability to thrive.

Denollet, J. (2005). DS14: Standard Assessment of Negative Affectivity, Social Inhibition, and Type D Personality. Psychosomatic Medicine , 67 (1), 89–97. doi:10.1097/01.psy.0000149256.81953.49

O'Riordan, A., Gallagher, S., & Howard, S. (2023). Type D personality and cardiovascular reactivity to acute psychological stress: A systematic review and meta-analysis. Health Psychology, 42 (9), 628–641. https://doi.org/10.1037/hea0001328

Susan Krauss Whitbourne, Ph.D. , is a Professor Emerita of Psychological and Brain Sciences at the University of Massachusetts Amherst. Her latest book is The Search for Fulfillment.

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Sapir-Whorf & Our View of the World (GCSE Psychology)

Subject: Psychology

Age range: 14-16

Resource type: Lesson (complete)

Last updated

12 May 2024

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Two 1 hour lessons.

The first outlines & evaluates the Sapir Whorf Hypothesis. This includes:-

knowledge retrieval starter
Video to introduce the theory
teacher led notes/discussion
application task
discussion & independent written task for AO3

The second lesson outlines our view of the world i.e. recognition of colour & recall of events. This includes:-

practical activities to demonstrate research into this area
link back to Sapir Whorf (i.e. how this research can be used to evaluate Sapir Whorf)
9 mark exam question on Sapir Whorf

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How the incels warped my research

The ‘manosphere’ is claiming it has scientific legitimacy for its dangerous ideology. my field hasn’t done enough to defend our work from misappropriation..

Feminism has ruined dating for American men. American women are too entitled, they don’t respect their men, and they just don’t understand their role in life. If you want to find a good, traditional woman who will treat you right, you need to go abroad. But don’t bring her home or wokeness will corrupt her, too.

This is the world according to “passport bros,” a viral social media movement that advocates that men give up on American women. The sinister core of the movement is a group calling themselves “incels”, or involuntary celibates, an identity they’ve cultivated in a larger online ecosystem dubbed the “manosphere.” Composed of Reddit groups, TikToks, blogs, podcasts, wikis, and influencers, the manosphere tells men that women’s psychology, empowered by feminism, makes happy relationships impossible. Because of evolution, women will never date a guy like you; even if they do, they’ll eventually leave you for someone better.

Incels have been behind horrific attacks like the Isla Vista killings , when six students were murdered and over a dozen more injured near the campus of the University of California, Santa Barbara, 10 years ago. The perpetrator explained in a book-length manifesto that his motivation for the massacre was frustration with women who didn’t find him attractive and envy of the men they did. This year, a man in Sydney stabbed six women to death in a shopping mall. The attacker’s father suggested his motivations were similar to the Isla Vista killer’s: frustration over his failure with women.

The manosphere claims its worldview is grounded in science, specifically the discipline of evolutionary psychology. That’s my discipline — I am an evolutionary psychologist and associate professor at UC Santa Barbara, the home of evolutionary psychology. In fact, it turns out incels have coopted some of my research to justify their ideology.

I have generally tried to ignore the manosphere. But as an evolutionary psychologist, I’ve found that hard to do. You can hardly read two paragraphs of incel ideology without coming across references to my field.

Louis Bachaud and Sarah Johns recently published a content analysis of manosphere messaging in the journal Evolutionary Human Sciences , explaining the ways in which our research gets appropriated by manosphere circles.

For example, incels maintain a wiki page of scientific citations they claim support their worldview — an annotated bibliography of misogyny. In one case, in a sort of Russian nesting doll of misrepresentation, the incel wiki quotes a pape r citing a study of mine as demonstrating that women prefer dominant men — which they further twist into the incel notion that women actually prefer violent men as romantic partners.

Reading this entry, I thought, “That’s odd, I don’t remember ever publishing on dominance preferences — do the incels know my work better than I do?” No. I double-checked: That study didn’t even mention dominance preferences.

Curiously overlooked in this whole wiki section on women’s preferences is the fact that kindness is repeatedly found to be among the most desired qualities in large-scale , cross-cultural studies of mate preferences.

This is just one example. Peering into the manosphere has been like walking through a funhouse mirror version of my science. The manosphere view of evolutionary psychology is distorted, filtered, selective, and embellished.

What has made evolutionary psychology so popular with incels?

Evolutionary psychology is the academic subdiscipline of psychology that tries to understand how the human mind works by understanding the problems it evolved to solve. Over the past four decades, evolutionary psychologists have explored the psychology of relationships — including patterns in what people look for in a partner, attitudes towards casual sex and long-term relationships, relationship emotions like love and jealousy, and the connections between hormones and mating psychology. All of these topics are central to incel ideology.

Like any biological approach to behavior, evolutionary psychology has always been controversial. In part, this is owing to some truly bad actors in the field. All it takes is some thoughtless tweets or blog posts for the entire field to earn a reputation as a safe space for provocateurs. This initiates a vicious cycle, where rabble rousers flock to the field, establish academic journals where they publish inflammatory work, get invited to speak on popular manosphere podcasts , and then use the publicity to sell books and garner enough career success to inspire the next generation of charlatans.

This grift cycle produces a small number of loudmouths who end up being the public face of evolutionary psychology. From the inside, I can promise you that most of our research is genuinely boring. But cool as this work is to nerds like myself, the good research doesn’t get you booked on Joe Rogan.

This allows the manosphere to sell its audience a scientific consensus around its ideology that simply does not exist. Its members appropriate and mischaracterize the literature on evolutionary psychology to lend a scientific patina to their hateful, misogynistic, and dangerous ideas.

For instance, incels are obsessed with the “dual mating strategy” hypothesis, a divisive idea that interprets fluctuations in women’s sexual desire as evidence that women have evolved to seek out men with “good genes” at the most fertile point in their menstrual cycle. Incels use this hypothesis to explain, in their eyes, why relationships are doomed: No matter how good a partner you are, women will always be looking to sleep around with someone better.

Part of the problem is that the dual mating strategy hypothesis was indeed a popular idea among evolutionary psychologists until about 2016. After that, it became one of the more prominent epicenters of psychology’s replication crisis, which revealed that large swaths of psychology research were based on unreliable findings. But even before this major setback, the dual mating strategy hypothesis was critiqued by some evolutionary psychologists like my friend and colleague Jim Roney. Nonetheless, Jim’s work gets hardly any play in manosphere circles, and the hypothesis has since morphed into a version quite unlike the one promoted by incels.

At the end of the day, incels attempt to draw from evolutionary theory a power it does not have. Evolution is not destiny. It is a powerful tool for explaining how we came to be who we are today, but it cannot tell us who we should be today or who we can be tomorrow. In fact, we can leverage an understanding of our evolved psychology to create the world we want to live in. The manosphere interprets my science to mean that love is impossible — but a major focus of my lab is helping people form happy, enduring relationships.

I am embarrassed to have ignored the appropriation of my work for so long. My complacency and that of my peers has allowed the manosphere version of our science to fester, grow, and borrow against our field’s credibility to suit its own interests. Because of our negligence, our science has a body count.

So I’m sticking my neck out. And I’d encourage my level-headed colleagues to do the same. The manosphere and our peers who cater to it don’t represent our field. If I could teach the young men flicking through passport bro videos anything about evolutionary psychology, it would be that believing evolution is important for explaining human behavior need not commit you to a regressive worldview. Anyone who tells you otherwise is selling ideology, not science.

Daniel Conroy-Beam is an associate professor in the psychological and brain sciences department at the University of California, Santa Barbara, and a Public Voices Fellow at The OpEd Project.

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Interest Gaps in the Labor Market: Comparing People’s Vocational Interests with National Job Demands

Original Paper
Published: 08 May 2024

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Kevin A. Hoff ORCID: orcid.org/0000-0003-3265-2209 1 ,
Kenneth E. Granillo-Velasquez 2 ,
Alexis Hanna 3 ,
Michael L. Morris 4 ,
Frederick L. Oswald 5 &
James Rounds 6

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Vocational interest assessments are widely used to determine which jobs might be a good fit for people. However, showing a good fit to particular jobs does not necessarily mean that those jobs are available. In this respect, little is known about the alignment between people’s vocational interests and national labor demands. The current study used a national dataset comprising 1.21 million United States residents to investigate this issue empirically. Results revealed three major findings. First, around two-thirds of people were most interested in people-oriented jobs (i.e., artistic, social, or enterprising interests), with the remaining one-third being most interested in things-oriented jobs (i.e., realistic, investigative, or conventional interests). Second, the distribution of people’s interests did not align with U.S. job demands in 2014, 2019, and 2029 (projections), revealing large gaps between interest supply and demand. Notably, the most popular interest among people (artistic) was the least demanded among jobs, whereas the least popular interest among people (conventional) was highly demanded among jobs. Third, interest gaps were generally larger at lower education levels, indicating that higher education can provide more opportunities to achieve interest fit at work. We integrate these findings to discuss implications for individuals, organizations, and career guidance practitioners aimed at better coordinating people’s interests with available jobs to promote individual career success and national workforce readiness.

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Vocational Interests: Revisiting Assumptions About Their Development and What They Predict

Identity Exploration or Labor Market Reaction: Social Class Differences in College Student Participation in Peace Corps, Teach for America, and Other Service Programs

Parliament as a steppingstone? Patterns of post-parliamentary careers in The Netherlands between 1967 and 2017

The year 2010 was chosen for Census data because it approximates the middle of the interval (2005–2014) in which interest data was collected. In addition, the complete Census is only conducted every ten years.

Job zone 1 occupations require little-to-no experience; and in the current study, these jobs were matched to participants who had less than a high school degree. Job zone 2 occupations require a high school diploma, which was matched to participants with a high school diploma. Job zone 3 occupations typically require some college or a community college degree; in the current study, this included participants who had obtained a trade/technical school degree, an Associate’s degree, some college, or a community college degree. Job zone 4 occupations typically require a 4-year college degree, which was matched to those with a Bachelor’s degree. Finally, job zone 5 occupations require a graduate degree; this included participants with a Master’s, Doctoral, or Professional degree.

Note that the average degree of fit between employed participants’ and their reported occupation ( r = .20) was greater than the average degree of fit between all participants and all occupations ( r = − .02) because individuals tend to self-select into occupations based on their interests.

Examining interest supply and demand using profile correlations. Note. Total N = 1,208,465. To estimate these distributions, each participant’s RIASEC interest profile was correlated with each O*NET occupation. Then, occupations were organized based on their RIASEC high-point code, such that the red bars display the distributions of participants showing different levels of profile-based fit with all of the occupations in each RIASEC category. The vertical, dotted blue line represents the average degree of fit between employed participants in the sample and their occupation ( r = .20)

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Department of Psychology, Michigan State University, East Lansing, USA

Kevin A. Hoff

Department of Psychology, University of Houston, Houston, USA

Kenneth E. Granillo-Velasquez

Department of Management, University of Nevada-Reno, Reno, USA

Alexis Hanna

The Myers-Briggs Company, Sunnyvale, USA

Michael L. Morris

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Frederick L. Oswald

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Supplementary Material 1

Data transparency appendix.

The vocational interest dataset reported in this manuscript was included in one published study (Morris, 2016 ), as well as one submitted study under review (Hoff et al., 2024 ). The table below displays the variables used in the current study (Hoff et al., 2024 ) and how they differ from the other studies. To summarize, the current study has a unique focus on the popularity of RIASEC interest profiles which have not yet been analyzed using this dataset. In addition, we collected and aggregated an extensive amount of information about the interest profiles of jobs in the U.S. labor market using data from the Bureau of Labor Statistics and O*NET (which is also unique to this manuscript). More generally, no results from the current manuscript overlap with any other manuscript, with exception of basic descriptive information about the data collection procedure and measures. MS1 was published in 2016 and focused on demographic differences in mean interest scores, but does not contain any information about the popularity of interest profiles. MS3 focuses on gender differences in basic interest scales, which are distinct from the RIASEC scales used in the current study.

Note that in the table below, the O*NET Occupational Interest Profiles and Bureau of Labor Statistics Employment Data come from publicly available datasets that were linked and aggregated for use in the current study. All other variables are from the proprietary vocational interest dataset owned by the The Myers Briggs Company.

Description of sample variables used in previous, current, and planned work

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Hoff, K.A., Granillo-Velasquez, K.E., Hanna, A. et al. Interest Gaps in the Labor Market: Comparing People’s Vocational Interests with National Job Demands. J Bus Psychol (2024). https://doi.org/10.1007/s10869-024-09945-8

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