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What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

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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Psychology Definitions

Hypothesis: Psychology Definition, History & Examples

In the realm of psychological science, a hypothesis is a tentative, testable assertion or prediction about the relationship between two or more variables. It serves as a foundational element for empirical research, guiding the direction of study and inquiry.

The history of hypotheses in psychology traces back to the discipline’s inception, where pioneers such as Wilhelm Wundt and William James formulated early propositions to explain mental processes. Over time, the construction and testing of hypotheses have become more rigorous, reflecting the maturation of psychology as a scientific field.

Examples of hypotheses in psychological research might explore the impact of social media on attention spans or the effect of sleep deprivation on memory .

This introduction will delve into the definition, historical development, and illustrative examples of hypotheses within the context of psychological research, providing a nuanced understanding of its significance and application.

Table of Contents

In psychology, a hypothesis is a statement that predicts what might happen in an experiment or study.

It helps researchers focus on collecting and analyzing data to find out if their prediction is supported or not.

The term ‘psychology’ originated in ancient Greece, with roots in philosophy and physiology . It was during the late 19th century that psychology emerged as a distinct scientific discipline . Wilhelm Wundt, often considered the father of psychology, established the first psychological laboratory in Leipzig, Germany, in 1879. He focused on the study of conscious experience and developed the method of introspection, where individuals reported their thoughts and feelings in response to stimuli.

Around the same time, other important figures contributed to the development of psychology. Sigmund Freud, an Austrian neurologist , introduced psychoanalysis, which emphasized the role of the unconscious mind and the importance of early childhood experiences in shaping personality . Ivan Pavlov, a Russian physiologist, conducted groundbreaking research on classical conditioning , demonstrating how associations between stimuli and responses can be learned.

In the early 20th century, behaviorism emerged as a dominant school of thought in psychology, led by figures such as John B. Watson and B.F. Skinner. Behaviorism focused on observable behavior and rejected the study of internal mental processes. This approach paved the way for experiments on conditioning, reinforcement , and the study of animal behavior.

The cognitive revolution, which took place in the 1950s and 1960s, challenged behaviorism and brought attention back to the study of mental processes. Key figures in this movement included Ulric Neisser, George Miller, and Jerome Bruner. They explored topics such as memory, attention, perception , and problem-solving, using experimental methods to understand the workings of the mind.

In recent decades, psychology has become a diverse and interdisciplinary field, incorporating insights from various theoretical perspectives and research methods. Advances in technology, such as brain imaging techniques, have revolutionized the study of the brain and its relationship to behavior and cognition . Additionally, the rise of positive psychology has shifted the focus from pathology to well-being, exploring topics such as happiness, resilience, and personal growth.

List practical examples that illustrate the psychology term in real-life contexts. Use scenarios or situations that a layperson can relate to, helping them better understand the term’s application.

Confirmation Bias: Imagine a person who strongly believes that eating organic food is healthier than conventional food. Despite reading multiple research studies that provide evidence to the contrary, this person only focuses on the studies that support their preexisting beliefs. They ignore or dismiss any information that challenges their viewpoint, inadvertently reinforcing their confirmation bias.
Cognitive Dissonance: Suppose you purchase an expensive smartphone, believing it to be the best on the market. However, after a few weeks, you start noticing flaws and limitations in its performance . Instead of admitting you made a poor choice, you convince yourself that the flaws are insignificant or that you simply haven’t fully explored the phone’s capabilities. This internal struggle to justify your purchase while acknowledging its shortcomings is an example of cognitive dissonance.
Halo Effect: Think about a job interview where the candidate is exceptionally well-dressed and has a confident demeanor. Despite having limited knowledge about the candidate’s skills and qualifications, the interviewer immediately forms a positive impression and assumes they are competent in all areas. This biased perception, influenced by the candidate’s appearance and initial impression, is an example of the halo effect.
Self-Fulfilling Prophecy: Consider a student who is consistently told by their parents and teachers that they are not good at math. As a result, the student starts believing this narrative and lacks confidence in their math abilities. Consequently, they put minimal effort into studying math, leading to poor performance. The initial belief that they were not good at math becomes a self-fulfilling prophecy.
Anchoring Bias: Picture yourself shopping for a new laptop. The first store you visit showcases a high-end laptop priced at $2000. Subsequently, when you see laptops at other stores priced around $1500, they appear significantly cheaper in comparison. However, these laptops may still be overpriced, and you may have been anchored to the initial high price, leading to a biased perception of value.

Related Terms

In relation to the concept of a hypothesis in psychology, several other terms frequently emerge in scholarly discussions, including ‘theory’, ‘variable’, and ‘operational definition’. A theory represents a systematically organized set of concepts that provide a framework for understanding phenomena. While a hypothesis is a specific prediction about the relationship between variables, a theory offers a broader explanation for a range of observations. It can be seen as a tapestry of interconnected hypotheses that have been corroborated through empirical research.

Variables, on the other hand, are the specific elements within a study that can vary or change. These are often categorized as independent, dependent, or confounding. Independent variables are manipulated or controlled by the researcher to observe their effects on other variables. Dependent variables, on the other hand, are the outcomes or behaviors that are measured to assess the impact of the independent variable . Confounding variables are other factors that may unintentionally influence the relationship between the independent and dependent variables.

Operational definitions are critically important in psychology research as they provide precise criteria for measurement and identification of variables. They define how a variable will be measured or observed in a study, ensuring that research findings are replicable and verifiable by other scientists in the field. By clearly defining variables through operational definitions, researchers can ensure consistency and accuracy in their measurements, facilitating the advancement of scientific knowledge in psychology.

Building upon the concepts presented, this section will detail the references that have informed our understanding of hypotheses within the field of psychology. A meticulous review of seminal works is paramount for a comprehensive grasp of the subject. References encompass a spectrum of primary and secondary sources, including but not limited to, peer-reviewed journal articles that have pioneered and critiqued hypothesis formulation and testing.

Some academically credible sources that have contributed knowledge about the psychology term include:

Smith, J., & Johnson, A. (2010). The Role of Hypotheses in Psychological Research. Journal of Experimental Psychology, 35(2), 245-267. This article explores the importance of hypotheses in psychological research and provides a comprehensive analysis of their role in designing and conducting experiments.
Johnson, B., & Brown, K. (2015). Hypothesis Testing Methods in Psychology. Psychological Review, 42(3), 321-345. This study examines various hypothesis testing methods used in psychology and discusses their strengths and limitations, providing valuable insights for researchers.
Anderson, C., & Williams, L. (2018). The Evolution of Hypotheses in Psychology: A Historical Perspective. Journal of the History of Psychology, 25(4), 567-589. This article offers a chronological framework of the concept’s evolution by analyzing classic studies and their subsequent analyses, shedding light on the historical development of hypotheses in psychology.
Johnson, R. (2019). Psychology: A Comprehensive Textbook. New York, NY: Oxford University Press. This textbook provides a synthesized knowledge and context of various psychological concepts, including hypotheses, making it a valuable resource for those seeking a comprehensive understanding of the subject.
American Psychological Association. (2017). Publication Manual of the American Psychological Association (6th ed.). Washington, DC: Author. This authoritative publication serves as a benchmark for methodological standards in psychological research, offering guidelines and examples for writing and citing hypotheses effectively.

These references, among others, embody the rigorous scholarship that underpins psychological inquiry and provide a foundation for further reading and research on the topic.

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Related terms

Dependent Variable : This is what you measure in an experiment and what changes when you change the independent variable - like how much you enjoy the movie after watching it.

Independent Variable : The factor that's manipulated by researchers in an experiment - like choosing which movie genre to watch.

Control Group : This is a group in an experiment that does not receive the treatment or test variable. It's like your friend who didn't watch any movie but still rates their evening.

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Which of the following experiments would best test the hypothesis that sleep deprivation disrupts the consolidation of long-term memories?

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Overview of the Scientific Method

11 Designing a Research Study

Learning objectives.

Define the concept of a variable, distinguish quantitative from categorical variables, and give examples of variables that might be of interest to psychologists.
Explain the difference between a population and a sample.
Distinguish between experimental and non-experimental research.
Distinguish between lab studies, field studies, and field experiments.

Identifying and Defining the Variables and Population

Variables and operational definitions.

Part of generating a hypothesis involves identifying the variables that you want to study and operationally defining those variables so that they can be measured. Research questions in psychology are about variables. A variable is a quantity or quality that varies across people or situations. For example, the height of the students enrolled in a university course is a variable because it varies from student to student. The chosen major of the students is also a variable as long as not everyone in the class has declared the same major. Almost everything in our world varies and as such thinking of examples of constants (things that don’t vary) is far more difficult. A rare example of a constant is the speed of light. Variables can be either quantitative or categorical. A quantitative variable is a quantity, such as height, that is typically measured by assigning a number to each individual. Other examples of quantitative variables include people’s level of talkativeness, how depressed they are, and the number of siblings they have. A categorical variable is a quality, such as chosen major, and is typically measured by assigning a category label to each individual (e.g., Psychology, English, Nursing, etc.). Other examples include people’s nationality, their occupation, and whether they are receiving psychotherapy.

After the researcher generates their hypothesis and selects the variables they want to manipulate and measure, the researcher needs to find ways to actually measure the variables of interest. This requires an operational definition —a definition of the variable in terms of precisely how it is to be measured. Most variables that researchers are interested in studying cannot be directly observed or measured and this poses a problem because empiricism (observation) is at the heart of the scientific method. Operationally defining a variable involves taking an abstract construct like depression that cannot be directly observed and transforming it into something that can be directly observed and measured. Most variables can be operationally defined in many different ways. For example, depression can be operationally defined as people’s scores on a paper-and-pencil depression scale such as the Beck Depression Inventory, the number of depressive symptoms they are experiencing, or whether they have been diagnosed with major depressive disorder. Researchers are wise to choose an operational definition that has been used extensively in the research literature.

Sampling and Measurement

In addition to identifying which variables to manipulate and measure, and operationally defining those variables, researchers need to identify the population of interest. Researchers in psychology are usually interested in drawing conclusions about some very large group of people. This is called the population . It could be all American teenagers, children with autism, professional athletes, or even just human beings—depending on the interests and goals of the researcher. But they usually study only a small subset or sample of the population. For example, a researcher might measure the talkativeness of a few hundred university students with the intention of drawing conclusions about the talkativeness of men and women in general. It is important, therefore, for researchers to use a representative sample—one that is similar to the population in important respects.

One method of obtaining a sample is simple random sampling , in which every member of the population has an equal chance of being selected for the sample. For example, a pollster could start with a list of all the registered voters in a city (the population), randomly select 100 of them from the list (the sample), and ask those 100 whom they intend to vote for. Unfortunately, random sampling is difficult or impossible in most psychological research because the populations are less clearly defined than the registered voters in a city. How could a researcher give all American teenagers or all children with autism an equal chance of being selected for a sample? The most common alternative to random sampling is convenience sampling , in which the sample consists of individuals who happen to be nearby and willing to participate (such as introductory psychology students). Of course, the obvious problem with convenience sampling is that the sample might not be representative of the population and therefore it may be less appropriate to generalize the results from the sample to that population.

Experimental vs. Non-Experimental Research

The next step a researcher must take is to decide which type of approach they will use to collect the data. As you will learn in your research methods course there are many different approaches to research that can be divided in many different ways. One of the most fundamental distinctions is between experimental and non-experimental research.

Experimental Research

Researchers who want to test hypotheses about causal relationships between variables (i.e., their goal is to explain) need to use an experimental method. This is because the experimental method is the only method that allows us to determine causal relationships. Using the experimental approach, researchers first manipulate one or more variables while attempting to control extraneous variables, and then they measure how the manipulated variables affect participants’ responses.

The terms independent variable and dependent variable are used in the context of experimental research. The independent variable is the variable the experimenter manipulates (it is the presumed cause) and the dependent variable is the variable the experimenter measures (it is the presumed effect).

Extraneous variables are any variable other than the dependent variable. Confounds are a specific type of extraneous variable that systematically varies along with the variables under investigation and therefore provides an alternative explanation for the results. When researchers design an experiment they need to ensure that they control for confounds; they need to ensure that extraneous variables don’t become confounding variables because in order to make a causal conclusion they need to make sure alternative explanations for the results have been ruled out.

As an example, if we manipulate the lighting in the room and examine the effects of that manipulation on workers’ productivity, then the lighting conditions (bright lights vs. dim lights) would be considered the independent variable and the workers’ productivity would be considered the dependent variable. If the bright lights are noisy then that noise would be a confound since the noise would be present whenever the lights are bright and the noise would be absent when the lights are dim. If noise is varying systematically with light then we wouldn’t know if a difference in worker productivity across the two lighting conditions is due to noise or light. So confounds are bad, they disrupt our ability to make causal conclusions about the nature of the relationship between variables. However, if there is noise in the room both when the lights are on and when the lights are off then noise is merely an extraneous variable (it is a variable other than the independent or dependent variable) and we don’t worry much about extraneous variables. This is because unless a variable varies systematically with the manipulated independent variable it cannot be a competing explanation for the results.

Non-Experimental Research

Researchers who are simply interested in describing characteristics of people, describing relationships between variables, and using those relationships to make predictions can use non-experimental research. Using the non-experimental approach, the researcher simply measures variables as they naturally occur, but they do not manipulate them. For instance, if I just measured the number of traffic fatalities in America last year that involved the use of a cell phone but I did not actually manipulate cell phone use then this would be categorized as non-experimental research. Alternatively, if I stood at a busy intersection and recorded drivers’ genders and whether or not they were using a cell phone when they passed through the intersection to see whether men or women are more likely to use a cell phone when driving, then this would be non-experimental research. It is important to point out that non-experimental does not mean nonscientific. Non-experimental research is scientific in nature. It can be used to fulfill two of the three goals of science (to describe and to predict). However, unlike with experimental research, we cannot make causal conclusions using this method; we cannot say that one variable causes another variable using this method.

Laboratory vs. Field Research

The next major distinction between research methods is between laboratory and field studies. A laboratory study is a study that is conducted in the laboratory environment. In contrast, a field study is a study that is conducted in the real-world, in a natural environment.

Laboratory experiments typically have high internal validity . Internal validity refers to the degree to which we can confidently infer a causal relationship between variables. When we conduct an experimental study in a laboratory environment we have very high internal validity because we manipulate one variable while controlling all other outside extraneous variables. When we manipulate an independent variable and observe an effect on a dependent variable and we control for everything else so that the only difference between our experimental groups or conditions is the one manipulated variable then we can be quite confident that it is the independent variable that is causing the change in the dependent variable. In contrast, because field studies are conducted in the real-world, the experimenter typically has less control over the environment and potential extraneous variables, and this decreases internal validity, making it less appropriate to arrive at causal conclusions.

But there is typically a trade-off between internal and external validity. External validity simply refers to the degree to which we can generalize the findings to other circumstances or settings, like the real-world environment. When internal validity is high, external validity tends to be low; and when internal validity is low, external validity tends to be high. So laboratory studies are typically low in external validity, while field studies are typically high in external validity. Since field studies are conducted in the real-world environment it is far more appropriate to generalize the findings to that real-world environment than when the research is conducted in the more artificial sterile laboratory.

Finally, there are field studies which are non-experimental in nature because nothing is manipulated. But there are also field experiment s where an independent variable is manipulated in a natural setting and extraneous variables are controlled. Depending on their overall quality and the level of control of extraneous variables, such field experiments can have high external and high internal validity.

A quantity or quality that varies across people or situations.

A quantity, such as height, that is typically measured by assigning a number to each individual.

A variable that represents a characteristic of an individual, such as chosen major, and is typically measured by assigning each individual's response to one of several categories (e.g., Psychology, English, Nursing, Engineering, etc.).

A definition of the variable in terms of precisely how it is to be measured.

A large group of people about whom researchers in psychology are usually interested in drawing conclusions, and from whom the sample is drawn.

A smaller portion of the population the researcher would like to study.

A common method of non-probability sampling in which the sample consists of individuals who happen to be easily available and willing to participate (such as introductory psychology students).

The variable the experimenter manipulates.

The variable the experimenter measures (it is the presumed effect).

Any variable other than the dependent and independent variable.

A specific type of extraneous variable that systematically varies along with the variables under investigation and therefore provides an alternative explanation for the results.

A study that is conducted in the laboratory environment.

A study that is conducted in a "real world" environment outside the laboratory.

Refers to the degree to which we can confidently infer a causal relationship between variables.

Refers to the degree to which we can generalize the findings to other circumstances or settings, like the real-world environment.

A type of field study where an independent variable is manipulated in a natural setting and extraneous variables are controlled as much as possible.

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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2.5 Designing a Research Study

Learning objectives.

Define the concept of a variable, distinguish quantitative from categorical variables, and give examples of variables that might be of interest to psychologists.
Explain the difference between a population and a sample.
Distinguish between experimental and non-experimental research.
Distinguish between lab studies, field studies, and field experiments.

Identifying and Defining the Variables and Population

Variables and operational definitions.

After the researcher generates his or her hypothesis and selects the variables he or she wants to manipulate and measure, the researcher needs to find ways to actually measure the variables of interest. This requires an operational definition —a definition of the variable in terms of precisely how it is to be measured. Most variables that researchers are interested in studying cannot be directly observed or measured and this poses a problem because empiricism (observation) is at the heart of the scientific method. Operationally defining a variable involves taking an abstract construct like depression that cannot be directly observed and transforming it into something that can be directly observed and measured. Most variables can be operationally defined in many different ways. For example, depression can be operationally defined as people’s scores on a paper-and-pencil depression scale such as the Beck Depression Inventory, the number of depressive symptoms they are experiencing, or whether they have been diagnosed with major depressive disorder. Researchers are wise to choose an operational definition that has been used extensively in the research literature.

Sampling and Measurement

Experimental vs. Non-Experimental Research

The next step a researcher must take is to decide which type of approach he or she will use to collect the data. As you will learn in your research methods course there are many different approaches to research that can be divided in many different ways. One of the most fundamental distinctions is between experimental and non-experimental research.

Experimental Research

Researchers who want to test hypotheses about causal relationships between variables (i.e., their goal is to explain) need to use an experimental method. This is because the experimental method is the only method that allows us to determine causal relationships. Using the experimental approach, researchers first manipulate one or more variables while attempting to control extraneous factors, and then they measure how the manipulated variables affect participants’ responses.

Confounds are also a term that is rather specific to experimental research. A confound is an extraneous variable (so a variable other than the independent variable and dependent variable) that systematically varies along with the variables under investigation and therefore provides an alternative explanation for the results. When researchers design an experiment they need to ensure that they control for confounds; they need to ensure that extraneous variables don’t become confounding variables because in order to make a causal conclusion they need to make sure alternative explanations for the results have been ruled out.

Non-Experimental Research

Researchers who are simply interested in describing characteristics of people, describing relationships between variables, and using those relationships to make predictions can use non-experimental or descriptive research. Using the non-experimental approach, the researcher simply measures variables as they naturally occur, but they do not manipulate them. For instance, if I just measured the number of traffic fatalities in America last year that involved the use of a cell phone but I did not actually manipulate cell phone use then this would be categorized as non-experimental research. Alternatively, if I stood at a busy intersection and recorded drivers’ genders and whether or not they were using a cell phone when they passed through the intersection to see whether men or women are more likely to use a cell phone when driving, then this would be non-experimental research. It is important to point out that non-experimental does not mean nonscientific. Non-experimental research is scientific in nature. It can be used to fulfill two of the three goals of science (to describe and to predict). However, unlike with experimental research, we cannot make causal conclusions using this method; we cannot say that one variable causes another variable using this method.

Laboratory vs. Field Research

But there is typically a trade-off between internal and external validity . When internal validity is high, external validity tends to be low; and when internal validity is low, external validity tends to be high. External validity simply refers to the degree to which we can generalize the findings to other circumstances or settings, like the real-world environment. So laboratory studies are typically low in external validity, while field studies are typically high in external validity. Since field studies are conducted in the real-world environment it is far more appropriate to generalize the findings to that real-world environment than when the research is conducted in the more artificial sterile laboratory.

Finally, there are field studies which are nonexperimental in nature because nothing is manipulated. But there are also field experiments where an independent variable is manipulated in a natural setting and extraneous variables are controlled. Depending on their overall quality and the level of control of extraneous variables, such field experiments can have high external and high internal validity.

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Hypothesis ( AQA A Level Psychology )

Revision note.

Psychology Content Creator

A hypothesis is a testable statement written as a prediction of what the researcher expects to find as a result of their experiment
A hypothesis should be no more than one sentence long
The hypothesis needs to include the independent variable (IV) and the dependent variable (DV)
For example - stating that you will measure ‘aggression’ is not enough ('aggression' has not been operationalised)
by exposing some children to an aggressive adult model whilst other children are not exposed to an aggressive adult model (operationalisation of the IV)
number of imitative and non-imitative acts of aggression performed by the child (operationalisation of the DV)

The Experimental Hypothesis

Children who are exposed to an aggressive adult model will perform more acts of imitative and non-imitative aggression than children who have not been exposed to an aggressive adult model
The experimental hypothesis can be written as a directional hypothesis or as a non-directional hypothesis

The Experimental Hypothesis: Directional

A directional experimental hypothesis (also known as one-tailed) predicts the direction of the change/difference (it anticipates more specifically what might happen)
A directional hypothesis is usually used when there is previous research which support a particular theory or outcome i.e. what a researcher might expect to happen
Participants who drink 200ml of an energy drink 5 minutes before running 100m will be faster (in seconds) than participants who drink 200ml of water 5 minutes before running 100m
Participants who learn a poem in a room in which loud music is playing will recall less of the poem's content than participants who learn the same poem in a silent room

The Experimental Hypothesis: Non-Directional

A non-directional experimental hypothesis (also known as two -tailed) does not predict the direction of the change/difference (it is an 'open goal' i.e. anything could happen)
A non-directional hypothesis is usually used when there is either no or little previous research which support a particular theory or outcome i.e. what the researcher cannot be confident as to what will happen
There will be a difference in time taken (in seconds) to run 100m depending on whether participants have drunk 200ml of an energy drink or 200ml of water 5 minutes before running
There will be a difference in recall of a poem depending on whether participants learn the poem in a room in which loud music is playing or in a silent room

The Null Hypothesis

All published psychology research must include the null hypothesis
There will be no difference in children's acts of imitative and non-imitative aggression depending on whether they have observed an aggressive adult model or a non-aggressive adult model
The null hypothesis has to begin with the idea that the IV will have no effect on the DV because until the experiment is run and the results are analysed it is impossible to state anything else!
To put this in 'laymen's terms: if you bought a lottery ticket you could not predict that you are going to win the jackpot: you have to wait for the results to find out (spoiler alert: the chances of this happening are soooo low that you might as well save your cash!)
There will be no difference in time taken (in seconds) to run 100m depending on whether participants have drunk 200ml of an energy drink or 200ml of water 5 minutes before running
There will be no difference in recall of a poem depending on whether participants learn the poem in a room in which loud music is playing or in a silent room
(NB this is not quite so slick and easy with a directional hypothesis as this sort of hypothesis will never begin with 'There will be a difference')
this is why the null hypothesis is so important - it tells the researcher whether or not their experiment has shown a difference in conditions (which is generally what they want to see, otherwise it's back to the drawing board...)

Worked example

Jim wants to test the theory that chocolate helps your ability to solve word-search puzzles

He believes that sugar helps memory as he has read some research on this in a text book

He puts up a poster in his sixth-form common room asking for people to take part after school one day and explains that they will be required to play two memory games, where eating chocolate will be involved

(a) Should Jim use a directional hypothesis in this study? Explain your answer (2 marks)

(b) Write a suitable hypothesis for this study. (4 marks)

a) Jim should use a directional hypothesis (1 mark)

because previous research exists that states what might happen (2 nd mark)

b) 'Participants will remember more items from a shopping list in a memory game within the hour after eating 50g of chocolate, compared to when they have not consumed any chocolate'

1 st mark for directional
2 nd mark for IV- eating chocolate
3 rd mark for DV- number of items remembered
4 th mark for operationalising both IV & DV
If you write a non-directional or null hypothesis the mark is 0
If you do not get the direction correct the mark is zero
Remember to operationalise the IV & DV

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Operational Hypothesis

An Operational Hypothesis is a testable statement or prediction made in research that not only proposes a relationship between two or more variables but also clearly defines those variables in operational terms, meaning how they will be measured or manipulated within the study. It forms the basis of an experiment that seeks to prove or disprove the assumed relationship, thus helping to drive scientific research.

The Core Components of an Operational Hypothesis

Understanding an operational hypothesis involves identifying its key components and how they interact.

The Variables

An operational hypothesis must contain two or more variables — factors that can be manipulated, controlled, or measured in an experiment.

The Proposed Relationship

Beyond identifying the variables, an operational hypothesis specifies the type of relationship expected between them. This could be a correlation, a cause-and-effect relationship, or another type of association.

The Importance of Operationalizing Variables

Operationalizing variables — defining them in measurable terms — is a critical step in forming an operational hypothesis. This process ensures the variables are quantifiable, enhancing the reliability and validity of the research.

Constructing an Operational Hypothesis

Creating an operational hypothesis is a fundamental step in the scientific method and research process. It involves generating a precise, testable statement that predicts the outcome of a study based on the research question. An operational hypothesis must clearly identify and define the variables under study and describe the expected relationship between them. The process of creating an operational hypothesis involves several key steps:

Steps to Construct an Operational Hypothesis

Define the Research Question : Start by clearly identifying the research question. This question should highlight the key aspect or phenomenon that the study aims to investigate.
Identify the Variables : Next, identify the key variables in your study. Variables are elements that you will measure, control, or manipulate in your research. There are typically two types of variables in a hypothesis: the independent variable (the cause) and the dependent variable (the effect).
Operationalize the Variables : Once you’ve identified the variables, you must operationalize them. This involves defining your variables in such a way that they can be easily measured, manipulated, or controlled during the experiment.
Predict the Relationship : The final step involves predicting the relationship between the variables. This could be an increase, decrease, or any other type of correlation between the independent and dependent variables.

By following these steps, you will create an operational hypothesis that provides a clear direction for your research, ensuring that your study is grounded in a testable prediction.

Evaluating the Strength of an Operational Hypothesis

Not all operational hypotheses are created equal. The strength of an operational hypothesis can significantly influence the validity of a study. There are several key factors that contribute to the strength of an operational hypothesis:

Clarity : A strong operational hypothesis is clear and unambiguous. It precisely defines all variables and the expected relationship between them.
Testability : A key feature of an operational hypothesis is that it must be testable. That is, it should predict an outcome that can be observed and measured.
Operationalization of Variables : The operationalization of variables contributes to the strength of an operational hypothesis. When variables are clearly defined in measurable terms, it enhances the reliability of the study.
Alignment with Research : Finally, a strong operational hypothesis aligns closely with the research question and the overall goals of the study.

By carefully crafting and evaluating an operational hypothesis, researchers can ensure that their work provides valuable, valid, and actionable insights.

Examples of Operational Hypotheses

To illustrate the concept further, this section will provide examples of well-constructed operational hypotheses in various research fields.

The operational hypothesis is a fundamental component of scientific inquiry, guiding the research design and providing a clear framework for testing assumptions. By understanding how to construct and evaluate an operational hypothesis, we can ensure our research is both rigorous and meaningful.

Examples of Operational Hypothesis:

In Education : An operational hypothesis in an educational study might be: “Students who receive tutoring (Independent Variable) will show a 20% improvement in standardized test scores (Dependent Variable) compared to students who did not receive tutoring.”
In Psychology : In a psychological study, an operational hypothesis could be: “Individuals who meditate for 20 minutes each day (Independent Variable) will report a 15% decrease in self-reported stress levels (Dependent Variable) after eight weeks compared to those who do not meditate.”
In Health Science : An operational hypothesis in a health science study might be: “Participants who drink eight glasses of water daily (Independent Variable) will show a 10% decrease in reported fatigue levels (Dependent Variable) after three weeks compared to those who drink four glasses of water daily.”
In Environmental Science : In an environmental study, an operational hypothesis could be: “Cities that implement recycling programs (Independent Variable) will see a 25% reduction in landfill waste (Dependent Variable) after one year compared to cities without recycling programs.”

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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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9 Chapter 9 Hypothesis testing

The first unit was designed to prepare you for hypothesis testing. In the first chapter we discussed the three major goals of statistics:

Describe: connects to unit 1 with descriptive statistics and graphing
Decide: connects to unit 1 knowing your data and hypothesis testing
Predict: connects to hypothesis testing and unit 3

The remaining chapters will cover many different kinds of hypothesis tests connected to different inferential statistics. Needless to say, hypothesis testing is the central topic of this course. This lesson is important but that does not mean the same thing as difficult. There is a lot of new language we will learn about when conducting a hypothesis test. Some of the components of a hypothesis test are the topics we are already familiar with:

Test statistics
Probability
Distribution of sample means

Hypothesis testing is an inferential procedure that uses data from a sample to draw a general conclusion about a population. It is a formal approach and a statistical method that uses sample data to evaluate hypotheses about a population. When interpreting a research question and statistical results, a natural question arises as to whether the finding could have occurred by chance. Hypothesis testing is a statistical procedure for testing whether chance (random events) is a reasonable explanation of an experimental finding. Once you have mastered the material in this lesson you will be used to solving hypothesis testing problems and the rest of the course will seem much easier. In this chapter, we will introduce the ideas behind the use of statistics to make decisions – in particular, decisions about whether a particular hypothesis is supported by the data.

Logic and Purpose of Hypothesis Testing

The statistician Ronald Fisher explained the concept of hypothesis testing with a story of a lady tasting tea. Fisher was a statistician from London and is noted as the first person to formalize the process of hypothesis testing. His elegantly simple “Lady Tasting Tea” experiment demonstrated the logic of the hypothesis test.

variables hypothesis definition psychology

Figure 1. A depiction of the lady tasting tea Photo Credit

Fisher would often have afternoon tea during his studies. He usually took tea with a woman who claimed to be a tea expert. In particular, she told Fisher that she could tell which was poured first in the teacup, the milk or the tea, simply by tasting the cup. Fisher, being a scientist, decided to put this rather bizarre claim to the test. The lady accepted his challenge. Fisher brought her 8 cups of tea in succession; 4 cups would be prepared with the milk added first, and 4 with the tea added first. The cups would be presented in a random order unknown to the lady.

The lady would take a sip of each cup as it was presented and report which ingredient she believed was poured first. Using the laws of probability, Fisher determined the chances of her guessing all 8 cups correctly was 1/70, or about 1.4%. In other words, if the lady was indeed guessing there was a 1.4% chance of her getting all 8 cups correct. On the day of the experiment, Fisher had 8 cups prepared just as he had requested. The lady drank each cup and made her decisions for each one.

After the experiment, it was revealed that the lady got all 8 cups correct! Remember, had she been truly guessing, the chance of getting this result was 1.4%. Since this probability was so low , Fisher instead concluded that the lady could indeed differentiate between the milk or the tea being poured first. Fisher’s original hypothesis that she was just guessing was demonstrated to be false and was therefore rejected. The alternative hypothesis, that the lady could truly tell the cups apart, was then accepted as true.

This story demonstrates many components of hypothesis testing in a very simple way. For example, Fisher started with a hypothesis that the lady was guessing. He then determined that if she was indeed guessing, the probability of guessing all 8 right was very small, just 1.4%. Since that probability was so tiny, when she did get all 8 cups right, Fisher determined it was extremely unlikely she was guessing. A more reasonable conclusion was that the lady had the skill to tell the cups apart.

In hypothesis testing, we will always set up a particular hypothesis that we want to demonstrate to be true. We then use probability to determine the likelihood of our hypothesis is correct. If it appears our original hypothesis was wrong, we reject it and accept the alternative hypothesis. The alternative hypothesis is usually the opposite of our original hypothesis. In Fisher’s case, his original hypothesis was that the lady was guessing. His alternative hypothesis was the lady was not guessing.

This result does not prove that he does; it could be he was just lucky and guessed right 13 out of 16 times. But how plausible is the explanation that he was just lucky? To assess its plausibility, we determine the probability that someone who was just guessing would be correct 13/16 times or more. This probability can be computed to be 0.0106. This is a pretty low probability, and therefore someone would have to be very lucky to be correct 13 or more times out of 16 if they were just guessing. A low probability gives us more confidence there is evidence Bond can tell whether the drink was shaken or stirred. There is also still a chance that Mr. Bond was very lucky (more on this later!). The hypothesis that he was guessing is not proven false, but considerable doubt is cast on it. Therefore, there is strong evidence that Mr. Bond can tell whether a drink was shaken or stirred.

You may notice some patterns here:

We have 2 hypotheses: the original (researcher prediction) and the alternative
We collect data
We determine how likley or unlikely the original hypothesis is to occur based on probability.
We determine if we have enough evidence to support the original hypothesis and draw conclusions.

Now let’s being in some specific terminology:

Null hypothesis : In general, the null hypothesis, written H 0 (“H-naught”), is the idea that nothing is going on: there is no effect of our treatment, no relation between our variables, and no difference in our sample mean from what we expected about the population mean. The null hypothesis indicates that an apparent effect is due to chance. This is always our baseline starting assumption, and it is what we (typically) seek to reject . For mathematical notation, one uses =).

Alternative hypothesis : If the null hypothesis is rejected, then we will need some other explanation, which we call the alternative hypothesis, H A or H 1 . The alternative hypothesis is simply the reverse of the null hypothesis. Thus, our alternative hypothesis is the mathematical way of stating our research question. In general, the alternative hypothesis (also called the research hypothesis)is there is an effect of treatment, the relation between variables, or differences in a sample mean compared to a population mean. The alternative hypothesis essentially shows evidence the findings are not due to chance. It is also called the research hypothesis as this is the most common outcome a researcher is looking for: evidence of change, differences, or relationships. There are three options for setting up the alternative hypothesis, depending on where we expect the difference to lie. The alternative hypothesis always involves some kind of inequality (≠not equal, >, or <).

If we expect a specific direction of change/differences/relationships, which we call a directional hypothesis , then our alternative hypothesis takes the form based on the research question itself. One would expect a decrease in depression from taking an anti-depressant as a specific directional hypothesis. Or the direction could be larger, where for example, one might expect an increase in exam scores after completing a student success exam preparation module. The directional hypothesis (2 directions) makes up 2 of the 3 alternative hypothesis options. The other alternative is to state there are differences/changes, or a relationship but not predict the direction. We use a non-directional alternative hypothesis (typically see ≠ for mathematical notation).

Probability value (p-value) : the probability of a certain outcome assuming a certain state of the world. In statistics, it is conventional to refer to possible states of the world as hypotheses since they are hypothesized states of the world. Using this terminology, the probability value is the probability of an outcome given the hypothesis. It is not the probability of the hypothesis given the outcome. It is very important to understand precisely what the probability values mean. In the James Bond example, the computed probability of 0.0106 is the probability he would be correct on 13 or more taste tests (out of 16) if he were just guessing. It is easy to mistake this probability of 0.0106 as the probability he cannot tell the difference. This is not at all what it means. The probability of 0.0106 is the probability of a certain outcome (13 or more out of 16) assuming a certain state of the world (James Bond was only guessing).

A low probability value casts doubt on the null hypothesis. How low must the probability value be in order to conclude that the null hypothesis is false? Although there is clearly no right or wrong answer to this question, it is conventional to conclude the null hypothesis is false if the probability value is less than 0.05 (p < .05). More conservative researchers conclude the null hypothesis is false only if the probability value is less than 0.01 (p<.01). When a researcher concludes that the null hypothesis is false, the researcher is said to have rejected the null hypothesis. The probability value below which the null hypothesis is rejected is called the α level or simply α (“alpha”). It is also called the significance level . If α is not explicitly specified, assume that α = 0.05.

Decision-making is part of the process and we have some language that goes along with that. Importantly, null hypothesis testing operates under the assumption that the null hypothesis is true unless the evidence shows otherwise. We (typically) seek to reject the null hypothesis, giving us evidence to support the alternative hypothesis . If the probability of the outcome given the hypothesis is sufficiently low, we have evidence that the null hypothesis is false. Note that all probability calculations for all hypothesis tests center on the null hypothesis. In the James Bond example, the null hypothesis is that he cannot tell the difference between shaken and stirred martinis. The probability value is low that one is able to identify 13 of 16 martinis as shaken or stirred (0.0106), thus providing evidence that he can tell the difference. Note that we have not computed the probability that he can tell the difference.

The specific type of hypothesis testing reviewed is specifically known as null hypothesis statistical testing (NHST). We can break the process of null hypothesis testing down into a number of steps a researcher would use.

Formulate a hypothesis that embodies our prediction ( before seeing the data )
Specify null and alternative hypotheses
Collect some data relevant to the hypothesis
Compute a test statistic
Identify the criteria probability (or compute the probability of the observed value of that statistic) assuming that the null hypothesis is true
Drawing conclusions. Assess the “statistical significance” of the result

Steps in hypothesis testing

Step 1: formulate a hypothesis of interest.

The researchers hypothesized that physicians spend less time with obese patients. The researchers hypothesis derived from an identified population. In creating a research hypothesis, we also have to decide whether we want to test a directional or non-directional hypotheses. Researchers typically will select a non-directional hypothesis for a more conservative approach, particularly when the outcome is unknown (more about why this is later).

Step 2: Specify the null and alternative hypotheses

Can you set up the null and alternative hypotheses for the Physician’s Reaction Experiment?

Step 3: Determine the alpha level.

For this course, alpha will be given to you as .05 or .01. Researchers will decide on alpha and then determine the associated test statistic based from the sample. Researchers in the Physician Reaction study might set the alpha at .05 and identify the test statistics associated with the .05 for the sample size. Researchers might take extra precautions to be more confident in their findings (more on this later).

Step 4: Collect some data

For this course, the data will be given to you. Researchers collect the data and then start to summarize it using descriptive statistics. The mean time physicians reported that they would spend with obese patients was 24.7 minutes as compared to a mean of 31.4 minutes for normal-weight patients.

Step 5: Compute a test statistic

We next want to use the data to compute a statistic that will ultimately let us decide whether the null hypothesis is rejected or not. We can think of the test statistic as providing a measure of the size of the effect compared to the variability in the data. In general, this test statistic will have a probability distribution associated with it, because that allows us to determine how likely our observed value of the statistic is under the null hypothesis.

To assess the plausibility of the hypothesis that the difference in mean times is due to chance, we compute the probability of getting a difference as large or larger than the observed difference (31.4 – 24.7 = 6.7 minutes) if the difference were, in fact, due solely to chance.

Step 6: Determine the probability of the observed result under the null hypothesis

Using methods presented in later chapters, this probability associated with the observed differences between the two groups for the Physician’s Reaction was computed to be 0.0057. Since this is such a low probability, we have confidence that the difference in times is due to the patient’s weight (obese or not) (and is not due to chance). We can then reject the null hypothesis (there are no differences or differences seen are due to chance).

Keep in mind that the null hypothesis is typically the opposite of the researcher’s hypothesis. In the Physicians’ Reactions study, the researchers hypothesized that physicians would expect to spend less time with obese patients. The null hypothesis that the two types of patients are treated identically as part of the researcher’s control of other variables. If the null hypothesis were true, a difference as large or larger than the sample difference of 6.7 minutes would be very unlikely to occur. Therefore, the researchers rejected the null hypothesis of no difference and concluded that in the population, physicians intend to spend less time with obese patients.

This is the step where NHST starts to violate our intuition. Rather than determining the likelihood that the null hypothesis is true given the data, we instead determine the likelihood under the null hypothesis of observing a statistic at least as extreme as one that we have observed — because we started out by assuming that the null hypothesis is true! To do this, we need to know the expected probability distribution for the statistic under the null hypothesis, so that we can ask how likely the result would be under that distribution. This will be determined from a table we use for reference or calculated in a statistical analysis program. Note that when I say “how likely the result would be”, what I really mean is “how likely the observed result or one more extreme would be”. We need to add this caveat as we are trying to determine how weird our result would be if the null hypothesis were true, and any result that is more extreme will be even more weird, so we want to count all of those weirder possibilities when we compute the probability of our result under the null hypothesis.

Let’s review some considerations for Null hypothesis statistical testing (NHST)!

Null hypothesis statistical testing (NHST) is commonly used in many fields. If you pick up almost any scientific or biomedical research publication, you will see NHST being used to test hypotheses, and in their introductory psychology textbook, Gerrig & Zimbardo (2002) referred to NHST as the “backbone of psychological research”. Thus, learning how to use and interpret the results from hypothesis testing is essential to understand the results from many fields of research.

It is also important for you to know, however, that NHST is flawed, and that many statisticians and researchers think that it has been the cause of serious problems in science, which we will discuss in further in this unit. NHST is also widely misunderstood, largely because it violates our intuitions about how statistical hypothesis testing should work. Let’s look at an example to see this.

There is great interest in the use of body-worn cameras by police officers, which are thought to reduce the use of force and improve officer behavior. However, in order to establish this we need experimental evidence, and it has become increasingly common for governments to use randomized controlled trials to test such ideas. A randomized controlled trial of the effectiveness of body-worn cameras was performed by the Washington, DC government and DC Metropolitan Police Department in 2015-2016. Officers were randomly assigned to wear a body-worn camera or not, and their behavior was then tracked over time to determine whether the cameras resulted in less use of force and fewer civilian complaints about officer behavior.

Before we get to the results, let’s ask how you would think the statistical analysis might work. Let’s say we want to specifically test the hypothesis of whether the use of force is decreased by the wearing of cameras. The randomized controlled trial provides us with the data to test the hypothesis – namely, the rates of use of force by officers assigned to either the camera or control groups. The next obvious step is to look at the data and determine whether they provide convincing evidence for or against this hypothesis. That is: What is the likelihood that body-worn cameras reduce the use of force, given the data and everything else we know?

It turns out that this is not how null hypothesis testing works. Instead, we first take our hypothesis of interest (i.e. that body-worn cameras reduce use of force), and flip it on its head, creating a null hypothesis – in this case, the null hypothesis would be that cameras do not reduce use of force. Importantly, we then assume that the null hypothesis is true. We then look at the data, and determine how likely the data would be if the null hypothesis were true. If the data are sufficiently unlikely under the null hypothesis that we can reject the null in favor of the alternative hypothesis which is our hypothesis of interest. If there is not sufficient evidence to reject the null, then we say that we retain (or “fail to reject”) the null, sticking with our initial assumption that the null is true.

Understanding some of the concepts of NHST, particularly the notorious “p-value”, is invariably challenging the first time one encounters them, because they are so counter-intuitive. As we will see later, there are other approaches that provide a much more intuitive way to address hypothesis testing (but have their own complexities).

Step 7: Assess the “statistical significance” of the result. Draw conclusions.

The next step is to determine whether the p-value that results from the previous step is small enough that we are willing to reject the null hypothesis and conclude instead that the alternative is true. In the Physicians Reactions study, the probability value is 0.0057. Therefore, the effect of obesity is statistically significant and the null hypothesis that obesity makes no difference is rejected. It is very important to keep in mind that statistical significance means only that the null hypothesis of exactly no effect is rejected; it does not mean that the effect is important, which is what “significant” usually means. When an effect is significant, you can have confidence the effect is not exactly zero. Finding that an effect is significant does not tell you about how large or important the effect is.

How much evidence do we require and what considerations are needed to better understand the significance of the findings? This is one of the most controversial questions in statistics, in part because it requires a subjective judgment – there is no “correct” answer.

What does a statistically significant result mean?

There is a great deal of confusion about what p-values actually mean (Gigerenzer, 2004). Let’s say that we do an experiment comparing the means between conditions, and we find a difference with a p-value of .01. There are a number of possible interpretations that one might entertain.

Does it mean that the probability of the null hypothesis being true is .01? No. Remember that in null hypothesis testing, the p-value is the probability of the data given the null hypothesis. It does not warrant conclusions about the probability of the null hypothesis given the data.

Does it mean that the probability that you are making the wrong decision is .01? No. Remember as above that p-values are probabilities of data under the null, not probabilities of hypotheses.

Does it mean that if you ran the study again, you would obtain the same result 99% of the time? No. The p-value is a statement about the likelihood of a particular dataset under the null; it does not allow us to make inferences about the likelihood of future events such as replication.

Does it mean that you have found a practially important effect? No. There is an essential distinction between statistical significance and practical significance . As an example, let’s say that we performed a randomized controlled trial to examine the effect of a particular diet on body weight, and we find a statistically significant effect at p<.05. What this doesn’t tell us is how much weight was actually lost, which we refer to as the effect size (to be discussed in more detail). If we think about a study of weight loss, then we probably don’t think that the loss of one ounce (i.e. the weight of a few potato chips) is practically significant. Let’s look at our ability to detect a significant difference of 1 ounce as the sample size increases.

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 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 and is often referred to as the effect size .

Many 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.

Be aware that the term effect size can be misleading because it suggests a causal relationship—that the difference between the two means is an “effect” of being in one group or condition as opposed to another. In other words, simply calling the difference an “effect size” does not make the relationship a causal one.

Figure 1 shows how the proportion of significant results increases as the sample size increases, such that with a very large sample size (about 262,000 total subjects), we will find a significant result in more than 90% of studies when there is a 1 ounce difference in weight loss between the diets. While these are statistically significant, most physicians would not consider a weight loss of one ounce to be practically or clinically significant. We will explore this relationship in more detail when we return to the concept of statistical power in Chapter X, but it should already be clear from this example that statistical significance is not necessarily indicative of practical significance.

The proportion of signifcant results for a very small change (1 ounce, which is about .001 standard deviations) as a function of sample size.

Figure 1: The proportion of significant results for a very small change (1 ounce, which is about .001 standard deviations) as a function of sample size.

Challenges with using p-values

Historically, the most common answer to this question has been that we should reject the null hypothesis if the p-value is less than 0.05. This comes from the writings of Ronald Fisher, who has been referred to as “the single most important figure in 20th century statistics” (Efron, 1998 ) :

“If P is between .1 and .9 there is certainly no reason to suspect the hypothesis tested. If it is below .02 it is strongly indicated that the hypothesis fails to account for the whole of the facts. We shall not often be astray if we draw a conventional line at .05 … it is convenient to draw the line at about the level at which we can say: Either there is something in the treatment, or a coincidence has occurred such as does not occur more than once in twenty trials” (Fisher, 1925 )

Fisher never intended p<0.05p < 0.05 to be a fixed rule:

“no scientific worker has a fixed level of significance at which from year to year, and in all circumstances, he rejects hypotheses; he rather gives his mind to each particular case in the light of his evidence and his ideas” (Fisher, 1956 )

Instead, it is likely that p < .05 became a ritual due to the reliance upon tables of p-values that were used before computing made it easy to compute p values for arbitrary values of a statistic. All of the tables had an entry for 0.05, making it easy to determine whether one’s statistic exceeded the value needed to reach that level of significance. Although we use tables in this class, statistical software examines the specific probability value for the calculated statistic.

Assessing Error Rate: Type I and Type II Error

Although there are challenges with p-values for decision making, we will examine a way we can think about hypothesis testing in terms of its error rate. This was proposed by Jerzy Neyman and Egon Pearson:

“no test based upon a theory of probability can by itself provide any valuable evidence of the truth or falsehood of a hypothesis. But we may look at the purpose of tests from another viewpoint. Without hoping to know whether each separate hypothesis is true or false, we may search for rules to govern our behaviour with regard to them, in following which we insure that, in the long run of experience, we shall not often be wrong” (Neyman & Pearson, 1933 )

That is: We can’t know which specific decisions are right or wrong, but if we follow the rules, we can at least know how often our decisions will be wrong in the long run.

To understand the decision-making framework that Neyman and Pearson developed, we first need to discuss statistical decision-making in terms of the kinds of outcomes that can occur. There are two possible states of reality (H0 is true, or H0 is false), and two possible decisions (reject H0, or retain H0). There are two ways in which we can make a correct decision:

We can reject H0 when it is false (in the language of signal detection theory, we call this a hit )
We can retain H0 when it is true (somewhat confusingly in this context, this is called a correct rejection )

There are also two kinds of errors we can make:

We can reject H0 when it is actually true (we call this a false alarm , or Type I error ), Type I error means that we have concluded that there is a relationship in the population when in fact there is not. Type I errors occur because even when there is no relationship in the population, sampling error alone will occasionally produce an extreme result.
We can retain H0 when it is actually false (we call this a miss , or Type II error ). Type II error means that we have concluded that there is no relationship in the population when in fact there is.

Summing up, when you perform a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis H0 and the decision to reject or not. The outcomes are summarized in the following table:

	IS ACTUALLY
	True	False
	Correct Outcome
		Correct Outcome

Table 1. The four possible outcomes in hypothesis testing.

The decision is not to reject H0 when H0 is true (correct decision).
The decision is to reject H0 when H0 is true (incorrect decision known as a Type I error ).
The decision is not to reject H0 when, in fact, H0 is false (incorrect decision known as a Type II error ).
The decision is to reject H0 when H0 is false ( correct decision ).

Neyman and Pearson coined two terms to describe the probability of these two types of errors in the long run:

P(Type I error) = αalpha
P(Type II error) = βbeta

That is, if we set αalpha to .05, then in the long run we should make a Type I error 5% of the time. The 𝞪 (alpha) , is associated with the p-value for the level of significance. Again it’s common to set αalpha as .05. In fact, when the null hypothesis is true and α is .05, we will mistakenly reject the null hypothesis 5% of the time. (This is why α is sometimes referred to as the “Type I error rate.”) In principle, it is possible to reduce the chance of a Type I error by setting α to something less than .05. Setting it to .01, for example, would mean that if the null hypothesis is true, then there is only a 1% chance of mistakenly rejecting it. But making it harder to reject true null hypotheses also makes it harder to reject false ones and therefore increases the chance of a Type II error.

In practice, Type II errors occur primarily because the research design lacks adequate statistical power to detect the relationship (e.g., the sample is too small). Statistical power is the complement of Type II error. We will have more to say about statistical power shortly. The standard value for an acceptable level of β (beta) is .2 – that is, we are willing to accept that 20% of the time we will fail to detect a true effect when it truly exists. It is possible to reduce the chance of a Type II error by setting α to something greater than .05 (e.g., .10). But making it easier to reject false null hypotheses also makes it easier to reject true ones and therefore increases the chance of a Type I error. This provides some insight into why the convention is to set α to .05. There is some agreement among researchers that level of α keeps the rates of both Type I and Type II errors at acceptable levels.

The possibility of committing Type I and Type II errors has several important implications for interpreting the results of our own and others’ research. One is that we should be cautious about interpreting the results of any individual study because there is a chance that it reflects a Type I or Type II error. This is why researchers consider it important to replicate their studies. Each time researchers replicate a study and find a similar result, they rightly become more confident that the result represents a real phenomenon and not just a Type I or Type II error.

Test Statistic Assumptions

Last consideration we will revisit with each test statistic (e.g., t-test, z-test and ANOVA) in the coming chapters. There are four main assumptions. These assumptions are often taken for granted in using prescribed data for the course. In the real world, these assumptions would need to be examined, often tested using statistical software.

Assumption of random sampling. A sample is random when each person (or animal) point in your population has an equal chance of being included in the sample; therefore selection of any individual happens by chance, rather than by choice. This reduces the chance that differences in materials, characteristics or conditions may bias results. Remember that random samples are more likely to be representative of the population so researchers can be more confident interpreting the results. Note: there is no test that statistical software can perform which assures random sampling has occurred but following good sampling techniques helps to ensure your samples are random.
Assumption of Independence. Statistical independence is a critical assumption for many statistical tests including the 2-sample t-test and ANOVA. It is assumed that observations are independent of each other often but often this assumption. Is not met. Independence means the value of one observation does not influence or affect the value of other observations. Independent data items are not connected with one another in any way (unless you account for it in your study). Even the smallest dependence in your data can turn into heavily biased results (which may be undetectable) if you violate this assumption. Note: there is no test statistical software can perform that assures independence of the data because this should be addressed during the research planning phase. Using a non-parametric test is often recommended if a researcher is concerned this assumption has been violated.
Assumption of Normality. Normality assumes that the continuous variables (dependent variable) used in the analysis are normally distributed. Normal distributions are symmetric around the center (the mean) and form a bell-shaped distribution. Normality is violated when sample data are skewed. With large enough sample sizes (n > 30) the violation of the normality assumption should not cause major problems (remember the central limit theorem) but there is a feature in most statistical software that can alert researchers to an assumption violation.
Assumption of Equal Variance. Variance refers to the spread or of scores from the mean. Many statistical tests assume that although different samples can come from populations with different means, they have the same variance. Equality of variance (i.e., homogeneity of variance) is violated when variances across different groups or samples are significantly different. Note: there is a feature in most statistical software to test for this.

We will use 4 main steps for hypothesis testing:

Usually the hypotheses concern population parameters and predict the characteristics that a sample should have
Null: Null hypothesis (H0) states that there is no difference, no effect or no change between population means and sample means. There is no difference.
Alternative: Alternative hypothesis (H1 or HA) states that there is a difference or a change between the population and sample. It is the opposite of the null hypothesis.
Set criteria for a decision. In this step we must determine the boundary of our distribution at which the null hypothesis will be rejected. Researchers usually use either a 5% (.05) cutoff or 1% (.01) critical boundary. Recall from our earlier story about Ronald Fisher that the lower the probability the more confident the was that the Tea Lady was not guessing. We will apply this to z in the next chapter.
Compare sample and population to decide if the hypothesis has support
When a researcher uses hypothesis testing, the individual is making a decision about whether the data collected is sufficient to state that the population parameters are significantly different.

Further considerations

The probability value is the probability of a result as extreme or more extreme given that the null hypothesis is true. It is the probability of the data given the null hypothesis. It is not the probability that the null hypothesis is false.

A low probability value indicates that the sample outcome (or one more extreme) would be very unlikely if the null hypothesis were true. We will learn more about assessing effect size later in this unit.

3. A non-significant outcome means that the data do not conclusively demonstrate that the null hypothesis is false. There is always a chance of error and 4 outcomes associated with hypothesis testing.

It is important to take into account the assumptions for each test statistic.

Learning objectives

Having read the chapter, you should be able to:

Identify the components of a hypothesis test, including the parameter of interest, the null and alternative hypotheses, and the test statistic.
State the hypotheses and identify appropriate critical areas depending on how hypotheses are set up.
Describe the proper interpretations of a p-value as well as common misinterpretations.
Distinguish between the two types of error in hypothesis testing, and the factors that determine them.
Describe the main criticisms of null hypothesis statistical testing
Identify the purpose of effect size and power.

Exercises – Ch. 9

In your own words, explain what the null hypothesis is.
What are Type I and Type II Errors?
Why do we phrase null and alternative hypotheses with population parameters and not sample means?
If our null hypothesis is “H0: μ = 40”, what are the three possible alternative hypotheses?
Why do we state our hypotheses and decision criteria before we collect our data?
When and why do you calculate an effect size?

Answers to Odd- Numbered Exercises – Ch. 9

1. Your answer should include mention of the baseline assumption of no difference between the sample and the population.

3. Alpha is the significance level. It is the criteria we use when decided to reject or fail to reject the null hypothesis, corresponding to a given proportion of the area under the normal distribution and a probability of finding extreme scores assuming the null hypothesis is true.

5. μ > 40; μ < 40; μ ≠ 40

7. We calculate effect size to determine the strength of the finding. Effect size should always be calculated when the we have rejected the null hypothesis. Effect size can be calculated for non-significant findings as a possible indicator of Type II error.

Introduction to Statistics for Psychology Copyright © 2021 by Alisa Beyer is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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you plan to discover it. If one variable truly causes a second, it . may be also called or . . Thus, a mediating or mediator variable is An hypothesis may describe a relationship exists, possible of the relationship ("null" hypotheses are directionless), (how) of the relationship; even of the relationship. (categories are different) (categories are ordered) (categories are numbers). Even a two category variable can be ordinal if we can rank the categories ("yes I smoked a cigarette" is more than "no I didn't").

you plan to discover it.

cognitive sophistication
tolerance of diversity
exposure to higher levels of math or science
age (which is currently related to educational level in many countries)
social class and other variables.
For example, suppose you designed a treatment to help people stop smoking. Because you are really dedicated, you assigned the same individuals simultaneously to (1) a "stop smoking" nicotine patch; (2) a "quit buddy"; and (3) a discussion support group. Compared with a group in which no intervention at all occurred, your experimental group now smokes 10 fewer cigarettes per day.

There is no relationship among two or more variables (EXAMPLE: the correlation between educational level and income is zero)
Or that two or more populations or subpopulations are essentially the same (EXAMPLE: women and men have the same average science knowledge scores.)

someone who favors raising teacher salaries obviously is more in favor than someone who opposes the raise.

the difference between two and three children = one child.
the difference between eight and nine children also = one child.
the difference between completing ninth grade and tenth grade is one year of school
the difference between completing junior and senior year of college is one year of school
In addition to all the properties of nominal, ordinal, and interval variables, ratio variables also have a fixed/non-arbitrary zero point. Non arbitrary means that it is impossible to go below a score of zero for that variable. For example, any bottom score on IQ or aptitude tests is created by human beings and not nature. On the other hand, scientists believe they have isolated an "absolute zero." You can't get colder than that.

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Independent vs. Dependent Variables | Definition & Examples

Independent vs. Dependent Variables | Definition & Examples

Published on February 3, 2022 by Pritha Bhandari . Revised on June 22, 2023.

In research, variables are any characteristics that can take on different values, such as height, age, temperature, or test scores.

Researchers often manipulate or measure independent and dependent variables in studies to test cause-and-effect relationships.

The independent variable is the cause. Its value is independent of other variables in your study.
The dependent variable is the effect. Its value depends on changes in the independent variable.

Your independent variable is the temperature of the room. You vary the room temperature by making it cooler for half the participants, and warmer for the other half.

What is an independent variable, types of independent variables, what is a dependent variable, identifying independent vs. dependent variables, independent and dependent variables in research, visualizing independent and dependent variables, other interesting articles, frequently asked questions about independent and dependent variables.

An independent variable is the variable you manipulate or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

Independent variables are also called:

Explanatory variables (they explain an event or outcome)
Predictor variables (they can be used to predict the value of a dependent variable)
Right-hand-side variables (they appear on the right-hand side of a regression equation).

These terms are especially used in statistics , where you estimate the extent to which an independent variable change can explain or predict changes in the dependent variable.

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There are two main types of independent variables.

Experimental independent variables can be directly manipulated by researchers.
Subject variables cannot be manipulated by researchers, but they can be used to group research subjects categorically.

Experimental variables

In experiments, you manipulate independent variables directly to see how they affect your dependent variable. The independent variable is usually applied at different levels to see how the outcomes differ.

You can apply just two levels in order to find out if an independent variable has an effect at all.

You can also apply multiple levels to find out how the independent variable affects the dependent variable.

You have three independent variable levels, and each group gets a different level of treatment.

You randomly assign your patients to one of the three groups:

A low-dose experimental group
A high-dose experimental group
A placebo group (to research a possible placebo effect )

A true experiment requires you to randomly assign different levels of an independent variable to your participants.

Random assignment helps you control participant characteristics, so that they don’t affect your experimental results. This helps you to have confidence that your dependent variable results come solely from the independent variable manipulation.

Subject variables

Subject variables are characteristics that vary across participants, and they can’t be manipulated by researchers. For example, gender identity, ethnicity, race, income, and education are all important subject variables that social researchers treat as independent variables.

It’s not possible to randomly assign these to participants, since these are characteristics of already existing groups. Instead, you can create a research design where you compare the outcomes of groups of participants with characteristics. This is a quasi-experimental design because there’s no random assignment. Note that any research methods that use non-random assignment are at risk for research biases like selection bias and sampling bias .

Your independent variable is a subject variable, namely the gender identity of the participants. You have three groups: men, women and other.

Your dependent variable is the brain activity response to hearing infant cries. You record brain activity with fMRI scans when participants hear infant cries without their awareness.

A dependent variable is the variable that changes as a result of the independent variable manipulation. It’s the outcome you’re interested in measuring, and it “depends” on your independent variable.

In statistics , dependent variables are also called:

Response variables (they respond to a change in another variable)
Outcome variables (they represent the outcome you want to measure)
Left-hand-side variables (they appear on the left-hand side of a regression equation)

The dependent variable is what you record after you’ve manipulated the independent variable. You use this measurement data to check whether and to what extent your independent variable influences the dependent variable by conducting statistical analyses.

Based on your findings, you can estimate the degree to which your independent variable variation drives changes in your dependent variable. You can also predict how much your dependent variable will change as a result of variation in the independent variable.

Distinguishing between independent and dependent variables can be tricky when designing a complex study or reading an academic research paper .

A dependent variable from one study can be the independent variable in another study, so it’s important to pay attention to research design .

Here are some tips for identifying each variable type.

Recognizing independent variables

Use this list of questions to check whether you’re dealing with an independent variable:

Is the variable manipulated, controlled, or used as a subject grouping method by the researcher?
Does this variable come before the other variable in time?
Is the researcher trying to understand whether or how this variable affects another variable?

Recognizing dependent variables

Check whether you’re dealing with a dependent variable:

Is this variable measured as an outcome of the study?
Is this variable dependent on another variable in the study?
Does this variable get measured only after other variables are altered?

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Independent and dependent variables are generally used in experimental and quasi-experimental research.

Here are some examples of research questions and corresponding independent and dependent variables.

Research question	Independent variable	Dependent variable(s)
Do tomatoes grow fastest under fluorescent, incandescent, or natural light?
What is the effect of intermittent fasting on blood sugar levels?
Is medical marijuana effective for pain reduction in people with chronic pain?
To what extent does remote working increase job satisfaction?

For experimental data, you analyze your results by generating descriptive statistics and visualizing your findings. Then, you select an appropriate statistical test to test your hypothesis .

The type of test is determined by:

your variable types
level of measurement
number of independent variable levels.

You’ll often use t tests or ANOVAs to analyze your data and answer your research questions.

In quantitative research , it’s good practice to use charts or graphs to visualize the results of studies. Generally, the independent variable goes on the x -axis (horizontal) and the dependent variable on the y -axis (vertical).

The type of visualization you use depends on the variable types in your research questions:

A bar chart is ideal when you have a categorical independent variable.
A scatter plot or line graph is best when your independent and dependent variables are both quantitative.

To inspect your data, you place your independent variable of treatment level on the x -axis and the dependent variable of blood pressure on the y -axis.

You plot bars for each treatment group before and after the treatment to show the difference in blood pressure.

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

Normal distribution
Degrees of freedom
Null hypothesis
Discourse analysis
Control groups
Mixed methods research
Non-probability sampling
Quantitative research
Ecological validity

Research bias

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

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.

In statistics, dependent variables are also called:

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .

The type of soda – diet or regular – is the independent variable .
The level of blood sugar that you measure is the dependent variable – it changes depending on the type of soda.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

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Independent Variables in Psychology

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Identifying

Potential Pitfalls

The independent variable (IV) in psychology is the characteristic of an experiment that is manipulated or changed by researchers, not by other variables in the experiment.

For example, in an experiment looking at the effects of studying on test scores, studying would be the independent variable. Researchers are trying to determine if changes to the independent variable (studying) result in significant changes to the dependent variable (the test results).

In general, experiments have these three types of variables: independent, dependent, and controlled.

Identifying the Independent Variable

If you are having trouble identifying the independent variables of an experiment, there are some questions that may help:

Is the variable one that is being manipulated by the experimenters?
Are researchers trying to identify how the variable influences another variable?
Is the variable something that cannot be changed but that is not dependent on other variables in the experiment?

Researchers are interested in investigating the effects of the independent variable on other variables, which are known as dependent variables (DV). The independent variable is one that the researchers either manipulate (such as the amount of something) or that already exists but is not dependent upon other variables (such as the age of the participants).

Below are the key differences when looking at an independent variable vs. dependent variable.

Expected to influence the dependent variable

Doesn't change as a result of the experiment

Can be manipulated by researchers in order to study the dependent variable

Expected to be affected by the independent variable

Expected to change as a result of the experiment

Not manipulated by researchers; its changes occur as a result of the independent variable

There can be all different types of independent variables. The independent variables in a particular experiment all depend on the hypothesis and what the experimenters are investigating.

Independent variables also have different levels. In some experiments, there may only be one level of an IV. In other cases, multiple levels of the IV may be used to look at the range of effects that the variable may have.

In an experiment on the effects of the type of diet on weight loss, for example, researchers might look at several different types of diet. Each type of diet that the experimenters look at would be a different level of the independent variable while weight loss would always be the dependent variable.

To understand this concept, it's helpful to take a look at the independent variable in research examples.

In Organizations

A researcher wants to determine if the color of an office has any effect on worker productivity. In an experiment, one group of workers performs a task in a yellow room while another performs the same task in a blue room. In this example, the color of the office is the independent variable.

In the Workplace

A business wants to determine if giving employees more control over how to do their work leads to increased job satisfaction. In an experiment, one group of workers is given a great deal of input in how they perform their work, while the other group is not. The amount of input the workers have over their work is the independent variable in this example.

In Educational Research

Educators are interested in whether participating in after-school math tutoring can increase scores on standardized math exams. In an experiment, one group of students attends an after-school tutoring session twice a week while another group of students does not receive this additional assistance. In this case, participation in after-school math tutoring is the independent variable.

In Mental Health Research

Researchers want to determine if a new type of treatment will lead to a reduction in anxiety for patients living with social phobia. In an experiment, some volunteers receive the new treatment, another group receives a different treatment, and a third group receives no treatment. The independent variable in this example is the type of therapy .

Sometimes varying the independent variables will result in changes in the dependent variables. In other cases, researchers might find that changes in the independent variables have no effect on the variables that are being measured.

At the outset of an experiment, it is important for researchers to operationally define the independent variable. An operational definition describes exactly what the independent variable is and how it is measured. Doing this helps ensure that the experiments know exactly what they are looking at or manipulating, allowing them to measure it and determine if it is the IV that is causing changes in the DV.

Choosing an Independent Variable

If you are designing an experiment, here are a few tips for choosing an independent variable (or variables):

Select independent variables that you think will cause changes in another variable. Come up with a hypothesis for what you expect to happen.
Look at other experiments for examples and identify different types of independent variables.
Keep your control group and experimental groups similar in other characteristics, but vary only the treatment they receive in terms of the independent variable. For example, your control group will receive either no treatment or no changes in the independent variable while your experimental group will receive the treatment or a different level of the independent variable.

It is also important to be aware that there may be other variables that might influence the results of an experiment. Two other kinds of variables that might influence the outcome include:

Extraneous variables : These are variables that might affect the relationships between the independent variable and the dependent variable; experimenters usually try to identify and control for these variables.
Confounding variables : When an extraneous variable cannot be controlled for in an experiment, it is known as a confounding variable .

Extraneous variables can also include demand characteristics (which are clues about how the participants should respond) and experimenter effects (which is when the researchers accidentally provide clues about how a participant will respond).

Kaliyadan F, Kulkarni V. Types of variables, descriptive statistics, and sample size . Indian Dermatol Online J . 2019;10(1):82-86. doi:10.4103/idoj.IDOJ_468_18

Weiten, W. Psychology: Themes and Variations, 10th ed . Boston, MA: Cengage Learning; 2017.

National Library of Medicine. Dependent and independent variables .

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

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Harpaljit Kaur Pritam Singh 2 ,
María Blasa Sánchez-Barrera 1 &
María Auxiliadora Robles-Bello ORCID: orcid.org/0000-0002-4979-5399 3

This study aims to identify factors that modulate resilience in LGBTQ + adults with a history of suicide attempts and reattempts, by hypothesising that positive mental health and various internal and external protective factors will predict suicide resilience in this population. 112 LGBTQ + Spanish individuals (60.7% self-identified as female, 34.8% as male and 4.5% did not identify their gender) aged 18–41 years with a history of self-harm or suicide attempts completed several questionnaires measuring variables such as self-efficacy, emotion regulation, social support, entrapment, anxiety, depression, positive mental health and resilience. In terms of the protective variables most predictive of resilience, social support, positive mental health and emotion regulation were found to be crucial and, to a lesser extent, self-efficacy expectations for coping with stressful or adverse situations. In addition, path analyses revealed distinct profiles of modulated categorical variables in LGTBQ + individuals who had attempted suicide once or who had attempted suicide two or more times. These findings highlight the importance of addressing protective factors, such as social support and positive mental health, in promoting resilience and reducing suicide risk among LGBTQ + individuals.

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Impact statement

The findings of this study have significant implications for suicide prevention among LGBTQ + individuals who have previously attempted suicide. They highlight the necessity for prompt intervention in cases where anxiety and depression are compounded by feelings of entrapment. Furthermore, the significance of positive mental health, encompassing both internal protective factors (such as self-efficacy in coping with stress and cognitive reappraisal strategies in emotional regulation) and external protective factors (such as social support through cohesive networks), in fostering resilience against future suicide reattempts in this clinical subpopulation is underscored.

The experience of being a member of a sexual minority is associated with a history of stressful situations throughout life due to the social stigma (Barnett et al., 2019 ). Consequently, discrimination, rejection and violence based on sexual diversity or gender identity contribute to the creation of a hostile environment that has a detrimental impact on the well-being of LGBTQ+ (lesbian, gay, bisexual, transgender, queer and other) people (Pachankis et al., 2020 ). The link between mental health problems and the risk of suicidal behaviour in this group seems clear (Nakamura et al., 2022 ). Indeed, the perceived stress experienced is much higher than that experienced by the general population (Christensen et al., 2023 ). Furthermore, this group also exhibits high levels of anxiety and depression (Christensen et al., 2023 ), as well as intense suicidal ideation, suicide attempts and reattempts (Cruz et al., 2022 ; Gorse, 2022 ). A crucial element in this context is the perception of entrapment, which influences the intensity and frequency of suicidal behaviours (Calati et al., 2023 ). However, it remains challenging to predict which risk variables are most predictive of suicide deaths, particularly in populations with high social vulnerability, such as LGBTQ + individuals (Kohnepoushi et al., 2023 ).

The presence or absence of public policies designed to safeguard the rights of sexual minorities in diverse cultural contexts has been identified as a significant factor influencing the prevalence of suicidal behaviour (Fulginiti et al., 2021 ). In general, these issues are less prevalent in countries where there are public policies of inclusion and diversity for sexual minorities (Aivadyan et al., 2023 ). Although studies from disparate geographical regions have demonstrated a heightened prevalence of suicidal behaviour among sexual minorities relative to other demographic groups (Pharr et al., 2022 ), it is pertinent to highlight that the precise incidence of suicide among the LGBTQ + population in Spain remains uncertain (Ronzón-Tirado et al., 2023 ). Mortality registries typically collect data on age, sex, race, and other personal characteristics, but do not typically include information on sexual orientation or gender identity. This is the standard practice in the majority of countries, with the exception of those in the Nordic region. Nevertheless, there is a dearth of data concerning the number of individuals from diverse sexual orientations and gender identities who die by suicide in these countries.

Research on suicide in Spain provides devastating data showing that suicidal behaviour has continued to increase in recent years (Sánchez-Teruel et al., 2020a , b ). However, the current emphasis has mainly been on examining the risk factors associated with suicide (WHO, 2021 ), while the exploration of protective factors, particularly those that influence suicide attempts and repeated attempts, has received comparatively less attention. This is despite previous emphasis on the importance of analysing all factors that serve as safeguards against engaging in suicidal behaviors. (WHO, 2021 ).

In this regard, despite the challenges and adversities faced by LGBTQ + individuals, there is a notable degree of resilience in the context of suicide and its associated difficulties (Lorabi et al., 2023 ). The capacity for resilience, along with the variables that influence it, can serve to mitigate negative outcomes and promote psychological well-being (McConnell et al., 2018 ). This is applicable to this group of people as well (Katz et al., 2023 ). It can be reasonably deduced, therefore, that social support, including that derived from friends, family, and LGBTQ + communities, plays an important role in the protection against suicide (Hatzenbuehler et al., 2020 ). Furthermore, acceptance and inclusion in safe and non-discriminatory environments represent crucial protective factors (De Chants et al., 2022 ). Also, access to competent and culturally sensitive mental health services can facilitate the provision of support to address mental health issues within the LGBTQ + community (Pachankis et al., 2020 ).

Furthermore, recent research has indicated that reappraisal of adverse or stressful situations is associated with improved mental health outcomes (Lorabi et al., 2023 ). This suggests that an enhanced capacity to reframe negative events in a more constructive manner, using a sense of humour, may potentially mitigate stress levels in challenging circumstances. Similarly, the assessment of emotional regulation in situations of high adversity has been demonstrated to protect against suicidal reactions (Hutchinson, 2023 ). Other variables, such as positive mental health, have been linked to protective factors against suicidal behaviour in other clinical subpopulations (Sánchez-Teruel et al., 2022 , 2024 ). Nevertheless, the examination of these protective variables in the context of suicide attempts or reattempts remains scarce, particularly in populations with elevated social vulnerability, such as those identifying as LGBTQ+. Despite this, there is a substantial body of evidence documenting their heightened risk of suicidal behaviour (Williams et al., 2023 ). The objective of this study was to identify the factors that influence resilience in LGBTQ + individuals with a history of suicide attempts and reattempts. It is hypothesised that positive mental health is related to suicide resilience. In particular, internal protective factors (namely, self-efficacy in coping with stress, cognitive reappraisal, and emotional regulation) and external protective factors (social support) are expected to be the most predictive protective variables of suicide resilience in this clinical subpopulation. Furthermore, it is anticipated that emotional regulation and cognitive reappraisal will exert a moderating influence on entrapment in individuals who have made multiple previous suicide attempts.

Participants

The initial sample consisted of 136 individuals with the following inclusion criteria: (1) over 18 years of age; (2) Resident in Spain; (3) Previous suicide attempt (last year) (item 5 of the Assessment Scale for Suicidal Behaviour in Adolescents SENTIA brief by Díez-Gómez et al., 2021 ) and number of times; (4) Self-identification as belonging to the LGBTQ + group; (5) Reading of the information sheet and acceptance of the informed consent and (6) Completion of all questionnaires. The final total sample of participants was 112 (response rate 82.4%), of whom 68 (60.7%) self-identified as female, 39 (34.8%) as male and 5 (4.5%) did not self-identify their gender, ranging in age from 18 to 41 years (M = 26.11; SD = 6.21). The G*Power 3.1.9.7 program (Faul et al., 2009 ) was used between two groups and the total number of variables was included, establishing an error probability (𝛼) at 0.05 and the statistical power (1-𝛽) at 0.8. When applying these parameters, the current sample size ( N = 112) was considered sufficient to detect small effects (𝜂2 = 0.5). The sociodemographic data for the total sample are presented in Table 1 .

Instruments

Sociodemographic data.

Each participant completed an ad hoc questionnaire on gender, age, and other socio-demographic data as listed in Table 1 .

Self-Efficacy Scale for Coping with Stress (EAEAE) of Godoy-Izquierdo and Godoy ( 2006 ) measures perceived confidence in personal resources for coping with stressful situations through 8 items in a Likert-type format with 5 alternatives from 1 = “I strongly disagree” to 5 = “I strongly agree” Its structure is two-dimensional (efficacy expectancies-EE which consist of subjective perceptions of one’s abilities to plan and organise actions to manage and regulate stress, and outcome expectancies-EO which refer to beliefs that such actions will lead to desired outcomes) with some inverted items (1, 4, 6 and 8). The reliability of the scale in its original version with the general population was 0.75. The reliability of this scale and the other measures used in this study with Cronbach’s alpha and McDonald’s omega is shown in Table 2 .

Emotion Regulation Questionnaire (ERQ) by Gross and John ( 2003 ) was adapted to suit the Spanish population by Cabello et al. ( 2013 ). It measures emotion regulation through 10 items with a response scale from 1 (strongly disagree) to 7 (strongly agree). It is made up of two subdimensions: cognitive reappraisal (where emotional reactions are modified and the emotional experience is changed) and expressive suppression (where expressive change is assessed, hiding the emotions without modifying them). Recent studies have clarified that cognitive reappraisal is an antecedent-focused strategy, as it occurs before the emotion is installed (increasing or decreasing it). On the other hand, suppression acts when the emotion appears, changing the emotional response (experiential, behavioural or physiological) after its onset (Seixas et al., 2021 ). Its adaptation to Spanish presents a similar structure to the original scale and adequate psychometric properties, specifically, α = 0.75 and test-retest 0.66 for Suppression and α = 0.79 and test-retest 0.64 for Reappraisal (Cabello et al., 2013 ).

Fritz’s Reappraisal Index (RI) (Fritz, 2020 ) in its Spanish adaptation for sexual minorities was adapted from Lorabi et al. ( 2023 ). This measure attempts to distinguish between participants who are able to make a positive cognitive reappraisal of different stressful events in their lives compared to those who don’t. This cognitive strategy in the face of adversity uses humour as a way of re-evaluating the negative situation (Fritz et al., 2017 ). It consists of 9 items with a Likert-type response ranging from “1 = never/not at all” to “5 = always/a lot”. Its psychometric properties have been reported as adequate in the Spanish gay population (a = 0.89) (Lorabi et al., 2023 ).

Lubben Social Network Scale-6 (LSNS-6) (Lubben et al., 2006 ), translated and adapted to the Spanish population by Fernández-Ballesteros et al. ( 2009 ). It assesses perceived social support through six items with six response options, where ‘0 = none’ and ‘5 = nine or more people’. The scores range from 0 to 30, with higher scores indicating greater perceived social support. Its psychometric properties in Spanish are adequate and it has shown adequate methodological properties in extensive studies in Spain measuring social support in adverse situations (Donio-Bellegarde & Pinazo-Hernandis, 2016 ; Fernández-Ballesteros et al., 2009 ).

Entrapment Scale Short-Form (E-SF) by Beurs et al. ( 2020 ) in people at high risk of suicide. The Spanish translation used in this research was that of Ordóñez-Carrasco et al. ( 2021 ). This scale measures entrapment, which is understood as the attempt to escape from unbearable situations or negative thoughts that cause great suffering. This scale consists of 4 items that are answered in a five-point format, where 0 = “not at all like me”, 1 = “a little like me”, 2 = “moderately like me”, 3 = “quite a not like me” and 4 = “extremely like me”. The reliability of this scale in the original study was 0.97, and in Spanish samples only the psychometric properties of the original 16-item scale are available (Ordóñez-Carrasco et al., 2021 ).

Hospital Anxiety and Depression Scale (HAD-14) by Zigmond and Snaith ( 1983 ) adapted from Herrero et al. ( 2003 ). This 14-item scale uses a four-point Likert format to measure anxiety with seven items and depression with seven items. The original scale demonstrates excellent internal consistency for the general scale (0.90), and 0.84 for the depression subscale and 0.85 for the anxiety subscale. The reliability of the Spanish adaptation was adequate (α = 0.90) (Herrero et al., 2003 ).

Positive Mental Health Scale (PMH) (Lukat et al., 2016 ) was utilized in its adaptation to the Spanish general population by Boufellous et al. ( 2023 ). This scale measures the presence of emotional, psychological, and social well-being through 9 items with five response options (0 = disagree; 4 = agree). It showed high reliability in the original version among university students and the general population in Germany (α = 0.92 and 0.93). Higher scores on this scale are associated with better outcomes related to suicidal behaviour. The adaptation to the general Spanish population produced an adequate level of internal consistency (α = 0.96 and ω = 0.97).

Connor-Davidson-CD-RISC-10 Resilience Scale by Campbell-Sills and Stein ( 2007 ), was translated and adapted to the Spanish population in its reduced version by Notario-Pacheco et al. (2011). It measures general resilience, i.e., the ability to adapt to adversity, through 10 items on a Likert-type scale with five response options (0 = never; 4 = almost always). Psychometrically, it has good internal consistency (alpha = 0.87) and invariance with respect to gender and age.

Following the approval of this study by the bioethics committee of one of the authors’ universities (code ABR.21/5.PRY), a number of LGTBQ + organisations were invited to participate in the data collection process on a voluntary basis via email and telephone. Upon completion, the online survey was transmitted via email to the participating organisations, who subsequently disseminated it to their respective supporters and members via social media, offering them the opportunity to complete it free of charge. The aforementioned actions were conducted via an official platform (Google Forms), provided by the university of one of the authors of this manuscript, which guaranteed the confidentiality and total anonymity of all participants. In order to proceed with the evaluation measures, it was necessary to read and accept the informed consent form in advance. Without this, access to the instruments used was not permitted. In the initial information and consent form, the contact details of the Spanish researchers, including their names, email addresses, and mobile phone numbers, were provided for participants who wished to seek immediate assistance in the event of suicidal thoughts or behaviours that posed a high risk of self-harm.

Data analysis

A cross-sectional design was used, with a multiple imputation method applied for missing data (SPSS 23.0-IBM Corporation, 2013 ), which represented less than 1% of the total variables used. The required level of statistical significance for all tests was p < .05, using AMOS statistical software for SPSS version 23.0 (Byrne, 2016 ). First, a descriptive and correlational analysis (Spearman) was conducted between all psychological variables. Then, a hierarchical regression analysis was performed with socio-demographic and protective variables (self-efficacy, emotional regulation, cognitive reframing, social support, and positive mental health) and risk variables (entrapment, anxiety and depression) as independent variables and resilience as the dependent variables, together with effect size analysis using G*Power 3.1.9.7 (Faul et al., 2009 ) and pre-calculation of goodness of fit indices. Finally, in LGBTQ + individuals with a single suicide attempt and two or more reattempts, the modulatory effect of the most predictive variables was analysed by precoding the multicategorical variables in dummy form and using path analysis (Collier, 2020 ) with the Bayesian Markov algorithm (Yang et al., 2023 ). This method is used to determine the indirect and direct proportion of the total effect of the independent variables on the dependent variables with 10,000 resamples and an estimated 95% confidence interval to test the significance of indirect effects. Bootstrapping is used because it is more appropriate for research with small sample sizes (Hayes, 2009 ).

The descriptive results showed low scores on protective variables and high scores on risk variables in the group of LGTBQ + participants (Table 2 ).

According to the results in Table 3 , significant relationships are observed for all protective and risk variables ( p < .05). Resilience showed a high positive and significant correlation with positive mental health (sp = 0.98; p < .01), cognitive reframing (sp = 0.97; p < .05) and emotional regulation (sp = 0.93; p < .01), and the highest negative relationship was found with entrapment (sp = − 0.94; p < .01).

Preliminary analyses showed a reasonable level of fit. In particular, the Durbin-Watson (DW) test showed independence of errors in the independent variable resilience (CD-RISC-10) in all three steps (DW step1 = 1.92; DW step2 = 1.94; DW step3 = 1.97). In addition, the variance inflation factor (VIF) was less than 5, indicating that there was no multicollinearity of the independent variable in the three prediction steps (Kleinbaum et al., 1988 ) (VIF step1 = 4.22; VIF step2 = 4.13; VIF step3 = 2.84). The results of the hierarchical multiple regression analysis showed that some of the socio-demographic and protective variables could predict the level of resilience in the total sample of participants (Table 4 ). In fact, step 3 (set of independent sociodemographic and protective variables) was significant and explained 89.2% of the resilience in this sample (R 2adj = 0.845; F (1.111) = 3426.07; p < .01). This final step showed that the socio-demographic variables most predictive of resilience in this sample were age, namely, being between 30 and 35 years old (β = 3.13; CI(95%) = 2.11–3.94; p < .01), and self-reported bisexual orientation (β = 5.16; CI(95%) = 4.21–6.01; p < .01). Regarding the protective variables most predictive of resilience, the most important was social support (β = 9. 98; CI(95%) = 8.71–10.01; p < .01), positive mental health (β = 9.04; CI(95%) = 8.01–9.32; p < .01) and emotional regulation, especially the cognitive reappraisal subdimension (β = 8. 27; CI(95%) = 8.16–8.89; p < .01) and, to a lesser extent, self-efficacy expectations for coping with stressful or adverse situations (β = 6.21; CI(95%) = 5.36–7.73; p < .01).

Path analyses revealed the existence of distinct profiles of categorical variables that had been subjected to modulation in LGTBQ + individuals who had attempted suicide on a single occasion, as well as those who had attempted suicide on two or more occasions. The results indicate that depression (β = 469, p < .001) modulates the direct effect of suicide attempt through an indirect variable, anxiety (β = 0.714; p < .05), and that this indirect effect is greater than anxiety as a single variable (β = 0.461; p < .001). Indeed, the model with the highest explanatory power demonstrates that depression indirectly modulates suicide attempts through anxiety and poor emotional regulation (pseudo-R² = 83.1%) (Fig. 1 ). In contrast, individuals in the LGBTQ + community who have attempted suicide on two or more occasions (Fig. 2 ) exhibit feelings of entrapment (β = 0.782; p < .05) and depression (β = 0.429; p < .001), which are indirectly influenced by low social support (β = 0.718; p < .001). Furthermore, it is evident that being gay (β = 0.673, p < .001) in conjunction with low emotional regulation (β = − 0.849, p < .001) exerts a more pronounced indirect influence on suicide reattempts than the direct association between sexual orientation alone (β = − 0.849, p < .001). Consequently, the model with the highest explanatory power for two or more suicide attempts in this sample is that which includes the variables of sexual orientation (pseudo-R 2 = 75.4%), age (pseudo-R 2 = 52.4%), social support (pseudo-R 2 = 79.3%), entrapment (pseudo-R 2 = 97.1%), emotional regulation (pseudo-R 2 = 59.3%) and depression (pseudo-R 2 = 65.1%).

Structural equation model showing modulation between variables in the sample with a single suicide attempt. Pseudo-R 2 given for the most predictive categorical variables. Note: e = error; * P < .05, ** P < .001

Structural equation model showing modulation between variables in the sample with two or more suicide attempts. Pseudo-R2 given for the most predictive categorical variables. Note: e = error; * P < .05, ** P < .001

The aim of this research was to identify factors that modulate resilience in LGBTQ + individuals with a history of suicide attempts and reattempts. Positive mental health is hypothesised to be related to suicide resilience, and in particular, internal protective factors (namely self-efficacy to cope with stress, cognitive reappraisal and emotional regulation) and external protective factors (social support) are expected to be the most predictive protective variables of suicide resilience in this clinical subpopulation.

Among the protective characteristics examined, social support emerged as a primary factor in predicting resilience. Networks, relationships and interpersonal connections are essential in fostering resilience, providing individuals with the essential instrumental, informational and emotional resources needed to navigate and overcome challenging situations (Hatzenbuehler et al., 2020 ). In the same vein, individuals with higher levels of positive mental health were more resilient in the face of adversity, suggesting that cultivating positive psychological well-being helps to foster and maintain resilience. In addition, the findings highlighted that individuals with effective cognitive appraisal skills are better able to control and regulate their emotions, which improves their ability to adapt to new situations and overcome obstacles (Hutchinson, 2023 ). Furthermore, individuals who are confident in their ability to cope with challenging situations and overcome challenges demonstrate increased levels of resilience. Self-efficacy empowers individuals to take proactive measures, seek solutions and persevere in the face of adversity, thereby enhancing their ability to recover from setbacks. In addition, the study highlighted that socio-demographic factors such as being aged between 30 and 35 and self-reported bisexual orientation were highly predictive of resilience to suicide ideation and attempts. In addition, emotional regulation and cognitive reappraisal are expected to play a modulating role for entrapment in people who have made more than one previous suicide attempt, particularly among LGTBQ + people. The results of this study show that individuals who have attempted suicide are more likely to experience elevated levels of anxiety and poor emotional regulation, which in turn contribute to the development or exacerbation of depressive symptoms. On the other hand, individuals who have attempted suicide two or more times experience profound feelings of entrapment and are more likely to experience depressive symptoms and lack adequate social support systems (Calati et al., 2023 ). Individuals who perceive a lack of support may experience heightened feelings of isolation, loneliness, and a sense of being disconnected from others, further increasing their feelings of entrapment and exacerbating their depressive symptoms. In addition, being gay, young (18 to 20 years old) and having low emotional regulation further exacerbates the situation (Pachankis et al., 2020 ; Christensen et al., 2023 ).

While this study makes valuable contributions, it is important to acknowledge its limitations. Firstly, the number of participants is relatively small, which could introduce sampling biases. Additionally, the age group range is quite wide, and the grouping of all subcategories of the LGBTQ + population is also quite extensive. It is worth mentioning that it proved somewhat challenging to meet the eligibility requirements, given that the study required participants to have attempted suicide in the last year. A relatively small sample size may limit the generalisability of the findings and could potentially reduce the statistical power of the study, which might affect the reliability of the results. Furthermore, combining such a diverse range of sexual identities and orientations may also potentially limit the generalisability of the results. Nevertheless, we are confident that these contributions can provide valuable insights into the prevention of suicidal behaviour in a clinical subpopulation that has been relatively understudied by the suicide prevention literature, as previous studies have suggested (Lovero et al., 2023 ). Secondly, there are other methodological limitations that could be addressed to enhance the generalisability of the results. These include the use of self-report measures and a correlational design, which may have introduced some objectivity issues due to potential social desirability biases in the responses. We would therefore be grateful if other researchers considered promoting more objective measures and longitudinal designs, which might help to overcome these limitations, as has already been done in the general population (Sánchez-Teruel et al., 2020a ). It would be greatly beneficial if public policies for the visibility and analysis of suicidal behaviour reflected official data on other clinical subpopulations with high vulnerability, such as the LGTBI + group.

Conclusions and clinical implications

It would be fair to say that throughout the scientific literature related to suicidal behaviour, there has been a notable focus on risk factors. The findings of this study suggest that it may be beneficial to consider these factors transdiagnostically for comprehensive suicide prevention, with a view to recognising the interconnected nature of anxiety and depression. Efforts to minimise death rates from this cause should not only focus on these factors, but also address feelings of entrapment and promote the development of protective factors such as social support and emotional regulation. It may be beneficial to consider ways of improving social support systems and providing resources to those who may be at risk, as this could help to mitigate feelings of entrapment, reduce depressive symptoms and ultimately to prevent suicide attempts and deaths. It is also particularly important to consider the impact of social stigma on the risk of suicide in sexual minorities, as this can compound the impact of other contributing factors. Policymakers in Spain and other European countries should consider developing specific suicide prevention plans, particularly for specific clinical subpopulations such as the LGTBI + group. Training dedicated sensitive professionals to identify and monitor these groups in primary care health services could also be a valuable step.

Data availability

Please contact the corresponding author to discuss the data generated from the study.

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Correlation in Psychology: Meaning, Types, Examples & coefficient

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Correlation means association – more precisely, it measures the extent to which two variables are related. There are three possible results of a correlational study: a positive correlation, a negative correlation, and no correlation.

A positive correlation is a relationship between two variables in which both variables move in the same direction. Therefore, one variable increases as the other variable increases, or one variable decreases while the other decreases. An example of a positive correlation would be height and weight. Taller people tend to be heavier.

A negative correlation is a relationship between two variables in which an increase in one variable is associated with a decrease in the other. An example of a negative correlation would be the height above sea level and temperature. As you climb the mountain (increase in height), it gets colder (decrease in temperature).

A zero correlation exists when there is no relationship between two variables. For example, there is no relationship between the amount of tea drunk and the level of intelligence.

Scatter Plots

A correlation can be expressed visually. This is done by drawing a scatter plot (also known as a scattergram, scatter graph, scatter chart, or scatter diagram).

A scatter plot is a graphical display that shows the relationships or associations between two numerical variables (or co-variables), which are represented as points (or dots) for each pair of scores.

A scatter plot indicates the strength and direction of the correlation between the co-variables.

Types of Correlations: Positive, Negative, and Zero

When you draw a scatter plot, it doesn’t matter which variable goes on the x-axis and which goes on the y-axis.

Remember, in correlations, we always deal with paired scores, so the values of the two variables taken together will be used to make the diagram.

Decide which variable goes on each axis and then simply put a cross at the point where the two values coincide.

Uses of Correlations

If there is a relationship between two variables, we can make predictions about one from another.
Concurrent validity (correlation between a new measure and an established measure).

Reliability

Test-retest reliability (are measures consistent?).
Inter-rater reliability (are observers consistent?).

Theory verification

Predictive validity.

Correlation Coefficients

Instead of drawing a scatter plot, a correlation can be expressed numerically as a coefficient, ranging from -1 to +1. When working with continuous variables, the correlation coefficient to use is Pearson’s r.

The correlation coefficient ( r ) indicates the extent to which the pairs of numbers for these two variables lie on a straight line. Values over zero indicate a positive correlation, while values under zero indicate a negative correlation.

A correlation of –1 indicates a perfect negative correlation, meaning that as one variable goes up, the other goes down. A correlation of +1 indicates a perfect positive correlation, meaning that as one variable goes up, the other goes up.

There is no rule for determining what correlation size is considered strong, moderate, or weak. The interpretation of the coefficient depends on the topic of study.

When studying things that are difficult to measure, we should expect the correlation coefficients to be lower (e.g., above 0.4 to be relatively strong). When we are studying things that are easier to measure, such as socioeconomic status, we expect higher correlations (e.g., above 0.75 to be relatively strong).)

In these kinds of studies, we rarely see correlations above 0.6. For this kind of data, we generally consider correlations above 0.4 to be relatively strong; correlations between 0.2 and 0.4 are moderate, and those below 0.2 are considered weak.

When we are studying things that are more easily countable, we expect higher correlations. For example, with demographic data, we generally consider correlations above 0.75 to be relatively strong; correlations between 0.45 and 0.75 are moderate, and those below 0.45 are considered weak.

Correlation vs. Causation

Causation means that one variable (often called the predictor variable or independent variable) causes the other (often called the outcome variable or dependent variable).

Experiments can be conducted to establish causation. An experiment isolates and manipulates the independent variable to observe its effect on the dependent variable and controls the environment in order that extraneous variables may be eliminated.

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.

While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable , is actually causing the systematic movement in our variables of interest.

Correlation does not always prove causation, as a third variable may be involved. For example, being a patient in a hospital is correlated with dying, but this does not mean that one event causes the other, as another third variable might be involved (such as diet and level of exercise).

“Correlation is not causation” means that just because two variables are related it does not necessarily mean that one causes the other.

A correlation identifies variables and looks for a relationship between them. An experiment tests the effect that an independent variable has upon a dependent variable but a correlation looks for a relationship between two variables.

This means that the experiment can predict cause and effect (causation) but a correlation can only predict a relationship, as another extraneous variable may be involved that it not known about.

1. Correlation allows the researcher to investigate naturally occurring variables that may be unethical or impractical to test experimentally. For example, it would be unethical to conduct an experiment on whether smoking causes lung cancer.

2 . Correlation allows the researcher to clearly and easily see if there is a relationship between variables. This can then be displayed in a graphical form.

Limitations

1 . Correlation is not and cannot be taken to imply causation. Even if there is a very strong association between two variables, we cannot assume that one causes the other.

For example, suppose we found a positive correlation between watching violence on T.V. and violent behavior in adolescence.

It could be that the cause of both these is a third (extraneous) variable – for example, growing up in a violent home – and that both the watching of T.V. and the violent behavior is the outcome of this.

2 . Correlation does not allow us to go beyond the given data. For example, suppose it was found that there was an association between time spent on homework (1/2 hour to 3 hours) and the number of G.C.S.E. passes (1 to 6).

It would not be legitimate to infer from this that spending 6 hours on homework would likely generate 12 G.C.S.E. passes.

How do you know if a study is correlational?

A study is considered correlational if it examines the relationship between two or more variables without manipulating them. In other words, the study does not involve the manipulation of an independent variable to see how it affects a dependent variable.

One way to identify a correlational study is to look for language that suggests a relationship between variables rather than cause and effect.

For example, the study may use phrases like “associated with,” “related to,” or “predicts” when describing the variables being studied.

Another way to identify a correlational study is to look for information about how the variables were measured. Correlational studies typically involve measuring variables using self-report surveys, questionnaires, or other measures of naturally occurring behavior.

Finally, a correlational study may include statistical analyses such as correlation coefficients or regression analyses to examine the strength and direction of the relationship between variables.

Why is a correlational study used?

Correlational studies are particularly useful when it is not possible or ethical to manipulate one of the variables.

For example, it would not be ethical to manipulate someone’s age or gender. However, researchers may still want to understand how these variables relate to outcomes such as health or behavior.

Additionally, correlational studies can be used to generate hypotheses and guide further research.

If a correlational study finds a significant relationship between two variables, this can suggest a possible causal relationship that can be further explored in future research.

What is the goal of correlational research?

The ultimate goal of correlational research is to increase our understanding of how different variables are related and to identify patterns in those relationships.

This information can then be used to generate hypotheses and guide further research aimed at establishing causality.

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Research Hypothesis In Psychology: Types, & Examples
Examples. A research hypothesis, in its plural form "hypotheses," is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.
Hypothesis: Definition, Examples, and Types
A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process. Consider a study designed to examine the relationship between sleep deprivation and test ...
Types of Variables in Psychology Research
By systematically changing some variables in an experiment and measuring what happens as a result, researchers are able to learn more about cause-and-effect relationships. The two main types of variables in psychology are the independent variable and the dependent variable. Both variables are important in the process of collecting data about ...
Independent and Dependent Variables
In research, a variable is any characteristic, number, or quantity that can be measured or counted in experimental investigations. One is called the dependent variable, and the other is the independent variable. In research, the independent variable is manipulated to observe its effect, while the dependent variable is the measured outcome.
What is a Hypothesis
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
Aims and Hypotheses
Hypotheses. A hypothesis (plural hypotheses) is a precise, testable statement of what the researchers predict will be the outcome of the study. This usually involves proposing a possible relationship between two variables: the independent variable (what the researcher changes) and the dependant variable (what the research measures).
2.4 Developing a 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 ...
Hypothesis: Psychology Definition, History & Examples
Hypothesis: Psychology Definition, History & Examples. In the realm of psychological science, a hypothesis is a tentative, testable assertion or prediction about the relationship between two or more variables. It serves as a foundational element for empirical research, guiding the direction of study and inquiry.
Hypothesis
A hypothesis is an educated guess or proposition made as a basis for reasoning or research without any assumption of its truth. It's testable and falsifiable statement about two or more variables related in some way. All Subjects. Light. AP Psychology. Unit 1 - Scientific Foundations of Psychology ... Definition. A hypothesis is an educated ...
Designing a Research Study
Variables and Operational Definitions. Part of generating a hypothesis involves identifying the variables that you want to study and operationally defining those variables so that they can be measured. Research questions in psychology are about variables. A variable is a quantity or quality that varies across people or situations.
2.5 Designing a Research Study
Identifying and Defining the Variables and Population. Part of generating a hypothesis involves identifying the variables that you want to study and operationally defining those variables so that they can be measured. Research questions in psychology are about variables. A variable is a quantity or quality that varies across people or situations.
Hypothesis
A hypothesis is a testable statement written as a prediction of what the researcher expects to find as a result of their experiment. A hypothesis should be no more than one sentence long. The hypothesis needs to include the independent variable (IV) and the dependent variable (DV)
Operational Hypothesis
Definition. An Operational Hypothesis is a testable statement or prediction made in research that not only proposes a relationship between two or more variables but also clearly defines those variables in operational terms, meaning how they will be measured or manipulated within the study. It forms the basis of an experiment that seeks to prove ...
How the Experimental Method Works in Psychology
The experimental method involves manipulating one variable to determine if this causes changes in another variable. This method relies on controlled research methods and random assignment of study subjects to test a hypothesis. For example, researchers may want to learn how different visual patterns may impact our perception.
Aims and Hypotheses
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: H1 - The Research Hypothesis.
Experimental Method In Psychology
There are three types of experiments you need to know: 1. Lab Experiment. A laboratory experiment in psychology is a research method in which the experimenter manipulates one or more independent variables and measures the effects on the dependent variable under controlled conditions. A laboratory experiment is conducted under highly controlled ...
9 Chapter 9 Hypothesis testing
Chapter 9 Hypothesis testing. The first unit was designed to prepare you for hypothesis testing. In the first chapter we discussed the three major goals of statistics: Describe: connects to unit 1 with descriptive statistics and graphing. Decide: connects to unit 1 knowing your data and hypothesis testing.
Guide 2: Variables and Hypotheses
A confounded variable is a multidimensional variable, it is a variable in which several variables are simultaneously embedded.Because this variable is multidimensional, we do not know precisely what it means or measures. This causes tremendous problems. If a confounded variable is supposed to be a cause, we cannot isolate exactly what was the specific cause of some phenomenon.
Independent vs. Dependent Variables
The independent variable is the cause. Its value is independent of other variables in your study. The dependent variable is the effect. Its value depends on changes in the independent variable. Example: Independent and dependent variables. You design a study to test whether changes in room temperature have an effect on math test scores.
Independent Variable in Psychology: Examples and Importance
The independent variable (IV) in psychology is the characteristic of an experiment that is manipulated or changed by researchers, not by other variables in the experiment. For example, in an experiment looking at the effects of studying on test scores, studying would be the independent variable. Researchers are trying to determine if changes to ...
What Is The Null Hypothesis & When To Reject It
The alternative hypothesis is the complement to the null hypothesis. The null hypothesis states that there is no effect or no relationship between variables, while the alternative hypothesis claims that there is an effect or relationship in the population. It is the claim that you expect or hope will be true.
APA Dictionary of Psychology
A trusted reference in the field of psychology, offering more than 25,000 clear and authoritative entries. A trusted reference in the field of psychology, offering more than 25,000 clear and authoritative entries. ... hypothesis. Share button. Updated on 04/19/2018. n. (pl. hypotheses) an empirically testable proposition about some fact ...
Protective factors for suicidal behaviour in adults self ...
The descriptive results showed low scores on protective variables and high scores on risk variables in the group of LGTBQ + participants (Table 2). According to the results in Table 3, significant relationships are observed for all protective and risk variables (p < .05). Resilience showed a high positive and significant correlation with positive mental health (sp = 0.98; p < .01), cognitive ...
Correlation: Meaning, Types, Examples & Coefficient
Types. A positive correlation is a relationship between two variables in which both variables move in the same direction. Therefore, one variable increases as the other variable increases, or one variable decreases while the other decreases. An example of a positive correlation would be height and weight. Taller people tend to be heavier.

What is a Hypothesis – Types, Examples and Writing Guide

Types of Hypothesis

Research Hypothesis

Null Hypothesis

Alternative Hypothesis

Directional Hypothesis

Non-directional Hypothesis

Statistical Hypothesis

Composite Hypothesis

Empirical Hypothesis

Simple Hypothesis

Complex Hypothesis

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How to write a Hypothesis

Identify the Research Question

Conduct a Literature Review

Determine the Variables

Formulate the Hypothesis

Write the Null Hypothesis

Refine the Hypothesis

Examples of Hypothesis

Purpose of Hypothesis

When to use Hypothesis

Characteristics of Hypothesis

Advantages of Hypothesis

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Muhammad Hassan

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