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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

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

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

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

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

At a Glance

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

The Hypothesis in the Scientific Method

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

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

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

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

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

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

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

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

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

Elements of a Good Hypothesis

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

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

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

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

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

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

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

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

The Importance of Operational Definitions

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

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

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

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

Replicability

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

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

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

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

Hypothesis Checklist

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

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

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

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

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

A few examples of simple hypotheses:

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

Examples of a complex hypothesis include:

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

Examples of a null hypothesis include:

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

Examples of an alternative hypothesis:

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

Collecting Data on Your Hypothesis

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

Descriptive Research Methods

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

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

Experimental Research Methods

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

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

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

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

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

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

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

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

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

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

Definition of a Hypothesis

What it is and how it's used in sociology

• Key Concepts
• Major Sociologists
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• Research, Samples, and Statistics
• Recommended Reading
• Archaeology

A hypothesis is a prediction of what will be found at the outcome of a research project and is typically focused on the relationship between two different variables studied in the research. It is usually based on both theoretical expectations about how things work and already existing scientific evidence.

Within social science, a hypothesis can take two forms. It can predict that there is no relationship between two variables, in which case it is a null hypothesis . Or, it can predict the existence of a relationship between variables, which is known as an alternative hypothesis.

In either case, the variable that is thought to either affect or not affect the outcome is known as the independent variable, and the variable that is thought to either be affected or not is the dependent variable.

Researchers seek to determine whether or not their hypothesis, or hypotheses if they have more than one, will prove true. Sometimes they do, and sometimes they do not. Either way, the research is considered successful if one can conclude whether or not a hypothesis is true. 

Null Hypothesis

A researcher has a null hypothesis when she or he believes, based on theory and existing scientific evidence, that there will not be a relationship between two variables. For example, when examining what factors influence a person's highest level of education within the U.S., a researcher might expect that place of birth, number of siblings, and religion would not have an impact on the level of education. This would mean the researcher has stated three null hypotheses.

Alternative Hypothesis

Taking the same example, a researcher might expect that the economic class and educational attainment of one's parents, and the race of the person in question are likely to have an effect on one's educational attainment. Existing evidence and social theories that recognize the connections between wealth and cultural resources , and how race affects access to rights and resources in the U.S. , would suggest that both economic class and educational attainment of the one's parents would have a positive effect on educational attainment. In this case, economic class and educational attainment of one's parents are independent variables, and one's educational attainment is the dependent variable—it is hypothesized to be dependent on the other two.

Conversely, an informed researcher would expect that being a race other than white in the U.S. is likely to have a negative impact on a person's educational attainment. This would be characterized as a negative relationship, wherein being a person of color has a negative effect on one's educational attainment. In reality, this hypothesis proves true, with the exception of Asian Americans , who go to college at a higher rate than whites do. However, Blacks and Hispanics and Latinos are far less likely than whites and Asian Americans to go to college.

Formulating a Hypothesis

Formulating a hypothesis can take place at the very beginning of a research project , or after a bit of research has already been done. Sometimes a researcher knows right from the start which variables she is interested in studying, and she may already have a hunch about their relationships. Other times, a researcher may have an interest in ​a particular topic, trend, or phenomenon, but he may not know enough about it to identify variables or formulate a hypothesis.

Whenever a hypothesis is formulated, the most important thing is to be precise about what one's variables are, what the nature of the relationship between them might be, and how one can go about conducting a study of them.

Updated by Nicki Lisa Cole, Ph.D

• Null Hypothesis Examples
• Examples of Independent and Dependent Variables
• Difference Between Independent and Dependent Variables
• What Is a Hypothesis? (Science)
• Understanding Path Analysis
• What Are the Elements of a Good Hypothesis?
• What It Means When a Variable Is Spurious
• What 'Fail to Reject' Means in a Hypothesis Test
• How Intervening Variables Work in Sociology
• Null Hypothesis Definition and Examples
• Understanding Simple vs Controlled Experiments
• Scientific Method Vocabulary Terms
• Null Hypothesis and Alternative Hypothesis
• Six Steps of the Scientific Method
• What Are Examples of a Hypothesis?
• Structural Equation Modeling

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Developing a Research Question

18 Hypotheses

When researchers do not have predictions about what they will find, they conduct research to answer a question or questions, with an open-minded desire to know about a topic, or to help develop hypotheses for later testing. In other situations, the purpose of research is to test a specific hypothesis or hypotheses.  A hypothesis is a statement, sometimes but not always causal, describing a researcher’s expectations regarding anticipated finding. Often hypotheses are written to describe the expected relationship between two variables (though this is not a requirement). To develop a hypothesis, one needs to understand the differences between independent and dependent variables and between units of observation and units of analysis. Hypotheses are typically drawn from theories and usually describe how an independent variable is expected to affect some dependent variable or variables. Researchers following a deductive approach to their research will hypothesize about what they expect to find based on the theory or theories that frame their study. If the theory accurately reflects the phenomenon it is designed to explain, then the researcher’s hypotheses about what would be observed in the real world should bear out.

Sometimes researchers will hypothesize that a relationship will take a specific direction. As a result, an increase or decrease in one area might be said to cause an increase or decrease in another. For example, you might choose to study the relationship between age and legalization of marijuana. Perhaps you have done some reading in your spare time, or in another course you have taken.  Based on the theories you have read, you hypothesize that “age is negatively related to support for marijuana legalization.” What have you just hypothesized? You have hypothesized that as people get older, the likelihood of their support for marijuana legalization decreases. Thus, as age moves in one direction (up), support for marijuana legalization moves in another direction (down). If writing hypotheses feels tricky, it is sometimes helpful to draw them out. and depict each of the two hypotheses we have just discussed.

Note that you will almost never hear researchers say that they have proven their hypotheses. A statement that bold implies that a relationship has been shown to exist with absolute certainty and that there is no chance that there are conditions under which the hypothesis would not bear out. Instead, researchers tend to say that their hypotheses have been supported (or not) . This more cautious way of discussing findings allows for the possibility that new evidence or new ways of examining a relationship will be discovered. Researchers may also discuss a null hypothesis, one that predicts no relationship between the variables being studied. If a researcher rejects the null hypothesis, he or she is saying that the variables in question are somehow related to one another.

Quantitative and qualitative researchers tend to take different approaches when it comes to hypotheses. In quantitative research, the goal often is to empirically test hypotheses generated from theory. With a qualitative approach, on the other hand, a researcher may begin with some vague expectations about what he or she will find, but the aim is not to test one’s expectations against some empirical observations. Instead, theory development or construction is the goal. Qualitative researchers may develop theories from which hypotheses can be drawn and quantitative researchers may then test those hypotheses. Both types of research are crucial to understanding our social world, and both play an important role in the matter of hypothesis development and testing.  In the following section, we will look at qualitative and quantitative approaches to research, as well as mixed methods.

Text Attributions

• This chapter has been adapted from Chapter 5.2 in Principles of Sociological Inquiry , which was adapted by the Saylor Academy without attribution to the original authors or publisher, as requested by the licensor. © Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License .

An Introduction to Research Methods in Sociology Copyright © 2019 by Valerie A. Sheppard is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Share This Book

A hypothesis is an educated guess or proposition that attempts to explain a set of facts or phenomena in sociology. It is a testable statement that can be supported or refuted through empirical research and observation.

Related terms

Empirical Research : The collection and analysis of data from the real world to evaluate the validity of a hypothesis.

Sociological Theory : A framework or system of ideas that helps to explain social phenomena, often forming the basis for generating hypotheses.

Variable : An element, feature, or factor that is liable to vary or change, which researchers manipulate or measure in their studies to assess the effects on another variable

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3.1.3: Developing Theories and Hypotheses

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2.5: Developing a Hypothesis

Learning objectives.

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

Theories and Hypotheses

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

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

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

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

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

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

Theory Testing

The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method (although this term is much more likely to be used by philosophers of science than by scientists themselves). Researchers begin with a set of phenomena and either construct a theory to explain or interpret them or choose an existing theory to work with. They then make a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researchers then conduct an empirical study to test the hypothesis. Finally, they reevaluate the theory in light of the new results and revise it if necessary. This process is usually conceptualized as a cycle because the researchers can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As Figure \(\PageIndex{1}\) shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.

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

Incorporating Theory into Your Research

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

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

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

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

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• How to Write a Strong Hypothesis | Guide & Examples

How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

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

Table of contents

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

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

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

Variables in hypotheses

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

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

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Step 1: ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2: Do some preliminary research

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

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

Step 3: Formulate your hypothesis

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

Step 4: Refine your hypothesis

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

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

Step 5: Phrase your hypothesis in three ways

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

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

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

Step 6. Write a null hypothesis

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

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

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

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

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

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

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

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

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

Research Hypothesis 101

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

What is a hypothesis?

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

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

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

Hypothesis: sleep impacts academic performance.

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

But that’s not good enough…

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

What is a research hypothesis?

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes – specificity , clarity and testability .

Let’s take a look at these more closely.

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Hypothesis Essential #1: Specificity & Clarity

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

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

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

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

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

Hypothesis Essential #2: Testability (Provability)

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

For example, consider the hypothesis we mentioned earlier:

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

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

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

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

Defining A Research Hypothesis

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

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

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

What about the null hypothesis?

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

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

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

And there you have it – hypotheses in a nutshell. 

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

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This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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16 Comments

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

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

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

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

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

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

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

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

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

could you please elaborate it more

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

This is very helpful

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

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

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

this is very important note help me much more

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2.1 Approaches to Sociological Research

Learning objectives.

By the end of this section, you should be able to:

• Define and describe the scientific method.
• Explain how the scientific method is used in sociological research.
• Describe the function and importance of an interpretive framework.
• Describe the differences in accuracy, reliability and validity in a research study.

When sociologists apply the sociological perspective and begin to ask questions, no topic is off limits. Every aspect of human behavior is a source of possible investigation. Sociologists question the world that humans have created and live in. They notice patterns of behavior as people move through that world. Using sociological methods and systematic research within the framework of the scientific method and a scholarly interpretive perspective, sociologists have discovered social patterns in the workplace that have transformed industries, in families that have enlightened family members, and in education that have aided structural changes in classrooms.

Sociologists often begin the research process by asking a question about how or why things happen in this world. It might be a unique question about a new trend or an old question about a common aspect of life. Once the question is formed, the sociologist proceeds through an in-depth process to answer it. In deciding how to design that process, the researcher may adopt a scientific approach or an interpretive framework. The following sections describe these approaches to knowledge.

The Scientific Method

Sociologists make use of tried and true methods of research, such as experiments, surveys, and field research. But humans and their social interactions are so diverse that these interactions can seem impossible to chart or explain. It might seem that science is about discoveries and chemical reactions or about proving ideas right or wrong rather than about exploring the nuances of human behavior.

However, this is exactly why scientific models work for studying human behavior. A scientific process of research establishes parameters that help make sure results are objective and accurate. Scientific methods provide limitations and boundaries that focus a study and organize its results.

The scientific method involves developing and testing theories about the social world based on empirical evidence. It is defined by its commitment to systematic observation of the empirical world and strives to be objective, critical, skeptical, and logical. It involves a series of six prescribed steps that have been established over centuries of scientific scholarship.

Sociological research does not reduce knowledge to right or wrong facts. Results of studies tend to provide people with insights they did not have before—explanations of human behaviors and social practices and access to knowledge of other cultures, rituals and beliefs, or trends and attitudes.

In general, sociologists tackle questions about the role of social characteristics in outcomes or results. For example, how do different communities fare in terms of psychological well-being, community cohesiveness, range of vocation, wealth, crime rates, and so on? Are communities functioning smoothly? Sociologists often look between the cracks to discover obstacles to meeting basic human needs. They might also study environmental influences and patterns of behavior that lead to crime, substance abuse, divorce, poverty, unplanned pregnancies, or illness. And, because sociological studies are not all focused on negative behaviors or challenging situations, social researchers might study vacation trends, healthy eating habits, neighborhood organizations, higher education patterns, games, parks, and exercise habits.

Sociologists can use the scientific method not only to collect but also to interpret and analyze data. They deliberately apply scientific logic and objectivity. They are interested in—but not attached to—the results. They work outside of their own political or social agendas. This does not mean researchers do not have their own personalities, complete with preferences and opinions. But sociologists deliberately use the scientific method to maintain as much objectivity, focus, and consistency as possible in collecting and analyzing data in research studies.

With its systematic approach, the scientific method has proven useful in shaping sociological studies. The scientific method provides a systematic, organized series of steps that help ensure objectivity and consistency in exploring a social problem. They provide the means for accuracy, reliability, and validity. In the end, the scientific method provides a shared basis for discussion and analysis (Merton 1963). Typically, the scientific method has 6 steps which are described below.

Step 1: Ask a Question or Find a Research Topic

The first step of the scientific method is to ask a question, select a problem, and identify the specific area of interest. The topic should be narrow enough to study within a geographic location and time frame. “Are societies capable of sustained happiness?” would be too vague. The question should also be broad enough to have universal merit. “What do personal hygiene habits reveal about the values of students at XYZ High School?” would be too narrow. Sociologists strive to frame questions that examine well-defined patterns and relationships.

In a hygiene study, for instance, hygiene could be defined as “personal habits to maintain physical appearance (as opposed to health),” and a researcher might ask, “How do differing personal hygiene habits reflect the cultural value placed on appearance?”

Step 2: Review the Literature/Research Existing Sources

The next step researchers undertake is to conduct background research through a literature review , which is a review of any existing similar or related studies. A visit to the library, a thorough online search, and a survey of academic journals will uncover existing research about the topic of study. This step helps researchers gain a broad understanding of work previously conducted, identify gaps in understanding of the topic, and position their own research to build on prior knowledge. Researchers—including student researchers—are responsible for correctly citing existing sources they use in a study or that inform their work. While it is fine to borrow previously published material (as long as it enhances a unique viewpoint), it must be referenced properly and never plagiarized.

To study crime, a researcher might also sort through existing data from the court system, police database, prison information, interviews with criminals, guards, wardens, etc. It’s important to examine this information in addition to existing research to determine how these resources might be used to fill holes in existing knowledge. Reviewing existing sources educates researchers and helps refine and improve a research study design.

Step 3: Formulate a Hypothesis

A hypothesis is an explanation for a phenomenon based on a conjecture about the relationship between the phenomenon and one or more causal factors. In sociology, the hypothesis will often predict how one form of human behavior influences another. For example, a hypothesis might be in the form of an “if, then statement.” Let’s relate this to our topic of crime: If unemployment increases, then the crime rate will increase.

In scientific research, we formulate hypotheses to include an independent variables (IV) , which are the cause of the change, and a dependent variable (DV) , which is the effect , or thing that is changed. In the example above, unemployment is the independent variable and the crime rate is the dependent variable.

In a sociological study, the researcher would establish one form of human behavior as the independent variable and observe the influence it has on a dependent variable. How does gender (the independent variable) affect rate of income (the dependent variable)? How does one’s religion (the independent variable) affect family size (the dependent variable)? How is social class (the dependent variable) affected by level of education (the independent variable)?

Taking an example from Table 12.1, a researcher might hypothesize that teaching children proper hygiene (the independent variable) will boost their sense of self-esteem (the dependent variable). Note, however, this hypothesis can also work the other way around. A sociologist might predict that increasing a child’s sense of self-esteem (the independent variable) will increase or improve habits of hygiene (now the dependent variable). Identifying the independent and dependent variables is very important. As the hygiene example shows, simply identifying related two topics or variables is not enough. Their prospective relationship must be part of the hypothesis.

Step 4: Design and Conduct a Study

Researchers design studies to maximize reliability , which refers to how likely research results are to be replicated if the study is reproduced. Reliability increases the likelihood that what happens to one person will happen to all people in a group or what will happen in one situation will happen in another. Cooking is a science. When you follow a recipe and measure ingredients with a cooking tool, such as a measuring cup, the same results is obtained as long as the cook follows the same recipe and uses the same type of tool. The measuring cup introduces accuracy into the process. If a person uses a less accurate tool, such as their hand, to measure ingredients rather than a cup, the same result may not be replicated. Accurate tools and methods increase reliability.

Researchers also strive for validity , which refers to how well the study measures what it was designed to measure. To produce reliable and valid results, sociologists develop an operational definition , that is, they define each concept, or variable, in terms of the physical or concrete steps it takes to objectively measure it. The operational definition identifies an observable condition of the concept. By operationalizing the concept, all researchers can collect data in a systematic or replicable manner. Moreover, researchers can determine whether the experiment or method validly represent the phenomenon they intended to study.

A study asking how tutoring improves grades, for instance, might define “tutoring” as “one-on-one assistance by an expert in the field, hired by an educational institution.” However, one researcher might define a “good” grade as a C or better, while another uses a B+ as a starting point for “good.” For the results to be replicated and gain acceptance within the broader scientific community, researchers would have to use a standard operational definition. These definitions set limits and establish cut-off points that ensure consistency and replicability in a study.

We will explore research methods in greater detail in the next section of this chapter.

Step 5: Draw Conclusions

After constructing the research design, sociologists collect, tabulate or categorize, and analyze data to formulate conclusions. If the analysis supports the hypothesis, researchers can discuss the implications of the results for the theory or policy solution that they were addressing. If the analysis does not support the hypothesis, researchers may consider repeating the experiment or think of ways to improve their procedure.

However, even when results contradict a sociologist’s prediction of a study’s outcome, these results still contribute to sociological understanding. Sociologists analyze general patterns in response to a study, but they are equally interested in exceptions to patterns. In a study of education, a researcher might predict that high school dropouts have a hard time finding rewarding careers. While many assume that the higher the education, the higher the salary and degree of career happiness, there are certainly exceptions. People with little education have had stunning careers, and people with advanced degrees have had trouble finding work. A sociologist prepares a hypothesis knowing that results may substantiate or contradict it.

Sociologists carefully keep in mind how operational definitions and research designs impact the results as they draw conclusions. Consider the concept of “increase of crime,” which might be defined as the percent increase in crime from last week to this week, as in the study of Swedish crime discussed above. Yet the data used to evaluate “increase of crime” might be limited by many factors: who commits the crime, where the crimes are committed, or what type of crime is committed. If the data is gathered for “crimes committed in Houston, Texas in zip code 77021,” then it may not be generalizable to crimes committed in rural areas outside of major cities like Houston. If data is collected about vandalism, it may not be generalizable to assault.

Step 6: Report Results

Researchers report their results at conferences and in academic journals. These results are then subjected to the scrutiny of other sociologists in the field. Before the conclusions of a study become widely accepted, the studies are often repeated in the same or different environments. In this way, sociological theories and knowledge develops as the relationships between social phenomenon are established in broader contexts and different circumstances.

Interpretive Framework

While many sociologists rely on empirical data and the scientific method as a research approach, others operate from an interpretive framework . While systematic, this approach doesn’t follow the hypothesis-testing model that seeks to find generalizable results. Instead, an interpretive framework, sometimes referred to as an interpretive perspective , seeks to understand social worlds from the point of view of participants, which leads to in-depth knowledge or understanding about the human experience.

Interpretive research is generally more descriptive or narrative in its findings. Rather than formulating a hypothesis and method for testing it, an interpretive researcher will develop approaches to explore the topic at hand that may involve a significant amount of direct observation or interaction with subjects including storytelling. This type of researcher learns through the process and sometimes adjusts the research methods or processes midway to optimize findings as they evolve.

Critical Sociology

Critical sociology focuses on deconstruction of existing sociological research and theory. Informed by the work of Karl Marx, scholars known collectively as the Frankfurt School proposed that social science, as much as any academic pursuit, is embedded in the system of power constituted by the set of class, caste, race, gender, and other relationships that exist in the society. Consequently, it cannot be treated as purely objective. Critical sociologists view theories, methods, and the conclusions as serving one of two purposes: they can either legitimate and rationalize systems of social power and oppression or liberate humans from inequality and restriction on human freedom. Deconstruction can involve data collection, but the analysis of this data is not empirical or positivist.

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

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

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

What is a Research Hypothesis?

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

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

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

Importance of Hypothesis in Research

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

• A research hypothesis helps test theories.

A hypothesis plays a pivotal role in the scientific method by providing a basis for testing existing theories. For example, a hypothesis might test the predictive power of a psychological theory on human behavior.

• It serves as a great platform for investigation activities.

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

• Hypothesis guides the research work or study.

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

• Hypothesis sometimes suggests theories.

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

• It helps in knowing the data needs.

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

• The hypothesis explains social phenomena.

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

• Hypothesis provides a relationship between phenomena for empirical Testing.

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

• It helps in knowing the most suitable analysis technique.

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

Characteristics of a Good Research Hypothesis

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

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

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

Types of Research Hypotheses

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

01. Null Hypothesis

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

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

02. Alternative Hypothesis

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

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

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

03. Directional Hypothesis

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

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

04. Non-directional Hypothesis

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

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

05. Simple Hypothesis

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

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

06. Complex Hypothesis

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

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

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

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

07. Associative Hypothesis

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

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

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

08. Causal Hypothesis

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

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

09. Empirical Hypothesis

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

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

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

10. Statistical Hypothesis

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

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

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

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

How to Develop a Research Hypotheses?

Step 1: identify your research problem or topic..

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

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

Step 2: Conduct a literature review

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

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

Step 3: Formulate your research question

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

Step 4: Identify variables

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

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

Step 5: State the Null hypothesis

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

Step 6: Select appropriate methods for testing the hypothesis

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

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

Testing and Evaluating Hypotheses

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

• State your research hypothesis.

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

• Collect data strategically.

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

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

• Perform an appropriate statistical test.

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

• Decide if your idea was right or wrong.

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

• Share what you found.

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

The Role of QuestionPro to Develop a Good Research Hypothesis

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

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

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

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

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

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Research Hypothesis In Psychology: Types, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

On This Page:

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

Some key points about hypotheses:

• A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
• It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
• A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
• Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
• For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
• Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.

Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Types of Research Hypotheses

Alternative hypothesis.

The research hypothesis is often called the alternative or experimental hypothesis in experimental research.

It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.

The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).

A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:

• Important hypotheses lead to predictions that can be tested empirically. The evidence can then confirm or disprove the testable predictions.

In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and are significant in supporting the theory being investigated.

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.

Null Hypothesis

The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.

It states results are due to chance and are not significant in supporting the idea being investigated.

The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.

Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.

This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.

Nondirectional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.

It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.

For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.

Directional Hypothesis

A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)

It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.

For example, “Exercise increases weight loss” is a directional hypothesis.

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.

Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.

It means that there should exist some potential evidence or experiment that could prove the proposition false.

However many confirming instances exist for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.

For Popper, science should attempt to disprove a theory rather than attempt to continually provide evidence to support a research hypothesis.

Can a Hypothesis be Proven?

Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.

All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.

In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.

• Disproving a hypothesis is definitive. Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence.
• However, confirming evidence is always open to revision. Other explanations may account for the same results, and additional or contradictory evidence may emerge over time.

We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.

If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.

How to Write a Hypothesis

• Identify variables . The researcher manipulates the independent variable and the dependent variable is the measured outcome.
• Operationalized the variables being investigated . Operationalization of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression, you might count the number of punches given by participants.
• Decide on a direction for your prediction . If there is evidence in the literature to support a specific effect of the independent variable on the dependent variable, write a directional (one-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
• Make it Testable : Ensure your hypothesis can be tested through experimentation or observation. It should be possible to prove it false (principle of falsifiability).
• Clear & concise language . A strong hypothesis is concise (typically one to two sentences long), and formulated using clear and straightforward language, ensuring it’s easily understood and testable.

Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:

• The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
• The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

More Examples

• Memory : Participants exposed to classical music during study sessions will recall more items from a list than those who studied in silence.
• Social Psychology : Individuals who frequently engage in social media use will report higher levels of perceived social isolation compared to those who use it infrequently.
• Developmental Psychology : Children who engage in regular imaginative play have better problem-solving skills than those who don’t.
• Clinical Psychology : Cognitive-behavioral therapy will be more effective in reducing symptoms of anxiety over a 6-month period compared to traditional talk therapy.
• Cognitive Psychology : Individuals who multitask between various electronic devices will have shorter attention spans on focused tasks than those who single-task.
• Health Psychology : Patients who practice mindfulness meditation will experience lower levels of chronic pain compared to those who don’t meditate.
• Organizational Psychology : Employees in open-plan offices will report higher levels of stress than those in private offices.
• Behavioral Psychology : Rats rewarded with food after pressing a lever will press it more frequently than rats who receive no reward.

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Open Education Sociology Dictionary

Table of Contents

Definition of Hypothesis

( noun ) A proposed and testable explanation between two or more variables that predicts an outcome or explains a phenomenon.

Examples of Hypothesis

• Note : The  variables are the students, the time spent studying, and the test grades. To test the hypothesis, collect information from each student about how much time they spent studying prior to the test and compare that to the the testing outcomes.
• Sapir-Whorf hypothesis

Types of Hypothesis

• asymmetry hypothesis
• null hypothesis
• substantive hypothesis

Hypothesis Pronunciation

Pronunciation Usage Guide

Syllabification : hy·poth·e·sis

Audio Pronunciation

Phonetic Spelling

• American English – /hie-pAHth-uh-suhs/
• British English – /hie-pOth-i-sis/

International Phonetic Alphabet

• American English – /haɪˈpɑθəsəs/
• British English – /hʌɪˈpɒθᵻsᵻs/

Usage Notes

• Plural: hypotheses
• A hypothesis must have the capacity to be disconfirmed or proven false to have meaning. For example, “criminals” commit more crimes than “non-criminals” cannot be proven wrong.
• A hypothesis can either come from theory ( deduction ) or lead to theory ( induction ).
• A working hypothesis refers to a hypothesis that has not been thoroughly tested and verified.
• Hypothesis testing is the process of testing a hypothesis in a scientific manner that requires a link between the concepts or  variables under investigation and rigorous testing methodology .
• An ( noun ) hypothesist ( verb ) hypothesizes ( adverb ) hypothetically about social issues to create an ( adjective ) hypothetical explanation.

Related Videos

Additional Information

• Quantitative Research Resources – Books, Journals, and Helpful Links
• Word origin of “hypothesis” – Online Etymology Dictionary: etymonline.com
• Gauch, Hugh G., Jr. 2003. Scientific Method in Practice . Cambridge: Cambridge University Press.
• Lehmann, E. L., and Joseph P. Romano. 2010. Testing Statistical Hypotheses . 3rd ed. New York: Springer.
• Poletiek, Fenna. 2001. Hypothesis-testing Behaviour . Philadelphia: Psychology.
• Popper, Karl R. 1959.  The Logic of Scientific Discovery . New York: Basic Books.

Related Terms

• correlation
• dependent variable
• hypothetico-deductive model
• independent variable
• inferential statistics
• statistical analysis

Contributor: C. E. Seaman

Works Consulted

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Cite the Definition of Hypothesis

ASA – American Sociological Association (5th edition)

Seaman, C. E. 2015. “hypothesis.” In Open Education Sociology Dictionary , edited by Kenton Bell. Retrieved June 1, 2024 ( https://sociologydictionary.org/hypothesis/ ).

APA – American Psychological Association (6th edition)

Seaman, C. E. (2015). hypothesis. In K. Bell (Ed.), Open education sociology dictionary . Retrieved from https://sociologydictionary.org/hypothesis/

Chicago/Turabian: Author-Date – Chicago Manual of Style (16th edition)

Seaman, C. E. 2015. “hypothesis.” In Open Education Sociology Dictionary , edited by Kenton Bell. Accessed June 1, 2024. https://sociologydictionary.org/hypothesis/ .

MLA – Modern Language Association (7th edition)

Seaman, C. E. “hypothesis.” Open Education Sociology Dictionary . Ed. Kenton Bell. 2015. Web. 1 Jun. 2024. < https://sociologydictionary.org/hypothesis/ >.

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Hypotheses: meaning, types and sources | social research.

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After reading this article you will learn about:- 1. Meaning of Hypotheses 2. Types of Hypotheses 3. Sources.

Meaning of Hypotheses:

Once the problem to be answered in the course of research is finally instituted, the researcher may, if feasible proceed to formulate tentative solutions or answers to it. These proposed solutions or explanations are called hypotheses which the researcher is obliged to test on the basis of fact already known or which can be made known.

If such answers are not formulated, even implicitly, the researcher cannot effectively go ahead with the investigation of his problem because, in the absence of direction which hypotheses typically provide, the researcher would not know what facts to look for and what relation or order to search for amongst them.

The hypotheses guide the researcher through a bewildering Jungle of facts to see and select only those that are relevant to the problem or difficulty he proposes to solve. Collection of facts merely for the sake of collecting them will yield no fruits.

To be fruitful, one should collect such facts as are for or against some point of view or proposition. Such a point of view or proposition is the hypothesis. The task of the inquiry or research is to test its accord with facts.

Lundberg aptly observes, “The only difference between gathering data without a hypothesis and gathering them with one, is that in the latter case, we deliberately recognize the limitations of our senses and attempt to reduce their fallibility by limiting our field of investigation so as to prevent greater concentration for attention on particular aspects which past experience leads us to believe are irrelevant as insignificant for our purpose.”

Simply stated, an hypothesis helps us see and appreciate:

(1) The kind of data that need be collected in order to answer the research question and

(2) The way in which they should be organized most efficiently and meaningfully.

Webster’s New International Dictionary of English Language, 1956, defines the term “hypothesis” as “proposition, condition or principle which is assumed, perhaps without belief, in order to draw out its logical consequences and by this method to test its accord with facts which are known or may be determined.”

Cohen and Nagel bring out the value of hypothesis thus:

“We cannot take a single step forward in any inquiry unless we begin with a suggested explanation or solution of the difficulty which originated it. Such tentative explanations are suggested to us by something in the subject-matter and by our previous knowledge. When they are formulated as propositions, they are called hypotheses.”

Once the scientist knows what his question (problem) is, he can make a guess, or a number of guesses as to its possible answers. According to Werkmeister, “The guesses he makes are the hypotheses which either solve the problems or guide him in further investigation.”

It is clear now that a hypothesis is a provisional formulation; a tentative solution of the problem posed by the scientist. ‘The scientist starts by assuming that the solution is true without, of course, personally believing in its truthfulness.

Based on this assumption, the scientist anticipates that certain logical consequences will be observed on the plane of observable events or objects. Whether these anticipations or expectations really materialize is the test of the hypothesis, its proof or disproof.

If the hypothesis is proved, the problem of which it was a tentative solution is answered. If it is not proved, i.e., falsified owing to non-support of proof, alternative hypotheses may be formulated by the researcher. An hypothesis thus stands somewhere at the midpoint of research; from here, one can look back to the problem as also look forward to data.

The hypothesis may be stated in the form of a principle, that is, the tentative explanation or solution to the questions how? Or why? May be presented in the form of a principle that X varies with Y. The inquiry established that an empirical referent of X varies with the empirical referent of Y in a concrete observable situation (i.e., the hypothesis is proved) then the question is answered.

Hypotheses, however, may take other forms, such as intelligent guesses, conditions, propositions deduced from theories, observations and findings of other scholars etc.

Proceeding on the basis of hypotheses has been the slow and hard way of science. While some scientific conclusions and premises seem to have arisen in the mind of the investigator as if by flashes of insight, in a majority of cases the process of discovery has been a slower one.

“The scientific imagination devises a possible solution, a hypothesis and the investigator proceeds to test it. He makes intellectual keys and then tries to see whether they fit the lock. If the hypothesis does not fit, it is rejected and another is made. The scientific workshop is full of discarded keys.”

Cohen and Nagel’s statement that one cannot take a single step forward in any inquiry without a hypothesis may well be a correct statement of the value of hypothesis in scientific investigation generally, but it hardly does justice to an important function of scientific research, i.e., the “formulation hypotheses.”

Hypotheses are not given to us readymade. Of course in fields with a highly developed theoretic structure it is reasonable to expect that most empirical studies will have at least some sharp hypotheses to be tested.

This is so especially in social sciences where there has not yet evolved a highly developed theoretic system in many areas of its subject-matter which can afford fruitful bases for hypothesis-formulation.

As such, attempts to force research into this mould are either deceitful or stultifying and hypotheses are likely to be no more than hunches as to where to look for sharper hypotheses in which case the study may be described as an intelligent fishing trip.

As a result, in the social sciences at least, a considerable quantum of research endeavour is directed understandably toward ‘making’ hypotheses rather than at testing them.

A very important type of research has as its goal, the formulation of significant hypotheses relating to a particular problem. Hence, we will do well to bear in mind that research can begin with well formulated hypotheses or it may come out with hypotheses as its end product.

Let us recapitulate the role of hypotheses for research in the words of Chaddock who summarizes it thus:

“(A hypothesis) in the scientific sense is … an explanation held after careful canvass of known facts, in full knowledge of other explanations that have been offered and with a mind open to change of view, if the facts disclosed by the inquiry warrant a different explanation. Another hypothesis as an explanation is proposed including investigation all available and pertinent data either to prove or disprove the hypothesis…. (A hypothesis) gives point to the inquiry and if founded on sufficient previous knowledge, guides the line of investigation. Without it much useless data maybe collected in the hope that nothing essential will be omitted or important data may be omitted which could have been easily included if the purpose of inquiry had been more clearly defined” and thus hypotheses are likely to be no more than hunches as to where to look for pertinent data.

An hypothesis is therefore held with the definite purpose of including in the investigating all available and pertinent data either to prove or disprove the hypothesis.

Types of Hypotheses :

There are many kinds of hypotheses the social researcher has to be working with. One type of hypotheses asserts that something is the case in a given instance; that a particular object, person or situation has a particular characteristic.

Another type of hypotheses deals with the frequency of occurrences or of association among variables; this type of hypotheses may state that X is associated with y a certain (Y) proportion of times, e.g., that urbanism tends to be accompanied by mental disease or that something is greater or lesser than some thing else in a specific setting.

Yet another type of hypotheses assert that a particular characteristic is one of the factors which determine another characteristic, i.e., S is the producer of Y (product). Hypotheses of this type are known as causal hypotheses.

Hypotheses can be classified in a variety of ways. But classification of hypotheses on the basis of their levels of abstraction is regarded as especially fruitful. Goode arid Hatt have identified three differential levels of abstraction reached by hypotheses. We shall here be starting from the lowest level of abstraction and go over to the higher ones.

(a) At the lowest level of abstraction are the hypotheses which state existence of certain empirical uniformities. Many types of such empirical uniformities are common in social research, for instance, it may be hypothesized with reference to India that in the cities men will get married between the age of 22 and 24 years.

Or, the hypotheses of this type may state that certain behaviour pattern may be expected in a specified community. Thus, hypotheses of this type frequently seem to invite scientific verification of what are called “common sense propositions,” indeed without much justification.

It has often been said by way of a criticism of such hypotheses that these are not useful in as much as they merely state what everyone seems to know already. Such an objection may however be overruled by pointing out that what everyone knows is not often put in precise terms nor is it adequately integrated into the framework of science.

Secondly, what everyone knows may well be mistaken. To put common sense ideas into precisely defined concepts and subject the proposition to test is an important task of science.

This is particularly applicable to social sciences which are at present in their earlier stage of development. Not only social science but all sciences have found such commonsense knowledge a fruitful item of study. It was commonsense knowledge in the olden days that sun revolved round the earth. But this and many other beliefs based on commonsense have been exploded by patient, plodding, empirical checking of facts.

The monumental work, The American Soldier by Stouffer and associates was criticized in certain quarters, for it was according to them mere elaboration of the obvious. But to this study goes the credit of exploding some of the commonsense propositions and shocking many people who had never thought that what was so obvious a commonsense could be totally wrong or unfounded in fact.

(b) At a relatively higher level of abstraction are hypotheses concerned with complex ‘ideal types.’ These hypotheses aim at testing whether logically derived relationship between empirical uniformities obtain. This level of hypothesizing moves beyond the level of anticipating a simple empirical uniformity by visualizing a complex referent in society.

Such hypotheses are indeed purposeful distortions of empirical exactness and owing to their remoteness from empirical reality, these constructs are termed ‘ideal types.’ The function of such hypotheses is to create tools and formulate problems for further research in complex areas of investigation.

An example of one such hypothesis may be cited. Analyses of minority groups brought to light empirical uniformities in the behaviour of members of a wide variety of minorities. It was subsequently hypothesized that these uniformities pointed to an ‘ideal type’.

First called by H. A. Miller the ‘oppression psychosis,’ this ideal-typical construction was subsequently modified as the ‘Marginal man’ by E. Stone Quist and associates. Empirical evidence marshaled later substantiated the hypothesis, and so the concept of marginality (marginal man) has very much come to stay as a theoretic construct in social sciences, and as part of sociological theory.

(c) We now come to the class of hypotheses at the highest level of abstraction. This category of hypotheses is concerned with the relation obtaining amongst analytic variables. Such hypotheses are statements about, how one property affects other, e.g., a statement of relationship between education and social mobility or between wealth and fertility.

It is easy to see that this level of hypothesizing is not only more abstract compared to others; it is also the most sophisticated and vastly flexible mode of formulation.

This does not mean, however, that this type of hypotheses is ‘superior’ or ‘better’ than the other types. Each type of hypotheses has its own importance depending in turn upon the nature of investigation and the level of development the subject has achieved.

The sophisticated hypotheses of analytical variables owe much of their existence to the building-blocks contributed by the hypotheses existed at the lower orders of abstraction.

Sources of Hypotheses :

Hypotheses may be developed from a variety of sources. We examine here, some of the major ones.

(1) The history of sciences provides an eloquent testimony to the fact that personal and idiosyncratic experiences of the scientist contribute a great deal to type and form of questions he may ask, as also to the kinds of tentative answers to these questions (hypotheses) that he might provide. Some scientists may perceive an interesting pattern in what may merely, seem a jumble of facts to the common man.

The history of science is full of instances of discoveries made just because the ‘right’ person happened to make the ‘right’ observation owing to his characteristic life-history and exposure to a unique mosaic of events. Personal life-histories are a factor in determining the kinds of a person’s perception and conception and this factor may in turn direct him to certain hypotheses quite readily.

An illustration of such individual perspectives in social sciences may be seen in the work of Thorstein Veblen whom Merton describes as a sociologist with a keen eye for the unusual and paradoxical.

A product of an isolated Norwegian community, Veblen lived at a time when the capitalistic system was barely subjected to any criticism. His own community background was replete with derivational experiences attributable to the capitalist system.

Veblen being an outsider, was able to look at the capitalist economic system more objectively and with dispassionate detachment. Veblen was thus strategically positioned to attack the fundamental concepts and postulates of classical economics.

He was an alien who could bring a different experience to bear upon the economic world. Consequently, he made penetrating analyses of society and economy which have ever since profoundly influenced social science.

(2) Analogies are often a fountainhead of valuable hypotheses. Students of sociology and political science in the course of their studies would have come across analogies wherein society and state are compared to a biological organism, the natural law to the social law, thermodynamics to social dynamics, etc. such analogies, notwithstanding the fact that analogies as a class suffer from serious limitations, do provide certain fruitful insight which formulated as hypotheses stimulate and guide inquiries.

One of the recent orientations to hypotheses formulation is provided by cybernetics, the communication models now so well entrenched in the social science testify to the importance of analogies as a source of fruitful hypotheses. The hypothesis that similar human types or activities may be found occupying the same territory was derived from plant ecology.

When the hypothesis was borne out by observations in society, the concept of segregation as it is called in plant ecology was admitted into sociology. It has now become an important idea in sociological theory. Such examples may be multiplied.

In sum, analogy may be very suggestive but care needs to be taken not to accept models from other disciplines without a careful scrutiny of the concepts in terms of their applicability to the new frame of reference in which they are proposed to be deployed.

(3) Hypotheses may rest also on the findings of other studies. The researcher on the basis of the findings of other studies may hypothesize that similar relationship between specified variables will hold good in the present study too. This is a common way of researchers who design their study with a view of replicating another study conducted in a different concrete context or setting.

It was said that many a study in social science is exploratory in character, i.e., they start without explicit hypotheses, the findings of such studies may be formulated as hypotheses for more structured investigations directed at testing certain hypotheses.

(4) An hypothesis may stem from a body of theory which may afford by way of logical deduction, the prediction that if certain conditions are present, certain results will follow. Theory represents what is known; logical deductions from this constitute the hypotheses which must be true if the theory was true.

Dubin aptly remarks, “Hypothesis is the feature of the theoretical model closest to the ‘things observable’ that the theory is trying to model.” Merton illustrates this function of theory with his customary felicity. Basing his deductions on Durham’s theoretic orientation, Merton shows how hypotheses may be derived as deductions from theoretic system.

(1) Social cohesion provides psychic support to group members subjected to acute stresses and anxieties.

(2) Suicide rates are functions of unrelieved anxieties to which persons are subjected.

(3) Catholics have greater social cohesion than protestants.

(4) Therefore, lower suicide rates should be expected among Catholics than among protestants.

If theories purport to model the empirical world, then there must be a linkage between the two. This linkage is to be found in the hypotheses that mirror the propositions of the theoretical model.

It may thus appear that the points of departure vis-a-vis hypotheses-construction are in two opposite directions:

(a) Conclusions based on concrete or empirical observations lead through the process of induction to more abstract hypotheses and

(b) The theoretical model through the process of logical deduction affords more concrete hypotheses.

It may be well to bear in mind, however, that although these two approaches to hypotheses formulation seem diametrically opposed to each other, the two points of departure, i.e., empirical, observations and the theoretical structure, represent the poles of a continuum and hypotheses lie somewhere in the middle of this continuum.

Both these approaches to hypotheses-construction have proved their worth. The Chicago School in American sociology represents a strong empirical orientation whereas the Mertonian and Parsonian approach is typified by a stress on theoretic models as initial bases for hypotheses-construction. Hence hypotheses can be deductively derived from theoretic models.

(5) It is worthy of note that value-orientation of the culture in which a science develops may furnish many of its basic hypotheses.

That certain hypotheses and not others capture the attention of scientists or occur to them in particular societies or culture may well be attributed to the cultural emphases. Goode and Hatt contend that the American emphasis upon personal happiness had had considerable effect upon social science in that country.

The phenomenon of personal happiness has been studied in great detail. In every branch of social science, the problem of personal happiness came to occupy a position meriting central focus. Happiness has been correlated with income, education, occupation, social class, and so on. It is evident that the culture emphasis on happiness has been productive of a very wide range of hypotheses for the American social science.

Folk-wisdom prevalent in a culture may also serve as source of hypotheses. The sum and substance of the discussion is aptly reflected in Larrabee’s remark that the ideal source of fruitful and relevant hypotheses is a fusion of two elements: past experience and imagination in the disciplined mind of the scientist.

Related Articles:

• Role of Hypothesis in Social Research
• 6 Main Characteristics of a Usable Hypotheses | Social Research

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3 Social science theories, methods, and values

Learning Objectives for this Chapter

After reading this Chapter, you should be able to:

• understand, apply, and evaluate core social science values, concepts, and theories, which can help inform and guide our understanding of how the world works, how power is defined and exercised, and how we can critically understand and engage with these concepts when examining the world around us.

Social science theory: theories to explain the world around us

As we have discussed in previous chapters, social science research is concerned with discovering things about the social world: for instance, how people act in different situations, why people act the way they do, how their actions relate to broader social structures, and how societies function at both the micro and macro levels. However, without theory, the ‘social facts’ that we discover cannot be woven together into broader understandings about the world around us.

Theory is the ‘glue’ that holds social facts together. Theory helps us to conceptualise and explain why things are the way they are, rather than only focusing on how things are. In this sense, different theoretical perspectives, such as those discussed in this Chapter, act as different lenses through which we can see and interpret the world around us.

Theory testing and generation is also an important part of social scientific research. As shown in the image below, different theories are rooted in different philosophical foundations. That is, various theories arise in accordance with different ways of seeing and living in the world, as well as different understandings about how knowledge is understood and constructed. As we learned earlier in the book, these concern both ontological and epistemological considerations, but also axiological considerations; that is, questions about the nature of value,  and what things in the world hold value (including in relation to one another). While theory is rooted in these philosophical foundations, however, it also gives way to different ways of doing research, both in terms of the methodology and methods employed. Overall, using different theoretical perspectives to consider social questions is a bit like putting on different pairs of glasses to see the world afresh.

Below we consider some foundational social science theories. While these are certainly not the only  theoretical perspectives that exist, they are often considered to be amongst the most influential. They also provide helpful building blocks for understanding other theoretical perspectives, as well as how theory can be applied to guide and build social scientific knowledge.

Structural functionalism

Structural functionalism is a theory about social institutions, ‘social norms’ (i.e., the often unspoken rules that govern social behaviours), and social stability. We talk more about social institutions in the next Chapter of this book, but essentially they are the ‘big building blocks’ of society that act as both repositories and creators/instigators of social norms. These include things like school/education, the state (often called a meta-institution), the family, the economy, and more. In this regard, structural functionalism is considered a macro theory; that is, it considers macro (large) structures in society, and concerns how they work in an interdependent way to produce what structural functionalists believe to be ‘harmonious’ and stable societies. Structural functionalists are particularly concerned with social institutions’ manifest and latent functions, as well as their functions and dysfunctions (Merton [1910-2003]).

Manifest functions of social institutions include things that are overt and obvious. By contrast, latent functions of social institutions are those that are more hidden or secondary. For instance, a manifest function of the social institution of school is to teach students new knowledge and skills, which can assist them to move into chosen careers. Alternatively, we might also argue that school has other latent functions, such as socialisation and conformity to social norms, and building relationships with peers.

In addition to manifest and latent functions, structural functionalists are also concerned with the  functions  and  dysfunctions  of social institutions. They believe, for instance, that dysfunctions play just as much of an important role as functions, because they enable social institutions to identify and punish them, thereby making an example of dysfunctional elements (e.g., punishing those committing crime). This serves to reinforce social norms around how society should function.

Reflection exercise

Take a piece of paper and, in your own words, write down a brief definition of structural functionalism. Then re-read the above sub-section. How does your understanding fit with the information above?

Structural functionalism: want to learn more?

If you’d like to reinforce your understanding of structural functionalism, the below video provides a good summary that might be helpful.

Functionalism (YouTube, 5:40) :

Phenomenology

Phenomenology is the study of our experiences and how our consciousness makes sense of the phenomena (be they objects, people or ideas) around us. As a methodology or approach in the social sciences it has garnered renewed interest in the last few decades to better understand the world around us by studying how we experience the world in a subjective and often individual manner. It is, thus, considered a ‘micro’ theory.

This philosophical approach was developed by Edmund Husserl (1859–1938), and his students and critics in France and Germany (key figures were philosophers Martin Heidegger (1889-1976), Jean-Paul Sartre (1905-1980) and Maurice Merleau-Ponty (1908-1961)) and later made it to the US via influential sociologists, such as Alfred Schütz (1899–1959).

Phenomenologists reject objectivity and instead focus on the subjective and intersubjective, the relations between people, and between people and objects. So, rather than trying to come to some objective truth, they are more interested in relationships and connections between the individual and the world around them. Indeed, there is a strong centering of and focus on the individual and their experiences of the world that phenomenologists believe can tell us about society at large. The individual is also key, as there is a focus on the sensory and the body both as instruments of enquiring as well as enquiry. Thus, we are always already part of the world around us and have to make sense of being here, but also want to go beyond ourselves by understanding others and how they relate to the world. The body features as a key site for such enquiries as it is the physical connection we have with people and objects around us. Further, there is a focus on everyday, mundane experiences as they have much to tell us about how society operates. This background environment in which we as people operate is called a lifeworld,  the shared horizon of experience we share and inhabit. It is marked by linguistic, cultural, and social codes and norms.

One key method inherent to Husserl’s early approaches is ‘bracketing’ , the process of standing back or aside from phenomena to understand it better. Such processes of ‘reflexivity’ and understanding our taken for granted attitudes and beliefs about certain phenomena are crucial to enable the social sciences to better understand the world around us. Debates in philosophy continue around whether such a bracketing is ever fully possible, especially considering that we as humans remain trapped in our minds and  bodies. Nonetheless, phenomenology has had a profound impact in most social sciences to redirect the focus towards the intersubjective nature of life and the lifeworld, within which we experience the world around us.

Take a piece of paper and, in your own words, write down a brief definition of phenomenology. Then re-read the above sub-section. How does your understanding fit with the information above?

Phenomenology: want to learn more?

If you’d like to reinforce your understanding of phenomenology, the below video provides a good summary that might be helpful.

Understanding Phenomenology (YouTube, 2:59) :

Symbolic interactionism

Symbolic interactionism is related to phenomenology as it is also a theory focused on the self. In this regard, it’s also a micro theory – it has particular focus on individuals and how they interact with one another. Symbolic interactionists say that symbolism is fundamental to how we see ourselves and how we see and interact with others. George Herbert Mead (1863-1931) is often regarded as the founder of this theory and his focus was on the relationship between the self and others in society. He considered our individual minds to function through interactions with others and through the shared meanings and symbols we create for the people and objects around us. Mead’s best known book Mind, Self, Society, was posthumously put together by his students and demonstrates how our individual minds allow us to use language and symbols to make sense of the world around us and how we construct a self based on how others perceive us.

Charles Cooley’s (1864-1929) concept of the “ looking glass self ” points out, for instance, that other peoples’ perceptions of us can also influence and change our perceptions of ourselves. Other sociologists, such as Erving Goffman (1922-1982), have built on this understanding, suggesting that ‘all of life is a stage’ and that each of us play different parts, like actors in a play. Goffman argued that we adapt our personality, behaviours, actions, and beliefs to suit the different contexts we find ourselves in. This understanding is often referred to as a ‘dramaturgical model’ of social interaction; it understands our social interactions to be performative – they are the outcomes of our ‘play acting’ different roles.

In explaining this theory, Goffman also referred to what he called ‘impression management’. As part of this, for instance, Goffman drew a crucial distinction between what he referred to as our ‘ front stage selves ‘ and our ‘ backstage selves ‘. For Goffman, our ‘front stage selves’ are those that we are willing to share with the ‘audience’ (e.g., the person or group with whom we are interacting). Alternatively, our ‘backstage selves’ are those that we keep for ourselves; this is the way we act when we are alone and have no audience.

Goffman also pointed to the important role that stigma can play in how we see ourselves and thus, how we act and behave in relation to others. Stigma occurs when “the reaction of others spoils normal identity”. Goffman argued that those who feel stigmatised by others (e.g., through public discourses and ‘frames’ of social issues that vilify certain groups of people) also experience changes in the way they see themselves – that is, their own sense of self-identity is ‘spoiled’. This can lead to other negative effects, such as social withdrawal and poorer health and wellbeing.

Take a piece of paper and, in your own words, write down a brief definition of symbolic interactionism. Then re-read the above sub-section. How does your understanding fit with the information above?

This exercise is to be conducted in small groups. First, get into a small group with other students. Then, do the following:

• Think about your daily life, activities, and interactions with others.
• Take a few moments to identify at least three examples of social symbols that you and other group members frequently use to interpret the world around you.
• Talk about how each of the group members interprets/responds to these symbols. Are there similarities? Are there differences?

Students should share/discuss their thoughts within the group, and if undertaken in a class environment, then report back to the class.

Symbolic interactionism: want to learn more?

If you’d like to reinforce your understanding of symbolic interactionism, the below videos provide good summaries that might be helpful.

Symbolic Interactionism (YouTube, 3:33) provides an easy-to-understand summary of symbolic interactionism:

What does it mean to be me? Erving Goffman and the Performed Self (YouTube, 1:58) provides a helpful summary of Erving Goffman’s conception of the ‘performed self’ – including his notions of a ‘front stage’ and ‘backstage’ self:

Conflict theories

Conflict theories focus particularly on conflict within and across societies and, thus, are particularly interested in power: where it does and doesn’t exist, who does and doesn’t hold it, and what they do or don’t do with it, for example. These theories hold that societies will always be characterised by states of conflict and competition over goods, resources, and more. These conflicts can arise along various lines, though

this group of theories emanate from the work of Karl Marx (1818-1883), who saw the capitalist economy as a primary site of conflict.

In Marx’s view, social ills emanated particularly from what he described as an upper- and lower-class structure, which had been perpetuated across multiple societies (e.g., in ancient societies in terms of slave owners/slaves, or in pre-Enlightenment times between the feudal peasantry/aristocracy). He saw capitalism as replicating this upper/lower class structure through the creation of a bourgeoisie (upper class, who own the means of production) and proletariat (lower class, who supply labour to the capitalist market). Marx also talked about a lumpenproletariat , an underclass without class consciousness and/or organised political power. Classical Marxism takes a macro lens: it is particularly concerned with how power is invested in the social institution of the capitalist economy. In this sense, classical Marxism represents a structural theory of power.

Marx argued that the only way for society to be fairer and more equal was if the proletariat was to rise up and revolt against the bourgeoisie; to “smash the chains of capitalism”! Thus, he strongly advocated for revolution as a means of creating a fairer, utopic society. He stated, “Philosophers have hitherto only interpreted the world, in various ways; the point is to change it” (Marx 1968: 662). Nevertheless, a series of revolutions in the early 20th century that drew on Marxist thinking resulted in power vacuums that made way for violent, totalitarian regimes, as political philosopher Hannah Arendt (1906-1975) argued in On the Origins of Totalitarianism . On this basis, subsequent conflict theorists (and critical theorists) have tended towards advocating for more incremental reforms, as opposed to revolution.

Take a few moments to watch the below two videos, which explain conflict theory in greater detail.

Key concepts: Conflict theory – definition and critiques (YouTube, 2:49) :

Political theory – Karl Marx (YouTube, 9:27) :

After watching these videos, take a piece of paper and, in your own words, write down a definition of conflict theories. After doing so, re-read the above sub-section. How does your understanding fit with the information in the above sub-section, and in the videos? Was anything missing? Is anything still unclear?

Critical theories

Marx saw the capitalist economy as a primary site of oppression, between the working class and the property owning class. Marx advocated for revolution, where the proletariat were urged to rise up and break the chains of capitalism by overthrowing the bourgeoisie. Marx saw this as being necessary for ensuring the freedom of the working classes. Critical theory develops from the work of Karl Marx, supplementing his theory of capitalism with other sociological and philosophical concepts.

Gramsci and cultural hegemony

In addition to Marx, critical theory utilised the work of Italian political philosopher Antonio Gramsci, specifically his concept of ‘Cultural Hegemony’. When we refer to ‘hegemonic’ social norms, we’re referring to social norms that are regarded as ‘common sense’ and thus, which overshadow and suppress alternative norms. Hegemonic norms typically reflect the values of the ruling classes (in Marxist terms, the bourgeoisie). To learn more, you might like to watch the video below:

Hegemony: WTF? An introduction to Gramsci and cultural hegemony (YouTube, 6:25)

Developing from this, critical theory also considers how power and oppression can operate in more subtle ways across the whole of society. Critical theory does not seek to actively bring about revolution, as the possibility for a revolution in the years post-World War Two was unlikely. Whilst critical theorists are by no means opposed to revolution, their focus lies more in identifying how capitalist society and its institutions limits advancement of human civilisation. In this respect, conflict theorists see more opportunities for praxis than classical Marxists.

Critical theory observes how the Enlightenment ideals of freedom, reason, and liberalism have developed throughout the first half of the 1900s. Ultimately, critical theorists see that reason has not necessarily progressed in a positive way throughout history. In fact, reason has developed to become increasingly technical, interested in classifying, regulating, and standardising all aspects of human society and culture. German philosopher Theodor Adorno (1903-1969) thought that Nazi Germany and the holocaust is a devastating example of the potential evils of rationality if developed without a critical perspective.

Another, less extreme, example of this tendency toward standardisation is in the production of art and culture. Big budget films, typically in the superhero or science fiction genre, all appear to be virtually identical: extravagant special effects, epic soundtracks, and relatively simple plots. However, this is not to say that such films are of a poor quality. Rather the similarity and popularity of these films indicates a homogenisation of culture. If culture is merely the reproduction of the same, how can society progress beyond its current point?

This critique of the development of reason throughout the 20th century does not mean that we must abandon reason entirely. To do so would be to discount the vast wealth of knowledge that humanity has come to grasp, as well as prevent further knowledge production. Instead, critical theorists argue that reason should be critiqued to uncover what has been left out of its development thus far, as well as open up the possibility for a more free, progressive form of society.

At its core, then, critical theory can be thought about as being an additional theoretical lens through which we can look at and understand the social world around us. In tune with Flyvbjerg’s (2001) conception of phronetic social science, critical theorists are also concerned with disrupting the systems they observe as a means of achieving social change. Critical theory urges us to recognise, understand and address how capitalist society reproduces itself and limits the free organisation of human beings.

Take a few moments to watch Critical theory definition and critiques (YouTube, 3:26) , which explains critical theory in greater detail.

Take a piece of paper and, in your own words, write down a brief definition of critical theories. Then re-read the above sub-section. How does your understanding fit with the information above and the video?

Critical theory can be applied in myriad different ways to better understand the world around us. In  Critical theory and the production of mass culture (YouTube, 2:12) , critical theory is adopted as a lens to understand and critique the production of mass culture. Watch the video and then consider the questions below.

• Can you think of examples where you could argue that the primary objective of producing art is to preserve the economic structure of the capitalist system?
• Do you agree with the proposition that mass-consumed entertainment, like popular television shows, are only  produced as a source of light entertainment and escapism from work, and thus serve to placate and pacify the worker? Why or why not? (What other  purposes might such entertainment serve, if any?)
• Do you agree with Adorno’s proposition that the products of the ‘culture industry’ are not only the artworks, but also the consumers themselves? Why or why not?

Critical race theory

Critical race theory applies a critical theory lens to the notion of race, seeking to understand how the concept of race itself can act as a site of power and oppression. Arising from the work of American legal scholars during the 1980s (including key thinkers like Derrick Bell [1930-2011] and Kimberlé Crenshaw [1959-]), it originally sought to understand and challenge “the ways in which race and racial power [were]… cosnstructed and represented in American legal culture and, more generally, in American society as a whole.” (Crenshaw et al. 1995: xiii) In particular, it questioned whether the civil rights afforded to African Americans in the aftermath of the civil rights movement had made a substantive impact on their experiences of social justice. Critical race theorists argued that more needed to be done; that civil rights had not had the desired impacts because (amongst other reasons) they:

• were imagined, shaped and brought into being by (predominantly) white, male middle- or upper-class lawyers, and thus, were only imagined within the bounds of white ontology,
• did not move beyond race – race still mattered, and
• implicitly perpetuated white privilege (e.g. they were constrained to only imagine redress and justice within the existing oppressive, white hegmonic system).

Crenshaw (1995: xiii) writes that, although critical race scholars’ work is heterogenous, they are nevertheless united by the following common interests:

• “The first is to understand how a regime of white supremacy and its subordination of people of color have been created and maintained in America, and, in particular, to examine the relationship between that social structure and professed ideas such as ‘the rule of law’ and ‘equal protection’.”
• “The second is a desire not merely to understand the vexed bond between law and racial power but to change it.”

In Australia, scholars have also taken up aspects of a critical race lens to understand how privilege is bound up with race. As Moreton-Robinson (2015: xiii) puts it, in Australia:

Race matters in the lives of all peoples; for some people it confers unearned privileges, and for others it is the mark of inferiority. Daily newspapers, radio, television, and social media usually portray Indigenous peoples as a deficit model of humanity. We are overrepresented as always lacking, dysfunctional, alcoholic, violent, needy, and lazy… For Indigenous people, white possession is not unmarked, unnamed or invisible; it is hypervisible…

Crenshaw has been crucial in also stressing the key importance of understanding how race can also intersect with other aspects of social identity, such as gender, to produce a ‘double’ or ‘triple’ oppression. In Australia, Professor Aileen Moreton-Robinson’s 2000 book, Talkin’ up to the white woman, was also crucial in understanding how Australian feminism could also be oppressive of Indigenous Australian women by not seeing and hearing them or the specific issues they face/d. She called for the need for “white feminists to relinquish some power, dominance and privilege in Australian feminism to give Indigenous women’s interest some priority” (Moreton-Robinson 2000: xxv). This emphasised that an intersectional lens was needed to acknowledge the different but cumulative impacts of both racial oppression and sexism. At the centre of this argument is the reality that “all white feminists [in Australia] benefit from colonisation; they are overwhelmingly represented and disproportionately predominant, have the key roles, and constitute the norm, the ordinary and the standard of womanhood in Australia” (Moreton-Robinson 2000: xxv).

Uproar over critical race theory

During 2020, racial sensitivity training in the USA prompted widespread discussion about critical race theory. Former US President, Donald Trump, posits in the video below that the theory, and the kinds of racial sensitivity training it promotes, are fundamentally racist – against white people. Others argued that this represented a deep misunderstanding of the theory, but also an ignorance of the extent and power of white privilege.

For an example of former President Trump’s views, watch  Trump: Racial sensitivity training on white privilege is ‘racist’ (YouTube, 3:16) :

Postmodern critique of critical race theory

Postmodernists have levelled critique at critical race theory on the basis that understanding/explaining power as being rooted in racial difference has the consequence of reinforcing and perpetuating the validity of ‘race’. Postmodernism, however, rejects the distinct, conceptual bounds of ‘race’ and racialised identities. Instead, it sees race itself as a social construction, which should be questioned and disrupted, thereby leading to new insights that aren’t constrained by socially constructed definitions of race.

Kwame Anthony Appiah, for example, seeks to “probe the very definitions of race itself. He bypasses the empirical question of whether racism exists to ask the theoretical question of what race and racism are” (in Chong-Soon Lee 1995: 441)

Take a piece of paper and, in your own words, write down a brief definition of critical race theory . Then re-read the above sub-section. How does your understanding fit with the information above?

Putting theory into action: rethinking crime through a critical lens

Critical criminologists apply a critical theory lens to the study of crime and criminality. In this regard, critical criminology is concerned with understanding how the criminal justice system can act as a site of power and oppression; a perspective that tends to sit in contrast with western (non-critical) criminology, which sees the criminal justice system as a natural social institution that has the primarily purpose of protecting society against deviants (criminals) and making an example of those who fail to comply with hegemonic social norms. (This non-critical view draws parallels, for example, with the perceived ‘functions’ of the criminal justice system under a structural functionalist perspective, and its role in making examples of ‘dysfunctional’ elements of society.)

Critical criminologists in Australia have considered the role of the criminal justice system as a key site of oppression under, for example, Australian settler colonialism. For instance, Indigenous Australians are, per capita, the most incarcerated peoples in the entire world ( Anthony & Baldry 2017 ) and these incarceration rates are rising, not reducing (ABS 2018). In using a critical lens to understand the difference between incarceration rates for Indigenous and non-Indigenous Australians, however, we can seek better insight into how the criminal justice system operates as a site of oppression, perpetuating white settler colonial norms and values, which seek to punish alternative ontologies and epistemologies. Lynch (cited in Cunneen and Tauri 2016: 26) argued,

In short, criminology is one of the disciplines that established the conditions necessary for maintenance of the status quo of power. It can only do so by oppressing those who would undermine the status quo. In this sense, criminology must be viewed as a science of oppression.

In part, this oppression operates through the construction of knowledge and truth within (positivist) criminology (which relates to Foucault’s conception of power-knowledge, as we touched on last week). In turn, this also involves what Cunneen and Tauri (2016: 26) describe as “the ideologically driven dismissal of Indigenous knowledge about the social world as ‘subjective’, ‘unscientific’, and/or at best ‘folk epistemology’… which in turn paves the way for excluding other ways of knowing from the Western, criminological lexicon”.

In their book, Decolonising criminology, Blagg and Anthony (2019: 22-23) set out a taxonomy for what they see as a decolonised criminology (noting, though, that Blagg and Anthony themselves are non-Indigenous researchers, though they have worked closely with Indigenous peoples and communities for decades).  In their taxonomy (which we have included an adapted version of below), they include the following probing comparisons between a positivist (largely uncritical) criminology and a decolonised (critical) criminology:

A table comparing positivist and decolonial approaches to criminology.

Source: Authors’ adaptation from Blagg & Anthony (2019: 22-23 )

The probes and questions that Blagg & Anthony pose in the above taxonomy are critical in their focus and intent; they seek to critique the criminal justice system as a site of colonial power, but they also seek to change it — through research that produces knowledge about these truths. This is, in essence, a reframing (to use Bacchi’s term) of the nature of criminological research towards a richer, and more historically and culturally contextualised understanding of the Australian criminal justice system. As a result, this produces different knowledge about crime and justice in Australia: knowledge that shifts blame away from the individual (the ‘bad’ Indigenous citizen, to use Moreton-Robinson’s [2009] language) to the structures, history and continuation of colonial oppression.

Critical or radical criminology?

Radical criminology is rooted in the Marxist conflict tradition and sees the capitalist economy as being central to the definitions of crime (arrived at by the bourgeoisie) that constrict, control and suppress the working classes (proletariat).

In contrast (or in addition to), critical criminology is interested in more than just class relations and also sees different opportunities for praxis – tending to favour a more incremental approach to social change as opposed to widespread revolution ( Bernard 1981 )

Drawing on a critical criminology and decolonising perspective, consider the below graph, which shows the over-representation of Indigenous Australians in prisons, indicating an upward trend from 2008-2018. Then consider, from a critical criminology standpoint, what kinds of ‘truths’ might you draw on to help explain this trend?

(To guide your thinking, you may like to revisit the above taxonomy by Blagg and Anthony.)

Watch the below short clip of Senator Patrick Dodson talking in March 2021 about the issue of Aboriginal and Torres Strait Islander deaths in custody. Consider LNP Senator, Amanda Stoker’s response to Senator Pat Dodson, in particular her comment that she “understand[s] the outrage is real… because the lives of every person, though our justice system are important, no matter the colour of their skin.”

In #Estimates , @SenatorDodson fires up over a lack of action on deaths in custody. @stoker_aj ‘s response: “I understand the outrage is real…because the lives of every person, through our justice system are important, no matter the colour of their skin.” #Auspol @SBSNews @NITV pic.twitter.com/jgsb8y9YcD — Naveen Razik (@naveenjrazik) March 26, 2021

What do you think about Senator Stoker’s response to Senator Dodson? How might you analyse her response, through a critical race theory lens?

Choose one of the following social issues:

• The gender pay gap
• The workplace ‘stress’ epidemic
• Homelessness
• Childhood obesity

Consider how your chosen social issue might be explained by drawing on the different theoretical perspectives outlined earlier in this Chapter. Record your thoughts in a short, written explanation.

Reflection exercise: a critical reading of meritocracy

Kim and Choi (2017: 112) define meritocracy as “a social system in which advancement in society is based on an individual’s capabilities and merits rather than on the basis of family, wealth, or social background.” According to Kim and Choi (2017: 116), meritocracy has two key features: “impartial competition” and “equality of opportunity”.

The notion of meritocracy has arisen over the past few centuries primarily in response to feudalism and absolute monarchy, where power and privilege are handed down on the basis of familial lines (‘nepotism’) or friendships (‘cronyism’). This kind of system could (and often did) place people into positions of power, regardless of whether they were the most appropriate or ‘best’ person for the job. In essence, then, the notion of meritocracy is intended to tie social advancement to merit; that is, the focus is supposed to be on ‘what you know’ rather than ‘who you know’, which seems a noble cause, right? Many have argued, however, that a blinkered belief in meritocracy leaves a lot of things out of the ‘frame’.

The belief in meritocracy, and its focus on ‘what you know’ rather than ‘who you know’, can have both positive and negative impacts. Take a piece of paper and write a short list of each.

If critical theory operates according to the broad Marxist understanding of history as class struggle, post-structuralism is a theory that attempts to abandon the idea of grand historical narratives altogether. Fundamentally, post-structuralism differs from other social theories in its rejection of metanarratives , its critique of binaries, and its refusal to understand all human action as being shaped solely by universal social structures. Whilst there is much disagreement between post-structuralist thinkers, these three broad trends help us to understand this social theory.

Post-structuralism

Post-structural accounts of conflict and power can take a macro and micro lens. They see power as transcending social structures, like social institutions (e.g., the state, the economy) and instead being all around us at all times. Michel Foucault (1926-1984), for example, argued that power is everywhere and acts upon us to shape our identities, bodies, behaviours, and being. In terms of a liberal democratic society, therefore, where coercive (‘sovereign’) power is only exerted by the state under certain specific circumstances, Foucault argued that the state otherwise uses its power to create ‘responsibilised’ citizens who absorb hegemonic (i.e. authoritative/dominant) social norms and use these to govern themselves . This relates to what Fairclough (1995: 257) referred to as power by consent:

We live in an age in which power is predominantly exercised through the generation of consent rather than through coercion… through the inculcation of self-disciplining practices rather than through the breaking of skulls (though there is still unfortunately no shortage of the latter).

Foucault was also particularly interested in the link between power and knowledge. He argued that those who hold the power tend to construct knowledge and ‘truth’ in certain ways, which can reinforce their power by, for example, perpetuating certain social norms. This is elaborated on by Watts and Hodgson (2019) in reading 5.2, where they describe Foucault’s conception of power/knowledge as follows:

Truth is not neutral or objective, and is not simply a thing that can be verified scientifically because its ‘truth value’ is dependent on the operation and circulation of power (think, for example, the oft-quoted phrase that ‘truth is whatever the powerful say it is’). In the context of the human and social sciences, power creates knowledge and is also a force for the translation of knowledge of and about human beings into practice… For example, the moment we speak into existence the concept of something as commonplace as ‘human being’ or ‘human rights’ or ‘social justice’ we are using some form of power (truth) to render such things thinkable and knowable as things in the world (Watts and Hodgson 2019: 85-86).

Take a piece of paper and, in your own words, write down a brief definition of Foucault’s post-structural concept of power. Then, re-read the above account. Does your definition align with the information above?

Beck and Risk Society

The notion of risk society is outlined by Ulrich Beck in his 1992 book ‘Risk Society: Towards a New Modernity’. Where society was once organised around wealth distribution based on scarcity, Beck argues that society is becoming increasingly based on the distribution of risks. Risks are defined as “a systematic way of dealing with hazards and insecurities induced and introduced by modernization itself” (Beck 1992: 21). Beck argues that the process of modernisation is no longer focused exclusively on the creation of new technologies, but rather the focus lies in the management of risks of potential technologies. As such, modernisation is becoming increasingly reflexive, involved not only in the production of technologies to meet needs, but rather investigating the often unknown side-effects of technologies. For example, a nuclear energy plant might be built in order to meet society’s increasing energy demand. However, this solution to a specific problem then must deal with the new issue of disposing of this radioactive waste that modernisation itself has produced. This is just one example of the ecological risks inherent with the development of new technologies, which often have unintended side-effects, that must themselves be uncovered and solved.

Postmodernism

Before we can get to postmodernism, we need to define modernism to see what postmodernism wants to supersede. Modernism describes the social upheaval and major changes of 20th century life. It is marked by processes of industrialisation, rationalisation and bureaucratisation – in short a world in which the sciences seemed to provide ever more answers and ultimate truths about the world and us. Modernism or modernity was also about hope for a new society, unfettered technological and material progress and, with advances in scientific fields, led to longer lives and new and exciting materials to make new things to make life easier (think household machines). It was also punctured by some key social movements that brought the world to the brink of destruction in the epic fight over what ultimate truth should prevail. The key political ideologies of fascism, socialism and liberalism clashed in the second World War over their different visions for a new world order. In the post war climate of a new stand-off between socialism/communism and liberalism or the Soviet bloc and ‘the West’ many writers, academics and artists became disillusioned with the modernist project. Slowly critiques of these universalising truths and meta-narratives came to think of this time as a time of postmodernism. Jean-François Lyotard (1924-1998) defined postmodernism as the ‘incredulity towards meta-narratives’, by which he meant that increasingly people were no longer persuaded by grand or master narratives about themselves, a particular nation, people or even humanity. The singular, stable, coherent modern subject was thrown into a void and thus becomes fragmented, fluid and plural in the postmodern. No one truth exists anymore and the certainty of facts becomes disputed and muddied once more. Thus, postmodernity is about scepticism, deconstruction and questioning rather than offering answers and solutions. This has made it a controversial theory or topic as it offers little in the way of hope for a better world, indeed it is often seen as dystopic. Inherent in many postmodern critiques of current society is a critique of (late) capitalism and consumer or mass culture that pervade every aspect of our lives, whilst others focus on technology and its pervasive intrusion into our daily lives.

Resources for further learning

• Moreton-Robinson, A. 2015. ‘Introduction: white possession and Indigenous sovereignty matters.’ In. Moreton-Robinson, A.  The White Possessive: property, power and Indigenous sovereignty,  pp. xi-xxiv.
• Powers, C. 2009. Sociology as a coherent discipline: unifying themes. In. Powers, C. Making sense of social theory , Chapter 16.
• Watts, L. and Hodgson, D. 2019. ‘Power and knowledge’. In. Watts, L. and Hodgson, D. Social justice theory and practice for social work, Chapter 5.
• Cunneen, C. and Tauri, J. 2016. ‘Towards a critical Indigenous criminology.’ In. Cunneen, C. and Tauri, J. Indigenous criminology, pp. 23-43.
• Kim, C.H. and Choi, Y.B. 2017. How meritocracy is defined today – contemporary aspects of meritocracy. Economics and Sociology, 10(1): 112-121.
• Flyvbjerg, B. 2001. ‘Values in social and political inquiry.’ In. Flyvbjerg, B. Making social science matter, Chapter 5.

Other resources:

• Watego, C. 2021.  ‘Who are the real criminals? Making the case for abolishing criminology.’ (YouTube, 1:35:01),
• Anderson, E. 2017. ‘How good social science can and ought to be value-laden’ (YouTube, 17:00) .
• Zigon, J. and Throop, J. 2021. ‘ Phenomenology ‘ Open Encyclopedia of Anthropology .

Introduction to the Social Sciences Copyright © 2023 by The University of Queensland is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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13 4.2 Causality

Learning objectives.

• Define and provide an example of idiographic and nomothetic causal explanations
• Describe the role of causality in quantitative research as compared to qualitative research
• Identify, define, and describe each of the main criteria for nomothetic causal explanations
• Describe the difference between and provide examples of independent, dependent, and control variables
• Define hypothesis, be able to state a clear hypothesis, and discuss the respective roles of quantitative and qualitative research when it comes to hypotheses

Most social scientific studies attempt to provide some kind of causal explanation.  In other words, it is about cause and effect. A study on an intervention to prevent child abuse is trying to draw a connection between the intervention and changes in child abuse. Causality refers to the idea that one event, behavior, or belief will result in the occurrence of another, subsequent event, behavior, or belief.  It seems simple, but you may be surprised to learn there is more than one way to explain how one thing causes another. How can that be? How could there be many ways to understand causality?

Think back to our chapter on paradigms, which were analytic lenses comprised of assumptions about the world. You’ll remember the positivist paradigm as the one that believes in objectivity and social constructionist paradigm as the one that believes in subjectivity. Both paradigms are correct, though incomplete, viewpoints on the social world and social science.

A researcher operating in the social constructionist paradigm would view truth as subjective. In causality, that means that in order to try to understand what caused what, we would need to report what people tell us. Well, that seems pretty straightforward, right? Well, what if two different people saw the same event from the exact same viewpoint and came up with two totally different explanations about what caused what? A social constructionist might say that both people are correct. There is not one singular truth that is true for everyone, but many truths created and shared by people.

When social constructionists engage in science, they are trying to establish one type of causality—idiographic causality.  The word idiographic comes from the root word “idio” which means peculiar to one, personal, and distinct. An idiographic causal explanation means that you will attempt to explain or describe your phenomenon exhaustively, based on the subjective understandings of your participants. Idiographic causal explanations are intended to explain one particular context or phenomenon.  These explanations are bound with the narratives people create about their lives and experience, and are embedded in a cultural, historical, and environmental context. Idiographic causal explanations are so powerful because they convey a deep understanding of a phenomenon and its context. From a social constructionist perspective, the truth is messy. Idiographic research involves finding patterns and themes in the causal themes established by your research participants.

If that doesn’t sound like what you normally think of as “science,” you’re not alone. Although the ideas behind idiographic research are quite old in philosophy, they were only applied to the sciences at the start of the last century. If we think of famous Western scientists like Newton or Darwin, they never saw truth as subjective. They operated with the understanding there were objectively true laws of science that were applicable in all situations. In their time, another paradigm–the positivist paradigm–was dominant and continues its dominance today. When positivists try to establish causality, they are like Newton and Darwin, trying to come up with a broad, sweeping explanation that is universally true for all people. This is the hallmark of a nomothetic causal explanation .  The word nomothetic is derived from the root word “nomo” which means related to a law or legislative, and “thetic” which means something that establishes.  Put the root words together and it means something that is establishing a law, or in our case, a universal explanation.

Nomothetic causal explanations are incredibly powerful. They allow scientists to make predictions about what will happen in the future, with a certain margin of error. Moreover, they allow scientists to generalize —that is, make claims about a large population based on a smaller sample of people or items. Generalizing is important. We clearly do not have time to ask everyone their opinion on a topic, nor do we have the ability to look at every interaction in the social world. We need a type of causal explanation that helps us predict and estimate truth in all situations.

If these still seem like obscure philosophy terms, let’s consider an example. Imagine you are working for a community-based non-profit agency serving people with disabilities. You are putting together a report to help lobby the state government for additional funding for community support programs, and you need to support your argument for additional funding at your agency. If you looked at nomothetic research, you might learn how previous studies have shown that, in general, community-based programs like yours are linked with better health and employment outcomes for people with disabilities. Nomothetic research seeks to explain that community-based programs are better for everyone with disabilities. If you looked at idiographic research, you would get stories and experiences of people in community-based programs. These individual stories are full of detail about the lived experience of being in a community-based program. Using idiographic research, you can understand what it’s like to be a person with a disability and then communicate that to the state government. For example, a person might say “I feel at home when I’m at this agency because they treat me like a family member” or “this is the agency that helped me get my first paycheck.”

Neither kind of causal explanation is better than the other. A decision to conduct idiographic research means that you will attempt to explain or describe your phenomenon exhaustively, attending to cultural context and subjective interpretations. A decision to conduct nomothetic research, on the other hand, means that you will try to explain what is true for everyone and predict what will be true in the future. In short, idiographic explanations have greater depth, and nomothetic explanations have greater breadth. More importantly, social workers understand the value of both approaches to understanding the social world. A social worker helping a client with substance abuse issues seeks idiographic knowledge when they ask about that client’s life story, investigate their unique physical environment, or probe how they understand their addiction. At the same time, a social worker also uses nomothetic knowledge to guide their interventions. Nomothetic research may help guide them to minimize risk factors and maximize protective factors or use an evidence-based therapy, relying on knowledge about what in general helps people with substance abuse issues.

Nomothetic causal explanations

If you are trying to generalize about causality, or create a nomothetic causal explanation, then the rest of these statements are likely to be true: you will use quantitative methods, reason deductively, and engage in explanatory research. How can we make that prediction? Let’s take it part by part.

Because nomothetic causal explanations try to generalize, they must be able to reduce phenomena to a universal language, mathematics. Mathematics allows us to precisely measure, in universal terms, phenomena in the social world. Because explanatory researchers want a clean “x causes y” explanation, they need to use the universal language of mathematics to achieve their goal. That’s why nomothetic causal explanations use quantitative methods.  It’s helpful to note that not all quantitative studies are explanatory. For example, a descriptive study could reveal the number of people without homes in your county, though it won’t tell you why they are homeless. But nearly all explanatory studies are quantitative.

What we’ve been talking about here is an association between variables. When one variable precedes or predicts another, we have what researchers call independent and dependent variables. Two variables can be associated without having a causal relationship.  However, when certain conditions are met (which we describe later in this chapter), the independent variable is considered as a “ cause ” of the dependent variable.  For our example on spanking and aggressive behavior, spanking would be the independent variable and aggressive behavior addiction would be the dependent variable.  In causal explanations, the  independent variable is the cause, and the dependent variable is the effect.  Dependent variables depend on independent variables. If all of that gets confusing, just remember this graphical depiction:

The strength of the association between the independent variable and dependent variable is another important factor to take into consideration when attempting to make causal claims when your research approach is nomothetic.  In this context, strength refers to statistical significance . When the  association between two variables is shown to be statistically significant, we can have greater confidence that the data from our sample reflect a true association between those variables in the target population. Statistical significance is usually represented in statistics as the p- value .  Generally a p -value of .05 or less indicates the association between the two variables is statistically significant.

A hypothesis is a statement describing a researcher’s expectation regarding the research findings. Hypotheses in quantitative research are nomothetic causal explanations that the researcher expects to demonstrate. Hypotheses are written to describe the expected association between the independent and dependent variables. Your prediction should be taken from a theory or model of the social world. For example, you may hypothesize that treating clinical clients with warmth and positive regard is likely to help them achieve their therapeutic goals. That hypothesis would be using the humanistic theories of Carl Rogers. Using previous theories to generate hypotheses is an example of deductive research. If Rogers’ theory of unconditional positive regard is accurate, your hypothesis should be true.

Let’s consider a couple of examples. In research on sexual harassment (Uggen & Blackstone, 2004), one might hypothesize, based on feminist theories of sexual harassment, that more females than males will experience specific sexually harassing behaviors. What is the causal explanation being predicted here? Which is the independent and which is the dependent variable? In this case, we hypothesized that a person’s gender (independent variable) would predict their likelihood to experience sexual harassment (dependent variable).

Sometimes researchers will hypothesize that an association will take a specific direction. As a result, an increase or decrease in one area might be said to cause an increase or decrease in another. For example, you might choose to study the association between age and support for legalization of marijuana. Perhaps you’ve taken a sociology class and, based on the theories you’ve read, you hypothesize that age is negatively related to support for marijuana legalization. In fact, there are empirical data that support this hypothesis. Gallup has conducted research on this very question since the 1960s (Carroll, 2005). What have you just hypothesized? You have hypothesized that as people get older, the likelihood of their supporting marijuana legalization decreases. Thus, as age (your independent variable) moves in one direction (up), support for marijuana legalization (your dependent variable) moves in another direction (down). So, positive associations involve two variables going in the same direction and negative associations involve two variables going in opposite directions. If writing hypotheses feels tricky, it is sometimes helpful to draw them out and depict each of the two hypotheses we have just discussed.

It’s important to note that once a study starts, it is unethical to change your hypothesis to match the data that you found. For example, what happens if you conduct a study to test the hypothesis from Figure 4.3 on support for marijuana legalization, but you find no association between age and support for legalization? It means that your hypothesis was wrong, but that’s still valuable information. It would challenge what the existing literature says on your topic, demonstrating that more research needs to be done to figure out the factors that impact support for marijuana legalization. Don’t be embarrassed by negative results, and definitely don’t change your hypothesis to make it appear correct all along!

Establishing causality in nomothetic research

Let’s say you conduct your study and you find evidence that supports your hypothesis, as age increases, support for marijuana legalization decreases. Success! Causal explanation complete, right? Not quite. You’ve only established one of the criteria for causality. The main criteria for causality have to do with covariation, plausibility, temporality, and spuriousness. In our example from Figure 4.3, we have established only one criteria—covariation. When variables covary , they vary together. Both age and support for marijuana legalization vary in our study. Our sample contains people of varying ages and varying levels of support for marijuana legalization and they vary together in a patterned way–when age increases, support for legalization decreases.

Just because there might be some correlation between two variables does not mean that a causal explanation between the two is really plausible. Plausibility means that in order to make the claim that one event, behavior, or belief causes another, the claim has to make sense. It makes sense that people from previous generations would have different attitudes towards marijuana than younger generations. People who grew up in the time of Reefer Madness or the hippies may hold different views than those raised in an era of legalized medicinal and recreational use of marijuana.

Once we’ve established that there is a plausible association between the two variables, we also need to establish that the cause happened before the effect, the criterion of temporality . A person’s age is a quality that appears long before any opinions on drug policy, so temporally the cause comes before the effect. It wouldn’t make any sense to say that support for marijuana legalization makes a person’s age increase. Even if you could predict someone’s age based on their support for marijuana legalization, you couldn’t say someone’s age was caused by their support for legalization.

Finally, scientists must establish nonspuriousness. A spurious association is one in which an association between two variables appears to be causal but can in fact be explained by some third variable. For example, we could point to the fact that older cohorts are less likely to have used marijuana. Maybe it is actually use of marijuana that leads people to be more open to legalization, not their age. This is often referred to as the third variable problem, where a seemingly true causal explanation is actually caused by a third variable not in the hypothesis. In this example, the association between age and support for legalization could be more about having tried marijuana than the age of the person.

Quantitative researchers are sensitive to the effects of potentially spurious associations. They are an important form of critique of scientific work. As a result, they will often measure these third variables in their study, so they can control for their effects. These are called control variables , and they refer to variables whose effects are controlled for mathematically in the data analysis process. Control variables can be a bit confusing, but think about it as an argument between you, the researcher, and a critic.

Researcher: “The older a person is, the less likely they are to support marijuana legalization.” Critic: “Actually, it’s more about whether a person has used marijuana before. That is what truly determines whether someone supports marijuana legalization.” Researcher: “Well, I measured previous marijuana use in my study and mathematically controlled for its effects in my analysis. The association between age and support for marijuana legalization is still statistically significant and is the most important association here.”

Let’s consider a few additional, real-world examples of spuriousness. Did you know, for example, that high rates of ice cream sales have been shown to cause drowning? Of course, that’s not really true, but there is a positive association between the two. In this case, the third variable that causes both high ice cream sales and increased deaths by drowning is time of year, as the summer season sees increases in both (Babbie, 2010). Here’s another good one: it is true that as the salaries of Presbyterian ministers in Massachusetts rise, so too does the price of rum in Havana, Cuba. Well, duh, you might be saying to yourself. Everyone knows how much ministers in Massachusetts love their rum, right? Not so fast. Both salaries and rum prices have increased, true, but so has the price of just about everything else (Huff & Geis, 1993).

Finally, research shows that the more firefighters present at a fire, the more damage is done at the scene. What this statement leaves out, of course, is that as the size of a fire increases so too does the amount of damage caused as does the number of firefighters called on to help (Frankfort-Nachmias & Leon-Guerrero, 2011). In each of these examples, it is the presence of a third variable that explains the apparent association between the two original variables.

In sum, the following criteria must be met for a correlation to be considered causal:

• The two variables must vary together.
• The association must be plausible.
• The cause must precede the effect in time.
• The association must be nonspurious (not due to a third variable).

Once these criteria are met, there is a nomothetic causal explanation, one that is objectively true. However, this is difficult for researchers to achieve. You will almost never hear researchers say that they have proven their hypotheses. A statement that bold implies that a association has been shown to exist with absolute certainty and that there is no chance that there are conditions under which the hypothesis would not be true. Instead, researchers tend to say that their hypotheses have been supported (or not). This more cautious way of discussing findings allows for the possibility that new evidence or new ways of examining an association will be discovered. Researchers may also discuss a null hypothesis. The null hypothesis is one that predicts no association between the variables being studied. If a researcher fails to accept the null hypothesis, she is saying that the variables in question are likely to be related to one another.

Idiographic causal explanations

If you not trying to generalize, but instead are trying to establish an idiographic causal explanation, then you are likely going to use qualitative methods, reason inductively, and engage in exploratory or descriptive research. We can understand these assumptions by walking through them, one by one.

Researchers seeking idiographic causal explanation are not trying to generalize, so they have no need to reduce phenomena to mathematics. In fact, using the language of mathematics to reduce the social world down is a bad thing, as it robs the causality of its meaning and context. Idiographic causal explanations are bound within people’s stories and interpretations. Usually, these are expressed through words. Not all qualitative studies analyze words, as some can use interpretations of visual or performance art, but the vast majority of social science studies do.

But wait, we predicted that an idiographic causal explanation would use descriptive or exploratory research. How can we build causality if we are just describing or exploring a topic? Wouldn’t we need to do explanatory research to build any kind of causal explanation?  To clarify, explanatory research attempts to establish nomothetic causal explanations—an independent variable is demonstrated to cause changes a dependent variable. Exploratory and descriptive qualitative research are actually descriptions of the causal explanations established by the participants in your study. Instead of saying “x causes y,” your participants will describe their experiences with “x,” which they will tell you was caused by and influenced a variety of other factors, depending on time, environment, and subjective experience. As stated before, idiographic causal explanations are messy. The job of a social science researcher is to accurately identify patterns in what participants describe.

Let’s consider an example. What would you say if you were asked why you decided to become a social worker?  If we interviewed many social workers about their decisions to become social workers, we might begin to notice patterns. We might find out that many social workers begin their careers based on a variety of factors, such as: personal experience with a disability or social injustice, positive experiences with social workers, or a desire to help others. No one factor is the “most important factor,” like with nomothetic causal explanations. Instead, a complex web of factors, contingent on context, emerge in the dataset when you interpret what people have said.

Finding patterns in data, as you’ll remember from Chapter 2, is what inductive reasoning is all about. A qualitative researcher collects data, usually words, and notices patterns. Those patterns inform the theories we use in social work. In many ways, the idiographic causal explanations created in qualitative research are like the social theories we reviewed in Chapter 2  and other theories you use in your practice and theory courses. Theories are explanations about how different concepts are associated with each other how that network of associations works in the real world. While you can think of theories like Systems Theory as Theory (with a capital “T”), inductive causality is like theory with a small “t.” It may apply only to the participants, environment, and moment in time in which the data were gathered. Nevertheless, it contributes important information to the body of knowledge on the topic studied.

Unlike nomothetic causal explanations, there are no formal criteria (e.g., covariation) for establishing causality in idiographic causal explanations. In fact, some criteria like temporality and nonspuriousness may be violated. For example, if an adolescent client says, “It’s hard for me to tell whether my depression began before my drinking, but both got worse when I was expelled from my first high school,” they are recognizing that oftentimes it’s not so simple that one thing causes another. Sometimes, there is a reciprocal association where one variable (depression) impacts another (alcohol abuse), which then feeds back into the first variable (depression) and also into other variables (school). Other criteria, such as covariation and plausibility still make sense, as the associations you highlight as part of your idiographic causal explanation should still be plausibly true and it elements should vary together.

Similarly, idiographic causal explanations differ in terms of hypotheses. If you recall from the last section, hypotheses in nomothetic causal explanations are testable predictions based on previous theory. In idiographic research, instead of predicting that “x will decrease y,” researchers will use previous literature to figure out what concepts might be important to participants and how they believe participants might respond during the study. Based on an analysis of the literature a researcher may formulate a few tentative hypotheses about what they expect to find in their qualitative study. Unlike nomothetic hypotheses, these are likely to change during the research process. As the researcher learns more from their participants, they might introduce new concepts that participants talk about. Because the participants are the experts in idiographic causal explanation, a researcher should be open to emerging topics and shift their research questions and hypotheses accordingly.

Complementary approaches to causality

Over time, as more qualitative studies are done and patterns emerge across different studies and locations, more sophisticated theories emerge that explain phenomena across multiple contexts. In this way, qualitative researchers use idiographic causal explanations for theory building or the creation of new theories based on inductive reasoning. Quantitative researchers, on the other hand, use nomothetic causal explanations for theory testing , wherein a hypothesis is created from existing theory (big T or small t) and tested mathematically (i.e., deductive reasoning).  Once a theory is developed from qualitative data, a quantitative researcher can seek to test that theory. In this way, qualitatively-derived theory can inspire a hypothesis for a quantitative research project.

Two different baskets

Idiographic and nomothetic causal explanations form the “two baskets” of research design elements pictured in Figure 4.4 below. Later on, they will also determine the sampling approach, measures, and data analysis in your study.

In most cases, mixing components from one basket with the other would not make sense. If you are using quantitative methods with an idiographic question, you wouldn’t get the deep understanding you need to answer an idiographic question. Knowing, for example, that someone scores 20/35 on a numerical index of depression symptoms does not tell you what depression means to that person. Similarly, qualitative methods are not often used to deductive reasoning because qualitative methods usually seek to understand a participant’s perspective, rather than test what existing theory says about a concept.

However, these are not hard-and-fast rules. There are plenty of qualitative studies that attempt to test a theory. There are fewer social constructionist studies with quantitative methods, though studies will sometimes include quantitative information about participants. Researchers in the critical paradigm can fit into either bucket, depending on their research question, as they focus on the liberation of people from oppressive internal (subjective) or external (objective) forces.

We will explore later on in this chapter how researchers can use both buckets simultaneously in mixed methods research. For now, it’s important that you understand the logic that connects the ideas in each bucket. Not only is this fundamental to how knowledge is created and tested in social work, it speaks to the very assumptions and foundations upon which all theories of the social world are built!

Key Takeaways

• Idiographic research focuses on subjectivity, context, and meaning.
• Nomothetic research focuses on objectivity, prediction, and generalizing.
• In qualitative studies, the goal is generally to understand the multitude of causes that account for the specific instance the researcher is investigating.
• In quantitative studies, the goal may be to understand the more general causes of some phenomenon rather than the idiosyncrasies of one particular instance.
• For nomothetic causal explanations, an association must be plausible and nonspurious, and the cause must precede the effect in time.
• In a nomothetic causal explanations, the independent variable causes changes in a dependent variable.
• Hypotheses are statements, drawn from theory, which describe a researcher’s expectation about an association between two or more variables.
• Qualitative research may create theories that can be tested quantitatively.
• The choice of idiographic or nomothetic causal explanation requires a consideration of methods, paradigm, and reasoning.
• Depending on whether you seek a nomothetic or idiographic causal explanation, you are likely to employ specific research design components.
• Causality-the idea that one event, behavior, or belief will result in the occurrence of another, subsequent event, behavior, or belief
• Control variables- potential “third variables” effects are controlled for mathematically in the data analysis process to highlight the relationship between the independent and dependent variable
• Covariation- the degree to which two variables vary together
• Dependent variable- a variable that depends on changes in the independent variable
• Generalize- to make claims about a larger population based on an examination of a smaller sample
• Hypothesis- a statement describing a researcher’s expectation regarding what she anticipates finding
• Idiographic research- attempts to explain or describe your phenomenon exhaustively, based on the subjective understandings of your participants
• Independent variable- causes a change in the dependent variable
• Nomothetic research- provides a more general, sweeping explanation that is universally true for all people
• Plausibility- in order to make the claim that one event, behavior, or belief causes another, the claim has to make sense
• Spurious relationship- an association between two variables appears to be causal but can in fact be explained by some third variable
• Statistical significance- confidence researchers have in a mathematical relationship
• Temporality- whatever cause you identify must happen before the effect
• Theory building- the creation of new theories based on inductive reasoning
• Theory testing- when a hypothesis is created from existing theory and tested mathematically

Image attributions

Mikado by 3dman_eu CC-0

Weather TV Forecast by mohamed_hassan CC-0

Figures 4.2 and 4.3 were copied from Blackstone, A. (2012) Principles of sociological inquiry: Qualitative and quantitative methods. Saylor Foundation. Retrieved from: https://saylordotorg.github.io/text_principles-of-sociological-inquiry-qualitative-and-quantitative-methods/ Shared under CC-BY-NC-SA 3.0 License

Beatrice Birra Storytelling at African Art Museum by Anthony Cross public domain

Foundations of Social Work Research Copyright © 2020 by Rebecca L. Mauldin is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Practice  Questions  – Write short note on Importance and Sources of Hypothesis in Sociological Research. [ UPSC 2008]

Approach –  Introduction, What makes Hypothesis relevant in a sociological research?, What are the sources which aids us to derive hypothesis?, Conclusion

INTRODUCTION

A hypothesis is a prediction of what will be found at the outcome of a research project and is typically focused on the relationship between two different variables studied in the research. It is usually based on both theoretical expectations about how things work and already existing scientific evidence.

We know that research begins with a problem or a felt need or difficulty. The purpose of research is to find a solution to the difficulty. It is desirable that the researcher should propose a set of suggested solutions or explanations of the  difficulty which the research proposes to solve. Such tentative solutions formulated as a proposition are called hypotheses. The suggested solutions formulated as hypotheses may or may not be the real solutions to the problem. Whether they are or not is the task of research to test and establish.

DEFINTITIONS

• Lundberg- A Hypothesis is a tentative generalisation, the validity of which remains to be tested. In its most elementary stages, the hypothesis may be any hunch, guess imaginative idea or Intuition whatsoever which becomes the basis of action or Investigation.
• Bogardus- A Hypothesis is a proposition to be tested.
• Goode and Hatt- It is a proposition which can be put to test to determinants validity.
• P. V. Yaung- The idea of ​a temporary but central importance that becomes the basis of useful research is called a working hypothesis.

TYPES OF HYPOTHESIS

i)  Explanatory Hypothesis : The purpose of this hypothesis is to explain a certain fact. All hypotheses are in a way explanatory for a hypothesis is advanced only when we try to explain the observed fact. A large number of hypotheses are advanced to explain the individual facts in life. A theft, a murder, an accident are examples.

ii) Descriptive Hypothesis:  Some times a researcher comes across a complex phenomenon. He/ she does not understand the relations among the observed facts. But how to account for these facts? The answer is a descriptive hypothesis. A hypothesis is descriptive when it is based upon the points of resemblance of some thing. It describes the cause and effect relationship of a phenomenon e.g., the current unemployment rate of a state exceeds 25% of the work force. Similarly, the consumers of local made products constitute asignificant market segment.

iii) Analogical Hypothesis : When we formulate a hypothesis on the basis of similarities (analogy), it is called an analogical hypothesis e.g., families with higher earnings invest more surplus income on long term investments.

iv) Working hypothesis : Some times certain facts cannot be explained adequately by existing hypotheses, and no new hypothesis comes up. Thus, the investigation is held up. In this situation, a researcher formulates a hypothesis which enables to continue investigation. Such a hypothesis, though inadequate and formulated for the purpose of further investigation only, is called a working hypothesis. It is simply accepted as a starting point in the process of investigation.

v) Null Hypothesis:  It is an important concept that is used widely in the sampling theory. It forms the basis of many tests of significance. Under this type, the hypothesis is stated negatively. It is null because it may be nullified, if the evidence of a random sample is unfavourable to the hypothesis. It is a hypothesis being tested (H0). If the calculated value of the test is less than the permissible value, Null hypothesis is accepted, otherwise it is rejected. The rejection of a null hypothesis implies that the difference could not have arisen due to chance or sampling fluctuations.

USES OF HYPOTHESIS

i) It is a starting point for many a research work. ii) It helps in deciding the direction in which to proceed. iii) It helps in selecting and collecting pertinent facts. iv) It is an aid to explanation. v) It helps in drawing specific conclusions. vi) It helps in testing theories. vii) It works as a basis for future knowledge.

ROLE  OF HYPOTHESIS

In any scientific investigation, the role of hypothesis is indispensable as it always guides and gives direction to scientific research. Research remains unfocused without a hypothesis. Without it, the scientist is not in position to decide as to what to observe and how to observe. He may at best beat around the bush. In the words of Northrop, “The function of hypothesis is to direct our search for order among facts, the suggestions formulated in any hypothesis may be solution to the problem, whether they are, is the task of the enquiry”.

First ,  it is an operating tool of theory. It can be deduced from other hypotheses and theories. If it is correctly drawn and scientifically formulated, it enables the researcher to proceed on correct line of study. Due to this progress, the investigator becomes capable of drawing proper conclusions. In the words of Goode and Hatt, “without hypothesis the research is unfocussed, a random empirical wandering. The results cannot be studied as facts with clear meaning. Hypothesis is a necessary link between theory and investigation which leads to discovery and addition to knowledge.

Secondly,  the hypothesis acts as a pointer to enquiry. Scientific research has to proceed in certain definite lines and through hypothesis the researcher becomes capable of knowing specifically what he has to find out by determining the direction provided by the hypothesis. Hypotheses acts like a pole star or a compass to a sailor with the help of which he is able to head in the proper direction.

Thirdly , the hypothesis enables us to select relevant and pertinent facts and makes our task easier. Once, the direction and points are identified, the researcher is in a position to eliminate the irrelevant facts and concentrate only on the relevant facts. Highlighting the role of hypothesis in providing pertinent facts, P.V. Young has stated, “The use of hypothesis prevents a blind research and indiscriminate gathering of masses of data which may later prove irrelevant to the problem under study”. For example, if the researcher is interested in examining the relationship between broken home and juvenile delinquency, he can easily proceed in the proper direction and collect pertinent information succeeded only when he has succeed in formulating a useful hypothesis.

Fourthly , the hypothesis provides guidance by way of providing the direction, pointing to enquiry, enabling to select pertinent facts and helping to draw specific conclusions. It saves the researcher from the botheration of ‘trial and error’ which causes loss of money, energy and time.

Finally,  the hypothesis plays a significant role in facilitating advancement of knowledge beyond one’s value and opinions. In real terms, the science is incomplete without hypotheses.

STAGES OF HYPOTHESIS TESTING

• EXPERIMENTATION   : Research study focuses its study which is manageable and approachable to it and where it can test its hypothesis. The study gradually becomes more focused on its variables and influences on variables so that hypothesis may be tested. In this process, hypothesis can be disproved.
• REHEARSAL TESTING :   The researcher should conduct a pre testing or rehearsal before going for field work or data collection.
• FIELD RESEARCH :  To test and investigate hypothesis, field work with predetermined research methodology tools is conducted in which interviews, observations with stakeholders, questionnaires, surveys etc are used to follow. The documentation study may also happens at this stage.
• PRIMARY & SECONDARY DATA/INFORMATION ANALYSIS :  The primary or secondary data and information’s available prior to hypothesis testing may be used to ascertain validity of hypothesis itself.

Formulating a hypothesis can take place at the very beginning of a research project, or after a bit of research has already been done. Sometimes a researcher knows right from the start which variables she is interested in studying, and she may already have a hunch about their relationships. Other times, a researcher may have an interest in ​a particular topic, trend, or phenomenon, but he may not know enough about it to identify variables or formulate a hypothesis. Whenever a hypothesis is formulated, the most important thing is to be precise about what one’s variables are, what the nature of the relationship between them might be, and how one can go about conducting a study of them.

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Dictionary of the Social Sciences  

Edited by: craig calhoun.

Oxford's unprecedented Dictionary of the Social Sciences is designed to break down the barriers between social science disciplines, as well as to make social scientific language comprehensible to general readers. Collecting anthropology, sociology, political science, economics, human geography, cultural studies, and Marxism in one volume, the Dictionary presents concise, clearly written definitions of more than 1,500 important terms. Entries are true definitions, not extended essays or summaries. Ranging from 50 to 500 words, they succinctly define terms within each specific discipline and acquaint readers with the intellectual issues at stake when the terms are used. The entries draw on classic and contemporary scholarship, and include basic terms, concepts, theories, schools of thought, methodologies, techniques, topics, issues, and controversies. In addition to terminology, the Dictionary includes nearly 275 biographies of major figures—from Franz Boas to John Maynard Keynes to Max Weber, whose work has had a profound impact on the various fields.

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Craig Calhoun is president of the Social Science Research Council in New York and a professor of sociology and history at New York University.

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