hypothesis is the first step in social research

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The Craft of Writing a Strong Hypothesis

Table of Contents

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

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

What is a Hypothesis?

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

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

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

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

Different Types of Hypotheses‌

Types of hypotheses

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

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

1. Null hypothesis

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

2. Alternative hypothesis

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

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

3. Simple hypothesis

A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, “Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking.

4. Complex hypothesis

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

5. Associative and casual hypothesis

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

6. Empirical hypothesis

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

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

7. Statistical hypothesis

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

Characteristics of a Good Hypothesis

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

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

Separating a Hypothesis from a Prediction

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

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

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

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

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

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

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

2. Carry out a recce

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

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

3. Create a 3-dimensional hypothesis

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

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

4. Write the first draft

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

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

5. Proof your hypothesis

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

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

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

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

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

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

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

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

2. What is an example of hypothesis?

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

3. What is an example of null hypothesis?

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

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

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

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

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

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

7. Difference between research question and research hypothesis?

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

8. What is plural for hypothesis?

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

9. What is the red queen hypothesis?

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

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

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

11. When to reject null hypothesis?

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

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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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Chapter 2. Sociological Research

Learning objectives.

2.1. Approaches to Sociological Research

• Define and describe the scientific method
• Explain how the scientific method is used in sociological research
• Understand the difference between positivist and interpretive approaches to the scientific method in sociology
• Define what reliability and validity mean in a research study

2.2. Research Methods

• Differentiate between four kinds of research methods: surveys, experiments, field research, and secondary data and textual analysis
• Understand why different topics are better suited to different research approaches

2.3. Ethical Concerns

• Understand why ethical standards exist
• Demonstrate awareness of the Canadian Sociological Association’s Code of Ethics
• Define value neutrality
• Outline some of the issues of value neutrality in sociology

Introduction to Sociological Research

In the university cafeteria, you set your lunch tray down at a table, grab a chair, join a group of your classmates, and hear the start of two discussions. One person says, “It’s weird how Justin Bieber has 48 million followers on Twitter.” Another says, “Disney World is packed year round.” Those two seemingly benign statements are claims, or opinions, based on everyday observation of human behaviour. Perhaps the speakers had firsthand experience, talked to experts, conducted online research, or saw news segments on TV. In response, two conversations erupt. “I don’t see why anyone would want to go to Disney World and stand in those long lines.” “Are you kidding?! Going to Disney World is one of my favourite childhood memories.” “It’s the opposite for me with Justin Bieber. Seeing people camp out outside his hotel just to get a glimpse of him; it doesn’t make sense.” “Well, you’re not a teenage girl.” “Going to a theme park is way different than trying to see a teenage heart throb.” “But both are things people do for the same reason: they’re looking for a good time.” “If you call getting crushed by a crowd of strangers fun.”

As your classmates at the lunch table discuss what they know or believe, the two topics converge. The conversation becomes a debate. Someone compares Beliebers to Beatles fans. Someone else compares Disney World to a cruise. Students take sides, agreeing or disagreeing, as the conversation veers to topics such as crowd control, mob mentality, political protests, and group dynamics. If you contributed your expanding knowledge of sociological research to this conversation, you might make statements like these: “Justin Bieber’s fans long for an escape from the boredom of real teenage life. Beliebers join together claiming they want romance, except what they really want is a safe place to explore the confusion of teenage sexual feelings.” And this: “Mickey Mouse is a larger-than-life cartoon celebrity. Disney World is a place where families go to see what it would be like to live inside a cartoon.” You finish lunch, clear away your tray, and hurry to your next class. But you are thinking of Justin Bieber and Disney World. You have a new perspective on human behaviour and a list of questions that you want answered. That is the purpose of sociological research—to investigate and provide insights into how human societies function.

Although claims and opinions are part of sociology, sociologists use empirical evidence (that is, evidence corroborated by direct experience and/or observation) combined with the scientific method or an interpretive framework to deliver sound sociological research. They also rely on a theoretical foundation that provides an interpretive perspective through which they can make sense of scientific results. A truly scientific sociological study of the social situations up for discussion in the cafeteria would involve these prescribed steps: defining a specific question, gathering information and resources through observation, forming a hypothesis, testing the hypothesis in a reproducible manner, analyzing and drawing conclusions from the data, publishing the results, and anticipating further development when future researchers respond to and retest findings.

An appropriate starting point in this case might be the question “What do fans of Justin Bieber seek that drives them to follow his Twitter comments so faithfully?” As you begin to think like a sociologist, you may notice that you have tapped into your observation skills. You might assume that your observations and insights are valuable and accurate. But the results of casual observation are limited by the fact that there is no standardization—who is to say one person’s observation of an event is any more accurate than another’s? To mediate these concerns, sociologists rely on systematic research processes.

When sociologists apply the sociological perspective and begin to ask questions, no topic is off limits. Every aspect of human behaviour is a source of possible investigation. Sociologists question the world that humans have created and live in. They notice patterns of behaviour 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 workplace patterns that have transformed industries, family patterns that have enlightened parents, and education patterns that have aided structural changes in classrooms. The students at that university cafeteria discussion put forth a few loosely stated opinions.

If the human behaviours around those claims were tested systematically, a student could write a report and offer the findings to fellow sociologists and the world in general. The new perspective could help people understand themselves and their neighbours and help people make better decisions about their lives. It might seem strange to use scientific practices to study social trends, but, as we shall see, it’s extremely helpful to rely on systematic approaches that research methods provide. 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 a question is formed, a sociologist proceeds through an in-depth process to answer it. In deciding how to design that process, the researcher may adopt a positivist approach or an interpretive approach. 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, field research, and textual analysis. But humans and their social interactions are so diverse that they 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 behaviour. However, this is exactly why scientific models work for studying human behaviour. 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. This is the case for both positivist or quantitative methodologies and interpretive or qualitative methodologies. The scientific method involves developing and testing theories about the 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 prescribed steps that have been established over centuries of scholarship.

But just because sociological studies use scientific methods does not make the results less human. Sociological topics are not reduced to right or wrong facts. In this field, results of studies tend to provide people with access to knowledge they did not have before—knowledge of other cultures, knowledge of rituals and beliefs, knowledge of trends and attitudes. No matter what research approach is used, researchers want to maximize the study’s reliability (how likely research results are to be replicated if the study is reproduced). Reliability increases the likelihood that what is true of one person will be true of all people in a group. Researchers also strive for validity (how well the study measures what it was designed to measure).

Returning to the Disney World topic, reliability of a study would reflect how well the resulting experience represents the average experience of theme park-goers. Validity would ensure that the study’s design accurately examined what it was designed to study, so an exploration of adults’ interactions with costumed mascots should address that issue and not veer into other age groups’ interactions with them or into adult interactions with staff or other guests.

In general, sociologists tackle questions about the role of social characteristics in outcomes. 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 look between the cracks to discover obstacles to meeting basic human needs. They might study environmental influences and patterns of behaviour that lead to crime, substance abuse, divorce, poverty, unplanned pregnancies, or illness. And, because sociological studies are not all focused on problematic behaviours or challenging situations, researchers might study vacation trends, healthy eating habits, neighbourhood organizations, higher education patterns, games, parks, and exercise habits.

Sociologists can use the scientific method not only to collect but to interpret and analyze the data. They deliberately apply scientific logic and objectivity. They are interested in but not attached to the results. Their research work is independent of their own political or social beliefs. This does not mean researchers are not critical. Nor does it mean they 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 a particular study. 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 starts with these steps—1) ask a question, 2) research existing sources, 3) formulate a hypothesis—described below.

Ask a Question

The first step of the scientific method is to ask a question, describe a problem, and identify the specific area of interest. The topic should be narrow enough to study within a geography and timeframe. “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. That said, happiness and hygiene are worthy topics to study.

Sociologists do not rule out any topic, but would strive to frame these questions in better research terms. That is why sociologists are careful to define their terms. 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?” When forming these basic research questions, sociologists develop an operational definition ; that is, they define the concept in terms of the physical or concrete steps it takes to objectively measure it. The concept is translated into an observable variable , a measure that has different values. The operational definition identifies an observable condition of the concept.

By operationalizing a variable of the concept, all researchers can collect data in a systematic or replicable manner. The operational definition must be valid in the sense that it is an appropriate and meaningful measure of the concept being studied. It must also be reliable, meaning that results will be close to uniform when tested on more than one person. For example, “good drivers” might be defined in many ways: those who use their turn signals, those who don’t speed, or those who courteously allow others to merge. But these driving behaviours could be interpreted differently by different researchers and could be difficult to measure. Alternatively, “a driver who has never received a traffic violation” is a specific description that will lead researchers to obtain the same information, so it is an effective operational definition.

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 and a thorough online search will uncover existing research about the topic of study. This step helps researchers gain a broad understanding of work previously conducted on the topic at hand and enables them to position their own research to build on prior knowledge. It allows them to sharpen the focus of their research question and avoid duplicating previous research. 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 build on previously published material (as long as it enhances a unique viewpoint), it must be referenced properly and never plagiarized. To study hygiene and its value in a particular society, a researcher might sort through existing research and unearth studies about childrearing, vanity, obsessive-compulsive behaviours, and cultural attitudes toward beauty. It’s important to sift through this information and determine what is relevant. Using existing sources educates a researcher and helps refine and improve a study’s design.

Formulate a Hypothesis

A hypothesis is an assumption about how two or more variables are related; it makes a conjectural statement about the relationship between those variables. It is an “educated guess” because it is not random but based on theory, observations, patterns of experience, or the existing literature. The hypothesis formulates this guess in the form of a testable proposition. However, how the hypothesis is handled differs between the positivist and interpretive approaches. Positivist methodologies are often referred to as hypothetico-deductive methodologies . A hypothesis is derived from a theoretical proposition. On the basis of the hypothesis a prediction or generalization is logically deduced. In positivist sociology, the hypothesis predicts how one form of human behaviour influences another.

Successful prediction will determine the adequacy of the hypothesis and thereby test the theoretical proposition. Typically positivist approaches operationalize variables as quantitative data ; that is, by translating a social phenomenon like “health” into a quantifiable or numerically measurable variable like “number of visits to the hospital.” This permits sociologists to formulate their predictions using mathematical language like regression formulas, to present research findings in graphs and tables, and to perform mathematical or statistical techniques to demonstrate the validity of relationships.

Variables are examined to see if there is a correlation between them. When a change in one variable coincides with a change in another variable there is a correlation. This does not necessarily indicate that changes in one variable causes a change in another variable, however, just that they are associated. A key distinction here is between independent and dependent variables. In research, independent variables are the cause of the change. The dependent variable is the effect , or thing that is changed. For example, in a basic study, the researcher would establish one form of human behaviour 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)? For it to become possible to speak about causation, three criteria must be satisfied:

• There must be a relationship or correlation between the independent and dependent variables.
• The independent variable must be prior to the dependent variable.
• There must be no other intervening variable responsible for the causal relationship.

 Table 2.1. Examples of Dependent and Independent Variables Typically, the independent variable causes the dependent variable to change in some way.

At this point, a researcher’s operational definitions help measure the variables. In a study asking how tutoring improves grades, for instance, one researcher might define “good” grades as a C or better, while another uses a B+ as a starting point for “good.” Another operational definition might describe “tutoring” as “one-on-one assistance by an expert in the field, hired by an educational institution.” Those definitions set limits and establish cut-off points, ensuring consistency and replicability in a study. As the chart shows, an independent variable is the one that causes a dependent variable to change. For example, a researcher might hypothesize that teaching children proper hygiene (the independent variable) will boost their sense of self-esteem (the dependent variable). Or rephrased, a child’s sense of self-esteem depends, in part, on the quality and availability of hygienic resources.

Of course, this hypothesis can also work the other way around. Perhaps a sociologist believes that increasing a child’s sense of self-esteem (the independent variable) will automatically 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 two topics, or variables, is not enough: Their prospective relationship must be part of the hypothesis. Just because a sociologist forms an educated prediction of a study’s outcome doesn’t mean data contradicting the hypothesis are not welcome. 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 a rewarding career. While it has become at least a cultural assumption 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 will vary.

While many sociologists rely on the positivist hypothetico-deductive method in their research, others operate from an interpretive approach . While systematic, this approach does not follow the hypothesis-testing model that seeks to make generalizable predictions from quantitative variables. Instead, an interpretive framework seeks to understand social worlds from the point of view of participants, leading to in-depth knowledge. It focuses on qualitative data, or the meanings that guide people’s behaviour. Rather than relying on quantitative instruments like questionnaires or experiments, which can be artificial, the interpretive approach attempts to find ways to get closer to the informants’ lived experience and perceptions. Interpretive research is generally more descriptive or narrative in its findings. It can begin from a deductive approach, by deriving a hypothesis from theory and then seeking to confirm it through methodologies like in-depth interviews.

However, it is ideally suited to an inductive approach in which the hypothesis emerges only after a substantial period of direct observation or interaction with subjects. This type of approach is exploratory in that the researcher also learns as he or she proceeds, sometimes adjusting the research methods or processes midway to respond to new insights and findings as they evolve. Once the preliminary work is done, it’s time for the next research steps: designing and conducting a study, and drawing conclusions. These research methods are discussed below.

Sociologists examine the world, see a problem or interesting pattern, and set out to study it. They use research methods to design a study—perhaps a positivist, quantitative method for conducting research and obtaining data, or perhaps an ethnographic study utilizing an interpretive framework. Planning the research design is a key step in any sociological study. When entering a particular social environment, a researcher must be careful. There are times to remain anonymous and times to be overt. There are times to conduct interviews and times to simply observe. Some participants need to be thoroughly informed; others should not know they are being observed. A researcher would not stroll into a crime-ridden neighbourhood at midnight, calling out, “Any gang members around?” And if a researcher walked into a coffee shop and told the employees they would be observed as part of a study on work efficiency, the self-conscious, intimidated baristas might not behave naturally.

In the 1920s, leaders of a Chicago factory called Hawthorne Works commissioned a study to determine whether or not changing certain aspects of working conditions could increase or decrease worker productivity. Sociologists were surprised when the productivity of a test group increased when the lighting of their workspace was improved. They were even more surprised when productivity improved when the lighting of the workspace was dimmed. In fact almost every change of independent variable—lighting, breaks, work hours—resulted in an improvement of productivity. But when the study was over, productivity dropped again.

Why did this happen? In 1953, Henry A. Landsberger analyzed the study results to answer this question. He realized that employees’ productivity increased because sociologists were paying attention to them. The sociologists’ presence influenced the study results. Worker behaviours were altered not by the lighting but by the study itself. From this, sociologists learned the importance of carefully planning their roles as part of their research design (Franke and Kaul 1978). Landsberger called the workers’ response the Hawthorne effect —people changing their behaviour because they know they are being watched as part of a study.

The Hawthorne effect is unavoidable in some research. In many cases, sociologists have to make the purpose of the study known for ethical reasons. Subjects must be aware that they are being observed, and a certain amount of artificiality may result (Sonnenfeld 1985). Making sociologists’ presence invisible is not always realistic for other reasons. That option is not available to a researcher studying prison behaviours, early education, or the Ku Klux Klan. Researchers cannot just stroll into prisons, kindergarten classrooms, or Ku Klux Klan meetings and unobtrusively observe behaviours. In situations like these, other methods are needed. All studies shape the research design, while research design simultaneously shapes the study. Researchers choose methods that best suit their study topic and that fit with their overall goal for the research.

In planning a study’s design, sociologists generally choose from four widely used methods of social investigation: survey, experiment, field research, and textual or secondary data analysis (or use of existing sources). Every research method comes with plusses and minuses, and the topic of study strongly influences which method or methods are put to use.

As a research method, a survey collects data from subjects who respond to a series of questions about behaviours and opinions, often in the form of a questionnaire. The survey is one of the most widely used positivist research methods. The standard survey format allows individuals a level of anonymity in which they can express personal ideas.

At some point or another, everyone responds to some type of survey. The Statistics Canada census is an excellent example of a large-scale survey intended to gather sociological data. Customers also fill out questionnaires at stores or promotional events, responding to questions such as “How did you hear about the event?” and “Were the staff helpful?” You’ve probably picked up the phone and heard a caller ask you to participate in a political poll or similar type of survey: “Do you eat hot dogs? If yes, how many per month?” Not all surveys would be considered sociological research. Marketing polls help companies refine marketing goals and strategies; they are generally not conducted as part of a scientific study, meaning they are not designed to test a hypothesis or to contribute knowledge to the field of sociology. The results are not published in a refereed scholarly journal, where design, methodology, results, and analyses are vetted.

Often, polls on TV do not reflect a general population, but are merely answers from a specific show’s audience. Polls conducted by programs such as American Idol or Canadian Idol represent the opinions of fans but are not particularly scientific. A good contrast to these are the BBM Ratings, which determine the popularity of radio and television programming in Canada through scientific market research. Sociologists conduct surveys under controlled conditions for specific purposes. Surveys gather different types of information from people. While surveys are not great at capturing the ways people really behave in social situations, they are a great method for discovering how people feel and think—or at least how they say they feel and think. Surveys can track attitudes and opinions, political preferences, reported individual behaviours (such as sleeping, driving, or texting habits), or factual information such as employment status, income, and education levels. A survey targets a specific population , people who are the focus of a study, such as university athletes, international students, or teenagers living with type 1 (juvenile-onset) diabetes.

Most researchers choose to survey a small sector of the population, or a sample : that is, a manageable number of subjects who represent a larger population. The success of a study depends on how well a population is represented by the sample. In a random sample , every person in a population has the same chance of being chosen for the study. According to the laws of probability, random samples represent the population as a whole. For instance, an Ipsos Reid poll, if conducted as a nationwide random sampling, should be able to provide an accurate estimate of public opinion whether it contacts 2,000 or 10,000 people. However the validity of surveys can be threatened when part of the population is inadvertently excluded from the sample (e.g., telephone surveys that rely on land lines exclude people that use only cell phones) or when there is a low response rate. After selecting subjects, the researcher develops a specific plan to ask questions and record responses.

It is important to inform subjects of the nature and purpose of the study upfront. If they agree to participate, researchers thank subjects and offer them a chance to see the results of the study if they are interested. The researcher presents the subjects with an instrument (a means of gathering the information). A common instrument is a structured questionnaire, in which subjects answer a series of set questions. For some topics, the researcher might ask yes-or-no or multiple-choice questions, allowing subjects to choose possible responses to each question.

This kind of quantitative data —research collected in numerical form that can be counted—is easy to tabulate. Just count up the number of “yes” and “no” answers or tabulate the scales of “strongly agree,” “agree,” disagree,” etc. responses and chart them into percentages. This is also their chief drawback however: their artificiality. In real life, there are rarely any unambiguously yes-or-no answers. Questionnaires can also ask more complex questions with more complex answers—beyond “yes,” “no,” “agree,” “strongly agree,” or an option next to a checkbox. In those cases, the answers are subjective, varying from person to person. How do you plan to use your university education? Why do you follow Justin Bieber around the country and attend every concert? Those types of questions require short essay responses, and participants willing to take the time to write those answers will convey personal information about religious beliefs, political views, and morals.

Some topics that reflect internal thought are impossible to observe directly and are difficult to discuss honestly in a public forum. People are more likely to share honest answers if they can respond to questions anonymously. This type of information is qualitative data —results that are subjective and often based on what is seen in a natural setting. Qualitative information is harder to organize and tabulate. The researcher will end up with a wide range of responses, some of which may be surprising. The benefit of written opinions, though, is the wealth of material that they provide.

An interview is a one-on-one conversation between the researcher and the subject, and is a way of conducting surveys on a topic. Interviews are similar to the short answer questions on surveys in that the researcher asks subjects a series of questions. However, participants are free to respond as they wish, without being limited by predetermined choices. In the back-and-forth conversation of an interview, a researcher can ask for clarification, spend more time on a subtopic, or ask additional questions. In an interview, a subject will ideally feel free to open up and answer questions that are often complex. There are no right or wrong answers. The subject might not even know how to answer the questions honestly. Questions such as “How did society’s view of alcohol consumption influence your decision whether or not to take your first sip of alcohol?” or “Did you feel that the divorce of your parents would put a social stigma on your family?” involve so many factors that the answers are difficult to categorize. A researcher needs to avoid steering or prompting the subject to respond in a specific way; otherwise, the results will prove to be unreliable. And, obviously, a sociological interview is not an interrogation. The researcher will benefit from gaining a subject’s trust, from empathizing or commiserating with a subject, and from listening without judgment.

Experiments

You’ve probably tested personal social theories. “If I study at night and review in the morning, I’ll improve my retention skills.” Or, “If I stop drinking soda, I’ll feel better.” Cause and effect. If this, then that. When you test the theory, your results either prove or disprove your hypothesis. One way researchers test social theories is by conducting an experiment , meaning they investigate relationships to test a hypothesis—a scientific approach. There are two main types of experiments: lab-based experiments and natural or field experiments.

In a lab setting, the research can be controlled so that perhaps more data can be recorded in a certain amount of time. In a natural or field-based experiment, the generation of data cannot be controlled but the information might be considered more accurate since it was collected without interference or intervention by the researcher. As a research method, either type of sociological experiment is useful for testing if-then statements: if a particular thing happens, then another particular thing will result.

To set up a lab-based experiment, sociologists create artificial situations that allow them to manipulate variables. Classically, the sociologist selects a set of people with similar characteristics, such as age, class, race, or education. Those people are divided into two groups. One is the experimental group and the other is the control group . The experimental group is exposed to the independent variable(s) and the control group is not. This is similar to pharmaceutical drug trials in which the experimental group is given the test drug and the control group is given a placebo or sugar pill. To test the benefits of tutoring, for example, the sociologist might expose the experimental group of students to tutoring while the control group does not receive tutoring. Then both groups would be tested for differences in performance to see if tutoring had an effect on the experimental group of students. As you can imagine, in a case like this, the researcher would not want to jeopardize the accomplishments of either group of students, so the setting would be somewhat artificial. The test would not be for a grade reflected on their permanent record, for example.

The Stanford Prison Experiment is perhaps one of the most famous sociological experiments ever conducted. In 1971, 24 healthy, middle-class male university students were selected to take part in a simulated jail environment to examine the effects of social setting and social roles on individual psychology and behaviour. They were randomly divided into 12 guards and 12 prisoners. The prisoner subjects were arrested at home and transported blindfolded to the simulated prison in the basement of the psychology building on the campus of Stanford University. Within a day of arriving the prisoners and the guards began to display signs of trauma and sadism respectively. After some prisoners revolted by blockading themselves in their cells, the guards resorted to using increasingly humiliating and degrading tactics to control the prisoners through psychological manipulation. The experiment had to be abandoned after only six days because the abuse had grown out of hand (Haney, Banks, and Zimbardo 1973). While the insights into the social dynamics of authoritarianism it generated were fascinating, the Stanford Prison Experiment also serves as an example of the ethical issues that emerge when experimenting on human subjects.

Making Connections: Sociological Research

An experiment in action: mincome.

A real-life example will help illustrate the experimental process in sociology. Between 1974 and 1979 an experiment was conducted in the small town of Dauphin, Manitoba (the “garden capital of Manitoba”). Each family received a modest monthly guaranteed income—a “mincome”—equivalent to a maximum of 60 percent of the “low-income cut-off figure” (a Statistics Canada measure of poverty, which varies with family size). The income was 50 cents per dollar less for families who had incomes from other sources. Families earning over a certain income level did not receive mincome. Families that were already collecting welfare or unemployment insurance were also excluded. The test families in Dauphin were compared with control groups in other rural Manitoba communities on a range of indicators such as number of hours worked per week, school performance, high school dropout rates, and hospital visits (Forget 2011). A guaranteed annual income was seen at the time as a less costly, less bureaucratic public alternative for addressing poverty than the existing employment insurance and welfare programs. Today it is an active proposal being considered in Switzerland (Lowrey 2013).

Intuitively, it seems logical that lack of income is the cause of poverty and poverty-related issues. One of the main concerns, however, was whether a guaranteed income would create a disincentive to work. The concept appears to challenge the principles of the Protestant work ethic (see the discussion of Max Weber in Chapter 1). The study did find very small decreases in hours worked per week: about 1 percent for men, 3 percent for wives, and 5 percent for unmarried women. Forget (2011) argues this was because the income provided an opportunity for people to spend more time with family and school, especially for young mothers and teenage girls. There were also significant social benefits from the experiment, including better test scores in school, lower high school dropout rates, fewer visits to hospital, fewer accidents and injuries, and fewer mental health issues.

Ironically, due to lack of guaranteed funding (and lack of political interest by the late 1970s), the data and results of the study were not analyzed or published until 2011. The data were archived and sat gathering dust in boxes. The mincome experiment demonstrated the benefits that even a modest guaranteed annual income supplement could have on health and social outcomes in communities. People seem to live healthier lives and get a better education when they do not need to worry about poverty. In her summary of the research, Forget notes that the impact of the income supplement was surprisingly large given that at any one time only about a third of the families were receiving the income and, for some families, the income amount would have been very small. The income benefit was largest for low-income working families but the research showed that the entire community profited. The improvement in overall health outcomes for the community suggest that a guaranteed income would also result in savings for the public health system.

Field Research

The work of sociology rarely happens in limited, confined spaces. Sociologists seldom study subjects in their own offices or laboratories. Rather, sociologists go out into the world. They meet subjects where they live, work, and play. Field research refers to gathering primary data from a natural environment without doing a lab experiment or a survey. It is a research method suited to an interpretive approach rather than to positivist approaches. To conduct field research, the sociologist must be willing to step into new environments and observe, participate, or experience those worlds. In fieldwork, the sociologists, rather than the subjects, are the ones out of their element. The researcher interacts with or observes a person or people, gathering data along the way. The key point in field research is that it takes place in the subject’s natural environment, whether it’s a coffee shop or tribal village, a homeless shelter or a care home, a hospital, airport, mall, or beach resort.

While field research often begins in a specific setting , the study’s purpose is to observe specific behaviours in that setting. Fieldwork is optimal for observing how people behave. It is less useful, however, for developing causal explanations of why they behave that way. From the small size of the groups studied in fieldwork, it is difficult to make predictions or generalizations to a larger population. Similarly, there are difficulties in gaining an objective distance from research subjects. It is difficult to know whether another researcher would see the same things or record the same data. We will look at three types of field research: participant observation, ethnography, and the case study.

Making Connections: Sociology in the Real World

When is sharing not such a good idea.

Choosing a research methodology depends on a number of factors, including the purpose of the research and the audience for whom the research is intended. If we consider the type of research that might go into producing a government policy document on the effectiveness of safe injection sites for reducing the public health risks of intravenous drug use, we would expect public administrators to want “hard” (i.e., quantitative) evidence of high reliability to help them make a policy decision. The most reliable data would come from an experimental or quasi-experimental research model in which a control group can be compared with an experimental group using quantitative measures.

This approach has been used by researchers studying InSite in Vancouver (Marshall et al. 2011; Wood et al. 2006). InSite is a supervised safe-injection site where heroin addicts and other intravenous drug users can go to inject drugs in a safe, clean environment. Clean needles are provided and health care professionals are on hand to intervene in the case of overdose or other medical emergency. It is a controversial program both because heroin use is against the law (the facility operates through a federal ministerial exemption) and because the heroin users are not obliged to quit using or seek therapy. To assess the effectiveness of the program, researchers compared the risky usage of drugs in populations before and after the opening of the facility and geographically near and distant to the facility. The results from the studies have shown that InSite has reduced both deaths from overdose and risky behaviours, such as the sharing of needles, without increasing the levels of crime associated with drug use and addiction.

On the other hand, if the research question is more exploratory (for example, trying to discern the reasons why individuals in the crack smoking subculture engage in the risky activity of sharing pipes), the more nuanced approach of fieldwork is more appropriate. The research would need to focus on the subcultural context, rituals, and meaning of sharing pipes, and why these phenomena override known health concerns. Graduate student Andrew Ivsins at the University of Victoria studied the practice of sharing pipes among 13 habitual users of crack cocaine in Victoria, B.C. (Ivsins 2010). He met crack smokers in their typical setting downtown and used an unstructured interview method to try to draw out the informal norms that lead to sharing pipes. One factor he discovered was the bond that formed between friends or intimate partners when they shared a pipe. He also discovered that there was an elaborate subcultural etiquette of pipe use that revolved around the benefit of getting the crack resin smokers left behind. Both of these motives tended to outweigh the recognized health risks of sharing pipes (such as hepatitis) in the decision making of the users. This type of research was valuable in illuminating the unknown subcultural norms of crack use that could still come into play in a harm reduction strategy such as distributing safe crack kits to addicts.

Participant Observation

In 2000, a comic writer named Rodney Rothman wanted an insider’s view of white-collar work. He slipped into the sterile, high-rise offices of a New York “dot com” agency. Every day for two weeks, he pretended to work there. His main purpose was simply to see if anyone would notice him or challenge his presence. No one did. The receptionist greeted him. The employees smiled and said good morning. Rothman was accepted as part of the team. He even went so far as to claim a desk, inform the receptionist of his whereabouts, and attend a meeting. He published an article about his experience in The New Yorker called “My Fake Job” (2000). Later, he was discredited for allegedly fabricating some details of the story and The New Yorker issued an apology. However, Rothman’s entertaining article still offered fascinating descriptions of the inside workings of a “dot com” company and exemplified the lengths to which a sociologist will go to uncover material.

Rothman had conducted a form of study called participant observation , in which researchers join people and participate in a group’s routine activities for the purpose of observing them within that context. This method lets researchers study a naturally occurring social activity without imposing artificial or intrusive research devices, like fixed questionnaire questions, onto the situation. A researcher might go to great lengths to get a firsthand look into a trend, institution, or behaviour. Researchers temporarily put themselves into “native” roles and record their observations. A researcher might work as a waitress in a diner, or live as a homeless person for several weeks, or ride along with police officers as they patrol their regular beat. Often, these researchers try to blend in seamlessly with the population they study, and they may not disclose their true identity or purpose if they feel it would compromise the results of their research.

At the beginning of a field study, researchers might have a question: “What really goes on in the kitchen of the most popular diner on campus?” or “What is it like to be homeless?” Participant observation is a useful method if the researcher wants to explore a certain environment from the inside. Field researchers simply want to observe and learn. In such a setting, the researcher will be alert and open minded to whatever happens, recording all observations accurately. Soon, as patterns emerge, questions will become more specific, observations will lead to hypotheses, and hypotheses will guide the researcher in shaping data into results. In a study of small-town America conducted by sociological researchers John S. Lynd and Helen Merrell Lynd, the team altered their purpose as they gathered data. They initially planned to focus their study on the role of religion in American towns. As they gathered observations, they realized that the effect of industrialization and urbanization was the more relevant topic of this social group. The Lynds did not change their methods, but they revised their purpose. This shaped the structure of Middletown: A Study in Modern American Culture , their published results (Lynd and Lynd 1959).

The Lynds were upfront about their mission. The townspeople of Muncie, Indiana, knew why the researchers were in their midst. But some sociologists prefer not to alert people to their presence. The main advantage of covert participant observation is that it allows the researcher access to authentic, natural behaviours of a group’s members. The challenge, however, is gaining access to a setting without disrupting the pattern of others’ behaviour. Becoming an inside member of a group, organization, or subculture takes time and effort. Researchers must pretend to be something they are not. The process could involve role playing, making contacts, networking, or applying for a job. Once inside a group, some researchers spend months or even years pretending to be one of the people they are observing. However, as observers, they cannot get too involved. They must keep their purpose in mind and apply the sociological perspective. That way, they illuminate social patterns that are often unrecognized. Because information gathered during participant observation is mostly qualitative, rather than quantitative, the end results are often descriptive or interpretive. The researcher might present findings in an article or book, describing what he or she witnessed and experienced.

This type of research is what journalist Barbara Ehrenreich conducted for her book Nickel and Dimed . One day over lunch with her editor, as the story goes, Ehrenreich mentioned an idea. How can people exist on minimum-wage work? How do low-income workers get by? she wondered. Someone should do a study. To her surprise, her editor responded, Why don’t you do it? That is how Ehrenreich found herself joining the ranks of the low-wage service sector. For several months, she left her comfortable home and lived and worked among people who lacked, for the most part, higher education and marketable job skills. Undercover, she applied for and worked minimum wage jobs as a waitress, a cleaning woman, a nursing home aide, and a retail chain employee. During her participant observation, she used only her income from those jobs to pay for food, clothing, transportation, and shelter. She discovered the obvious: that it’s almost impossible to get by on minimum wage work. She also experienced and observed attitudes many middle- and upper-class people never think about. She witnessed firsthand the treatment of service work employees. She saw the extreme measures people take to make ends meet and to survive. She described fellow employees who held two or three jobs, worked seven days a week, lived in cars, could not pay to treat chronic health conditions, got randomly fired, submitted to drug tests, and moved in and out of homeless shelters. She brought aspects of that life to light, describing difficult working conditions and the poor treatment that low-wage workers suffer.

Ethnography

Ethnography is the extended observation of the social perspective and cultural values of an entire social setting. Researchers seek to immerse themselves in the life of a bounded group, by living and working among them. Often ethnography involves participant observation, but the focus is the systematic observation of an entire community.

The heart of an ethnographic study focuses on how subjects view their own social standing and how they understand themselves in relation to a community. An ethnographic study might observe, for example, a small Newfoundland fishing town, an Inuit community, a village in Thailand, a Buddhist monastery, a private boarding school, or Disney World. These places all have borders. People live, work, study, or vacation within those borders. People are there for a certain reason and therefore behave in certain ways and respect certain cultural norms. An ethnographer would commit to spending a determined amount of time studying every aspect of the chosen place, taking in as much as possible, and keeping careful notes on his or her observations.

A sociologist studying a tribe in the Amazon might learn the language, watch the way villagers go about their daily lives, ask individuals about the meaning of different aspects of activity, study the group’s cosmology and then write a paper about it. To observe a spiritual retreat centre, an ethnographer might sign up for a retreat and attend as a guest for an extended stay, observe and record how people experience spirituality in this setting, and collate the material into results.

The Feminist Perspective: Institutional Ethnography

Dorothy Smith elaborated on traditional ethnography to develop what she calls institutional ethnography (2005). In modern society the practices of everyday life in any particular local setting are often organized at a level that goes beyond what an ethnographer might observe directly. Everyday life is structured by “extralocal,” institutional forms; that is, by the practices of institutions that act upon people from a distance. It might be possible to conduct ethnographic research on the experience of domestic abuse by living in a women’s shelter and directly observing and interviewing victims to see how they form an understanding of their situation. However, to the degree that the women are seeking redress through the criminal justice system a crucial element of the situation would be missing. In order to activate a response from the police or the courts, a set of standard legal procedures must be followed, a “case file” must be opened, legally actionable evidence must be established, forms filled out, etc. All of this allows criminal justice agencies to organize and coordinate the response.

The urgent and immediate experience of the domestic abuse victims needs to be translated into a format that enables distant authorities to take action. Often this is a frustrating and mysterious process in which the immediate needs of individuals are neglected so that needs of institutional processes are met. Therefore to research the situation of domestic abuse victims, an ethnography needs to somehow operate at two levels: the close examination of the local experience of particular women and the simultaneous examination of the extralocal, institutional world through which their world is organized. In order to accomplish this, institutional ethnography focuses on the study of the way everyday life is coordinated through “textually mediated” practices: the use of written documents, standardized bureaucratic categories, and formalized relationships (Smith 1990).

Institutional paperwork translates the specific details of locally lived experience into a standardized format that enables institutions to apply the institution’s understandings, regulations, and operations in different local contexts. The study of these textual practices reveal otherwise inaccessible processes that formal organizations depend on: their formality, their organized character, and their ongoing methods of coordination, etc. An institutional ethnography often begins by following the paper trail that emerges when people interact with institutions: how does a person formulate a narrative about what has happened to him or her in a way that the institution will recognize? How is it translated into the abstract categories on a form or screen that enable an institutional response to be initiated? What is preserved in the translation to paperwork and what is lost? Where do the forms go next? What series of “processing interchanges” take place between different departments or agencies through the circulation of paperwork? How is the paperwork modified and made actionable through this process (e.g., an incident report, warrant request, motion for continuance)?

Smith’s insight is that the shift from the locally lived experience of individuals to the extralocal world of institutions is nothing short of a radical metaphysical shift in worldview. In institutional worlds, meanings are detached from directly lived processes and reconstituted in an organizational time, space, and consciousness that is fundamentally different from their original reference point. For example, the crisis that has led to a loss of employment becomes a set of anonymous criteria that determines one’s eligibility for Employment Insurance.

The unique life of a disabled child becomes a checklist that determines the content of an “individual education program” in the school system, which in turn determines whether funding will be provided for special aid assistants or therapeutic programs. Institutions put together a picture of what has occurred that is not at all the same as what was lived. The ubiquitous but obscure mechanism by which this is accomplished is textually mediated communication . The goal of institutional ethnography therefore is to making “documents or texts visible as constituents of social relations” (Smith 1990). Institutional ethnography is very useful as a critical research strategy. It is an analysis that gives grassroots organizations, or those excluded from the circles of institutional power, a detailed knowledge of how the administrative apparatuses actually work. This type of research enables more effective actions and strategies for change to be pursued.

The Case Study

Sometimes a researcher wants to study one specific person or event. A case study is an in-depth analysis of a single event, situation, or individual. To conduct a case study, a researcher examines existing sources like documents and archival records, conducts interviews, engages in direct observation, and even participant observation, if possible. Researchers might use this method to study a single case of, for example, a foster child, drug lord, cancer patient, criminal, or rape victim. However, a major criticism of the case study as a method is that a developed study of a single case, while offering depth on a topic, does not provide enough evidence to form a generalized conclusion. In other words, it is difficult to make universal claims based on just one person, since one person does not verify a pattern. This is why most sociologists do not use case studies as a primary research method.

However, case studies are useful when the single case is unique. In these instances, a single case study can add tremendous knowledge to a certain discipline. For example, a feral child, also called “wild child,” is one who grows up isolated from human beings. Feral children grow up without social contact and language, elements crucial to a “civilized” child’s development. These children mimic the behaviours and movements of animals, and often invent their own language. There are only about 100 cases of “feral children” in the world. As you may imagine, a feral child is a subject of great interest to researchers. Feral children provide unique information about child development because they have grown up outside of the parameters of “normal” child development. And since there are very few feral children, the case study is the most appropriate method for researchers to use in studying the subject. At age three, a Ukrainian girl named Oxana Malaya suffered severe parental neglect. She lived in a shed with dogs, eating raw meat and scraps. Five years later, a neighbour called authorities and reported seeing a girl who ran on all fours, barking. Officials brought Oxana into society, where she was cared for and taught some human behaviours, but she never became fully socialized. She has been designated as unable to support herself and now lives in a mental institution (Grice 2006). Case studies like this offer a way for sociologists to collect data that may not be collectable by any other method.

Secondary Data or Textual Analysis

While sociologists often engage in original research studies, they also contribute knowledge to the discipline through secondary data or textual analysis . Secondary data do not result from firsthand research collected from primary sources, but are drawn from the already-completed work of other researchers. Sociologists might study texts written by historians, economists, teachers, or early sociologists. They might search through periodicals, newspapers, or magazines from any period in history. Using available information not only saves time and money, but it can add depth to a study. Sociologists often interpret findings in a new way, a way that was not part of an author’s original purpose or intention. To study how women were encouraged to act and behave in the 1960s, for example, a researcher might watch movies, televisions shows, and situation comedies from that period. Or to research changes in behaviour and attitudes due to the emergence of television in the late 1950s and early 1960s, a sociologist would rely on new interpretations of secondary data. Decades from now, researchers will most likely conduct similar studies on the advent of mobile phones, the Internet, or Facebook.

One methodology that sociologists employ with secondary data is content analysis. Content analysis is a quantitative approach to textual research that selects an item of textual content (i.e., a variable) that can be reliably and consistently observed and coded, and surveys the prevalence of that item in a sample of textual output. For example, Gilens (1996) wanted to find out why survey research shows that the American public substantially exaggerates the percentage of African Americans among the poor. He examined whether media representations influence public perceptions and did a content analysis of photographs of poor people in American news magazines. He coded and then systematically recorded incidences of three variables: (1) Race: white, black, indeterminate; (2) Employed: working, not working; and (3) Age. Gilens discovered that not only were African Americans markedly overrepresented in news magazine photographs of poverty, but that the photos also tended to underrepresent “sympathetic” subgroups of the poor—the elderly and working poor—while overrepresenting less sympathetic groups—unemployed, working age adults. Gilens concluded that by providing a distorted representation of poverty, U.S. news magazines “reinforce negative stereotypes of blacks as mired in poverty and contribute to the belief that poverty is primarily a ‘black problem’” (1996).

Social scientists also learn by analyzing the research of a variety of agencies. Governmental departments and global groups, like Statistics Canada or the World Health Organization, publish studies with findings that are useful to sociologists. A public statistic that measures inequality of incomes might be useful for studying who benefited and who lost as a result of the 2008 recession; a demographic profile of different immigrant groups might be compared with data on unemployment to examine the reasons why immigration settlement programs are more effective for some communities than for others. One of the advantages of secondary data is that it is nonreactive (or unobtrusive) research, meaning that it does not include direct contact with subjects and will not alter or influence people’s behaviours. Unlike studies requiring direct contact with people, using previously published data does not require entering a population and the investment and risks inherent in that research process. Using available data does have its challenges. Public records are not always easy to access. A researcher needs to do some legwork to track them down and gain access to records. In some cases there is no way to verify the accuracy of existing data. It is easy, for example, to count how many drunk drivers are pulled over by the police. But how many are not? While it’s possible to discover the percentage of teenage students who drop out of high school, it might be more challenging to determine the number who return to school or get their GED later.

Another problem arises when data are unavailable in the exact form needed or do not include the precise angle the researcher seeks. For example, the salaries paid to professors at universities is often published. But the separate figures do not necessarily reveal how long it took each professor to reach the salary range, what their educational backgrounds are, or how long they have been teaching. In his research, sociologist Richard Sennett uses secondary data to shed light on current trends. In The Craftsman (2008), he studied the human desire to perform quality work, from carpentry to computer programming. He studied the line between craftsmanship and skilled manual labour. He also studied changes in attitudes toward craftsmanship that occurred not only during and after the Industrial Revolution, but also in ancient times. Obviously, he could not have firsthand knowledge of periods of ancient history; he had to rely on secondary data for part of his study. When conducting secondary data or textual analysis, it is important to consider the date of publication of an existing source and to take into account attitudes and common cultural ideals that may have influenced the research. For example, Robert S. Lynd and Helen Merrell Lynd gathered research for their book Middletown: A Study in Modern American Culture in the 1920s. Attitudes and cultural norms were vastly different then than they are now. Beliefs about gender roles, race, education, and work have changed significantly since then. At the time, the study’s purpose was to reveal the truth about small American communities. Today, it is an illustration of 1920s attitudes and values.

Sociologists conduct studies to shed light on human behaviours. Knowledge is a powerful tool that can be used toward positive change. And while a sociologist’s goal is often simply to uncover knowledge rather than to spur action, many people use sociological studies to help improve people’s lives. In that sense, conducting a sociological study comes with a tremendous amount of responsibility. Like any researchers, sociologists must consider their ethical obligation to avoid harming subjects or groups while conducting their research. The Canadian Sociological Association, or CSA, is the major professional organization of sociologists in Canada. The CSA is a great resource for students of sociology as well.

The CSA maintains a code of ethics —formal guidelines for conducting sociological research—consisting of principles and ethical standards to be used in the discipline. It also describes procedures for filing, investigating, and resolving complaints of unethical conduct. These are in line with the Tri-Council Policy Statement on Ethical Conduct for Research Involving Humans (2010) , which applies to any research with human subjects funded by one of the three federal research agencies – the Canadian Institutes of Health Research (CIHR), the Natural Sciences and Engineering Research Council of Canada (NSERC), and the Social Sciences and Humanities Research Council of Canada (SSHRC).

Practising sociologists and sociology students have a lot to consider. Some of the guidelines state that researchers must try to be skillful and fair-minded in their work, especially as it relates to their human subjects. Researchers must obtain participants’ informed consent, and inform subjects of the responsibilities and risks of research before they agree to participate. During a study, sociologists must ensure the safety of participants and immediately stop work if a subject becomes potentially endangered on any level. Researchers are required to protect the privacy of research participants whenever possible. Even if pressured by authorities, such as police or courts, researchers are not ethically allowed to release confidential information. Researchers must make results available to other sociologists, must make public all sources of financial support, and must not accept funding from any organization that might cause a conflict of interest or seek to influence the research results for its own purposes. The CSA’s ethical considerations shape not only the study but also the publication of results.

Pioneer German sociologist Max Weber (1864–1920) identified another crucial ethical concern. Weber understood that personal values could distort the framework for disclosing study results. While he accepted that some aspects of research design might be influenced by personal values, he declared it was entirely inappropriate to allow personal values to shape the interpretation of the responses. Sociologists, he stated, must establish value neutrality , a practice of remaining impartial, without bias or judgment, during the course of a study and in publishing results (1949). Sociologists are obligated to disclose research findings without omitting or distorting significant data. Value neutrality does not mean having no opinions. It means striving to overcome personal biases, particularly subconscious biases, when analyzing data. It means avoiding skewing data in order to match a predetermined outcome that aligns with a particular agenda, such as a political or moral point of view. Investigators are ethically obligated to report results, even when they contradict personal views, predicted outcomes, or widely accepted beliefs. Is value neutrality possible?

Many sociologists believe it is impossible to set aside personal values and retain complete objectivity. Individuals inevitably see the world from a partial perspective. Their interests are central to the types of topics they choose, the types of questions they ask, the way they frame their research and the research methodologies they select to pursue it. Moreover, facts, however objective, do not exist in a void. As we noted in Chapter 1, Jürgen Habermas (1972) argues that sociological research has built-in interests quite apart from the personal biases of individual researchers. Positivist sociology has an interest in pursuing types of knowledge that are useful for controlling and administering social life. Interpretive sociology has an interest in pursuing types of knowledge that promote greater mutual understanding and the possibility of consensus among members of society. Critical sociology has an interest in types of knowledge that enable emancipation from power relations and forms of domination in society. In Habermas’ view, sociological knowledge is not disinterested knowledge. This does not discredit the results of sociological research but allows readers to take into account the perspective of the research when judging the validity and applicability of its outcomes.

case study in-depth analysis of a single event, situation, or individual

code of ethics a set of guidelines that the Canadian Sociological Association has established to foster ethical research and professionally responsible scholarship in sociology

content analysis a quantitative approach to textual research that selects an item of textual content that can be reliably and consistently observed and coded, and surveys the prevalence of that item in a sample of textual output

control group an experimental group that is not exposed to the independent variable

correlation when a change in one variable coincides with a change in another variable, but does not necessarily indicate causation

d ependent variable variable changed by another variable

empirical evidence evidence corroborated by direct experience and/or observation

ethnography observing a complete social setting and all that it entails

experiment the testing of a hypothesis under controlled conditions

field research gathering data from a natural environment without doing a lab experiment or a survey

Hawthorne effect when study subjects behave in a certain manner due to their awareness of being observed by a researcher

hypothesis an educated guess with predicted outcomes about the relationship between two or more variables hypothetico-deductive methodologies methodologies based on deducing a prediction from a hypothesis and testing the  validity of the hypothesis by whether it correctly predicts observations

independent variable  variable that causes change in a dependent variable

inductive approach methodologies that derive a general statement from a series of empirical observations

institutional ethnography the study of the way everyday life is coordinated through institutional, textually mediated practices

interpretive approach a sociological research approach that seeks in-depth understanding of a topic or subject through observation or interaction

interview  a one-on-one conversation between a researcher and a subject

literature review a scholarly research step that entails identifying and studying all existing studies on a topic to create a basis for new research

nonreactive  unobtrusive research that does not include direct contact with subjects and will not alter or influence people’s behaviours

operational definitions specific explanations of abstract concepts that a researcher plans to study

participant observation immersion by a researcher in a group or social setting in order to make observations from an “insider” perspective

population a defined group serving as the subject of a study

positivist approach a research approach based on the natural science model of knowledge utilizing a hypothetico-deductive formulation of the research question and quantitative data

primary data data collected directly from firsthand experience

qualitative data  information based on interpretations of meaning

quantitative data information from research collected in numerical form that can be counted

random sample a study’s participants being randomly selected to serve as a representation of a larger population reliability a measure of a study’s consistency that considers how likely results are to be replicated if a study is reproduced research design a detailed, systematic method for conducting research and obtaining data

sample small, manageable number of subjects that represent the population

scientific method a systematic research method that involves asking a question, researching existing sources, forming a hypothesis, designing and conducting a study, and drawing conclusions

secondary data analysis using data collected by others but applying new interpretations

surveys data collections from subjects who respond to a series of questions about behaviours and opinions, often in the form of a questionnaire

textually mediated communication institutional forms of communication that rely on written documents, texts, and paperwork

validity the degree to which a sociological measure accurately reflects the topic of study

value neutrality a practice of remaining impartial, without bias or judgment during the course of a study and in publishing results

variable a characteristic or measure of a social phenomenon that can take different values

Section Summary

2.1. Approaches to Sociological Research Using the scientific method, a researcher conducts a study in five phases: asking a question, researching existing sources, formulating a hypothesis, conducting a study, and drawing conclusions. The scientific method is useful in that it provides a clear method of organizing a study. Some sociologists conduct scientific research through a positivist framework utilizing a hypothetico-deductive formulation of the research question. Other sociologists conduct scientific research by employing an interpretive framework that is often inductive in nature. Scientific sociological studies often observe relationships between variables. Researchers study how one variable changes another. Prior to conducting a study, researchers are careful to apply operational definitions to their terms and to establish dependent and independent variables.

2.2. Research Methods Sociological research is a fairly complex process. As you can see, a lot goes into even a simple research design. There are many steps and much to consider when collecting data on human behaviour, as well as in interpreting and analyzing data in order to form conclusive results. Sociologists use scientific methods for good reason. The scientific method provides a system of organization that helps researchers plan and conduct the study while ensuring that data and results are reliable, valid, and objective. The many methods available to researchers—including experiments, surveys, field studies, and secondary data analysis—all come with advantages and disadvantages. The strength of a study can depend on the choice and implementation of the appropriate method of gathering research. Depending on the topic, a study might use a single method or a combination of methods. It is important to plan a research design before undertaking a study. The information gathered may in itself be surprising, and the study design should provide a solid framework in which to analyze predicted and unpredicted data.

Table 2.2. Main Sociological Research Methods. Sociological research methods have advantages and disadvantages.

2.3. Ethical Concerns Sociologists and sociology students must take ethical responsibility for any study they conduct. They must first and foremost guarantee the safety of their participants. Whenever possible, they must ensure that participants have been fully informed before consenting to be part of a study. The CSA (Canadian Sociological Association) maintains ethical guidelines that sociologists must take into account as they conduct research. The guidelines address conducting studies, properly using existing sources, accepting funding, and publishing results. Sociologists must try to maintain value neutrality. They must gather and analyze data objectively, setting aside their personal preferences, beliefs, and opinions. They must report findings accurately, even if they contradict personal convictions.

Section Quiz

2.1. Approaches to Sociological Research 1. A measurement is considered ______­ if it actually measures what it is intended to measure, according to the topic of the study.

• sociological
• quantitative

2. Sociological studies test relationships in which change in one ______ causes change in another.

• test subject
• operational definition

3. In a study, a group of 10-year-old boys are fed doughnuts every morning for a week and then weighed to see how much weight they gained. Which factor is the dependent variable?

• the doughnuts
• the duration of a week
• the weight gained

4. Which statement provides the best operational definition of “childhood obesity”?

• children who eat unhealthy foods and spend too much time watching television and playing video games
• a distressing trend that can lead to health issues including type 2 diabetes and heart disease
• body weight at least 20 percent higher than a healthy weight for a child of that height
• the tendency of children today to weigh more than children of earlier generations

2.2. Research Methods 5. Which materials are considered secondary data?

• photos and letters given to you by another person
• books and articles written by other authors about their studies
• information that you have gathered and now have included in your results
• responses from participants whom you both surveyed and interviewed

6. What method did Andrew Ivsins use to study crack users in Victoria?

• field research
• content analysis

7. Why is choosing a random sample an effective way to select participants?

• Participants do not know they are part of a study
• The researcher has no control over who is in the study
• It is larger than an ordinary sample
• Everyone has the same chance of being part of the study

8. What research method did John S. Lynd and Helen Merrell Lynd mainly use in their Middletown study?

• secondary data
• participant observation

9. Which research approach is best suited to the positivist approach?

• questionnaire
• ethnography
• secondary data analysis

10. The main difference between ethnography and other types of participant observation is:

• ethnography isn’t based on hypothesis testing
• ethnography subjects are unaware they’re being studied
• ethnographic studies always involve minority ethnic groups
• there is no difference

11. Which best describes the results of a case study?

• it produces more reliable results than other methods because of its depth
• its results are not generally applicable
• it relies solely on secondary data analysis
• all of the above

12. Using secondary data is considered an unobtrusive or ________ research method.

• nonreactive
• nonparticipatory
• nonrestrictive
• nonconfrontive

2.3. Ethical Concerns 13. Which statement illustrates value neutrality?

• Obesity in children is obviously a result of parental neglect and, therefore, schools should take a greater role to prevent it.
• In 2003, states like Arkansas adopted laws requiring elementary schools to remove soft drink vending machines from schools.
• Merely restricting children’s access to junk food at school is not enough to prevent obesity.
• Physical activity and healthy eating are a fundamental part of a child’s education.

14. Which person or organization defined the concept of value neutrality?

• Institutional Review Board (IRB)
• Peter Rossi
• Canadian Sociological Association (CSA)

15. To study the effects of fast food on lifestyle, health, and culture, from which group would a researcher ethically be unable to accept funding?

• a fast-food restaurant
• a nonprofit health organization
• a private hospital
• a governmental agency like Health and Social Services

Short Answer

• Write down the first three steps of the scientific method. Think of a broad topic that you are interested in and which would make a good sociological study—for example, ethnic diversity in a college, homecoming rituals, athletic scholarships, or teen driving. Now, take that topic through the first steps of the process. For each step, write a few sentences or a paragraph: 1) Ask a question about the topic. 2) Do some research and write down the titles of some articles or books you’d want to read about the topic. 3) Formulate a hypothesis.

2.2.Research Methods

• What type of data do surveys gather? For what topics would surveys be the best research method? What drawbacks might you expect to encounter when using a survey? To explore further, ask a research question and write a hypothesis. Then create a survey of about six questions relevant to the topic. Provide a rationale for each question. Now define your population and create a plan for recruiting a random sample and administering the survey.
• Imagine you are about to do field research in a specific place for a set time. Instead of thinking about the topic of study itself, consider how you, as the researcher, will have to prepare for the study. What personal, social, and physical sacrifices will you have to make? How will you manage your personal effects? What organizational equipment and systems will you need to collect the data?
• Create a brief research design about a topic in which you are passionately interested. Now write a letter to a philanthropic or grant organization requesting funding for your study. How can you describe the project in a convincing yet realistic and objective way? Explain how the results of your study will be a relevant contribution to the body of sociological work already in existence.
• Why do you think the CSA crafted such a detailed set of ethical principles? What type of study could put human participants at risk? Think of some examples of studies that might be harmful. Do you think that, in the name of sociology, some researchers might be tempted to cross boundaries that threaten human rights? Why?
• Would you willingly participate in a sociological study that could potentially put your health and safety at risk, but had the potential to help thousands or even hundreds of thousands of people? For example, would you participate in a study of a new drug that could cure diabetes or cancer, even if it meant great inconvenience and physical discomfort for you or possible permanent damage?

Further Research

2.1. Approaches to Sociological Research For a historical perspective on the scientific method in sociology, read “The Elements of Scientific Method in Sociology” by F. Stuart Chapin (1914) in the American Journal of Sociology : http://openstaxcollege.org/l/Method-in-Sociology

2.2. Research Methods For information on current real-world sociology experiments, visit: http://openstaxcollege.org/l/Sociology-Experiments

2.3. Ethical Concerns Founded in 1966, the CSA is a nonprofit organization located in Montreal, Quebec, with a membership of 900 researchers, faculty members, students, and practitioners of sociology. Its mission is to promote “research, publication and teaching in Sociology in Canada.” Learn more about this organization at http://www.csa-scs.ca/ .

2.1. Approaches to Sociological Research Merton, Robert. 1968 [1949]. Social Theory and Social Structure . New York: Free Press.

2.2. Research Methods Forget, Evelyn. 2011. “The Town with no Poverty: Using Health Administration Data to Revisit Outcomes of a Canadian Guaranteed Annual Income Field Experiement.” Canadian Public Policy . 37(3): 282-305.

Franke, Richard and James Kaul. 1978. “The Hawthorne Experiments: First Statistical Interpretation.” American Sociological Review 43(5):632–643.

Gilens, Martin. 1996. “Race and Poverty in America: Public Misperceptions and the American News Media.” The Public Opinion Quarterly 60(4):515–541. Grice, Elizabeth. 2006. “Cry of an Enfant Sauvage.” The Telegraph . Retrieved July 20, 2011 ( http://www.telegraph.co.uk/culture/tvandradio/3653890/Cry-of-an-enfant-sauvage.html ).

Haney, C., Banks, W. C., and Zimbardo, P. G. 1973. “Interpersonal Dynamics in a Simulated Prison.” International Journal of Criminology and Penology  1:69–97.

Ivsins, A.K. 2010. “’Got a pipe?’ The social dimensions and functions of crack pipe sharing among crack users in Victoria, BC.” MA thesis, Department of Sociology, University of Victoria. Retrieved February 14, 2014 ( http://dspace.library.uvic.ca:8080/bitstream/handle/1828/3044/Full%20thesis%20Ivsins_CPS.2010_FINAL.pdf?sequence=1 )

Lowrey, Annie. 2013. “Switzerland’s Proposal to Pay People for Being Alive.” The  New York Times Magazine. Retrieved February 17, 2014 ( http://www.nytimes.com/2013/11/17/magazine/switzerlands-proposal-to-pay-people-for-being-alive.html?pagewanted=1&_r=2 ).

Lynd, Robert S. and Helen Merrell Lynd. 1959. Middletown: A Study in Modern American Culture . San Diego, CA: Harcourt Brace Javanovich.

Lynd, Staughton. 2005. “Making Middleton.” Indiana Magazine of History 101(3):226–238.

Marshall, B.D.L., M.J. Milloy,  E. Wood, J.S.G.  Montaner,  and T. Kerr. 2011. “Reduction in overdose mortality after the opening of North America’s first medically supervised safer injecting facility: A retrospective population-based study.” Lancet  377(9775):1429–1437.

Rothman, Rodney. 2000. “My Fake Job.” The New Yorker , November 27, 120.

Sennett, Richard. 2008. The Craftsman . New Haven, CT: Yale University Press. Retrieved July 18, 2011 ( http://www.richardsennett.com/site/SENN/Templates/General.aspx?pageid=40 ).

Smith, Dorothy. 1990. “Textually Mediated Social Organization” Pp. 209–234 in Texts, Facts and Femininity: Exploring the Relations of Ruling. London: Routledge.

Smith, Dorothy. 2005. Institutional Ethnography: A Sociology for People. Toronto: Altamira Press.

Sonnenfeld, Jeffery A. 1985. “Shedding Light on the Hawthorne Studies.” Journal of Occupational Behavior 6:125.

Wood, E., M.W. Tyndall, J.S. Montaner, and T. Kerr. 2006. “Summary of findings from the evaluation of a pilot medically supervised safer injecting facility.” Canadian Medical Association Journal  175(11):1399–1404.

2.3. Ethical Concerns Canadian Institutes of Health Research, Natural Sciences and Engineering Research Council of Canada, and Social Sciences and Humanities Research Council of Canada. 2010.  Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans . Retrieved February 15, 2014 ( http://www.pre.ethics.gc.ca/pdf/eng/tcps2/TCPS_2_FINAL_Web.pdf ).

Canadian Sociological Association. 2012. Statement of Professional Ethics . Retrieved February 15, 2014 ( http://www.csa-scs.ca/files/www/csa/documents/codeofethics/2012Ethics.pdf ).

Habermas, Jürgen. 1972. Knowledge and Human Interests. Boston: Beacon Press

Weber, Max. 1949. Methodology of the Social Sciences . Translated by H. Shils and E. Finch. Glencoe, IL: Free Press.

Solutions to Section Quiz

1. C | 2. C | 3. D | 4. C | 5. B | 6. C | 7. D | 8. C | 9. A | 10. A | 11. B | 12. A | 13. B | 14. D | 15. A

Image Attributions

Figure 2.3.  Didn’t they abolish the mandatory census? Then what’s this? by  Khosrow Ebrahimpour ( https://www.flickr.com/photos/xosrow/5685345306/in/photolist-9EoT5W-ow4tdu-oeGG4m-oeMEcK-oy2jM2-ovJC8w-oePSRQ-9J2V24-of1Hnu-of243u-of2K2B-of2FHn-owiBSA-owtQN3-of1Ktd-oitLSC-oeVJte-oep8KX-ovEz8w-oeohhF-oew5Xb-oewdWN-owavju-oeMEnV-oweLcN-ovEPGG-ovAQUX-oeo2eL-oeo3Fd-oeoqxh-oxCKnv-ovEzA5-oewFHa-ovHRSz-ow8QtY-oeQY6Y-oeZReR-oeQmHw-oeKXid-oeQLKa-oy6fNT-ow4sVT-oeQMQq-oeQPPr-oeQYbL-ow8hS1-ow4n8v-owiPKS-oeQF41-oeiH5z ) used under CC BY 2.0 ( https://creativecommons.org/licenses/by/2.0/ )

Figure 2.4. Dauphin Canadian Northern Railway Station by Bobak Ha’Eri ( http://commons.wikimedia.org/wiki/File:2009-0520-TrainStation-Dauphin.jpg ) used under CC BY 3.0 license ( http://creativecommons.org/licenses/by/3.0/deed.en )

Figure 2.5.  Punk Band by Patrick ( https://www.flickr.com/photos/lordkhan/181561343/in/photostream/ ) used under CC BY 2.0 ( https://creativecommons.org/licenses/by/2.0/ )

Figure 2.6.  Crack Cocaine Smokers in Vancouver Alleyway ( http://commons.wikimedia.org/wiki/File:Crack_Cocaine_Smokers_in_Vancouver_Alleyway.jpg ) is in the public domain ( http://en.wikipedia.org/wiki/Public_domain )

Figure 2.8.  Muncie, Indiana High School: 1917 by Don O’Brien ( https://www.flickr.com/photos/dok1/3694125269/ ) used under CC BY 2.0 license ( https://creativecommons.org/licenses/by/2.0/ )

Introduction to Sociology - 1st Canadian Edition Copyright © 2014 by William Little and Ron McGivern is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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Chapter 3: Developing a Research Question

3.4 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 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 Chapter5.2in Principles of Sociological Inquiry , which was adapted by the Saylor Academy without attribution to the original authors or publisher, as requested by the licensor. © CreativeCommonsAttribution-NonCommercial-ShareAlike 3.0 License .

Research Methods for the Social Sciences: An Introduction Copyright © 2020 by Valerie Sheppard is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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How to Write a Great Hypothesis

Hypothesis 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, operational definitions, types of hypotheses, hypotheses examples.

• Collecting Data

Frequently Asked Questions

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.

One hypothesis example would be a study designed to look at the relationship between sleep deprivation and test performance might have a hypothesis that states: "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."

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. It is only at this point that researchers 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 a number of 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 wisdom 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.

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.

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.

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

In order to measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming other people. In this situation, the researcher might utilize a simulated task to measure aggressiveness.

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 that there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type of hypothesis suggests a relationship between three or more variables, such as two independent variables and a dependent variable.
• 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 sample of the population 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."
• Complex hypothesis: "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."

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:

• "Children who receive a new reading intervention will have scores different than students who do not receive the intervention."
• "There will be no difference in scores on a memory recall task between children and adults."

Examples of an alternative hypothesis:

• "Children who receive a new reading intervention will perform better than students who did not receive the intervention."
• "Adults will perform better on a memory task than children." 

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 it would be impossible or difficult to  conduct an experiment . 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 then be used to look at how the variables are related. This type of 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.

A Word From Verywell

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.

Some examples of how to write a hypothesis include:

• "Staying up late will lead to worse test performance the next day."
• "People who consume one apple each day will visit the doctor fewer times each year."
• "Breaking study sessions up into three 20-minute sessions will lead to better test results than a single 60-minute study session."

The four parts of a hypothesis are:

• The research question
• The independent variable (IV)
• The dependent variable (DV)
• The proposed relationship between the IV and DV

Castillo M. The scientific method: a need for something better? . AJNR Am J Neuroradiol. 2013;34(9):1669-71. doi:10.3174/ajnr.A3401

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."

The Research Process and Stages of Social Research

Social research is the act of gathering data that can help a person to answer questions about the various aspects of society. These questions can be generalized, or very specific in terms of the problems.

The research methods, such as surveys and experiments have a major impact on society through their findings. They are increasingly being used outside of the social sciences, in the other sectors of the society. For example – surveys being conducted to determine proper voting hours in presidential elections. Traditionally, there were two ways of determining voting hours. The scientist would develop a theory concerning the voting hours that would best meet the goals of democracy, and then the survey could be used to gather data for testing the theory. This was one way. The other way could be by conducting surveys to directly ask people when it would be most convenient for them to vote, and to open polls at those hours to ensure a maximum number of voters.

In the presidential elections of 1980, the surveys turned out to have a beneficial impact on the voting pattern. Surveys that were conducted by major television networks showed that Ronald Regan was ahead of president carter. As a result, Carter conceded, and some of the potential voters believed that their vote would not play a part in the outcome of the election. There was a difference of three hours in the closing of east coast polls and the west coast polls. Therefore, there was the talk of closing the poll at the same time across the country. A study shows that exit polls suggest a clear winner when previously the race has been considered close.

Gathering of data is done through personal interviews of eyewitnesses and other ‘non-scientific’ manner. The Los Angeles riots of 1992 not only were widely reported by the international media, but also spawned a number of social surveys designed to gather data about not only opinions concerning the underlying cause of the riot, but a number of other matters as well.

It is not necessary that research methods, including exit polls, are always correct. For example- November 1989 exit poll in Virginia, US which accurately showed Democrat L. Douglas Wilder as the winner over the republican j. Marshall Coleman. However, there was an error of 5 percentage points between the victory, which is outside of the normally accepted standards of sampling error. The reason for the same could be the reluctance to accept that some of them were not voting for the black candidate (welder) for the fear of being labeled as racist.

SOCIAL SCIENCE AS SCIENCE

The society in which we live is very complex and is needed to be understood, since, the social environment affects us just as directly and deeply as the physical environment, although in different ways. It has been relatively recent, that we have started to study the social sciences. However, there has been much less funding for social science research than for other physical sciences. One explanatory answer for this has always been that physical scientists could expect a more satisfactory result than social sciences. As a result, most of the funding was spent on the space programs and as a result, social science was so poorly developed, that it would achieve very little. But soon, due to certain world events, they recognized the importance of the study of social sciences with the physical sciences and the interdependency of the two.

A crucial question in social sciences concerns the nature of social phenomena and how they can best be understood. Different sociologists had a different perspective on it. Wilhelm Dilthey, a nineteenth-century sociologist, believed that no one can generalize or predict their actions of humans. Whereas Emile Durkheim had the opposite view. He said, that social phenomena can be generalized. According to him, there was a little difference between physical science and social science except for subject matters. Weber, however, took an intermediate approach between the two extremes and stated his view that though humans have free will, the actions are in a rational pattern, that can be predicted.

Most social scientists believed that social phenomena are orderly enough to be explained and predicted. But some believed that not all social phenomena could be explained or predicted accurately. Others believed that the phenomena contain some random element or margin of error. The social scientists agreed to the view of positivism. This means that social phenomena are considered to be objectively occurring phenomena. It came to be seen as pure science rather than applied (its task is to gather information rather than to use it), abstract rather than concrete, it does not concern itself with history, but with the generations of scientific laws.

STAGES OF SOCIAL RESEARCH

Though different sociologists have a different point of view, they have a common goal of understanding society, and hence will have common stages of research. There are five stages of research, as discussed below. Each of the stages is dependent on the other.

Circularity

The research process is in the form of a circle. After the researcher completes stage 5, and the study is found to be unsuccessful or partially successful, the researcher has to return to the early stages of the investigation, and repeat all the stages beginning with the faulty stage.

Replication

Once the research is completed and proved successful, the researcher must re-analyze all the stages of his research to ensure better accuracy of the hypothesis. But due to lack of money and funding, it usually repeats the hypothesis with few modifications. Nevertheless, research is a never-ending process, it may be proved wrong in further investigations.

Examples: density research

While all social research projects share the five basic stages, but they are very different in how they are carried out. Different approaches were carried out for the study on the effects of density on the human population. One of the studies is “population density and pathology: what are the relations for man?’ by Galle, Gove, and McPherson (1972). The other study by Griffith and Veitch (1971) is “hot and crowded: influence of population density and temperature on interpersonal affective behavior”. There is a comparison made on these studies through the five stages of research.

• Choosing the problem and stating the hypothesis- it is obvious, that the first step towards research is to know the subject matter and form an adequate hypothesis to formulate the hypothesis and gather facts and information.
• Research design – in this stage, the researcher has to decide how to measure the two main variables (density and social effects) and on what group/how many people to test the hypothesis.
• Gathering the data – here, people are chosen for surveys, and the data is collected. Griffith and Veitch decided to conduct a laboratory study, about the degree of aggressiveness by how well the subject like the hypothetical stranger. There was a total of 8 experimental conditions by varying density and heat of the room. Galle also performed the experiment. Persons in Griffith’s study were randomly assigned the experimental conditions. In the Galle study, data had already been collected. The researchers could not collect exactly the data they wanted but had to use whatever was available in the factbook
• Coding and analyzing the data – through analyzing the data, Griffith and Veitch concluded, that high density increases the tendency of people to dislike one another. Density analysis was more complicated in the Galle study since there were many confounding factors that might affect the relationship between density and pathology.
• Interpreting the result and testing the hypothesis – Griffith and Veitch found evidence to support the hypothesis. Now the next step was to replicate the study, make some changes or conduct study with a larger sample of people to make sure that their finding was no fluke. The evidence in Galle’s study seemed to indicate that density does not cause pathology, but that both density level and pathology level vary with social class. They left the hypothesis unchanged and revised the study. They found that when density is measured by persons per room, there is a relation between density and pathology, hence the hypothesis was supported.

Bailey, K. (1994). The Research Process in Methods of social research. Simon and Schuster, 4th ed. The Free Press, New York 10020. Pp.3 -19

Sabnam, pursuing Sociology from Miranda House, Delhi University hails from the land of red River, Assam. 1500 dollar loans online . She is a pure non-realist, because, as she puts it, "reality hurts and pain is not what I endure but what I pour into paper!".

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2.2 Stages in the Sociological Research Process

Learning objectives.

• List the major stages of the sociological research process.
• Describe the different types of units of analysis in sociology.
• Explain the difference between an independent variable and a dependent variable.

Sociological research consists of several stages. The researcher must first choose a topic to investigate and then become familiar with prior research on the topic. Once appropriate data are gathered and analyzed, the researcher can then draw appropriate conclusions. This section discusses these various stages of the research process.

Choosing a Research Topic

The first step in the research process is choosing a topic. There are countless topics from which to choose, so how does a researcher go about choosing one? Many sociologists choose a topic based on a theoretical interest they may have. For example, Émile Durkheim’s interest in the importance of social integration motivated his monumental study of suicide that Chapter 1 “Sociology and the Sociological Perspective” discussed. Many sociologists since the 1970s have had a theoretical interest in gender, and this interest has motivated a huge volume of research on the difference that gender makes for behavior, attitudes, and life chances. The link between theory and research lies at the heart of the sociological research process, as it does for other social, natural, and physical sciences. Accordingly, this book discusses many examples of studies motivated by sociologists’ varied theoretical interests.

Many sociologists, such as the two pictured here, have a theoretical interest in gender that leads them to investigate the importance of gender for many aspects of the social world.

Francisco Osorio – CL Society 31: Sociologists – CC BY 2.0.

Many sociologists also choose a topic based on a social policy interest they may have. For example, sociologists concerned about poverty have investigated its effects on individuals’ health, educational attainment, and other outcomes during childhood, adolescence, and adulthood. Sociologists concerned about racial prejudice and discrimination have carried out many studies documenting their negative consequences for people of color. As Chapter 1 “Sociology and the Sociological Perspective” discussed and as this book emphasizes, the roots of sociology in the United States lie in the use of sociological knowledge to achieve social reform, and many sociologists today continue to engage in numerous research projects because of their social policy interests. The news story that began this chapter discussed an important example of this type of research. The “Sociology Making a Difference” box further discusses research of this type.

Sociology Making a Difference

Survey Research to Help the Poor

The Community Service Society (CSS) of New York City is a nonprofit organization that, according to its Web site ( http://www.cssny.org ), “engages in advocacy, research and direct service” to help low-income residents of the city. It was established about 160 years ago and has made many notable accomplishments over the years, including aiding the victims of the Titanic disaster in 1912, helping initiate the free school lunch program that is now found around the United States, and establishing the largest senior volunteer program in the nation.

A key component of the CSS’s efforts today involves gathering much information about the lives of poor New Yorkers through an annual survey of random samples of these residents. Because the needs of the poor are so often neglected and their voices so often unheard, the CSS calls this effort the Unheard Third survey, as the poor represent about one-third of the New York City population. The Unheard Third survey asks respondents their opinions about many issues affecting their lives and also asks them many questions about such matters as their health and health care needs, employment status and job satisfaction, debt, and housing. The CSS then uses all this information in reports about the needs of the poor and near-poor in New York that it prepares for city and state officials, the news media, and key individuals in the private sector. In these ways, the CSS uses survey research in the service of society. As its Web site ( http://www.cssny.org/research ) states, “research is a critical tool we use to increase our understanding of conditions that drive poverty as we advocate for public policy and programs that will improve the economic standing of low-income New Yorkers.”

A third source of inspiration for research topics is personal experience . Like other social scientists (and probably also natural and physical scientists), many sociologists have had various experiences during childhood, adolescence, or adulthood that lead them to study a topic from a sociological standpoint. For example, a sociologist whose parents divorced while the sociologist was in high school may become interested in studying the effects of divorce on children. A sociologist who was arrested during college for a political protest may become interested in studying how effective protest might be for achieving the aims of a social movement. A sociologist who acted in high school plays may choose a dissertation during graduate school that focuses on a topic involving social interaction. Although the exact number will never be known, many research studies in sociology are undoubtedly first conceived because personal experience led the author to become interested in the theory or social policy addressed by the study.

Conducting a Literature Review

Whatever topic is chosen, the next stage in the research process is a review of the literature. A researcher who begins a new project typically reads a good number of studies that have already been published on the topic that the researcher wants to investigate. In sociology, most of these studies are published in journals, but many are also published as books. The government and private research organizations also publish reports that researchers consult for their literature reviews.

Regardless of the type of published study, a literature review has several goals. First, the researcher needs to determine that the study she or he has in mind has not already been done. Second, the researcher needs to determine how the proposed study will add to what is known about the topic of the study. How will the study add to theoretical knowledge of the topic? How will the study improve on the methodology of earlier studies? How will the study aid social policy related to the topic? Typically, a research project must answer at least one of these questions satisfactorily for it to have a chance of publication in a scholarly journal, and a thorough literature review is necessary to determine the new study’s possible contribution. A third goal of a literature review is to see how prior studies were conducted. What research design did they use? From where did their data come? How did they measure key concepts and variables? A thorough literature review enhances the methodology of the researcher’s new study and enables the researcher to correct any possible deficiencies in the methodology of prior studies.

In “the old days,” researchers would conduct a literature review primarily by going to an academic library, consulting a printed index of academic journals, trudging through shelf after shelf of printed journals, and photocopying articles they found or taking notes on index cards. Those days are long gone, and thankfully so. Now researchers use any number of electronic indexes and read journal articles online or download a PDF version to read later. Literature reviews are still a lot of work, but the time they take is immeasurably shorter than just a decade ago.

Formulating a Hypothesis

After the literature review has been completed, it is time to formulate the hypothesis that will guide the study. As you might remember from a science class, a hypothesis is a statement of the relationship between two variables concerning the units of analysis the researcher is studying. To understand this definition, we must next define variable and unit of analysis . Let’s start with unit of analysis , which refers to the type of entity a researcher is studying. As we discuss further in a moment, the most common unit of analysis in sociology is a person, but other units of analysis include organizations and geographical locations. A variable is any feature or factor that may differ among the units of analysis that a researcher is studying. Key variables in sociological studies of people as the units of analysis include gender, race and ethnicity, social class, age, and any number of attitudes and behaviors. Whatever unit of analysis is being studied, sociological research aims to test relationships between variables or, more precisely, to test whether one variable affects another variable, and a hypothesis outlines the nature of the relationship that is to be tested.

Suppose we want to test the hypothesis that women were more likely than men to have voted for Obama in 2008. The first variable in this hypothesis is gender, whether someone is a woman or a man. (As Chapter 11 “Gender and Gender Inequality” discusses, gender is actually more complex than this, but let’s keep things simple for now.) The second variable is voting preference—for example, whether someone voted for Obama or McCain. In this example, gender is the independent variable and voting preference is the dependent variable. An independent variable is a variable we think can affect another variable. This other variable is the dependent variable , or the variable we think is affected by the independent variable (see Figure 2.3 “Causal Path for the Independent and Dependent Variable” ). When sociological research tests relationships between variables, it normally is testing whether an independent variable affects a dependent variable.

Figure 2.3 Causal Path for the Independent and Dependent Variable

Many hypotheses in sociology involve variables concerning people, but many also involve variables concerning organizations and geographical locations. As this statement is meant to suggest, sociological research is conducted at different levels, depending on the unit of analysis chosen. As noted earlier, the most common unit of analysis in sociology is the person ; this is probably the type of research with which you are most familiar. If we conduct a national poll to see how gender influences voting decisions or how race influences views on the state of the economy, we are studying characteristics, or variables, involving people, and the person is the unit of analysis. Another common unit of analysis in sociology is the organization . Suppose we conduct a study of hospitals to see whether the patient-to-nurse ratio (the number of patients divided by the number of nurses) is related to the average number of days that patients stay in the hospital. In this example, the patient-to-nurse ratio and the average number of days patients stay are both characteristics of the hospital, and the hospital is the unit of analysis. A third unit of analysis in sociology is the geographical location , whether it is cities, states, regions of a country, or whole societies. In the United States, for example, large cities generally have higher violent crime rates than small cities. In this example, the city is the unit of analysis.

One of the units of analysis in sociological research is the geographical location. The major regions of the United States are often compared on various characteristics. In one notable finding, the South has the highest regional homicide rate.

Source: Adapted from http://commons.wikimedia.org/wiki/File:Blank_US_Map.svg .

Measuring Variables and Gathering Data

After the hypothesis has been formulated, the sociologist is now ready to begin the actual research. Data must be gathered via one or more of the research designs examined later in this chapter, and variables must be measured. Data can either be quantitative (numerical) or qualitative (nonnumerical). Data gathered through a questionnaire are usually quantitative. The answers a respondent gives to a questionnaire are coded for computer analysis. For example, if a question asks whether respondents consider themselves to be politically conservative, moderate, or liberal, those who answer “conservative” might receive a “1” for computer analysis; those who choose “moderate” might receive a “2”; and those who say “liberal” might receive a “3.”

Data gathered through observation and/or intensive interviewing, research designs discussed later in this chapter, are usually qualitative. If a researcher interviews college students at length to see what they think about dating violence and how seriously they regard it, the researcher may make simple comparisons, such as “most” of the interviewed students take dating violence very seriously, but without really statistically analyzing the in-depth responses from such a study. Instead, the goal is to make sense of what the researcher observes or of the in-depth statements that people provide to an interviewer and then to relate the major findings to the hypothesis or topic the researcher is investigating.

The measurement of variables is a complex topic and lies far beyond the scope of this discussion. Suffice it to say that accurate measurement of variables is essential in any research project. In a questionnaire, for example, a question should be worded clearly and unambiguously. Take the following question, which has appeared in national surveys: “Do you ever drink more than you think you should?” This question is probably meant to measure whether the respondent has an alcohol problem. But some respondents might answer yes to this question even if they only have a few drinks per year if, for example, they come from a religious background that frowns on alcohol use; conversely, some respondents who drink far too much might answer no because they do not think they drink too much. A researcher who interpreted a yes response from the former respondents as an indicator of an alcohol problem or a no response from the latter respondents as an indicator of no alcohol problem would be in error.

As another example, suppose a researcher hypothesizes that younger couples are happier than older couples. Instead of asking couples how happy they are through a questionnaire, the researcher decides to observe couples as they walk through a shopping mall. Some interesting questions of measurement arise in this study. First, how does the researcher know who is a couple? Second, how sure can the researcher be of the approximate age of each person in the couple? The researcher might be able to distinguish people in their 20s or early 30s from those in their 50s and 60s, but age measurement beyond this gross comparison might often be in error. Third, how sure can the researcher be of the couple’s degree of happiness? Is it really possible to determine how happy a couple is by watching them for a few moments in the mall? What exactly does being happy look like, and do all people look this way when they are happy? These and other measurement problems in this particular study might be so severe that the study should not be done, at least if the researcher hopes to publish it.

After any measurement issues have been resolved, it is time to gather the data. For the sake of simplicity, let’s assume the unit of analysis is the person. A researcher who is doing a study “from scratch” must decide which people to study. Because it is certainly impossible to study everybody, the researcher only studies a sample , or subset of the population of people in whom the researcher is interested. Depending on the purpose of the study, the population of interest varies widely: it can be the adult population of the United States, the adult population of a particular state or city, all young women aged 13–18 in the nation, or countless other variations.

Many researchers who do survey research (discussed in a later section) study people selected for a random sample of the population of interest. In a random sample, everyone in the population (whether it be the whole U.S. population or just the population of a state or city, all the college students in a state or city or all the students at just one college, and so forth) has the same chance of being included in the survey. The ways in which random samples are chosen are too complex to fully discuss here, but suffice it to say the methods used to determine who is in the sample are equivalent to flipping a coin or rolling some dice. The beauty of a random sample is that it allows us to generalize the results of the sample to the population from which the sample comes. This means that we can be fairly sure of the attitudes of the whole U.S. population by knowing the attitudes of just 400 people randomly chosen from that population.

Other researchers use nonrandom samples, in which members of the population do not have the same chance of being included in the study. If you ever filled out a questionnaire after being approached in a shopping mall or campus student center, it is very likely that you were part of a nonrandom sample. While the results of the study (marketing research or social science research) for which you were interviewed might have been interesting, they could not necessarily be generalized to all students or all people in a state or in the nation because the sample for the study was not random.

High school classes often are used as a convenience sample in sociological and other social science research.

NWABR – 2009 Student Fellows – CC BY 2.0.

A specific type of nonrandom sample is the convenience sample , which refers to a nonrandom sample that is used because it is relatively quick and inexpensive to obtain. If you ever filled out a questionnaire during a high school or college class, as many students have done, you were very likely part of a convenience sample—a researcher can simply go into class, hand out a survey, and have the data available for coding and analysis within a few minutes. Convenience samples often include students, but they also include other kinds of people. When prisoners are studied, they constitute a convenience sample, because they are definitely not going anywhere. Partly because of this fact, convenience samples are also sometimes called captive-audience samples .

Another specific type of nonrandom sample is the quota sample . In this type of sample, a researcher tries to ensure that the makeup of the sample resembles one or more characteristics of the population as closely as possible. For example, on a campus of 10,000 students where 60% of the students are women and 40% are men, a researcher might decide to study 100 students by handing out a questionnaire to those who happen to be in the student center building on a particular day. If the researcher decides to have a quota sample based on gender, the researcher will select 60 women students and 40 male students to receive the questionnaire. This procedure might make the sample of 100 students more representative of all the students on campus than if it were not used, but it still does not make the sample entirely representative of all students. The students who happen to be in the student center on a particular day might be very different in many respects from most other students on the campus.

As we shall see later when research design is discussed, the choice of a design is very much related to the type of sample that is used. Surveys lend themselves to random samples, for example, while observation studies and experiments lend themselves to nonrandom samples.

Analyzing Data

After all data have been gathered, the next stage is to analyze the data. If the data are quantitative, the analysis will almost certainly use highly sophisticated statistical techniques beyond the scope of this discussion. Many statistical analysis software packages exist for this purpose, and sociologists learn to use one or more of these packages during graduate school. If the data are qualitative, researchers analyze their data (what they have observed and/or what people have told them in interviews) in ways again beyond our scope. Many researchers now use qualitative analysis software that helps them uncover important themes and patterns in the qualitative data they gather. However qualitative or quantitative data are analyzed, it is essential that the analysis be as accurate as possible. To go back to a point just made, this means that variable measurement must also be as accurate as possible, because even expert analysis of inaccurate data will yield inaccurate results. As a phrase from the field of computer science summarizes this problem, “garbage in, garbage out.” Data analysis can be accurate only if the data are accurate to begin with.

Criteria of Causality

As researchers analyze their data, they naturally try to determine whether their analysis supports their hypothesis. As noted above, when we test a hypothesis, we want to be able to conclude that an independent variable affects a dependent variable. Four criteria must be satisfied before we can conclude this (see Table 2.1 “Criteria of Causality” ).

Table 2.1 Criteria of Causality

First, the independent variable and the dependent variable must be statistically related . That means that the independent variable makes a statistical difference for where one ranks on the dependent variable. Suppose we hypothesize that age was related to voting preference in the 2008 presidential election. Here age is clearly the independent variable and voting preference the dependent variable. (It is possible for age to affect voting preference, but it is not possible for voting preference to affect age.) Exit poll data indicate that 66% of 18- to 24-year-olds voted for Obama in 2008, while only 45% of those 65 and older voted for him. The two variables are thus statistically related, as younger voters were more likely than older voters to prefer Obama.

The second criterion is called the causal order (or chicken-and-egg) problem and reflects the familiar saying that “correlation does not mean causation.” Just because an independent and a dependent variable are related does not automatically mean that the independent variable affects the dependent variable. It might well be that the dependent variable is affecting the independent. To satisfy this criterion, the researcher must be sure that the independent variable precedes the dependent variable in time or in logic. In the example just discussed, age might affect voting preference, but voting preference definitely cannot affect age. However, causal order is not as clear in other hypotheses. For example, suppose we find a statistical relationship between marital happiness and job satisfaction: the more happy people are in their marriage, the more satisfied they are with their jobs. Which makes more sense, that having a happy marriage leads you to like your job more, or that being satisfied with your work leads you to have a happier marriage? In this example, causal order is not very clear, and thus the second criterion is difficult to satisfy.

The third criterion involves spurious relationships . A relationship between an independent variable and dependent variable is spurious if a third variable accounts for the relationship because it affects both the independent and dependent variables. Although this sounds a bit complicated, an example or two should make it clear. If you did a survey of Americans 18 and older, you would find that people who attend college have worse acne than people who do not attend college. Does this mean that attending college causes worse acne? Certainly not. You would find this statistical relationship only because a third variable, age, affects both the likelihood of attending college and the likelihood of having acne: young people are more likely than older people to attend college, and also more likely—for very different reasons—to have acne. Controlling for age makes it clear that the original relationship between attending college and having acne was spurious. Figure 2.5 “Diagram of a Spurious Relationship” diagrams this particular spurious relationship; notice that there is no causal arrow between the attending college and having acne variables.

Figure 2.5 Diagram of a Spurious Relationship

In another example, the more fire trucks at a fire, the more damage the fire causes. Does that mean that fire trucks somehow make fires worse, as the familiar saying “too many cooks spoil the broth” might suggest? Of course not! The third variable here is the intensity of the fire: the more intense the fire, the more fire trucks respond to fight it, and the more intense the fire, the more damage it causes. The relationship between number of fire trucks and damage the fire causes is spurious.

The final criterion of causality is that our explanation for the relationship between the independent and dependent variables is the best explanation . Even if the first three criteria are satisfied, that does not necessarily mean the two variables are in fact related. For example, the U.S. crime rate dropped in the early 1980s, and in 1984 the reelection campaign of President Ronald Reagan took credit for this drop. This relationship satisfied the first three criteria: the crime rate fell after President Reagan took office in 1981, the drop in the crime rate could not have affected the election of this president, and there was no apparent third variable that influenced both why Reagan was elected and why the crime rate fell. However, social scientists pointed to another reason that accounted for the crime rate decrease during the 1980s: a drop in the birth rate some 15–20 years earlier, which led to a decrease during the early 1980s of the number of U.S. residents in the high-crime ages of 15–30 (Steffensmeier & Harer, 1991). The relationship between the election of Ronald Reagan and the crime rate drop was thus only a coincidence.

Drawing a Conclusion

Once the data are analyzed, the researcher finally determines whether the data analysis supports the hypothesis that has been tested, taking into account the criteria of causality just discussed. Whether or not the hypothesis is supported, the researcher (if writing for publication) typically also discusses what the results of the present research imply for both prior and future studies on the topic. If the primary purpose of the project has been to test or refine a particular theory, the conclusion will discuss the implications of the results for this theory. If the primary purpose has been to test or advance social policy, the conclusion will discuss the implications of the results for policy making relevant to the project’s subject matter.

Key Takeaways

• Several stages compose the sociological research process. These stages include (a) choosing a research topic, (b) conducting a literature review, (c) measuring variables and gathering data, (d) analyzing data, and (e) drawing a conclusion.
• Sociologists commonly base their choice of a research topic on one or more of the following: (a) a theoretical interest, (b) a social policy interest, and (c) one or more personal experiences.
• Accurate measurement of variables is essential for sound sociological research. As a minimum, measures should be as clear and unambiguous as possible.

For Your Review

• Consider the following question from a survey: “Generally speaking, are you very happy, somewhat happy, or not too happy?” Write a brief essay in which you evaluate how well this question measures happiness.
• Think of a personal experience you have had that lends itself to a possible research project. Write a brief essay in which you describe the experience and discuss the hypothesis that the research project based on the experience would address.

Steffensmeier, D., & Harer, M. D. (1991). Did crime rise or fall during the Reagan presidency? The effects of an “aging” U.S. population on the nation’s crime rate. Journal of Research in Crime and Delinquency, 28 (3), 330–359.

Sociology Copyright © 2016 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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.

Need a helping hand?

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.

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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15 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?

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Home » Research Process – Steps, Examples and Tips

Research Process – Steps, Examples and Tips

Table of Contents

Research Process

Definition:

Research Process is a systematic and structured approach that involves the collection, analysis, and interpretation of data or information to answer a specific research question or solve a particular problem.

Research Process Steps

Research Process Steps are as follows:

Identify the Research Question or Problem

This is the first step in the research process. It involves identifying a problem or question that needs to be addressed. The research question should be specific, relevant, and focused on a particular area of interest.

Conduct a Literature Review

Once the research question has been identified, the next step is to conduct a literature review. This involves reviewing existing research and literature on the topic to identify any gaps in knowledge or areas where further research is needed. A literature review helps to provide a theoretical framework for the research and also ensures that the research is not duplicating previous work.

Formulate a Hypothesis or Research Objectives

Based on the research question and literature review, the researcher can formulate a hypothesis or research objectives. A hypothesis is a statement that can be tested to determine its validity, while research objectives are specific goals that the researcher aims to achieve through the research.

Design a Research Plan and Methodology

This step involves designing a research plan and methodology that will enable the researcher to collect and analyze data to test the hypothesis or achieve the research objectives. The research plan should include details on the sample size, data collection methods, and data analysis techniques that will be used.

Collect and Analyze Data

This step involves collecting and analyzing data according to the research plan and methodology. Data can be collected through various methods, including surveys, interviews, observations, or experiments. The data analysis process involves cleaning and organizing the data, applying statistical and analytical techniques to the data, and interpreting the results.

Interpret the Findings and Draw Conclusions

After analyzing the data, the researcher must interpret the findings and draw conclusions. This involves assessing the validity and reliability of the results and determining whether the hypothesis was supported or not. The researcher must also consider any limitations of the research and discuss the implications of the findings.

Communicate the Results

Finally, the researcher must communicate the results of the research through a research report, presentation, or publication. The research report should provide a detailed account of the research process, including the research question, literature review, research methodology, data analysis, findings, and conclusions. The report should also include recommendations for further research in the area.

Review and Revise

The research process is an iterative one, and it is important to review and revise the research plan and methodology as necessary. Researchers should assess the quality of their data and methods, reflect on their findings, and consider areas for improvement.

Ethical Considerations

Throughout the research process, ethical considerations must be taken into account. This includes ensuring that the research design protects the welfare of research participants, obtaining informed consent, maintaining confidentiality and privacy, and avoiding any potential harm to participants or their communities.

Dissemination and Application

The final step in the research process is to disseminate the findings and apply the research to real-world settings. Researchers can share their findings through academic publications, presentations at conferences, or media coverage. The research can be used to inform policy decisions, develop interventions, or improve practice in the relevant field.

Research Process Example

Following is a Research Process Example:

Research Question : What are the effects of a plant-based diet on athletic performance in high school athletes?

Step 1: Background Research Conduct a literature review to gain a better understanding of the existing research on the topic. Read academic articles and research studies related to plant-based diets, athletic performance, and high school athletes.

Step 2: Develop a Hypothesis Based on the literature review, develop a hypothesis that a plant-based diet positively affects athletic performance in high school athletes.

Step 3: Design the Study Design a study to test the hypothesis. Decide on the study population, sample size, and research methods. For this study, you could use a survey to collect data on dietary habits and athletic performance from a sample of high school athletes who follow a plant-based diet and a sample of high school athletes who do not follow a plant-based diet.

Step 4: Collect Data Distribute the survey to the selected sample and collect data on dietary habits and athletic performance.

Step 5: Analyze Data Use statistical analysis to compare the data from the two samples and determine if there is a significant difference in athletic performance between those who follow a plant-based diet and those who do not.

Step 6 : Interpret Results Interpret the results of the analysis in the context of the research question and hypothesis. Discuss any limitations or potential biases in the study design.

Step 7: Draw Conclusions Based on the results, draw conclusions about whether a plant-based diet has a significant effect on athletic performance in high school athletes. If the hypothesis is supported by the data, discuss potential implications and future research directions.

Step 8: Communicate Findings Communicate the findings of the study in a clear and concise manner. Use appropriate language, visuals, and formats to ensure that the findings are understood and valued.

Applications of Research Process

The research process has numerous applications across a wide range of fields and industries. Some examples of applications of the research process include:

• Scientific research: The research process is widely used in scientific research to investigate phenomena in the natural world and develop new theories or technologies. This includes fields such as biology, chemistry, physics, and environmental science.
• Social sciences : The research process is commonly used in social sciences to study human behavior, social structures, and institutions. This includes fields such as sociology, psychology, anthropology, and economics.
• Education: The research process is used in education to study learning processes, curriculum design, and teaching methodologies. This includes research on student achievement, teacher effectiveness, and educational policy.
• Healthcare: The research process is used in healthcare to investigate medical conditions, develop new treatments, and evaluate healthcare interventions. This includes fields such as medicine, nursing, and public health.
• Business and industry : The research process is used in business and industry to study consumer behavior, market trends, and develop new products or services. This includes market research, product development, and customer satisfaction research.
• Government and policy : The research process is used in government and policy to evaluate the effectiveness of policies and programs, and to inform policy decisions. This includes research on social welfare, crime prevention, and environmental policy.

Purpose of Research Process

The purpose of the research process is to systematically and scientifically investigate a problem or question in order to generate new knowledge or solve a problem. The research process enables researchers to:

• Identify gaps in existing knowledge: By conducting a thorough literature review, researchers can identify gaps in existing knowledge and develop research questions that address these gaps.
• Collect and analyze data : The research process provides a structured approach to collecting and analyzing data. Researchers can use a variety of research methods, including surveys, experiments, and interviews, to collect data that is valid and reliable.
• Test hypotheses : The research process allows researchers to test hypotheses and make evidence-based conclusions. Through the systematic analysis of data, researchers can draw conclusions about the relationships between variables and develop new theories or models.
• Solve problems: The research process can be used to solve practical problems and improve real-world outcomes. For example, researchers can develop interventions to address health or social problems, evaluate the effectiveness of policies or programs, and improve organizational processes.
• Generate new knowledge : The research process is a key way to generate new knowledge and advance understanding in a given field. By conducting rigorous and well-designed research, researchers can make significant contributions to their field and help to shape future research.

Tips for Research Process

Here are some tips for the research process:

• Start with a clear research question : A well-defined research question is the foundation of a successful research project. It should be specific, relevant, and achievable within the given time frame and resources.
• Conduct a thorough literature review: A comprehensive literature review will help you to identify gaps in existing knowledge, build on previous research, and avoid duplication. It will also provide a theoretical framework for your research.
• Choose appropriate research methods: Select research methods that are appropriate for your research question, objectives, and sample size. Ensure that your methods are valid, reliable, and ethical.
• Be organized and systematic: Keep detailed notes throughout the research process, including your research plan, methodology, data collection, and analysis. This will help you to stay organized and ensure that you don’t miss any important details.
• Analyze data rigorously: Use appropriate statistical and analytical techniques to analyze your data. Ensure that your analysis is valid, reliable, and transparent.
• I nterpret results carefully : Interpret your results in the context of your research question and objectives. Consider any limitations or potential biases in your research design, and be cautious in drawing conclusions.
• Communicate effectively: Communicate your research findings clearly and effectively to your target audience. Use appropriate language, visuals, and formats to ensure that your findings are understood and valued.
• Collaborate and seek feedback : Collaborate with other researchers, experts, or stakeholders in your field. Seek feedback on your research design, methods, and findings to ensure that they are relevant, meaningful, and impactful.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Principles of Social Research Methodology pp 29–41 Cite as

Social Research: Definitions, Types, Nature, and Characteristics

• Kanamik Kani Khan 4 &
• Md. Mohsin Reza 5  
• First Online: 27 October 2022

2416 Accesses

Social research is often defined as a study of mankind that helps to identify the relations between social life and social systems. This kind of research usually creates new knowledge and theories or tests and verifies existing theories. However, social research is a broad spectrum that requires a discursive understanding of its varied nature and definitions. This chapter aims to explain the multifarious definitions of social research given by different scholars. The information used in this chapter is solely based on existing literature regarding social research. There are various stages discussed regarding how social research can be effectively conducted. The types and characteristics of social research are further analysed in this chapter. Social research plays a substantial role in investigating knowledge and theories relevant to social problems. Additionally, social research is important for its contribution to national and international policymaking, which explains the importance of social research.

• Social research
• Human and social behaviour
• Knowledge and theories

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Chapter 8: Inference for One Proportion

Hypothesis testing (2 of 5), learning objectives.

• Recognize the logic behind a hypothesis test and how it relates to the P-value.

In this section, our focus is hypothesis testing, which is part of inference. On the previous page, we practiced stating null and alternative hypotheses from a research question. Forming the hypotheses is the first step in a hypothesis test. Here are the general steps in the process of hypothesis testing. We will see that hypothesis testing is related to the thinking we did in Linking Probability to Statistical Inference .

Step 1: Determine the hypotheses.

The hypotheses come from the research question.

Step 2: Collect the data.

Ideally, we select a random sample from the population. The data comes from this sample. We calculate a statistic (a mean or a proportion) to summarize the data.

Step 3: Assess the evidence.

Assume that the null hypothesis is true. Could the data come from the population described by the null hypothesis? Use simulation or a mathematical model to examine the results from random samples selected from the population described by the null hypothesis. Figure out if results similar to the data are likely or unlikely. Note that the wording “likely or unlikely” implies that this step requires some kind of probability calculation.

Step 4: State a conclusion.

We use what we find in the previous step to make a decision. This step requires us to think in the following way. Remember that we assume that the null hypothesis is true. Then one of two outcomes can occur:

• One possibility is that results similar to the actual sample are extremely unlikely. This means that the data do not fit in with results from random samples selected from the population described by the null hypothesis. In this case, it is unlikely that the data came from this population, so we view this as strong evidence against the null hypothesis. We reject the null hypothesis in favor of the alternative hypothesis.
• The other possibility is that results similar to the actual sample are fairly likely (not unusual). This means that the data fit in with typical results from random samples selected from the population described by the null hypothesis. In this case, we do not have evidence against the null hypothesis, so we cannot reject it in favor of the alternative hypothesis.

Data Use on Smart Phones

According to an article by Andrew Berg (“Report: Teens Texting More, Using More Data,” Wireless Week , October 15, 2010), Nielsen Company analyzed cell phone usage for different age groups using cell phone bills and surveys. Nielsen found significant growth in data usage, particularly among teens, stating that “94 percent of teen subscribers self-identify as advanced data users, turning to their cellphones for messaging, Internet, multimedia, gaming, and other activities like downloads.” The study found that the mean cell phone data usage was 62 MB among teens ages 13 to 17. A researcher is curious whether cell phone data usage has increased for this age group since the original study was conducted. She plans to conduct a hypothesis test.

The null hypothesis is often a statement of “no change,” so the null hypothesis will state that there is no change in the mean cell phone data usage for this age group since the original study. In this case, the alternative hypothesis is that the mean has increased from 62 MB.

• H 0 : The mean data usage for teens with smart phones is still 62 MB.
• H a : The mean data usage for teens with smart phones is greater than 62 MB.

The next step is to obtain a sample and collect data that will allow the researcher to test the hypotheses. The sample must be representative of the population and, ideally, should be a random sample. In this case, the researcher must randomly sample teens who use smart phones.

For the purposes of this example, imagine that the researcher randomly samples 50 teens who use smart phones. She finds that the mean data usage for these teens was 75 MB with a standard deviation of 45 MB. Since it is greater than 62 MB, this sample mean provides some evidence in favor of the alternative hypothesis. But the researcher anticipates that samples will vary when the null hypothesis is true. So how much of a difference will make her doubt the null hypothesis? Does she have evidence strong enough to reject the null hypothesis?

To assess the evidence, the researcher needs to know how much variability to expect in random samples when the null hypothesis is true. She begins with the assumption that H 0 is true – in this case, that the mean data usage for teens is still 62 MB. She then determines how unusual the results of the sample are: If the mean for all teens with smart phones actually is 62 MB, what is the chance that a random sample of 50 teens will have a sample mean of 75 MB or higher? Obviously, this probability depends on how much variability there is in random samples of this size from this population.

The probability of observing a sample mean at least this high if the population mean is 62 MB is approximately 0.023 (later topics explain how to calculate this probability). The probability is quite small. It tells the researcher that if the population mean is actually 62 MB, a sample mean of 75 MB or higher will occur only about 2.3% of the time. This probability is called the P-value .

Note: The P-value is a conditional probability, discussed in the module Relationships in Categorical Data with Intro to Probability . The condition is the assumption that the null hypothesis is true.

Step 4: Conclusion.

The small P-value indicates that it is unlikely for a sample mean to be 75 MB or higher if the population has a mean of 62 MB. It is therefore unlikely that the data from these 50 teens came from a population with a mean of 62 MB. The evidence is strong enough to make the researcher doubt the null hypothesis, so she rejects the null hypothesis in favor of the alternative hypothesis. The researcher concludes that the mean data usage for teens with smart phones has increased since the original study. It is now greater than 62 MB. ( P = 0.023)

Notice that the P-value is included in the preceding conclusion, which is a common practice. It allows the reader to see the strength of the evidence used to draw the conclusion.

How Small Does the P-Value Have to Be to Reject the Null Hypothesis?

A small P-value indicates that it is unlikely that the actual sample data came from the population described by the null hypothesis. More specifically, a small P-value says that there is only a small chance that we will randomly select a sample with results at least as extreme as the data if H 0 is true. The smaller the P-value, the stronger the evidence against H 0 .

But how small does the P-value have to be in order to reject H 0 ?

In practice, we often compare the P-value to 0.05. We reject the null hypothesis in favor of the alternative if the P-value is less than (or equal to) 0.05.

Note: This means that sampling variability will produce results at least as extreme as the data 5% of the time. In other words, in the long run, 1 in 20 random samples will have results that suggest we should reject H 0 even when H 0 is true. This variability is just due to chance, but it is unusual enough that we are willing to say that results this rare suggest that H 0 is not true.

Statistical Significance: Another Way to Describe Unlikely Results

When the P-value is less than (or equal to) 0.05, we also say that the difference between the actual sample statistic and the assumed parameter value is statistically significant . In the previous example, the P-value is less than 0.05, so we say the difference between the sample mean (75 MB) and the assumed mean from the null hypothesis (62 MB) is statistically significant. You will also see this described as a significant difference . A significant difference is an observed difference that is too large to attribute to chance. In other words, it is a difference that is unlikely when we consider sampling variability alone. If the difference is statistically significant, we reject H 0 .

Other Observations about Stating Conclusions in a Hypothesis Test

In the example, the sample mean was greater than 62 MB. This fact alone does not suggest that the data supports the alternative hypothesis. We have to determine that the data is not only larger than 62 MB but larger than we would expect to see in a random sampling if the population mean is 62 MB. We therefore need to determine the P-value. If the sample mean was less than or equal to 62 MB, it would not support the alternative hypothesis. We don’t need to find a P-value in this case. The conclusion is clear without it.

We have to be very careful in how we state the conclusion. There are only two possibilities.

• We have enough evidence to reject the null hypothesis and support the alternative hypothesis.
• We do not have enough evidence to reject the null hypothesis, so there is not enough evidence to support the alternative hypothesis.

If the P-value in the previous example was greater than 0.05, then we would not have enough evidence to reject H 0 and accept H a . In this case our conclusion would be that “there is not enough evidence to show that the mean amount of data used by teens with smart phones has increased.” Notice that this conclusion answers the original research question. It focuses on the alternative hypothesis. It does not say “the null hypothesis is true.” We never accept the null hypothesis or state that it is true. When there is not enough evidence to reject H 0 , the conclusion will say, in essence, that “there is not enough evidence to support H a .” But of course we will state the conclusion in the specific context of the situation we are investigating.

We compared the P-value to 0.05 in the previous example. The number 0.05 is called the significance level for the test, because a P-value less than or equal to 0.05 is statistically significant (unlikely to have occurred solely by chance). The symbol we use for the significance level is α (the lowercase Greek letter alpha). We sometimes refer to the significance level as the α-level. We call this value the significance level because if the P-value is less than the significance level, we say the results of the test showed a significance difference.

If the P-value ≤ α, we reject the null hypothesis in favor of the alternative hypothesis.

If the P-value > α, we fail to reject the null hypothesis.

In practice, it is common to see 0.05 for the significance level. Occasionally, researchers use other significance levels. In particular, if rejecting H 0 will be controversial or expensive, we may require stronger evidence. In this case, a smaller significance level, such as 0.01, is used. As with the hypotheses, we should choose the significance level before collecting data. It is treated as an agreed-upon benchmark prior to conducting the hypothesis test. In this way, we can avoid arguments about the strength of the data.

We look more at how to choose the significance level later. On this page we continue to use a significance level of 0.05.

First let’s look at some exercises that focus on the P-value and its meaning. Then we’ll try some that cover the conclusion.

Learn By Doing

For many years, working full-time has meant working 40 hours per week. Nowadays, it seems that corporate employers expect their employees to work more than this amount. A researcher decides to investigate this hypothesis.

• H 0 : The average time full-time corporate employees work per week is 40 hours.
• H a : The average time full-time corporate employees work per week is more than 40 hours.

To substantiate his claim, the researcher randomly selects 250 corporate employees and finds that they work an average of 47 hours per week with a standard deviation of 3.2 hours.

According to the Centers for Disease Control (CDC), roughly 21.5% of all high school seniors in the United States have used marijuana. (The data were collected in 2002. The figure represents those who smoked during the month prior to the survey, so the actual figure might be higher.) A sociologist suspects that the rate among African American high school seniors is lower. In this case, then,

• H 0 : The rate of African American high-school seniors who have used marijuana is 21.5% (same as the overall rate of seniors).
• H a : The rate of African American high-school seniors who have used marijuana is lower than 21.5%.

To check his claim, the sociologist chooses a random sample of 375 African American high school seniors,and finds that 16.5% of them have used marijuana.

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Hypothesis Testing | A Step-by-Step Guide with Easy Examples

Published on November 8, 2019 by Rebecca Bevans . Revised on June 22, 2023.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics . It is most often used by scientists to test specific predictions, called hypotheses, that arise from theories.

There are 5 main steps in hypothesis testing:

• State your research hypothesis as a null hypothesis and alternate hypothesis (H o ) and (H a  or H 1 ).
• Collect data in a way designed to test the hypothesis.
• Perform an appropriate statistical test .
• Decide whether to reject or fail to reject your null hypothesis.
• Present the findings in your results and discussion section.

Though the specific details might vary, the procedure you will use when testing a hypothesis will always follow some version of these steps.

Table of contents

Step 1: state your null and alternate hypothesis, step 2: collect data, step 3: perform a statistical test, step 4: decide whether to reject or fail to reject your null hypothesis, step 5: present your findings, other interesting articles, frequently asked questions about hypothesis testing.

After developing your initial research hypothesis (the prediction that you want to investigate), it is important to restate it as a null (H o ) and alternate (H a ) hypothesis so that you can test it mathematically.

The alternate hypothesis is usually your initial hypothesis that predicts a relationship between variables. The null hypothesis is a prediction of no relationship between the variables you are interested in.

• H 0 : Men are, on average, not taller than women. H a : Men are, on average, taller than women.

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For a statistical test to be valid , it is important to perform sampling and collect data in a way that is designed to test your hypothesis. If your data are not representative, then you cannot make statistical inferences about the population you are interested in.

There are a variety of statistical tests available, but they are all based on the comparison of within-group variance (how spread out the data is within a category) versus between-group variance (how different the categories are from one another).

If the between-group variance is large enough that there is little or no overlap between groups, then your statistical test will reflect that by showing a low p -value . This means it is unlikely that the differences between these groups came about by chance.

Alternatively, if there is high within-group variance and low between-group variance, then your statistical test will reflect that with a high p -value. This means it is likely that any difference you measure between groups is due to chance.

Your choice of statistical test will be based on the type of variables and the level of measurement of your collected data .

• an estimate of the difference in average height between the two groups.
• a p -value showing how likely you are to see this difference if the null hypothesis of no difference is true.

Based on the outcome of your statistical test, you will have to decide whether to reject or fail to reject your null hypothesis.

In most cases you will use the p -value generated by your statistical test to guide your decision. And in most cases, your predetermined level of significance for rejecting the null hypothesis will be 0.05 – that is, when there is a less than 5% chance that you would see these results if the null hypothesis were true.

In some cases, researchers choose a more conservative level of significance, such as 0.01 (1%). This minimizes the risk of incorrectly rejecting the null hypothesis ( Type I error ).

The results of hypothesis testing will be presented in the results and discussion sections of your research paper , dissertation or thesis .

In the results section you should give a brief summary of the data and a summary of the results of your statistical test (for example, the estimated difference between group means and associated p -value). In the discussion , you can discuss whether your initial hypothesis was supported by your results or not.

In the formal language of hypothesis testing, we talk about rejecting or failing to reject the null hypothesis. You will probably be asked to do this in your statistics assignments.

However, when presenting research results in academic papers we rarely talk this way. Instead, we go back to our alternate hypothesis (in this case, the hypothesis that men are on average taller than women) and state whether the result of our test did or did not support the alternate hypothesis.

If your null hypothesis was rejected, this result is interpreted as “supported the alternate hypothesis.”

These are superficial differences; you can see that they mean the same thing.

You might notice that we don’t say that we reject or fail to reject the alternate hypothesis . This is because hypothesis testing is not designed to prove or disprove anything. It is only designed to test whether a pattern we measure could have arisen spuriously, or by chance.

If we reject the null hypothesis based on our research (i.e., we find that it is unlikely that the pattern arose by chance), then we can say our test lends support to our hypothesis . But if the pattern does not pass our decision rule, meaning that it could have arisen by chance, then we say the test is inconsistent with our hypothesis .

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

• Normal distribution
• Descriptive statistics
• Measures of central tendency
• Correlation coefficient

Methodology

• Cluster sampling
• Stratified sampling
• Types of interviews
• Cohort study
• Thematic analysis

Research bias

• Implicit bias
• Cognitive bias
• Survivorship bias
• Availability heuristic
• Nonresponse bias
• Regression to the mean

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 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).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

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2 Thinking like a researcher

Conducting good research requires first retraining your brain to think like a researcher. This requires visualising the abstract from actual observations, mentally ‘connecting the dots’ to identify hidden concepts and patterns, and synthesising those patterns into generalisable laws and theories that apply to other contexts beyond the domain of the initial observations. Research involves constantly moving back and forth from an empirical plane where observations are conducted to a theoretical plane where these observations are abstracted into generalizable laws and theories. This is a skill that takes many years to develop, is not something that is taught in postgraduate or doctoral programs or acquired in industry training, and is by far the biggest deficit amongst PhD students. Some of the mental abstractions needed to think like a researcher include unit of analysis, constructs, hypotheses, operationalisation, theories, models, induction, deduction, and so forth, which we will examine in this chapter.

Unit of analysis

One of the first decisions in any social science research is the unit of analysis of a scientific study. The unit of analysis refers to the person, collective, or object that is the target of the investigation. Typical units of analysis include individuals, groups, organisations, countries, technologies, objects, and such. For instance, if we are interested in studying people’s shopping behaviour, their learning outcomes, or their attitudes to new technologies, then the unit of analysis is the individual . If we want to study characteristics of street gangs or teamwork in organisations, then the unit of analysis is the group . If the goal of research is to understand how firms can improve profitability or make good executive decisions, then the unit of analysis is the firm . In this case, even though decisions are made by individuals in these firms, these individuals are presumed to represent their firm’s decision rather than their personal decisions. If research is directed at understanding differences in national cultures, then the unit of analysis becomes a country . Even inanimate objects can serve as units of analysis. For instance, if a researcher is interested in understanding how to make web pages more attractive to users, then the unit of analysis is a web page rather than users. If we wish to study how knowledge transfer occurs between two firms, then our unit of analysis becomes the dyad —the combination of firms that is sending and receiving knowledge.

Understanding the units of analysis can sometimes be fairly complex. For instance, if we wish to study why certain neighbourhoods have high crime rates, then our unit of analysis becomes the neighbourhood , and not crimes or criminals committing such crimes. This is because the object of our inquiry is the neighbourhood and not criminals. However, if we wish to compare different types of crimes in different neighbourhoods, such as homicide, robbery, assault, and so forth, our unit of analysis becomes the crime . If we wish to study why criminals engage in illegal activities, then the unit of analysis becomes the individual (i.e., the criminal). Likewise, if we want to study why some innovations are more successful than others, then our unit of analysis is an innovation . However, if we wish to study how some organisations innovate more consistently than others, then the unit of analysis is the organisation . Hence, two related research questions within the same research study may have two entirely different units of analysis.

Understanding the unit of analysis is important because it shapes what type of data you should collect for your study and who you collect it from. If your unit of analysis is a web page, you should be collecting data about web pages from actual web pages, and not surveying people about how they use web pages. If your unit of analysis is the organisation, then you should be measuring organisational-level variables such as organisational size, revenues, hierarchy, or absorptive capacity. This data may come from a variety of sources such as financial records or surveys of Chief Executive Officers (CEO), who are presumed to be representing their organisation rather than themselves. Some variables such as CEO pay may seem like individual level variables, but in fact, it can also be an organisational level variable because each organisation has only one CEO to pay at any time. Sometimes, it is possible to collect data from a lower level of analysis and aggregate that data to a higher level of analysis. For instance, in order to study teamwork in organisations, you can survey individual team members in different organisational teams, and average their individual scores to create a composite team-level score for team-level variables like cohesion and conflict. We will examine the notion of ‘variables’ in greater depth in the next section.

Concepts, constructs, and variables

We discussed in Chapter 1 that although research can be exploratory, descriptive, or explanatory, most scientific research tends to be of the explanatory type in that it searches for potential explanations for observed natural or social phenomena. Explanations require development of concepts or generalisable properties or characteristics associated with objects, events, or people. While objects such as a person, a firm, or a car are not concepts, their specific characteristics or behaviour such as a person’s attitude toward immigrants, a firm’s capacity for innovation, and a car’s weight can be viewed as concepts.

Knowingly or unknowingly, we use different kinds of concepts in our everyday conversations. Some of these concepts have been developed over time through our shared language. Sometimes, we borrow concepts from other disciplines or languages to explain a phenomenon of interest. For instance, the idea of gravitation borrowed from physics can be used in business to describe why people tend to ‘gravitate’ to their preferred shopping destinations. Likewise, the concept of distance can be used to explain the degree of social separation between two otherwise collocated individuals. Sometimes, we create our own concepts to describe a unique characteristic not described in prior research. For instance, technostress is a new concept referring to the mental stress one may face when asked to learn a new technology.

Concepts may also have progressive levels of abstraction. Some concepts such as a person’s weight are precise and objective, while other concepts such as a person’s personality may be more abstract and difficult to visualise. A construct is an abstract concept that is specifically chosen (or ‘created’) to explain a given phenomenon. A construct may be a simple concept, such as a person’s weight , or a combination of a set of related concepts such as a person’s communication skill , which may consist of several underlying concepts such as the person’s vocabulary, syntax , and spelling . The former instance (weight) is a unidimensional construct , while the latter (communication skill) is a multidimensional construct (i.e., it consists of multiple underlying concepts). The distinctions between constructs and concepts are clearer in multi-dimensional constructs, where the higher order abstraction is called a construct and the lower order abstractions are called concepts. However, this distinction tends to blur in the case of unidimensional constructs.

Constructs used for scientific research must have precise and clear definitions that others can use to understand exactly what it means and what it does not mean. For instance, a seemingly simple construct such as income may refer to monthly or annual income, before-tax or after-tax income, and personal or family income, and is therefore neither precise nor clear. There are two types of definitions: dictionary definitions and operational definitions. In the more familiar dictionary definition, a construct is often defined in terms of a synonym. For instance, attitude may be defined as a disposition, a feeling, or an affect, and affect in turn is defined as an attitude. Such definitions of a circular nature are not particularly useful in scientific research for elaborating the meaning and content of that construct. Scientific research requires operational definitions that define constructs in terms of how they will be empirically measured. For instance, the operational definition of a construct such as temperature must specify whether we plan to measure temperature in Celsius, Fahrenheit, or Kelvin scale. A construct such as income should be defined in terms of whether we are interested in monthly or annual income, before-tax or after-tax income, and personal or family income. One can imagine that constructs such as learning , personality , and intelligence can be quite hard to define operationally.

A term frequently associated with, and sometimes used interchangeably with, a construct is a variable. Etymologically speaking, a variable is a quantity that can vary (e.g., from low to high, negative to positive, etc.), in contrast to constants that do not vary (i.e., remain constant). However, in scientific research, a variable is a measurable representation of an abstract construct. As abstract entities, constructs are not directly measurable, and hence, we look for proxy measures called variables. For instance, a person’s intelligence is often measured as his or her IQ (intelligence quotient) score , which is an index generated from an analytical and pattern-matching test administered to people. In this case, intelligence is a construct, and IQ score is a variable that measures the intelligence construct. Whether IQ scores truly measure one’s intelligence is anyone’s guess (though many believe that they do), and depending on whether or how well it measures intelligence, the IQ score may be a good or a poor measure of the intelligence construct. As shown in Figure 2.1, scientific research proceeds along two planes: a theoretical plane and an empirical plane. Constructs are conceptualised at the theoretical (abstract) plane, while variables are operationalised and measured at the empirical (observational) plane. Thinking like a researcher implies the ability to move back and forth between these two planes.

Depending on their intended use, variables may be classified as independent, dependent, moderating, mediating, or control variables. Variables that explain other variables are called independent variables , those that are explained by other variables are dependent variables , those that are explained by independent variables while also explaining dependent variables are mediating variables (or intermediate variables), and those that influence the relationship between independent and dependent variables are called moderating variables . As an example, if we state that higher intelligence causes improved learning among students, then intelligence is an independent variable and learning is a dependent variable. There may be other extraneous variables that are not pertinent to explaining a given dependent variable, but may have some impact on the dependent variable. These variables must be controlled in a scientific study, and are therefore called control variables .

To understand the differences between these different variable types, consider the example shown in Figure 2.2. If we believe that intelligence influences (or explains) students’ academic achievement, then a measure of intelligence such as an IQ score is an independent variable, while a measure of academic success such as a GPA ( grade point average) is a dependent variable. If we believe that the effect of intelligence on academic achievement also depends on the effort invested by the student in the learning process (i.e., between two equally intelligent students, the student who puts in more effort achieves higher academic achievement than one who puts in less effort), then effort becomes a moderating variable. Incidentally, one may also view effort as an independent variable and intelligence as a moderating variable. If academic achievement is viewed as an intermediate step to higher earning potential, then earning potential becomes the dependent variable for the independent variable academic achievement , and academic achievement becomes the mediating variable in the relationship between intelligence and earning potential. Hence, variables are defined as an independent, dependent, moderating, or mediating variable based on their nature of association with each other. The overall network of relationships between a set of related constructs is called a nomological network (see Figure 2.2). Thinking like a researcher requires not only being able to abstract constructs from observations, but also being able to mentally visualise a nomological network linking these abstract constructs.

Propositions and hypotheses

Figure 2.2 shows how theoretical constructs such as intelligence, effort, academic achievement, and earning potential are related to each other in a nomological network. Each of these relationships is called a proposition. In seeking explanations to a given phenomenon or behaviour, it is not adequate just to identify key concepts and constructs underlying the target phenomenon or behaviour. We must also identify and state patterns of relationships between these constructs. Such patterns of relationships are called propositions. A proposition is a tentative and conjectural relationship between constructs that is stated in a declarative form. An example of a proposition is: ‘An increase in student intelligence causes an increase in their academic achievement’. This declarative statement does not have to be true, but must be empirically testable using data, so that we can judge whether it is true or false. Propositions are generally derived based on logic (deduction) or empirical observations (induction).

Because propositions are associations between abstract constructs, they cannot be tested directly. Instead, they are tested indirectly by examining the relationship between corresponding measures (variables) of those constructs. The empirical formulations of propositions, stated as relationships between variables, are called hypotheses (see Figure 2.1). Since IQ scores and GPAs are operational measures of intelligence and academic achievement respectively, the above proposition can be specified in form of the hypothesis: ‘An increase in students’ IQ score causes an increase in their GPA’. Propositions are specified in the theoretical plane, while hypotheses are specified in the empirical plane. Hence, hypotheses are empirically testable using observed data, and may be rejected if not supported by empirical observations. Of course, the goal of hypothesis testing is to infer whether the corresponding proposition is valid.

Hypotheses can be strong or weak. ‘Students’ IQ scores are related to their academic achievement’ is an example of a weak hypothesis, since it indicates neither the directionality of the hypothesis (i.e., whether the relationship is positive or negative), nor its causality (i.e., whether intelligence causes academic achievement or academic achievement causes intelligence). A stronger hypothesis is ‘students’ IQ scores are positively related to their academic achievement’, which indicates the directionality but not the causality. A still better hypothesis is ‘students’ IQ scores have positive effects on their academic achievement’, which specifies both the directionality and the causality (i.e., intelligence causes academic achievement, and not the reverse). The signs in Figure 2.2 indicate the directionality of the respective hypotheses.

Also note that scientific hypotheses should clearly specify independent and dependent variables. In the hypothesis, ‘students’ IQ scores have positive effects on their academic achievement’, it is clear that intelligence is the independent variable (the ‘cause’) and academic achievement is the dependent variable (the ‘effect’). Further, it is also clear that this hypothesis can be evaluated as either true (if higher intelligence leads to higher academic achievement) or false (if higher intelligence has no effect on or leads to lower academic achievement). Later on in this book, we will examine how to empirically test such cause-effect relationships. Statements such as ‘students are generally intelligent’ or ‘all students can achieve academic success’ are not scientific hypotheses because they do not specify independent and dependent variables, nor do they specify a directional relationship that can be evaluated as true or false.

Theories and models

A theory is a set of systematically interrelated constructs and propositions intended to explain and predict a phenomenon or behaviour of interest, within certain boundary conditions and assumptions. Essentially, a theory is a systemic collection of related theoretical propositions. While propositions generally connect two or three constructs, theories represent a system of multiple constructs and propositions. Hence, theories can be substantially more complex and abstract and of a larger scope than propositions or hypotheses.

I must note here that people unfamiliar with scientific research often view a theory as a speculation or the opposite of fact . For instance, people often say that teachers need to be less theoretical and more practical or factual in their classroom teaching. However, practice or fact are not opposites of theory, but in a scientific sense, are essential components needed to test the validity of a theory. A good scientific theory should be well supported using observed facts and should also have practical value, while a poorly defined theory tends to be lacking in these dimensions. Famous organisational researcher Kurt Lewin once said, ‘Theory without practice is sterile; practice without theory is blind’. Hence, both theory and facts (or practice) are essential for scientific research.

Theories provide explanations of social or natural phenomenon. As emphasised in Chapter 1, these explanations may be good or poor. Hence, there may be good or poor theories. Chapter 3 describes some criteria that can be used to evaluate how good a theory really is. Nevertheless, it is important for researchers to understand that theory is not ‘truth’, there is nothing sacrosanct about any theory, and theories should not be accepted just because they were proposed by someone. In the course of scientific progress, poorer theories are eventually replaced by better theories with higher explanatory power. The essential challenge for researchers is to build better and more comprehensive theories that can explain a target phenomenon better than prior theories.

A term often used in conjunction with theory is a model. A model is a representation of all or part of a system that is constructed to study that system (e.g., how the system works or what triggers the system). While a theory tries to explain a phenomenon, a model tries to represent a phenomenon. Models are often used by decision makers to make important decisions based on a given set of inputs. For instance, marketing managers may use models to decide how much money to spend on advertising for different product lines based on parameters such as prior year’s advertising expenses, sales, market growth, and competing products. Likewise, weather forecasters can use models to predict future weather patterns based on parameters such as wind speeds, wind direction, temperature, and humidity. While these models are useful, they may not necessarily explain advertising expenditure or weather forecasts. Models may be of different kinds, such as mathematical models, network models, and path models. Models can also be descriptive, predictive, or normative. Descriptive models are frequently used for representing complex systems, and for visualising variables and relationships in such systems. An advertising expenditure model may be a descriptive model. Predictive models (e.g., a regression model) allow forecast of future events. Weather forecasting models are predictive models. Normative models are used to guide our activities along commonly accepted norms or practices. Models may also be static if they represent the state of a system at one point in time, or dynamic, if they represent a system’s evolution over time.

The process of theory or model development may involve inductive and deductive reasoning. Recall from Chapter 1 that deduction is the process of drawing conclusions about a phenomenon or behaviour based on theoretical or logical reasons and an initial set of premises. As an example, if a certain bank enforces a strict code of ethics for its employees (Premise 1), and Jamie is an employee at that bank (Premise 2), then Jamie can be trusted to follow ethical practices (Conclusion). In deduction, the conclusions must be true if the initial premises and reasons are correct.

In contrast, induction is the process of drawing conclusions based on facts or observed evidence. For instance, if a firm spent a lot of money on a promotional campaign (Observation 1), but the sales did not increase (Observation 2), then possibly the promotion campaign was poorly executed (Conclusion). However, there may be rival explanations for poor sales, such as economic recession or the emergence of a competing product or brand or perhaps a supply chain problem. Inductive conclusions are therefore only a hypothesis, and may be disproven. Deductive conclusions generally tend to be stronger than inductive conclusions, but a deductive conclusion based on an incorrect premise is also incorrect.

As shown in Figure 2.3, inductive and deductive reasoning go hand in hand in theory and model building. Induction occurs when we observe a fact and ask, ‘Why is this happening?’. In answering this question, we advance one or more tentative explanations (hypotheses). We then use deduction to narrow down the tentative explanations to the most plausible explanation based on logic and reasonable premises (based on our understanding of the phenomenon under study). Researchers must be able to move back and forth between inductive and deductive reasoning if they are to post extensions or modifications to a given model or theory, or build better ones, both of which are the essence of scientific research.

Social Science Research: Principles, Methods and Practices (Revised edition) Copyright © 2019 by Anol Bhattacherjee is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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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. ↵
• Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach. Journal of Personality and Social Psychology, 13 , 83–92. ↵
• Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168. ↵