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Variables in Research – Definition, Types and Examples

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Variables in Research

Definition:

In Research, Variables refer to characteristics or attributes that can be measured, manipulated, or controlled. They are the factors that researchers observe or manipulate to understand the relationship between them and the outcomes of interest.

Types of Variables in Research

Types of Variables in Research are as follows:

Independent Variable

This is the variable that is manipulated by the researcher. It is also known as the predictor variable, as it is used to predict changes in the dependent variable. Examples of independent variables include age, gender, dosage, and treatment type.

Dependent Variable

This is the variable that is measured or observed to determine the effects of the independent variable. It is also known as the outcome variable, as it is the variable that is affected by the independent variable. Examples of dependent variables include blood pressure, test scores, and reaction time.

Confounding Variable

This is a variable that can affect the relationship between the independent variable and the dependent variable. It is a variable that is not being studied but could impact the results of the study. For example, in a study on the effects of a new drug on a disease, a confounding variable could be the patient’s age, as older patients may have more severe symptoms.

Mediating Variable

This is a variable that explains the relationship between the independent variable and the dependent variable. It is a variable that comes in between the independent and dependent variables and is affected by the independent variable, which then affects the dependent variable. For example, in a study on the relationship between exercise and weight loss, the mediating variable could be metabolism, as exercise can increase metabolism, which can then lead to weight loss.

Moderator Variable

This is a variable that affects the strength or direction of the relationship between the independent variable and the dependent variable. It is a variable that influences the effect of the independent variable on the dependent variable. For example, in a study on the effects of caffeine on cognitive performance, the moderator variable could be age, as older adults may be more sensitive to the effects of caffeine than younger adults.

Control Variable

This is a variable that is held constant or controlled by the researcher to ensure that it does not affect the relationship between the independent variable and the dependent variable. Control variables are important to ensure that any observed effects are due to the independent variable and not to other factors. For example, in a study on the effects of a new teaching method on student performance, the control variables could include class size, teacher experience, and student demographics.

Continuous Variable

This is a variable that can take on any value within a certain range. Continuous variables can be measured on a scale and are often used in statistical analyses. Examples of continuous variables include height, weight, and temperature.

Categorical Variable

This is a variable that can take on a limited number of values or categories. Categorical variables can be nominal or ordinal. Nominal variables have no inherent order, while ordinal variables have a natural order. Examples of categorical variables include gender, race, and educational level.

Discrete Variable

This is a variable that can only take on specific values. Discrete variables are often used in counting or frequency analyses. Examples of discrete variables include the number of siblings a person has, the number of times a person exercises in a week, and the number of students in a classroom.

Dummy Variable

This is a variable that takes on only two values, typically 0 and 1, and is used to represent categorical variables in statistical analyses. Dummy variables are often used when a categorical variable cannot be used directly in an analysis. For example, in a study on the effects of gender on income, a dummy variable could be created, with 0 representing female and 1 representing male.

Extraneous Variable

This is a variable that has no relationship with the independent or dependent variable but can affect the outcome of the study. Extraneous variables can lead to erroneous conclusions and can be controlled through random assignment or statistical techniques.

Latent Variable

This is a variable that cannot be directly observed or measured, but is inferred from other variables. Latent variables are often used in psychological or social research to represent constructs such as personality traits, attitudes, or beliefs.

Moderator-mediator Variable

This is a variable that acts both as a moderator and a mediator. It can moderate the relationship between the independent and dependent variables and also mediate the relationship between the independent and dependent variables. Moderator-mediator variables are often used in complex statistical analyses.

Variables Analysis Methods

There are different methods to analyze variables in research, including:

• Descriptive statistics: This involves analyzing and summarizing data using measures such as mean, median, mode, range, standard deviation, and frequency distribution. Descriptive statistics are useful for understanding the basic characteristics of a data set.
• Inferential statistics : This involves making inferences about a population based on sample data. Inferential statistics use techniques such as hypothesis testing, confidence intervals, and regression analysis to draw conclusions from data.
• Correlation analysis: This involves examining the relationship between two or more variables. Correlation analysis can determine the strength and direction of the relationship between variables, and can be used to make predictions about future outcomes.
• Regression analysis: This involves examining the relationship between an independent variable and a dependent variable. Regression analysis can be used to predict the value of the dependent variable based on the value of the independent variable, and can also determine the significance of the relationship between the two variables.
• Factor analysis: This involves identifying patterns and relationships among a large number of variables. Factor analysis can be used to reduce the complexity of a data set and identify underlying factors or dimensions.
• Cluster analysis: This involves grouping data into clusters based on similarities between variables. Cluster analysis can be used to identify patterns or segments within a data set, and can be useful for market segmentation or customer profiling.
• Multivariate analysis : This involves analyzing multiple variables simultaneously. Multivariate analysis can be used to understand complex relationships between variables, and can be useful in fields such as social science, finance, and marketing.

Examples of Variables

• Age : This is a continuous variable that represents the age of an individual in years.
• Gender : This is a categorical variable that represents the biological sex of an individual and can take on values such as male and female.
• Education level: This is a categorical variable that represents the level of education completed by an individual and can take on values such as high school, college, and graduate school.
• Income : This is a continuous variable that represents the amount of money earned by an individual in a year.
• Weight : This is a continuous variable that represents the weight of an individual in kilograms or pounds.
• Ethnicity : This is a categorical variable that represents the ethnic background of an individual and can take on values such as Hispanic, African American, and Asian.
• Time spent on social media : This is a continuous variable that represents the amount of time an individual spends on social media in minutes or hours per day.
• Marital status: This is a categorical variable that represents the marital status of an individual and can take on values such as married, divorced, and single.
• Blood pressure : This is a continuous variable that represents the force of blood against the walls of arteries in millimeters of mercury.
• Job satisfaction : This is a continuous variable that represents an individual’s level of satisfaction with their job and can be measured using a Likert scale.

Applications of Variables

Variables are used in many different applications across various fields. Here are some examples:

• Scientific research: Variables are used in scientific research to understand the relationships between different factors and to make predictions about future outcomes. For example, scientists may study the effects of different variables on plant growth or the impact of environmental factors on animal behavior.
• Business and marketing: Variables are used in business and marketing to understand customer behavior and to make decisions about product development and marketing strategies. For example, businesses may study variables such as consumer preferences, spending habits, and market trends to identify opportunities for growth.
• Healthcare : Variables are used in healthcare to monitor patient health and to make treatment decisions. For example, doctors may use variables such as blood pressure, heart rate, and cholesterol levels to diagnose and treat cardiovascular disease.
• Education : Variables are used in education to measure student performance and to evaluate the effectiveness of teaching strategies. For example, teachers may use variables such as test scores, attendance, and class participation to assess student learning.
• Social sciences : Variables are used in social sciences to study human behavior and to understand the factors that influence social interactions. For example, sociologists may study variables such as income, education level, and family structure to examine patterns of social inequality.

Purpose of Variables

Variables serve several purposes in research, including:

• To provide a way of measuring and quantifying concepts: Variables help researchers measure and quantify abstract concepts such as attitudes, behaviors, and perceptions. By assigning numerical values to these concepts, researchers can analyze and compare data to draw meaningful conclusions.
• To help explain relationships between different factors: Variables help researchers identify and explain relationships between different factors. By analyzing how changes in one variable affect another variable, researchers can gain insight into the complex interplay between different factors.
• To make predictions about future outcomes : Variables help researchers make predictions about future outcomes based on past observations. By analyzing patterns and relationships between different variables, researchers can make informed predictions about how different factors may affect future outcomes.
• To test hypotheses: Variables help researchers test hypotheses and theories. By collecting and analyzing data on different variables, researchers can test whether their predictions are accurate and whether their hypotheses are supported by the evidence.

Characteristics of Variables

Characteristics of Variables are as follows:

• Measurement : Variables can be measured using different scales, such as nominal, ordinal, interval, or ratio scales. The scale used to measure a variable can affect the type of statistical analysis that can be applied.
• Range : Variables have a range of values that they can take on. The range can be finite, such as the number of students in a class, or infinite, such as the range of possible values for a continuous variable like temperature.
• Variability : Variables can have different levels of variability, which refers to the degree to which the values of the variable differ from each other. Highly variable variables have a wide range of values, while low variability variables have values that are more similar to each other.
• Validity and reliability : Variables should be both valid and reliable to ensure accurate and consistent measurement. Validity refers to the extent to which a variable measures what it is intended to measure, while reliability refers to the consistency of the measurement over time.
• Directionality: Some variables have directionality, meaning that the relationship between the variables is not symmetrical. For example, in a study of the relationship between smoking and lung cancer, smoking is the independent variable and lung cancer is the dependent variable.

Advantages of Variables

Here are some of the advantages of using variables in research:

• Control : Variables allow researchers to control the effects of external factors that could influence the outcome of the study. By manipulating and controlling variables, researchers can isolate the effects of specific factors and measure their impact on the outcome.
• Replicability : Variables make it possible for other researchers to replicate the study and test its findings. By defining and measuring variables consistently, other researchers can conduct similar studies to validate the original findings.
• Accuracy : Variables make it possible to measure phenomena accurately and objectively. By defining and measuring variables precisely, researchers can reduce bias and increase the accuracy of their findings.
• Generalizability : Variables allow researchers to generalize their findings to larger populations. By selecting variables that are representative of the population, researchers can draw conclusions that are applicable to a broader range of individuals.
• Clarity : Variables help researchers to communicate their findings more clearly and effectively. By defining and categorizing variables, researchers can organize and present their findings in a way that is easily understandable to others.

Disadvantages of Variables

Here are some of the main disadvantages of using variables in research:

• Simplification : Variables may oversimplify the complexity of real-world phenomena. By breaking down a phenomenon into variables, researchers may lose important information and context, which can affect the accuracy and generalizability of their findings.
• Measurement error : Variables rely on accurate and precise measurement, and measurement error can affect the reliability and validity of research findings. The use of subjective or poorly defined variables can also introduce measurement error into the study.
• Confounding variables : Confounding variables are factors that are not measured but that affect the relationship between the variables of interest. If confounding variables are not accounted for, they can distort or obscure the relationship between the variables of interest.
• Limited scope: Variables are defined by the researcher, and the scope of the study is therefore limited by the researcher’s choice of variables. This can lead to a narrow focus that overlooks important aspects of the phenomenon being studied.
• Ethical concerns: The selection and measurement of variables may raise ethical concerns, especially in studies involving human subjects. For example, using variables that are related to sensitive topics, such as race or sexuality, may raise concerns about privacy and discrimination.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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2.2: Concepts, Constructs, and Variables

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• Page ID 26212

• Anol Bhattacherjee
• University of South Florida via Global Text Project

We discussed in Chapter 1 that although research can be exploratory, descriptive, or explanatory, most scientific research tend to be of the explanatory type in that they search for potential explanations of observed natural or social phenomena. Explanations require development of concepts or generalizable 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 behavior 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 visualize. 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 multi-dimensional construct (i.e., it consists of multiple underlying concepts). The distinction 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 measures one’s intelligence is anyone’s guess (though many believe that they do), and depending on whether 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 conceptualized at the theoretical (abstract) plane, while variables are operationalized 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 for 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 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 is 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, variable 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 visualize a nomological network linking these abstract constructs.

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• Types of Variables in Research | Definitions & Examples

Types of Variables in Research | Definitions & Examples

Published on 19 September 2022 by Rebecca Bevans . Revised on 28 November 2022.

In statistical research, a variable is defined as an attribute of an object of study. Choosing which variables to measure is central to good experimental design .

You need to know which types of variables you are working with in order to choose appropriate statistical tests and interpret the results of your study.

You can usually identify the type of variable by asking two questions:

• What type of data does the variable contain?
• What part of the experiment does the variable represent?

Table of contents

Types of data: quantitative vs categorical variables, parts of the experiment: independent vs dependent variables, other common types of variables, frequently asked questions about variables.

Data is a specific measurement of a variable – it is the value you record in your data sheet. Data is generally divided into two categories:

• Quantitative data represents amounts.
• Categorical data represents groupings.

A variable that contains quantitative data is a quantitative variable ; a variable that contains categorical data is a categorical variable . Each of these types of variable can be broken down into further types.

Quantitative variables

When you collect quantitative data, the numbers you record represent real amounts that can be added, subtracted, divided, etc. There are two types of quantitative variables: discrete and continuous .

Categorical variables

Categorical variables represent groupings of some kind. They are sometimes recorded as numbers, but the numbers represent categories rather than actual amounts of things.

There are three types of categorical variables: binary , nominal , and ordinal variables.

*Note that sometimes a variable can work as more than one type! An ordinal variable can also be used as a quantitative variable if the scale is numeric and doesn’t need to be kept as discrete integers. For example, star ratings on product reviews are ordinal (1 to 5 stars), but the average star rating is quantitative.

Example data sheet

To keep track of your salt-tolerance experiment, you make a data sheet where you record information about the variables in the experiment, like salt addition and plant health.

To gather information about plant responses over time, you can fill out the same data sheet every few days until the end of the experiment. This example sheet is colour-coded according to the type of variable: nominal , continuous , ordinal , and binary .

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Experiments are usually designed to find out what effect one variable has on another – in our example, the effect of salt addition on plant growth.

You manipulate the independent variable (the one you think might be the cause ) and then measure the dependent variable (the one you think might be the effect ) to find out what this effect might be.

You will probably also have variables that you hold constant ( control variables ) in order to focus on your experimental treatment.

In this experiment, we have one independent and three dependent variables.

The other variables in the sheet can’t be classified as independent or dependent, but they do contain data that you will need in order to interpret your dependent and independent variables.

What about correlational research?

When you do correlational research , the terms ‘dependent’ and ‘independent’ don’t apply, because you are not trying to establish a cause-and-effect relationship.

However, there might be cases where one variable clearly precedes the other (for example, rainfall leads to mud, rather than the other way around). In these cases, you may call the preceding variable (i.e., the rainfall) the predictor variable and the following variable (i.e., the mud) the outcome variable .

Once you have defined your independent and dependent variables and determined whether they are categorical or quantitative, you will be able to choose the correct statistical test .

But there are many other ways of describing variables that help with interpreting your results. Some useful types of variable are listed below.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g., the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g., water volume or weight).

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

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Unit 8: Theory…and Research…and Methods (oh my!)

31 Variables; Operational and Conceptual Definitions

Listen, this whole “conceptual and operational definition” stuff might seem painfully boring but it’s actually one of the most useful Superpowers in your SYBI toolbox. The disconnect between the actual concept, the conceptual definition, and the operational definition is more prevalent than you think! And the disconnect between the scholar’s ConceptConceptualDefinitionOperationalDefinition and the average journalist’s perception? Oi ve! It’s enough to make you want to laterally read EVERYTHING that comes your way. At least, I hope it does. Let’s start in nice and slow and think about what are variables anyway? Student textbook authors: Take it away!

Learning Objectives

What is a variable?

Variables; Operational and Conceptual Definitions

Many of you have probably heard of or know what a variable from other classes like algebra. Variables are important in research because they help define and measure what is being researched. In this unit you should be able to define a variable and know the two main components of  variable.

Variables in social scientific research are similar to what you have learned in math classes, meaning they change depending on another element.

There are two components of a variable:

• A conceptual definition
• An operational definition

Conceptual Definitions- How we define something. It is the foundation of your research question because you must know  what  something is before you study its’ impact.

Example: How do Americans define the term freedom?

Operational Definitions- How we measure the variable. This is what you would typically think of when asked about the relationship between research and the research question. It relies on the conceptual definition.

Example: How do we measure what it means to have freedom?

Find the variables memory game .

Link to the “test” I mention in the video below:

https://www.idrlabs.com/gender-coordinates/test.php

“Researchers H. Heilman, Ph.D. and C. Peus, Ph.D. used a multidimensional framework to assess how people view men and women respectively. Their research results found that men and women consistently ascribe the same characteristics to each gender.”

Give it a whirl , take the “test.” What do YOU think about how they have operationalized the concept of gender?

Humor me and read the information below the start of the questions when you visit that link [1] .

This second link takes you to a different test but of the same basic concept. This is the one I referenced as “Bem’s Sex Role Inventory [2] .” https://www.idrlabs.com/gender/test.php

Ok, so take this one too (it really doesn’t take long, I promise). What do you think about the questions? Did you “score” the same? If not, why do you think that is? What does that say about operationalizing the concept? In future chapters I’ll ask you to think about what this would say about results and implications! I know – you are so excited!!

Also. Was not exaggerating my results:

First image is coordinates (IN the blue box), second is Bem’s (under the blue box)

Got ideas for questions to include on the exam?

Click this link to add them!

… Unit 1 … Unit 2 …. Unit 3 … Unit 4 … Unit 5 … Unit 6 … Unit 7 … Unit 8 … Unit 9 … Unit 10 … Unit 11 … Unit 12 … Unit 13 … Unit 14 … Unit 15 … Unit 16 …

VIII . Unit 8: Theory…and Research…and Methods (oh my!)

28. Logical Systems: Induction and Deduction

29. Variables; Operational and Conceptual Definitions

30. Variable oh variable! Wherefore art thou o’ variable?

31. On being skeptical [about concepts and variables]

Gender Coordinates Test

Based on the work of heilman and peus, question 1 of 35.

Self-confident

• "Drawing on the work of Dr. Sandra Lipsitz Bem, this test classifies your personality as masculine or feminine. Though gender stereotyping is controversial, it is important to note that Bem's work has been tested in several countries and has repeatedly been shown to have high levels of validity and test-retest reliability. The test exclusively tests for immanent conceptions of gender (meaning that it doesn't theorize about whether gender roles are biological, cultural, or both). Consequently, the test has been used both by feminists as an instrument of cultural criticism and by gender traditionalists who seek to confirm that gender roles are natural and heritable." ↵

Introduction to Social Scientific Research Methods in the field of Communication 3rd Ed - under construction for Fall 2023 Copyright © 2023 by Kate Magsamen-Conrad. All Rights Reserved.

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2 Chapter 2: Principles of Research

Principles of research, 2.1  basic concepts.

Before we address where research questions in psychology come from—and what makes them more or less interesting—it is important to understand the kinds of questions that researchers in psychology typically ask. This requires a quick introduction to several basic concepts, many of which we will return to in more detail later in the book.

Research questions in psychology are about variables. A variable is a quantity or quality that varies across people or situations. For example, the height of the students in a psychology class is a variable because it varies from student to student. The sex of the students is also a variable as long as there are both male and female students in the class. A quantitative variable is a quantity, such as height, that is typically measured by assigning a number to each individual. Other examples of quantitative variables include people’s level of talkativeness, how depressed they are, and the number of siblings they have. A categorical variable is a quality, such as sex, and is typically measured by assigning a category label to each individual. Other examples include people’s nationality, their occupation, and whether they are receiving psychotherapy.

“Lots of Candy Could Lead to Violence”

Although researchers in psychology know that  correlation does not imply causation , many journalists do not. Many headlines suggest that a causal relationship has been demonstrated, when a careful reading of the articles shows that it has not because of the directionality and third-variable problems.

One article is about a study showing that children who ate candy every day were more likely than other children to be arrested for a violent offense later in life. But could candy really “lead to” violence, as the headline suggests? What alternative explanations can you think of for this statistical relationship? How could the headline be rewritten so that it is not misleading?

As we will see later in the book, there are various ways that researchers address the directionality and third-variable problems. The most effective, however, is to conduct an experiment. An experiment is a study in which the researcher manipulates the independent variable. For example, instead of simply measuring how much people exercise, a researcher could bring people into a laboratory and randomly assign half of them to run on a treadmill for 15 minutes and the rest to sit on a couch for 15 minutes. Although this seems like a minor addition to the research design, it is extremely important. Now if the exercisers end up in more positive moods than those who did not exercise, it cannot be because their moods affected how much they exercised (because it was the researcher who determined how much they exercised). Likewise, it cannot be because some third variable (e.g., physical health) affected both how much they exercised and what mood they were in (because, again, it was the researcher who determined how much they exercised). Thus experiments eliminate the directionality and third-variable problems and allow researchers to draw firm conclusions about causal relationships.

2.2  Generating Good Research Questions

Good research must begin with a good research question. Yet coming up with good research questions is something that novice researchers often find difficult and stressful. One reason is that this is a creative process that can appear mysterious—even magical—with experienced researchers seeming to pull interesting research questions out of thin air. However, psychological research on creativity has shown that it is neither as mysterious nor as magical as it appears. It is largely the product of ordinary thinking strategies and persistence (Weisberg, 1993). This section covers some fairly simple strategies for finding general research ideas, turning those ideas into empirically testable research questions, and finally evaluating those questions in terms of how interesting they are and how feasible they would be to answer.

Finding Inspiration

Research questions often begin as more general research ideas—usually focusing on some behaviour or psychological characteristic: talkativeness, memory for touches, depression, bungee jumping, and so on. Before looking at how to turn such ideas into empirically testable research questions, it is worth looking at where such ideas come from in the first place. Three of the most common sources of inspiration are informal observations, practical problems, and previous research.

Informal observations include direct observations of our own and others’ behaviour as well as secondhand observations from nonscientific sources such as newspapers, books, and so on. For example, you might notice that you always seem to be in the slowest moving line at the grocery store. Could it be that most people think the same thing? Or you might read in the local newspaper about people donating money and food to a local family whose house has burned down and begin to wonder about who makes such donations and why. Some of the most famous research in psychology has been inspired by informal observations. Stanley Milgram’s famous research on obedience, for example, was inspired in part by journalistic reports of the trials of accused Nazi war criminals—many of whom claimed that they were only obeying orders. This led him to wonder about the extent to which ordinary people will commit immoral acts simply because they are ordered to do so by an authority figure (Milgram, 1963).

Practical problems can also inspire research ideas, leading directly to applied research in such domains as law, health, education, and sports. Can human figure drawings help children remember details about being physically or sexually abused? How effective is psychotherapy for depression compared to drug therapy? To what extent do cell phones impair people’s driving ability? How can we teach children to read more efficiently? What is the best mental preparation for running a marathon?

Probably the most common inspiration for new research ideas, however, is previous research. Recall that science is a kind of large-scale collaboration in which many different researchers read and evaluate each other’s work and conduct new studies to build on it. Of course, experienced researchers are familiar with previous research in their area of expertise and probably have a long list of ideas. This suggests that novice researchers can find inspiration by consulting with a more experienced researcher (e.g., students can consult a faculty member). But they can also find inspiration by picking up a copy of almost any professional journal and reading the titles and abstracts. In one typical issue of Psychological Science, for example, you can find articles on the perception of shapes, anti-Semitism, police lineups, the meaning of death, second-language learning, people who seek negative emotional experiences, and many other topics. If you can narrow your interests down to a particular topic (e.g., memory) or domain (e.g., health care), you can also look through more specific journals, such as Memory Cognition or Health Psychology.

Generating Empirically Testable Research Questions

Once you have a research idea, you need to use it to generate one or more empirically testable research questions, that is, questions expressed in terms of a single variable or relationship between variables. One way to do this is to look closely at the discussion section in a recent research article on the topic. This is the last major section of the article, in which the researchers summarize their results, interpret them in the context of past research, and suggest directions for future research. These suggestions often take the form of specific research questions, which you can then try to answer with additional research. This can be a good strategy because it is likely that the suggested questions have already been identified as interesting and important by experienced researchers.

But you may also want to generate your own research questions. How can you do this? First, if you have a particular behaviour or psychological characteristic in mind, you can simply conceptualize it as a variable and ask how frequent or intense it is. How many words on average do people speak per day? How accurate are children’s memories of being touched? What percentage of people have sought professional help for depression? If the question has never been studied scientifically—which is something that you will learn in your literature review—then it might be interesting and worth pursuing.

If scientific research has already answered the question of how frequent or intense the behaviour or characteristic is, then you should consider turning it into a question about a statistical relationship between that behaviour or characteristic and some other variable. One way to do this is to ask yourself the following series of more general questions and write down all the answers you can think of.

·         What are some possible causes of the behaviour or characteristic?

·         What are some possible effects of the behaviour or characteristic?

·         What types of people might exhibit more or less of the behaviour or characteristic?

·         What types of situations might elicit more or less of the behaviour or characteristic?

In general, each answer you write down can be conceptualized as a second variable, suggesting a question about a statistical relationship. If you were interested in talkativeness, for example, it might occur to you that a possible cause of this psychological characteristic is family size. Is there a statistical relationship between family size and talkativeness? Or it might occur to you that people seem to be more talkative in same-sex groups than mixed-sex groups. Is there a difference in the average level of talkativeness of people in same-sex groups and people in mixed-sex groups? This approach should allow you to generate many different empirically testable questions about almost any behaviour or psychological characteristic.

If through this process you generate a question that has never been studied scientifically—which again is something that you will learn in your literature review—then it might be interesting and worth pursuing. But what if you find that it has been studied scientifically? Although novice researchers often want to give up and move on to a new question at this point, this is not necessarily a good strategy. For one thing, the fact that the question has been studied scientifically and the research published suggests that it is of interest to the scientific community. For another, the question can almost certainly be refined so that its answer will still contribute something new to the research literature. Again, asking yourself a series of more general questions about the statistical relationship is a good strategy.

·         Are there other ways to operationally define the variables?

·         Are there types of people for whom the statistical relationship might be stronger or weaker?

·         Are there situations in which the statistical relationship might be stronger or weaker—including situations with practical importance?

For example, research has shown that women and men speak about the same number of words per day—but this was when talkativeness was measured in terms of the number of words spoken per day among college students in the United States and Mexico. We can still ask whether other ways of measuring talkativeness—perhaps the number of different people spoken to each day—produce the same result. Or we can ask whether studying elderly people or people from other cultures produces the same result. Again, this approach should help you generate many different research questions about almost any statistical relationship.

2.3  Evaluating Research Questions

Researchers usually generate many more research questions than they ever attempt to answer. This means they must have some way of evaluating the research questions they generate so that they can choose which ones to pursue. In this section, we consider two criteria for evaluating research questions: the interestingness of the question and the feasibility of answering it.

Interestingness

How often do people tie their shoes? Do people feel pain when you punch them in the jaw? Are women more likely to wear makeup than men? Do people prefer vanilla or chocolate ice cream? Although it would be a fairly simple matter to design a study and collect data to answer these questions, you probably would not want to because they are not interesting. We are not talking here about whether a research question is interesting to us personally but whether it is interesting to people more generally and, especially, to the scientific community. But what makes a research question interesting in this sense? Here we look at three factors that affect the interestingness of a research question: the answer is in doubt, the answer fills a gap in the research literature, and the answer has important practical implications.

First, a research question is interesting to the extent that its answer is in doubt. Obviously, questions that have been answered by scientific research are no longer interesting as the subject of new empirical research. But the fact that a question has not been answered by scientific research does not necessarily make it interesting. There has to be some reasonable chance that the answer to the question will be something that we did not already know. But how can you assess this before actually collecting data? One approach is to try to think of reasons to expect different answers to the question—especially ones that seem to conflict with common sense. If you can think of reasons to expect at least two different answers, then the question might be interesting. If you can think of reasons to expect only one answer, then it probably is not. The question of whether women are more talkative than men is interesting because there are reasons to expect both answers. The existence of the stereotype itself suggests the answer could be yes, but the fact that women’s and men’s verbal abilities are fairly similar suggests the answer could be no. The question of whether people feel pain when you punch them in the jaw is not interesting because there is absolutely no reason to think that the answer could be anything other than a resounding yes.

A second important factor to consider when deciding if a research question is interesting is whether answering it will fill a gap in the research literature. Again, this means in part that the question has not already been answered by scientific research. But it also means that the question is in some sense a natural one for people who are familiar with the research literature. For example, the question of whether human figure drawings can help children recall touch information would be likely to occur to anyone who was familiar with research on the unreliability of eyewitness memory (especially in children) and the ineffectiveness of some alternative interviewing techniques.

A final factor to consider when deciding whether a research question is interesting is whether its answer has important practical implications. Again, the question of whether human figure drawings help children recall information about being touched has important implications for how children are interviewed in physical and sexual abuse cases. The question of whether cell phone use impairs driving is interesting because it is relevant to the personal safety of everyone who travels by car and to the debate over whether cell phone use should be restricted by law.

Feasibility

A second important criterion for evaluating research questions is the feasibility of successfully answering them. There are many factors that affect feasibility, including time, money, equipment and materials, technical knowledge and skill, and access to research participants. Clearly, researchers need to take these factors into account so that they do not waste time and effort pursuing research that they cannot complete successfully.

Looking through a sample of professional journals in psychology will reveal many studies that are complicated and difficult to carry out. These include longitudinal designs in which participants are tracked over many years, neuroimaging studies in which participants’ brain activity is measured while they carry out various mental tasks, and complex non-experimental studies involving several variables and complicated statistical analyses. Keep in mind, though, that such research tends to be carried out by teams of highly trained researchers whose work is often supported in part by government and private grants. Keep in mind also that research does not have to be complicated or difficult to produce interesting and important results. Looking through a sample of professional journals will also reveal studies that are relatively simple and easy to carry out—perhaps involving a convenience sample of college students and a paper-and-pencil task.

A final point here is that it is generally good practice to use methods that have already been used successfully by other researchers. For example, if you want to manipulate people’s moods to make some of them happy, it would be a good idea to use one of the many approaches that have been used successfully by other researchers (e.g., paying them a compliment). This is good not only for the sake of feasibility—the approach is “tried and true”—but also because it provides greater continuity with previous research. This makes it easier to compare your results with those of other researchers and to understand the implications of their research for yours, and vice versa.

Key Takeaways

·         Research ideas can come from a variety of sources, including informal observations, practical problems, and previous research.

·         Research questions expressed in terms of variables and relationships between variables can be suggested by other researchers or generated by asking a series of more general questions about the behaviour or psychological characteristic of interest.

·         It is important to evaluate how interesting a research question is before designing a study and collecting data to answer it. Factors that affect interestingness are the extent to which the answer is in doubt, whether it fills a gap in the research literature, and whether it has important practical implications.

·         It is also important to evaluate how feasible a research question will be to answer. Factors that affect feasibility include time, money, technical knowledge and skill, and access to special equipment and research participants.

References from Chapter 2

Milgram, S. (1963). Behavioral study of obedience. Journal of Abnormal and Social Psychology, 67, 371–378.

Stanovich, K. E. (2010). How to think straight about psychology (9th ed.). Boston, MA: Allyn Bacon.

Weisberg, R. W. (1993). Creativity: Beyond the myth of genius. New York, NY: Freeman.

Research Methods in Psychology & Neuroscience Copyright © by Dalhousie University Introduction to Psychology and Neuroscience Team. All Rights Reserved.

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Scientific Research and Methodology

2.2 conceptual and operational definitions.

Research studies usually include terms that must be carefully and precisely defined, so that others know exactly what has been done and there are no ambiguities. Two types of definitions can be given: conceptual definitions and operational definitions .

Loosely speaking, a conceptual definition explains what to measure or observe (what a word or a term means for your study), and an operational definitions defines exactly how to measure or observe it.

For example, in a study of stress in students during a university semester. A conceptual definition would describe what is meant by ‘stress.’ An operational definition would describe how the ‘stress’ would be measured.

Sometimes the definitions themselves aren’t important, provided a clear definition is given. Sometimes, commonly-accepted definitions exist, so should be used unless there is a good reason to use a different definition (for example, in criminal law, an ‘adult’ in Australia is someone aged 18 or over ).

Sometimes, a commonly-accepted definition does not exist, so the definition being used should be clearly articulated.

Example 2.2 (Operational and conceptual definitions) Players and fans have become more aware of concussions and head injuries in sport. A Conference on concussion in sport developed this conceptual definition ( McCrory et al. 2013 ) :

Concussion is a brain injury and is defined as a complex pathophysiological process affecting the brain, induced by biomechanical forces. Several common features that incorporate clinical, pathologic and biomechanical injury constructs that may be utilised in defining the nature of a concussive head injury include: Concussion may be caused either by a direct blow to the head, face, neck or elsewhere on the body with an “impulsive” force transmitted to the head. Concussion typically results in the rapid onset of short-lived impairment of neurological function that resolves spontaneously. However, in some cases, symptoms and signs may evolve over a number of minutes to hours. Concussion may result in neuropathological changes, but the acute clinical symptoms largely reflect a functional disturbance rather than a structural injury and, as such, no abnormality is seen on standard structural neuroimaging studies. Concussion results in a graded set of clinical symptoms that may or may not involve loss of consciousness. Resolution of the clinical and cognitive symptoms typically follows a sequential course. However, it is important to note that in some cases symptoms may be prolonged.

While this is all helpful… it does not explain how to identify a player with concussion during a game.

Rugby decided on this operational definition ( Raftery et al. 2016 ) :

… a concussion applies with any of the following: The presence, pitch side, of any Criteria Set 1 signs or symptoms (table 1)… [ Note : This table includes symptoms such as ‘convulsion,’ ‘clearly dazed,’ etc.]; An abnormal post game, same day assessment…; An abnormal 36–48 h assessment…; The presence of clinical suspicion by the treating doctor at any time…

Example 2.3 (Operational and conceptual definitions) Consider a study requiring water temperature to be measured.

An operational definition would explain how the temperature is measured: the thermometer type, how the thermometer was positioned, how long was it left in the water, and so on.

Example 2.4 (Operational definitions) Consider a study measuring stress in first-year university students.

Stress cannot be measured directly, but could be assessed using a survey (like the Perceived Stress Scale (PSS) ( Cohen et al. 1983 ) ).

The operational definition of stress is the score on the ten-question PSS. Other means of measuring stress are also possible (such as heart rate or blood pressure).

Meline ( 2006 ) discusses five studies about stuttering, each using a different operational definition:

• Study 1: As diagnosed by speech-language pathologist.
• Study 2: Within-word disfluences greater than 5 per 150 words.
• Study 3: Unnatural hesitation, interjections, restarted or incomplete phrases, etc.
• Study 4: More than 3 stuttered words per minute.
• Study 5: State guidelines for fluency disorders.

A study of snacking in Australia ( Fayet-Moore et al. 2017 ) used this operational definition of ‘snacking’:

…an eating occasion that occurred between meals based on time of day. — Fayet-Moore et al. ( 2017 ) (p. 3)

A study examined the possible relationship between the ‘pace of life’ and the incidence of heart disease ( Levine 1990 ) in 36 US cities. The researchers used four different operational definitions for ‘pace of life’ (remember the article was published in 1990!):

• The walking speed of randomly chosen pedestrians.
• The speed with which bank clerks gave ‘change for two $20 bills or [gave] two $20 bills for change.’
• The talking speed of postal clerks.
• The proportion of men and women wearing a wristwatch.

None of these perfectly measure ‘pace of life,’ of course. Nonetheless, the researchers found that, compared to people on the West Coast,

… people in the Northeast walk faster, make change faster, talk faster and are more likely to wear a watch… — Levine ( 1990 ) (p. 455)

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Roles of Independent and Dependent Variables in Research

Explore the essential roles of independent and dependent variables in research. This guide delves into their definitions, significance in experiments, and their critical relationship. Learn how these variables are the foundation of research design, influencing hypothesis testing, theory development, and statistical analysis, empowering researchers to understand and predict outcomes of research studies.

Table of Contents

Introduction.

At the very base of scientific inquiry and research design , variables act as the fundamental steps, guiding the rhythm and direction of research. This is particularly true in human behavior research, where the quest to understand the complexities of human actions and reactions hinges on the meticulous manipulation and observation of these variables. At the heart of this endeavor lie two different types of variables, namely: independent and dependent variables, whose roles and interplay are critical in scientific discovery.

Understanding the distinction between independent and dependent variables is not merely an academic exercise; it is essential for anyone venturing into the field of research. This article aims to demystify these concepts, offering clarity on their definitions, roles, and the nuances of their relationship in the study of human behavior, and in science generally. We will cover hypothesis testing and theory development, illuminating how these variables serve as the cornerstone of experimental design and statistical analysis.

The significance of grasping the difference between independent and dependent variables extends beyond the confines of academia. It empowers researchers to design robust studies, enables critical evaluation of research findings, and fosters an appreciation for the complexity of human behavior research. As we delve into this exploration, our objective is clear: to equip readers with a deep understanding of these fundamental concepts, enhancing their ability to contribute to the ever-evolving field of human behavior research.

Chapter 1: The Role of Independent Variables in Human Behavior Research

In the realm of human behavior research, independent variables are the keystones around which studies are designed and hypotheses are tested. Independent variables are the factors or conditions that researchers manipulate or observe to examine their effects on dependent variables, which typically reflect aspects of human behavior or psychological phenomena. Understanding the role of independent variables is crucial for designing robust research methodologies, ensuring the reliability and validity of findings.

Defining Independent Variables

Independent variables are those variables that are changed or controlled in a scientific experiment to test the effects on dependent variables. In studies focusing on human behavior, these can range from psychological interventions (e.g., cognitive-behavioral therapy), environmental adjustments (e.g., noise levels, lighting, smells, etc), to societal factors (e.g., social media use). For example, in an experiment investigating the impact of sleep on cognitive performance, the amount of sleep participants receive is the independent variable. 

Selection and Manipulation

Selecting an independent variable requires careful consideration of the research question and the theoretical framework guiding the study. Researchers must ensure that their chosen variable can be effectively, and consistently manipulated or measured and is ethically and practically feasible, particularly when dealing with human subjects.

Manipulating an independent variable involves creating different conditions (e.g., treatment vs. control groups) to observe how changes in the variable affect outcomes. For instance, researchers studying the effect of educational interventions on learning outcomes might vary the type of instructional material (digital vs. traditional) to assess differences in student performance.

Challenges in Human Behavior Research

Manipulating independent variables in human behavior research presents unique challenges. Ethical considerations are paramount, as interventions must not harm participants. For example, studies involving vulnerable populations or sensitive topics require rigorous ethical oversight to ensure that the manipulation of independent variables does not result in adverse effects.

Practical limitations also come into play, such as controlling for extraneous variables that could influence the outcomes. In the aforementioned example of sleep and cognitive performance, factors like caffeine consumption or stress levels could confound the results. Researchers employ various methodological strategies, such as random assignment and controlled environments, to mitigate these influences.

Chapter 2: Dependent Variables: Measuring Human Behavior

The dependent variable in human behavior research acts as a mirror, reflecting the outcomes or effects resulting from variations in the independent variable. It is the aspect of human experience or behavior that researchers aim to understand, predict, or change through their studies. This section explores how dependent variables are measured, the significance of their accurate measurement, and the inherent challenges in capturing the complexities of human behavior.

Defining Dependent Variables

Dependent variables are the responses or outcomes that researchers measure in an experiment, expecting them to vary as a direct result of changes in the independent variable. In the context of human behavior research, dependent variables could include measures of emotional well-being, cognitive performance, social interactions, or any other aspect of human behavior influenced by the experimental manipulation. For instance, in a study examining the effect of exercise on stress levels, stress level would be the dependent variable, measured through various psychological assessments or physiological markers.

Measurement Methods and Tools

Measuring dependent variables in human behavior research involves a diverse array of methodologies, ranging from self-reported questionnaires and interviews to physiological measurements and behavioral observations. The choice of measurement tool depends on the nature of the dependent variable and the objectives of the study.

• Self-reported Measures: Often used for assessing psychological states or subjective experiences, such as anxiety, satisfaction, or mood. These measures rely on participants’ introspection and honesty, posing challenges in terms of accuracy and bias.
• Behavioral Observations: Involve the direct observation and recording of participants’ behavior in natural or controlled settings. This method is used for behaviors that can be externally observed and quantified, such as social interactions or task performance.
• Physiological Measurements: Include the use of technology to measure physical responses that indicate psychological states, such as heart rate, cortisol levels, or brain activity. These measures can provide objective data about the physiological aspects of human behavior.

Reliability and Validity

The reliability and validity of the measurement of dependent variables are critical to the integrity of human behavior research.

• Reliability refers to the consistency of a measure; a reliable tool yields similar results under consistent conditions.
• Validity pertains to the accuracy of the measure; a valid tool accurately reflects the concept it aims to measure.

Ensuring reliability and validity often involves the use of established measurement instruments with proven track records, pilot testing new instruments, and applying rigorous statistical analyses to evaluate measurement properties.

Challenges in Measuring Human Behavior

Measuring human behavior presents challenges due to its complexity and the influence of multiple, often interrelated, variables. Researchers must contend with issues such as participant bias, environmental influences, and the subjective nature of many psychological constructs. Additionally, the dynamic nature of human behavior means that it can change over time, necessitating careful consideration of when and how measurements are taken.

Section 3: Relationship between Independent and Dependent Variables

Understanding the relationship between independent and dependent variables is at the core of research in human behavior. This relationship is what researchers aim to elucidate, whether they seek to explain, predict, or influence human actions and psychological states. This section explores the nature of this relationship, the means by which it is analyzed, and common misconceptions that may arise.

The Nature of the Relationship

The relationship between independent and dependent variables can manifest in various forms—direct, indirect, linear, nonlinear, and may be moderated or mediated by other variables. At its most basic, this relationship is often conceptualized as cause and effect: the independent variable (the cause) influences the dependent variable (the effect). For instance, increased physical activity (independent variable) may lead to decreased stress levels (dependent variable).

Analyzing the Relationship

Statistical analyses play a pivotal role in examining the relationship between independent and dependent variables. Techniques vary depending on the nature of the variables and the research design, ranging from simple correlation and regression analyses for quantifying the strength and form of relationships, to complex multivariate analyses for exploring relationships among multiple variables simultaneously.

• Correlation Analysis : Used to determine the degree to which two variables are related. However, it’s crucial to note that correlation does not imply causation.
• Regression Analysis : Goes a step further by not only assessing the strength of the relationship but also predicting the value of the dependent variable based on the independent variable.
• Experimental Design : Provides a more robust framework for inferring causality, where manipulation of the independent variable and control of confounding factors allow researchers to directly observe the impact on the dependent variable.

Causality vs. Correlation

A fundamental consideration in human behavior research is the distinction between causality and correlation. Causality implies that changes in the independent variable cause changes in the dependent variable. Correlation, on the other hand, indicates that two variables are related but does not establish a cause-effect relationship. Confounding variables may influence both, creating the appearance of a direct relationship where none exists. Understanding this distinction is crucial for accurate interpretation of research findings.

Common Misinterpretations

The complexity of human behavior and the myriad factors that influence it often lead to challenges in interpreting the relationship between independent and dependent variables. Researchers must be wary of:

• Overestimating the strength of causal relationships based on correlational data.
• Ignoring potential confounding variables that may influence the observed relationship.
• Assuming the directionality of the relationship without adequate evidence.

This exploration highlights the importance of understanding independent and dependent variables in human behavior research. Independent variables act as the initiating factors in experiments, influencing the observed behaviors, while dependent variables reflect the results of these influences, providing insights into human emotions and actions. 

Ethical and practical challenges arise, especially in experiments involving human participants, necessitating careful consideration to respect participants’ well-being. The measurement of these variables is critical for testing theories and validating hypotheses, with their relationship offering potential insights into causality and correlation within human behavior. 

Rigorous statistical analysis and cautious interpretation of findings are essential to avoid misconceptions. Overall, the study of these variables is fundamental to advancing human behavior research, guiding researchers towards deeper understanding and potential interventions to improve the human condition.

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Here is the abstract of a 2014 article in the journal Psychological Science.

Taking notes on laptops rather than in longhand is increasingly common. Many researchers have suggested that laptop note taking is less effective than longhand note taking for learning. Prior studies have primarily focused on students’ capacity for multitasking and distraction when using laptops. The present research suggests that even when laptops are used solely to take notes, they may still be impairing learning because their use results in shallower processing. In three studies, we found that students who took notes on laptops performed worse on conceptual questions than students who took notes longhand. We show that whereas taking more notes can be beneficial, laptop note takers’ tendency to transcribe lectures verbatim rather than processing information and reframing it in their own words is detrimental to learning. (Mueler & Oppenheimer, 2014, p. 1159) [1]

In this abstract, the researcher has identified a research question—about the effect of taking notes on a laptop on learning—and identified why it is worthy of investigation—because the practice is ubiquitous and may be harmful to learning. In this chapter, we give you a broad overview of the various stages of the research process. These include finding a topic of investigation, reviewing the literature, refining your research question and generating a hypothesis, designing and conducting a study, analyzing the data, coming to conclusions, and reporting the results.

• Mueller, P. A., & Oppenheimer, D. M. (2014). The pen is mightier than the keyboard: Advantages of longhand over laptop note taking. Psychological Science, 25 (6), 1159-1168. ↵

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• First Online: 11 May 2023

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• Maribel Mendoza Solis   ORCID: orcid.org/0000-0001-5407-023X 5 ,
• Jorge Luis García Alcaraz 6 ,
• Juan Manuel Madrid Solórzano 7 &
• Emilio Jiménez Macías 8  

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This chapter analyzes six latent variables associated with supply chains (SC). These variables included resilience (SCR), flexibility (SCF), agility (SCA), efficiency (SCE), alertness (SCAL), transactional leadership (TSSCL), and transformational leadership (TFSCL). The primary studies on these topics are discussed, the most influential authors are identified, and possible future research directions for each topic are discussed. Similarly, the research problem and context of the Mexican maquiladora industry are presented, and the general objective of the research and the motivation of this book is stated according to the lack of research on this topic.

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Solis, M.M., García Alcaraz, J.L., Solórzano, J.M.M., Macías, E.J. (2023). Definition of Variables and Research Problem. In: Leadership and Operational Indexes for Supply Chain Resilience. SpringerBriefs in Applied Sciences and Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-32364-5_2

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

Chapter 2. Research Design

Getting started.

When I teach undergraduates qualitative research methods, the final product of the course is a “research proposal” that incorporates all they have learned and enlists the knowledge they have learned about qualitative research methods in an original design that addresses a particular research question. I highly recommend you think about designing your own research study as you progress through this textbook. Even if you don’t have a study in mind yet, it can be a helpful exercise as you progress through the course. But how to start? How can one design a research study before they even know what research looks like? This chapter will serve as a brief overview of the research design process to orient you to what will be coming in later chapters. Think of it as a “skeleton” of what you will read in more detail in later chapters. Ideally, you will read this chapter both now (in sequence) and later during your reading of the remainder of the text. Do not worry if you have questions the first time you read this chapter. Many things will become clearer as the text advances and as you gain a deeper understanding of all the components of good qualitative research. This is just a preliminary map to get you on the right road.

Research Design Steps

Before you even get started, you will need to have a broad topic of interest in mind. [1] . In my experience, students can confuse this broad topic with the actual research question, so it is important to clearly distinguish the two. And the place to start is the broad topic. It might be, as was the case with me, working-class college students. But what about working-class college students? What’s it like to be one? Why are there so few compared to others? How do colleges assist (or fail to assist) them? What interested me was something I could barely articulate at first and went something like this: “Why was it so difficult and lonely to be me?” And by extension, “Did others share this experience?”

Once you have a general topic, reflect on why this is important to you. Sometimes we connect with a topic and we don’t really know why. Even if you are not willing to share the real underlying reason you are interested in a topic, it is important that you know the deeper reasons that motivate you. Otherwise, it is quite possible that at some point during the research, you will find yourself turned around facing the wrong direction. I have seen it happen many times. The reason is that the research question is not the same thing as the general topic of interest, and if you don’t know the reasons for your interest, you are likely to design a study answering a research question that is beside the point—to you, at least. And this means you will be much less motivated to carry your research to completion.

Researcher Note

Why do you employ qualitative research methods in your area of study? What are the advantages of qualitative research methods for studying mentorship?

Qualitative research methods are a huge opportunity to increase access, equity, inclusion, and social justice. Qualitative research allows us to engage and examine the uniquenesses/nuances within minoritized and dominant identities and our experiences with these identities. Qualitative research allows us to explore a specific topic, and through that exploration, we can link history to experiences and look for patterns or offer up a unique phenomenon. There’s such beauty in being able to tell a particular story, and qualitative research is a great mode for that! For our work, we examined the relationships we typically use the term mentorship for but didn’t feel that was quite the right word. Qualitative research allowed us to pick apart what we did and how we engaged in our relationships, which then allowed us to more accurately describe what was unique about our mentorship relationships, which we ultimately named liberationships ( McAloney and Long 2021) . Qualitative research gave us the means to explore, process, and name our experiences; what a powerful tool!

How do you come up with ideas for what to study (and how to study it)? Where did you get the idea for studying mentorship?

Coming up with ideas for research, for me, is kind of like Googling a question I have, not finding enough information, and then deciding to dig a little deeper to get the answer. The idea to study mentorship actually came up in conversation with my mentorship triad. We were talking in one of our meetings about our relationship—kind of meta, huh? We discussed how we felt that mentorship was not quite the right term for the relationships we had built. One of us asked what was different about our relationships and mentorship. This all happened when I was taking an ethnography course. During the next session of class, we were discussing auto- and duoethnography, and it hit me—let’s explore our version of mentorship, which we later went on to name liberationships ( McAloney and Long 2021 ). The idea and questions came out of being curious and wanting to find an answer. As I continue to research, I see opportunities in questions I have about my work or during conversations that, in our search for answers, end up exposing gaps in the literature. If I can’t find the answer already out there, I can study it.

—Kim McAloney, PhD, College Student Services Administration Ecampus coordinator and instructor

When you have a better idea of why you are interested in what it is that interests you, you may be surprised to learn that the obvious approaches to the topic are not the only ones. For example, let’s say you think you are interested in preserving coastal wildlife. And as a social scientist, you are interested in policies and practices that affect the long-term viability of coastal wildlife, especially around fishing communities. It would be natural then to consider designing a research study around fishing communities and how they manage their ecosystems. But when you really think about it, you realize that what interests you the most is how people whose livelihoods depend on a particular resource act in ways that deplete that resource. Or, even deeper, you contemplate the puzzle, “How do people justify actions that damage their surroundings?” Now, there are many ways to design a study that gets at that broader question, and not all of them are about fishing communities, although that is certainly one way to go. Maybe you could design an interview-based study that includes and compares loggers, fishers, and desert golfers (those who golf in arid lands that require a great deal of wasteful irrigation). Or design a case study around one particular example where resources were completely used up by a community. Without knowing what it is you are really interested in, what motivates your interest in a surface phenomenon, you are unlikely to come up with the appropriate research design.

These first stages of research design are often the most difficult, but have patience . Taking the time to consider why you are going to go through a lot of trouble to get answers will prevent a lot of wasted energy in the future.

There are distinct reasons for pursuing particular research questions, and it is helpful to distinguish between them.  First, you may be personally motivated.  This is probably the most important and the most often overlooked.   What is it about the social world that sparks your curiosity? What bothers you? What answers do you need in order to keep living? For me, I knew I needed to get a handle on what higher education was for before I kept going at it. I needed to understand why I felt so different from my peers and whether this whole “higher education” thing was “for the likes of me” before I could complete my degree. That is the personal motivation question. Your personal motivation might also be political in nature, in that you want to change the world in a particular way. It’s all right to acknowledge this. In fact, it is better to acknowledge it than to hide it.

There are also academic and professional motivations for a particular study.  If you are an absolute beginner, these may be difficult to find. We’ll talk more about this when we discuss reviewing the literature. Simply put, you are probably not the only person in the world to have thought about this question or issue and those related to it. So how does your interest area fit into what others have studied? Perhaps there is a good study out there of fishing communities, but no one has quite asked the “justification” question. You are motivated to address this to “fill the gap” in our collective knowledge. And maybe you are really not at all sure of what interests you, but you do know that [insert your topic] interests a lot of people, so you would like to work in this area too. You want to be involved in the academic conversation. That is a professional motivation and a very important one to articulate.

Practical and strategic motivations are a third kind. Perhaps you want to encourage people to take better care of the natural resources around them. If this is also part of your motivation, you will want to design your research project in a way that might have an impact on how people behave in the future. There are many ways to do this, one of which is using qualitative research methods rather than quantitative research methods, as the findings of qualitative research are often easier to communicate to a broader audience than the results of quantitative research. You might even be able to engage the community you are studying in the collecting and analyzing of data, something taboo in quantitative research but actively embraced and encouraged by qualitative researchers. But there are other practical reasons, such as getting “done” with your research in a certain amount of time or having access (or no access) to certain information. There is nothing wrong with considering constraints and opportunities when designing your study. Or maybe one of the practical or strategic goals is about learning competence in this area so that you can demonstrate the ability to conduct interviews and focus groups with future employers. Keeping that in mind will help shape your study and prevent you from getting sidetracked using a technique that you are less invested in learning about.

STOP HERE for a moment

I recommend you write a paragraph (at least) explaining your aims and goals. Include a sentence about each of the following: personal/political goals, practical or professional/academic goals, and practical/strategic goals. Think through how all of the goals are related and can be achieved by this particular research study . If they can’t, have a rethink. Perhaps this is not the best way to go about it.

You will also want to be clear about the purpose of your study. “Wait, didn’t we just do this?” you might ask. No! Your goals are not the same as the purpose of the study, although they are related. You can think about purpose lying on a continuum from “ theory ” to “action” (figure 2.1). Sometimes you are doing research to discover new knowledge about the world, while other times you are doing a study because you want to measure an impact or make a difference in the world.

Basic research involves research that is done for the sake of “pure” knowledge—that is, knowledge that, at least at this moment in time, may not have any apparent use or application. Often, and this is very important, knowledge of this kind is later found to be extremely helpful in solving problems. So one way of thinking about basic research is that it is knowledge for which no use is yet known but will probably one day prove to be extremely useful. If you are doing basic research, you do not need to argue its usefulness, as the whole point is that we just don’t know yet what this might be.

Researchers engaged in basic research want to understand how the world operates. They are interested in investigating a phenomenon to get at the nature of reality with regard to that phenomenon. The basic researcher’s purpose is to understand and explain ( Patton 2002:215 ).

Basic research is interested in generating and testing hypotheses about how the world works. Grounded Theory is one approach to qualitative research methods that exemplifies basic research (see chapter 4). Most academic journal articles publish basic research findings. If you are working in academia (e.g., writing your dissertation), the default expectation is that you are conducting basic research.

Applied research in the social sciences is research that addresses human and social problems. Unlike basic research, the researcher has expectations that the research will help contribute to resolving a problem, if only by identifying its contours, history, or context. From my experience, most students have this as their baseline assumption about research. Why do a study if not to make things better? But this is a common mistake. Students and their committee members are often working with default assumptions here—the former thinking about applied research as their purpose, the latter thinking about basic research: “The purpose of applied research is to contribute knowledge that will help people to understand the nature of a problem in order to intervene, thereby allowing human beings to more effectively control their environment. While in basic research the source of questions is the tradition within a scholarly discipline, in applied research the source of questions is in the problems and concerns experienced by people and by policymakers” ( Patton 2002:217 ).

Applied research is less geared toward theory in two ways. First, its questions do not derive from previous literature. For this reason, applied research studies have much more limited literature reviews than those found in basic research (although they make up for this by having much more “background” about the problem). Second, it does not generate theory in the same way as basic research does. The findings of an applied research project may not be generalizable beyond the boundaries of this particular problem or context. The findings are more limited. They are useful now but may be less useful later. This is why basic research remains the default “gold standard” of academic research.

Evaluation research is research that is designed to evaluate or test the effectiveness of specific solutions and programs addressing specific social problems. We already know the problems, and someone has already come up with solutions. There might be a program, say, for first-generation college students on your campus. Does this program work? Are first-generation students who participate in the program more likely to graduate than those who do not? These are the types of questions addressed by evaluation research. There are two types of research within this broader frame; however, one more action-oriented than the next. In summative evaluation , an overall judgment about the effectiveness of a program or policy is made. Should we continue our first-gen program? Is it a good model for other campuses? Because the purpose of such summative evaluation is to measure success and to determine whether this success is scalable (capable of being generalized beyond the specific case), quantitative data is more often used than qualitative data. In our example, we might have “outcomes” data for thousands of students, and we might run various tests to determine if the better outcomes of those in the program are statistically significant so that we can generalize the findings and recommend similar programs elsewhere. Qualitative data in the form of focus groups or interviews can then be used for illustrative purposes, providing more depth to the quantitative analyses. In contrast, formative evaluation attempts to improve a program or policy (to help “form” or shape its effectiveness). Formative evaluations rely more heavily on qualitative data—case studies, interviews, focus groups. The findings are meant not to generalize beyond the particular but to improve this program. If you are a student seeking to improve your qualitative research skills and you do not care about generating basic research, formative evaluation studies might be an attractive option for you to pursue, as there are always local programs that need evaluation and suggestions for improvement. Again, be very clear about your purpose when talking through your research proposal with your committee.

Action research takes a further step beyond evaluation, even formative evaluation, to being part of the solution itself. This is about as far from basic research as one could get and definitely falls beyond the scope of “science,” as conventionally defined. The distinction between action and research is blurry, the research methods are often in constant flux, and the only “findings” are specific to the problem or case at hand and often are findings about the process of intervention itself. Rather than evaluate a program as a whole, action research often seeks to change and improve some particular aspect that may not be working—maybe there is not enough diversity in an organization or maybe women’s voices are muted during meetings and the organization wonders why and would like to change this. In a further step, participatory action research , those women would become part of the research team, attempting to amplify their voices in the organization through participation in the action research. As action research employs methods that involve people in the process, focus groups are quite common.

If you are working on a thesis or dissertation, chances are your committee will expect you to be contributing to fundamental knowledge and theory ( basic research ). If your interests lie more toward the action end of the continuum, however, it is helpful to talk to your committee about this before you get started. Knowing your purpose in advance will help avoid misunderstandings during the later stages of the research process!

The Research Question

Once you have written your paragraph and clarified your purpose and truly know that this study is the best study for you to be doing right now , you are ready to write and refine your actual research question. Know that research questions are often moving targets in qualitative research, that they can be refined up to the very end of data collection and analysis. But you do have to have a working research question at all stages. This is your “anchor” when you get lost in the data. What are you addressing? What are you looking at and why? Your research question guides you through the thicket. It is common to have a whole host of questions about a phenomenon or case, both at the outset and throughout the study, but you should be able to pare it down to no more than two or three sentences when asked. These sentences should both clarify the intent of the research and explain why this is an important question to answer. More on refining your research question can be found in chapter 4.

Chances are, you will have already done some prior reading before coming up with your interest and your questions, but you may not have conducted a systematic literature review. This is the next crucial stage to be completed before venturing further. You don’t want to start collecting data and then realize that someone has already beaten you to the punch. A review of the literature that is already out there will let you know (1) if others have already done the study you are envisioning; (2) if others have done similar studies, which can help you out; and (3) what ideas or concepts are out there that can help you frame your study and make sense of your findings. More on literature reviews can be found in chapter 9.

In addition to reviewing the literature for similar studies to what you are proposing, it can be extremely helpful to find a study that inspires you. This may have absolutely nothing to do with the topic you are interested in but is written so beautifully or organized so interestingly or otherwise speaks to you in such a way that you want to post it somewhere to remind you of what you want to be doing. You might not understand this in the early stages—why would you find a study that has nothing to do with the one you are doing helpful? But trust me, when you are deep into analysis and writing, having an inspirational model in view can help you push through. If you are motivated to do something that might change the world, you probably have read something somewhere that inspired you. Go back to that original inspiration and read it carefully and see how they managed to convey the passion that you so appreciate.

At this stage, you are still just getting started. There are a lot of things to do before setting forth to collect data! You’ll want to consider and choose a research tradition and a set of data-collection techniques that both help you answer your research question and match all your aims and goals. For example, if you really want to help migrant workers speak for themselves, you might draw on feminist theory and participatory action research models. Chapters 3 and 4 will provide you with more information on epistemologies and approaches.

Next, you have to clarify your “units of analysis.” What is the level at which you are focusing your study? Often, the unit in qualitative research methods is individual people, or “human subjects.” But your units of analysis could just as well be organizations (colleges, hospitals) or programs or even whole nations. Think about what it is you want to be saying at the end of your study—are the insights you are hoping to make about people or about organizations or about something else entirely? A unit of analysis can even be a historical period! Every unit of analysis will call for a different kind of data collection and analysis and will produce different kinds of “findings” at the conclusion of your study. [2]

Regardless of what unit of analysis you select, you will probably have to consider the “human subjects” involved in your research. [3] Who are they? What interactions will you have with them—that is, what kind of data will you be collecting? Before answering these questions, define your population of interest and your research setting. Use your research question to help guide you.

Let’s use an example from a real study. In Geographies of Campus Inequality , Benson and Lee ( 2020 ) list three related research questions: “(1) What are the different ways that first-generation students organize their social, extracurricular, and academic activities at selective and highly selective colleges? (2) how do first-generation students sort themselves and get sorted into these different types of campus lives; and (3) how do these different patterns of campus engagement prepare first-generation students for their post-college lives?” (3).

Note that we are jumping into this a bit late, after Benson and Lee have described previous studies (the literature review) and what is known about first-generation college students and what is not known. They want to know about differences within this group, and they are interested in ones attending certain kinds of colleges because those colleges will be sites where academic and extracurricular pressures compete. That is the context for their three related research questions. What is the population of interest here? First-generation college students . What is the research setting? Selective and highly selective colleges . But a host of questions remain. Which students in the real world, which colleges? What about gender, race, and other identity markers? Will the students be asked questions? Are the students still in college, or will they be asked about what college was like for them? Will they be observed? Will they be shadowed? Will they be surveyed? Will they be asked to keep diaries of their time in college? How many students? How many colleges? For how long will they be observed?

Recommendation

Take a moment and write down suggestions for Benson and Lee before continuing on to what they actually did.

Have you written down your own suggestions? Good. Now let’s compare those with what they actually did. Benson and Lee drew on two sources of data: in-depth interviews with sixty-four first-generation students and survey data from a preexisting national survey of students at twenty-eight selective colleges. Let’s ignore the survey for our purposes here and focus on those interviews. The interviews were conducted between 2014 and 2016 at a single selective college, “Hilltop” (a pseudonym ). They employed a “purposive” sampling strategy to ensure an equal number of male-identifying and female-identifying students as well as equal numbers of White, Black, and Latinx students. Each student was interviewed once. Hilltop is a selective liberal arts college in the northeast that enrolls about three thousand students.

How did your suggestions match up to those actually used by the researchers in this study? It is possible your suggestions were too ambitious? Beginning qualitative researchers can often make that mistake. You want a research design that is both effective (it matches your question and goals) and doable. You will never be able to collect data from your entire population of interest (unless your research question is really so narrow to be relevant to very few people!), so you will need to come up with a good sample. Define the criteria for this sample, as Benson and Lee did when deciding to interview an equal number of students by gender and race categories. Define the criteria for your sample setting too. Hilltop is typical for selective colleges. That was a research choice made by Benson and Lee. For more on sampling and sampling choices, see chapter 5.

Benson and Lee chose to employ interviews. If you also would like to include interviews, you have to think about what will be asked in them. Most interview-based research involves an interview guide, a set of questions or question areas that will be asked of each participant. The research question helps you create a relevant interview guide. You want to ask questions whose answers will provide insight into your research question. Again, your research question is the anchor you will continually come back to as you plan for and conduct your study. It may be that once you begin interviewing, you find that people are telling you something totally unexpected, and this makes you rethink your research question. That is fine. Then you have a new anchor. But you always have an anchor. More on interviewing can be found in chapter 11.

Let’s imagine Benson and Lee also observed college students as they went about doing the things college students do, both in the classroom and in the clubs and social activities in which they participate. They would have needed a plan for this. Would they sit in on classes? Which ones and how many? Would they attend club meetings and sports events? Which ones and how many? Would they participate themselves? How would they record their observations? More on observation techniques can be found in both chapters 13 and 14.

At this point, the design is almost complete. You know why you are doing this study, you have a clear research question to guide you, you have identified your population of interest and research setting, and you have a reasonable sample of each. You also have put together a plan for data collection, which might include drafting an interview guide or making plans for observations. And so you know exactly what you will be doing for the next several months (or years!). To put the project into action, there are a few more things necessary before actually going into the field.

First, you will need to make sure you have any necessary supplies, including recording technology. These days, many researchers use their phones to record interviews. Second, you will need to draft a few documents for your participants. These include informed consent forms and recruiting materials, such as posters or email texts, that explain what this study is in clear language. Third, you will draft a research protocol to submit to your institutional review board (IRB) ; this research protocol will include the interview guide (if you are using one), the consent form template, and all examples of recruiting material. Depending on your institution and the details of your study design, it may take weeks or even, in some unfortunate cases, months before you secure IRB approval. Make sure you plan on this time in your project timeline. While you wait, you can continue to review the literature and possibly begin drafting a section on the literature review for your eventual presentation/publication. More on IRB procedures can be found in chapter 8 and more general ethical considerations in chapter 7.

Once you have approval, you can begin!

Research Design Checklist

Before data collection begins, do the following:

• Write a paragraph explaining your aims and goals (personal/political, practical/strategic, professional/academic).
• Define your research question; write two to three sentences that clarify the intent of the research and why this is an important question to answer.
• Review the literature for similar studies that address your research question or similar research questions; think laterally about some literature that might be helpful or illuminating but is not exactly about the same topic.
• Find a written study that inspires you—it may or may not be on the research question you have chosen.
• Consider and choose a research tradition and set of data-collection techniques that (1) help answer your research question and (2) match your aims and goals.
• Define your population of interest and your research setting.
• Define the criteria for your sample (How many? Why these? How will you find them, gain access, and acquire consent?).
• If you are conducting interviews, draft an interview guide.
•  If you are making observations, create a plan for observations (sites, times, recording, access).
• Acquire any necessary technology (recording devices/software).
• Draft consent forms that clearly identify the research focus and selection process.
• Create recruiting materials (posters, email, texts).
• Apply for IRB approval (proposal plus consent form plus recruiting materials).
• Block out time for collecting data.
• At the end of the chapter, you will find a " Research Design Checklist " that summarizes the main recommendations made here ↵
• For example, if your focus is society and culture , you might collect data through observation or a case study. If your focus is individual lived experience , you are probably going to be interviewing some people. And if your focus is language and communication , you will probably be analyzing text (written or visual). ( Marshall and Rossman 2016:16 ). ↵
• You may not have any "live" human subjects. There are qualitative research methods that do not require interactions with live human beings - see chapter 16 , "Archival and Historical Sources." But for the most part, you are probably reading this textbook because you are interested in doing research with people. The rest of the chapter will assume this is the case. ↵

One of the primary methodological traditions of inquiry in qualitative research, ethnography is the study of a group or group culture, largely through observational fieldwork supplemented by interviews. It is a form of fieldwork that may include participant-observation data collection. See chapter 14 for a discussion of deep ethnography. 

A methodological tradition of inquiry and research design that focuses on an individual case (e.g., setting, institution, or sometimes an individual) in order to explore its complexity, history, and interactive parts.  As an approach, it is particularly useful for obtaining a deep appreciation of an issue, event, or phenomenon of interest in its particular context.

The controlling force in research; can be understood as lying on a continuum from basic research (knowledge production) to action research (effecting change).

In its most basic sense, a theory is a story we tell about how the world works that can be tested with empirical evidence.  In qualitative research, we use the term in a variety of ways, many of which are different from how they are used by quantitative researchers.  Although some qualitative research can be described as “testing theory,” it is more common to “build theory” from the data using inductive reasoning , as done in Grounded Theory .  There are so-called “grand theories” that seek to integrate a whole series of findings and stories into an overarching paradigm about how the world works, and much smaller theories or concepts about particular processes and relationships.  Theory can even be used to explain particular methodological perspectives or approaches, as in Institutional Ethnography , which is both a way of doing research and a theory about how the world works.

Research that is interested in generating and testing hypotheses about how the world works.

A methodological tradition of inquiry and approach to analyzing qualitative data in which theories emerge from a rigorous and systematic process of induction.  This approach was pioneered by the sociologists Glaser and Strauss (1967).  The elements of theory generated from comparative analysis of data are, first, conceptual categories and their properties and, second, hypotheses or generalized relations among the categories and their properties – “The constant comparing of many groups draws the [researcher’s] attention to their many similarities and differences.  Considering these leads [the researcher] to generate abstract categories and their properties, which, since they emerge from the data, will clearly be important to a theory explaining the kind of behavior under observation.” (36).

An approach to research that is “multimethod in focus, involving an interpretative, naturalistic approach to its subject matter.  This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them.  Qualitative research involves the studied use and collection of a variety of empirical materials – case study, personal experience, introspective, life story, interview, observational, historical, interactional, and visual texts – that describe routine and problematic moments and meanings in individuals’ lives." ( Denzin and Lincoln 2005:2 ). Contrast with quantitative research .

Research that contributes knowledge that will help people to understand the nature of a problem in order to intervene, thereby allowing human beings to more effectively control their environment.

Research that is designed to evaluate or test the effectiveness of specific solutions and programs addressing specific social problems.  There are two kinds: summative and formative .

Research in which an overall judgment about the effectiveness of a program or policy is made, often for the purpose of generalizing to other cases or programs.  Generally uses qualitative research as a supplement to primary quantitative data analyses.  Contrast formative evaluation research .

Research designed to improve a program or policy (to help “form” or shape its effectiveness); relies heavily on qualitative research methods.  Contrast summative evaluation research

Research carried out at a particular organizational or community site with the intention of affecting change; often involves research subjects as participants of the study.  See also participatory action research .

Research in which both researchers and participants work together to understand a problematic situation and change it for the better.

The level of the focus of analysis (e.g., individual people, organizations, programs, neighborhoods).

The large group of interest to the researcher.  Although it will likely be impossible to design a study that incorporates or reaches all members of the population of interest, this should be clearly defined at the outset of a study so that a reasonable sample of the population can be taken.  For example, if one is studying working-class college students, the sample may include twenty such students attending a particular college, while the population is “working-class college students.”  In quantitative research, clearly defining the general population of interest is a necessary step in generalizing results from a sample.  In qualitative research, defining the population is conceptually important for clarity.

A fictional name assigned to give anonymity to a person, group, or place.  Pseudonyms are important ways of protecting the identity of research participants while still providing a “human element” in the presentation of qualitative data.  There are ethical considerations to be made in selecting pseudonyms; some researchers allow research participants to choose their own.

A requirement for research involving human participants; the documentation of informed consent.  In some cases, oral consent or assent may be sufficient, but the default standard is a single-page easy-to-understand form that both the researcher and the participant sign and date.   Under federal guidelines, all researchers "shall seek such consent only under circumstances that provide the prospective subject or the representative sufficient opportunity to consider whether or not to participate and that minimize the possibility of coercion or undue influence. The information that is given to the subject or the representative shall be in language understandable to the subject or the representative.  No informed consent, whether oral or written, may include any exculpatory language through which the subject or the representative is made to waive or appear to waive any of the subject's rights or releases or appears to release the investigator, the sponsor, the institution, or its agents from liability for negligence" (21 CFR 50.20).  Your IRB office will be able to provide a template for use in your study .

An administrative body established to protect the rights and welfare of human research subjects recruited to participate in research activities conducted under the auspices of the institution with which it is affiliated. The IRB is charged with the responsibility of reviewing all research involving human participants. The IRB is concerned with protecting the welfare, rights, and privacy of human subjects. The IRB has the authority to approve, disapprove, monitor, and require modifications in all research activities that fall within its jurisdiction as specified by both the federal regulations and institutional policy.

Introduction to Qualitative Research Methods Copyright © 2023 by Allison Hurst is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

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Research methods and Statistics in Psychology

Variables and definitions - all with video answers.

Chapter Questions

Identify the assumed independent and dependent variables in the following statements: a) Attitudes can be influenced by propaganda messages b) Noise affects efficiency of work c) Time of day affects span of attention d) Performance is improved with practice e) Smiles given tend to produce smiles in return f) Aggression can be the result of frustration g) Birth order in the family influences the individual's personality and intellectual achievement h) People's behaviour in crowds is different from behaviour when alone

In exercise I, what could be an operational definition of: 'noise', 'span of attention', 'smile'?

Two groups of six-year-old children are assessed for their cognitive skills and sociability. One group has attended some form of preschool education for at least a year before starting school. The other group has not received any preschool experience. The preschool educated group are superior on both variables. a) Identify the independent and dependent variables b) Identify possible confounding variables c) Outline ways in which the confounding variables could be eliminated as possible explanations of the differences