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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.

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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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Definitions

Dependent Variable The variable that depends on other factors that are measured. These variables are expected to change as a result of an experimental manipulation of the independent variable or variables. It is the presumed effect.

Independent Variable The variable that is stable and unaffected by the other variables you are trying to measure. It refers to the condition of an experiment that is systematically manipulated by the investigator. It is the presumed cause.

Cramer, Duncan and Dennis Howitt. The SAGE Dictionary of Statistics . London: SAGE, 2004; Penslar, Robin Levin and Joan P. Porter. Institutional Review Board Guidebook: Introduction . Washington, DC: United States Department of Health and Human Services, 2010; "What are Dependent and Independent Variables?" Graphic Tutorial.

Identifying Dependent and Independent Variables

Don't feel bad if you are confused about what is the dependent variable and what is the independent variable in social and behavioral sciences research . However, it's important that you learn the difference because framing a study using these variables is a common approach to organizing the elements of a social sciences research study in order to discover relevant and meaningful results. Specifically, it is important for these two reasons:

• You need to understand and be able to evaluate their application in other people's research.
• You need to apply them correctly in your own research.

A variable in research simply refers to a person, place, thing, or phenomenon that you are trying to measure in some way. The best way to understand the difference between a dependent and independent variable is that the meaning of each is implied by what the words tell us about the variable you are using. You can do this with a simple exercise from the website, Graphic Tutorial. Take the sentence, "The [independent variable] causes a change in [dependent variable] and it is not possible that [dependent variable] could cause a change in [independent variable]." Insert the names of variables you are using in the sentence in the way that makes the most sense. This will help you identify each type of variable. If you're still not sure, consult with your professor before you begin to write.

Fan, Shihe. "Independent Variable." In Encyclopedia of Research Design. Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 592-594; "What are Dependent and Independent Variables?" Graphic Tutorial; Salkind, Neil J. "Dependent Variable." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 348-349;

Structure and Writing Style

The process of examining a research problem in the social and behavioral sciences is often framed around methods of analysis that compare, contrast, correlate, average, or integrate relationships between or among variables . Techniques include associations, sampling, random selection, and blind selection. Designation of the dependent and independent variable involves unpacking the research problem in a way that identifies a general cause and effect and classifying these variables as either independent or dependent.

The variables should be outlined in the introduction of your paper and explained in more detail in the methods section . There are no rules about the structure and style for writing about independent or dependent variables but, as with any academic writing, clarity and being succinct is most important.

After you have described the research problem and its significance in relation to prior research, explain why you have chosen to examine the problem using a method of analysis that investigates the relationships between or among independent and dependent variables . State what it is about the research problem that lends itself to this type of analysis. For example, if you are investigating the relationship between corporate environmental sustainability efforts [the independent variable] and dependent variables associated with measuring employee satisfaction at work using a survey instrument, you would first identify each variable and then provide background information about the variables. What is meant by "environmental sustainability"? Are you looking at a particular company [e.g., General Motors] or are you investigating an industry [e.g., the meat packing industry]? Why is employee satisfaction in the workplace important? How does a company make their employees aware of sustainability efforts and why would a company even care that its employees know about these efforts?

Identify each variable for the reader and define each . In the introduction, this information can be presented in a paragraph or two when you describe how you are going to study the research problem. In the methods section, you build on the literature review of prior studies about the research problem to describe in detail background about each variable, breaking each down for measurement and analysis. For example, what activities do you examine that reflect a company's commitment to environmental sustainability? Levels of employee satisfaction can be measured by a survey that asks about things like volunteerism or a desire to stay at the company for a long time.

The structure and writing style of describing the variables and their application to analyzing the research problem should be stated and unpacked in such a way that the reader obtains a clear understanding of the relationships between the variables and why they are important. This is also important so that the study can be replicated in the future using the same variables but applied in a different way.

Fan, Shihe. "Independent Variable." In Encyclopedia of Research Design. Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 592-594; "What are Dependent and Independent Variables?" Graphic Tutorial; “Case Example for Independent and Dependent Variables.” ORI Curriculum Examples. U.S. Department of Health and Human Services, Office of Research Integrity; Salkind, Neil J. "Dependent Variable." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 348-349; “Independent Variables and Dependent Variables.” Karl L. Wuensch, Department of Psychology, East Carolina University [posted email exchange]; “Variables.” Elements of Research. Dr. Camille Nebeker, San Diego State University.

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Research Variables 101

Independent variables, dependent variables, control variables and more

By: Derek Jansen (MBA) | Expert Reviewed By: Kerryn Warren (PhD) | January 2023

If you’re new to the world of research, especially scientific research, you’re bound to run into the concept of variables , sooner or later. If you’re feeling a little confused, don’t worry – you’re not the only one! Independent variables, dependent variables, confounding variables – it’s a lot of jargon. In this post, we’ll unpack the terminology surrounding research variables using straightforward language and loads of examples .

Overview: Variables In Research

What (exactly) is a variable.

The simplest way to understand a variable is as any characteristic or attribute that can experience change or vary over time or context – hence the name “variable”. For example, the dosage of a particular medicine could be classified as a variable, as the amount can vary (i.e., a higher dose or a lower dose). Similarly, gender, age or ethnicity could be considered demographic variables, because each person varies in these respects.

Within research, especially scientific research, variables form the foundation of studies, as researchers are often interested in how one variable impacts another, and the relationships between different variables. For example:

• How someone’s age impacts their sleep quality
• How different teaching methods impact learning outcomes
• How diet impacts weight (gain or loss)

As you can see, variables are often used to explain relationships between different elements and phenomena. In scientific studies, especially experimental studies, the objective is often to understand the causal relationships between variables. In other words, the role of cause and effect between variables. This is achieved by manipulating certain variables while controlling others – and then observing the outcome. But, we’ll get into that a little later…

The “Big 3” Variables

Variables can be a little intimidating for new researchers because there are a wide variety of variables, and oftentimes, there are multiple labels for the same thing. To lay a firm foundation, we’ll first look at the three main types of variables, namely:

• Independent variables (IV)
• Dependant variables (DV)
• Control variables

What is an independent variable?

Simply put, the independent variable is the “ cause ” in the relationship between two (or more) variables. In other words, when the independent variable changes, it has an impact on another variable.

For example:

• Increasing the dosage of a medication (Variable A) could result in better (or worse) health outcomes for a patient (Variable B)
• Changing a teaching method (Variable A) could impact the test scores that students earn in a standardised test (Variable B)
• Varying one’s diet (Variable A) could result in weight loss or gain (Variable B).

It’s useful to know that independent variables can go by a few different names, including, explanatory variables (because they explain an event or outcome) and predictor variables (because they predict the value of another variable). Terminology aside though, the most important takeaway is that independent variables are assumed to be the “cause” in any cause-effect relationship. As you can imagine, these types of variables are of major interest to researchers, as many studies seek to understand the causal factors behind a phenomenon.

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What is a dependent variable?

While the independent variable is the “ cause ”, the dependent variable is the “ effect ” – or rather, the affected variable . In other words, the dependent variable is the variable that is assumed to change as a result of a change in the independent variable.

Keeping with the previous example, let’s look at some dependent variables in action:

• Health outcomes (DV) could be impacted by dosage changes of a medication (IV)
• Students’ scores (DV) could be impacted by teaching methods (IV)
• Weight gain or loss (DV) could be impacted by diet (IV)

In scientific studies, researchers will typically pay very close attention to the dependent variable (or variables), carefully measuring any changes in response to hypothesised independent variables. This can be tricky in practice, as it’s not always easy to reliably measure specific phenomena or outcomes – or to be certain that the actual cause of the change is in fact the independent variable.

As the adage goes, correlation is not causation . In other words, just because two variables have a relationship doesn’t mean that it’s a causal relationship – they may just happen to vary together. For example, you could find a correlation between the number of people who own a certain brand of car and the number of people who have a certain type of job. Just because the number of people who own that brand of car and the number of people who have that type of job is correlated, it doesn’t mean that owning that brand of car causes someone to have that type of job or vice versa. The correlation could, for example, be caused by another factor such as income level or age group, which would affect both car ownership and job type.

To confidently establish a causal relationship between an independent variable and a dependent variable (i.e., X causes Y), you’ll typically need an experimental design , where you have complete control over the environmen t and the variables of interest. But even so, this doesn’t always translate into the “real world”. Simply put, what happens in the lab sometimes stays in the lab!

As an alternative to pure experimental research, correlational or “ quasi-experimental ” research (where the researcher cannot manipulate or change variables) can be done on a much larger scale more easily, allowing one to understand specific relationships in the real world. These types of studies also assume some causality between independent and dependent variables, but it’s not always clear. So, if you go this route, you need to be cautious in terms of how you describe the impact and causality between variables and be sure to acknowledge any limitations in your own research.

What is a control variable?

In an experimental design, a control variable (or controlled variable) is a variable that is intentionally held constant to ensure it doesn’t have an influence on any other variables. As a result, this variable remains unchanged throughout the course of the study. In other words, it’s a variable that’s not allowed to vary – tough life 🙂

As we mentioned earlier, one of the major challenges in identifying and measuring causal relationships is that it’s difficult to isolate the impact of variables other than the independent variable. Simply put, there’s always a risk that there are factors beyond the ones you’re specifically looking at that might be impacting the results of your study. So, to minimise the risk of this, researchers will attempt (as best possible) to hold other variables constant . These factors are then considered control variables.

Some examples of variables that you may need to control include:

• Temperature
• Time of day
• Noise or distractions

Which specific variables need to be controlled for will vary tremendously depending on the research project at hand, so there’s no generic list of control variables to consult. As a researcher, you’ll need to think carefully about all the factors that could vary within your research context and then consider how you’ll go about controlling them. A good starting point is to look at previous studies similar to yours and pay close attention to which variables they controlled for.

Of course, you won’t always be able to control every possible variable, and so, in many cases, you’ll just have to acknowledge their potential impact and account for them in the conclusions you draw. Every study has its limitations, so don’t get fixated or discouraged by troublesome variables. Nevertheless, always think carefully about the factors beyond what you’re focusing on – don’t make assumptions!

Other types of variables

As we mentioned, independent, dependent and control variables are the most common variables you’ll come across in your research, but they’re certainly not the only ones you need to be aware of. Next, we’ll look at a few “secondary” variables that you need to keep in mind as you design your research.

• Moderating variables
• Mediating variables
• Confounding variables
• Latent variables

Let’s jump into it…

What is a moderating variable?

A moderating variable is a variable that influences the strength or direction of the relationship between an independent variable and a dependent variable. In other words, moderating variables affect how much (or how little) the IV affects the DV, or whether the IV has a positive or negative relationship with the DV (i.e., moves in the same or opposite direction).

For example, in a study about the effects of sleep deprivation on academic performance, gender could be used as a moderating variable to see if there are any differences in how men and women respond to a lack of sleep. In such a case, one may find that gender has an influence on how much students’ scores suffer when they’re deprived of sleep.

It’s important to note that while moderators can have an influence on outcomes , they don’t necessarily cause them ; rather they modify or “moderate” existing relationships between other variables. This means that it’s possible for two different groups with similar characteristics, but different levels of moderation, to experience very different results from the same experiment or study design.

What is a mediating variable?

Mediating variables are often used to explain the relationship between the independent and dependent variable (s). For example, if you were researching the effects of age on job satisfaction, then education level could be considered a mediating variable, as it may explain why older people have higher job satisfaction than younger people – they may have more experience or better qualifications, which lead to greater job satisfaction.

Mediating variables also help researchers understand how different factors interact with each other to influence outcomes. For instance, if you wanted to study the effect of stress on academic performance, then coping strategies might act as a mediating factor by influencing both stress levels and academic performance simultaneously. For example, students who use effective coping strategies might be less stressed but also perform better academically due to their improved mental state.

In addition, mediating variables can provide insight into causal relationships between two variables by helping researchers determine whether changes in one factor directly cause changes in another – or whether there is an indirect relationship between them mediated by some third factor(s). For instance, if you wanted to investigate the impact of parental involvement on student achievement, you would need to consider family dynamics as a potential mediator, since it could influence both parental involvement and student achievement simultaneously.

What is a confounding variable?

A confounding variable (also known as a third variable or lurking variable ) is an extraneous factor that can influence the relationship between two variables being studied. Specifically, for a variable to be considered a confounding variable, it needs to meet two criteria:

• It must be correlated with the independent variable (this can be causal or not)
• It must have a causal impact on the dependent variable (i.e., influence the DV)

Some common examples of confounding variables include demographic factors such as gender, ethnicity, socioeconomic status, age, education level, and health status. In addition to these, there are also environmental factors to consider. For example, air pollution could confound the impact of the variables of interest in a study investigating health outcomes.

Naturally, it’s important to identify as many confounding variables as possible when conducting your research, as they can heavily distort the results and lead you to draw incorrect conclusions . So, always think carefully about what factors may have a confounding effect on your variables of interest and try to manage these as best you can.

What is a latent variable?

Latent variables are unobservable factors that can influence the behaviour of individuals and explain certain outcomes within a study. They’re also known as hidden or underlying variables , and what makes them rather tricky is that they can’t be directly observed or measured . Instead, latent variables must be inferred from other observable data points such as responses to surveys or experiments.

For example, in a study of mental health, the variable “resilience” could be considered a latent variable. It can’t be directly measured , but it can be inferred from measures of mental health symptoms, stress, and coping mechanisms. The same applies to a lot of concepts we encounter every day – for example:

• Emotional intelligence
• Quality of life
• Business confidence
• Ease of use

One way in which we overcome the challenge of measuring the immeasurable is latent variable models (LVMs). An LVM is a type of statistical model that describes a relationship between observed variables and one or more unobserved (latent) variables. These models allow researchers to uncover patterns in their data which may not have been visible before, thanks to their complexity and interrelatedness with other variables. Those patterns can then inform hypotheses about cause-and-effect relationships among those same variables which were previously unknown prior to running the LVM. Powerful stuff, we say!

Let’s recap

In the world of scientific research, there’s no shortage of variable types, some of which have multiple names and some of which overlap with each other. In this post, we’ve covered some of the popular ones, but remember that this is not an exhaustive list .

To recap, we’ve explored:

• Independent variables (the “cause”)
• Dependent variables (the “effect”)
• Control variables (the variable that’s not allowed to vary)

If you’re still feeling a bit lost and need a helping hand with your research project, check out our 1-on-1 coaching service , where we guide you through each step of the research journey. Also, be sure to check out our free dissertation writing course and our collection of free, fully-editable chapter templates .

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Neag School of Education

Educational Research Basics by Del Siegle

Each person/thing we collect data on is called an OBSERVATION (in our work these are usually people/subjects. Currently, the term participant rather than subject is used when describing the people from whom we collect data).

OBSERVATIONS (participants) possess a variety of CHARACTERISTICS .

If a CHARACTERISTIC of an OBSERVATION (participant) is the same for every member of the group (doesn’t vary) it is called a CONSTANT .

If a CHARACTERISTIC of an OBSERVATION (participant) differs for group members it is called a VARIABLE . In research we don’t get excited about CONSTANTS (since everyone is the same on that characteristic); we’re more interested in VARIABLES. Variables can be classified as QUANTITATIVE or QUALITATIVE (also known as CATEGORICAL).

QUANTITATIVE variables are ones that exist along a continuum that runs from low to high. Ordinal, interval, and ratio variables are quantitative.  QUANTITATIVE variables are sometimes called CONTINUOUS VARIABLES because they have a variety (continuum) of characteristics. Height in inches and scores on a test would be examples of quantitative variables.

QUALITATIVE variables do not express differences in amount, only differences. They are sometimes referred to as CATEGORICAL variables because they classify by categories. Nominal variables such as gender, religion, or eye color are CATEGORICAL variables. Generally speaking, categorical variables

A special case of a CATEGORICAL variable is a DICHOTOMOUS VARIABLE. DICHOTOMOUS variables have only two CHARACTERISTICS (male or female). When naming QUALITATIVE variables, it is important to name the category rather than the levels (i.e., gender is the variable name, not male and female).

Variables have different purposes or roles…

Independent (Experimental, Manipulated, Treatment, Grouping) Variable- That factor which is measured, manipulated, or selected by the experimenter to determine its relationship to an observed phenomenon. “In a research study, independent variables are antecedent conditions that are presumed to affect a dependent variable. They are either manipulated by the researcher or are observed by the researcher so that their values can be related to that of the dependent variable. For example, in a research study on the relationship between mosquitoes and mosquito bites, the number of mosquitoes per acre of ground would be an independent variable” (Jaeger, 1990, p. 373)

While the independent variable is often manipulated by the researcher, it can also be a classification where subjects are assigned to groups. In a study where one variable causes the other, the independent variable is the cause. In a study where groups are being compared, the independent variable is the group classification.

Dependent (Outcome) Variable- That factor which is observed and measured to determine the effect of the independent variable, i.e., that factor that appears, disappears, or varies as the experimenter introduces, removes, or varies the independent variable. “In a research study, the independent variable defines a principal focus of research interest. It is the consequent variable that is presumably affected by one or more independent variables that are either manipulated by the researcher or observed by the researcher and regarded as antecedent conditions that determine the value of the dependent variable. For example, in a study of the relationship between mosquitoes and mosquito bites, the number of mosquito bites per hour would be the dependent variable” (Jaeger, 1990, p. 370). The dependent variable is the participant’s response.

The dependent variable is the outcome. In an experiment, it may be what was caused or what changed as a result of the study. In a comparison of groups, it is what they differ on.

Moderator Variable- That factor which is measured, manipulated, or selected by the experimenter to discover whether it modifies the relationship of the independent variable to an observed phenomenon. It is a special type of independent variable.

The independent variable’s relationship with the dependent variable may change under different conditions. That condition is the moderator variable. In a study of two methods of teaching reading, one of the methods of teaching reading may work better with boys than girls. Method of teaching reading is the independent variable and reading achievement is the dependent variable. Gender is the moderator variable because it moderates or changes the relationship between the independent variable (teaching method) and the dependent variable (reading achievement).

Suppose we do a study of reading achievement where we compare whole language with phonics, and we also include students’ social economic status (SES) as a variable. The students are randomly assigned to either whole language instruction or phonics instruction. There are students of high and low SES in each group.

Let’s assume that we found that whole language instruction worked better than phonics instruction with the high SES students, but phonics instruction worked better than whole language instruction with the low SES students. Later you will learn in statistics that this is an interaction effect. In this study, language instruction was the independent variable (with two levels: phonics and whole language). SES was the moderator variable (with two levels: high and low). Reading achievement was the dependent variable (measured on a continuous scale so there aren’t levels).

With a moderator variable, we find the type of instruction did make a difference, but it worked differently for the two groups on the moderator variable. We select this moderator variable because we think it is a variable that will moderate the effect of the independent on the dependent. We make this decision before we start the study.

If the moderator had not been in the study above, we would have said that there was no difference in reading achievement between the two types of reading instruction. This would have happened because the average of the high and low scores of each SES group within a reading instruction group would cancel each other an produce what appears to be average reading achievement in each instruction group (i.e., Phonics: Low—6 and High—2; Whole Language:   Low—2 and High—6; Phonics has an average of 4 and Whole Language has an average of 4. If we just look at the averages (without regard to the moderator), it appears that the instruction types produced similar results).

Extraneous Variable- Those factors which cannot be controlled. Extraneous variables are independent variables that have not been controlled. They may or may not influence the results. One way to control an extraneous variable which might influence the results is to make it a constant (keep everyone in the study alike on that characteristic). If SES were thought to influence achievement, then restricting the study to one SES level would eliminate SES as an extraneous variable.

Here are some examples similar to your homework:

Null Hypothesis: Students who receive pizza coupons as a reward do not read more books than students who do not receive pizza coupon rewards. Independent Variable: Reward Status Dependent Variable: Number of Books Read

High achieving students do not perform better than low achieving student when writing stories regardless of whether they use paper and pencil or a word processor. Independent Variable: Instrument Used for Writing Moderator Variable: Ability Level of the Students Dependent Variable:  Quality of Stories Written When we are comparing two groups, the groups are the independent variable. When we are testing whether something influences something else, the influence (cause) is the independent variable. The independent variable is also the one we manipulate. For example, consider the hypothesis “Teachers given higher pay will have more positive attitudes toward children than teachers given lower pay.” One approach is to ask ourselves “Are there two or more groups being compared?” The answer is “Yes.” “What are the groups?” Teachers who are given higher pay and teachers who are given lower pay. Therefore, the independent variable is teacher pay (it has two levels– high pay and low pay). The dependent variable (what the groups differ on) is attitude towards school.

We could also approach this another way. “Is something causing something else?” The answer is “Yes.” “What is causing what?” Teacher pay is causing attitude towards school. Therefore, teacher pay is the independent variable (cause) and attitude towards school is the dependent variable (outcome).

Research Questions and Hypotheses

The research question drives the study. It should specifically state what is being investigated. Statisticians often convert their research questions to null and alternative hypotheses. The null hypothesis states that no relationship (correlation study) or difference (experimental study) exists. Converting research questions to hypotheses is a simple task. Take the questions and make it a positive statement that says a relationship exists (correlation studies) or a difference exists (experiment study) between the groups and we have the alternative hypothesis. Write a statement  that a relationship does not exist or a difference does not exist and we have the null hypothesis.

Format for sample research questions and accompanying hypotheses:

Research Question for Relationships: Is there a relationship between height and weight? Null Hypothesis:  There is no relationship between height and weight. Alternative Hypothesis:   There is a relationship between height and weight.

When a researcher states a nondirectional hypothesis in a study that compares the performance of two groups, she doesn’t state which group she believes will perform better. If the word “more” or “less” appears in the hypothesis, there is a good chance that we are reading a directional hypothesis. A directional hypothesis is one where the researcher states which group she believes will perform better.  Most researchers use nondirectional hypotheses.

We usually write the alternative hypothesis (what we believe might happen) before we write the null hypothesis (saying it won’t happen).

Directional Research Question for Differences: Do boys like reading more than girls? Null Hypothesis:   Boys do not like reading more than girls. Alternative Hypothesis:   Boys do like reading more than girls.

Nondirectional Research Question for Differences: Is there a difference between boys’ and girls’ attitude towards reading? –or– Do boys’ and girls’ attitude towards reading differ? Null Hypothesis:   There is no difference between boys’ and girls’ attitude towards reading.  –or–  Boys’ and girls’ attitude towards reading do not differ. Alternative Hypothesis:   There is a difference between boys’ and girls’ attitude towards reading.  –or–  Boys’ and girls’ attitude towards reading differ.

Del Siegle, Ph.D. Neag School of Education – University of Connecticut [email protected] www.delsiegle.com

Independent and Dependent Variables

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

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Associate Editor for Simply Psychology

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

On This Page:

In research, a variable is any characteristic, number, or quantity that can be measured or counted in experimental investigations . One is called the dependent variable, and the other is the independent variable.

In research, the independent variable is manipulated to observe its effect, while the dependent variable is the measured outcome. Essentially, the independent variable is the presumed cause, and the dependent variable is the observed effect.

Variables provide the foundation for examining relationships, drawing conclusions, and making predictions in research studies.

Independent Variable

In psychology, the independent variable is the variable the experimenter manipulates or changes and is assumed to directly affect the dependent variable.

It’s considered the cause or factor that drives change, allowing psychologists to observe how it influences behavior, emotions, or other dependent variables in an experimental setting. Essentially, it’s the presumed cause in cause-and-effect relationships being studied.

For example, allocating participants to drug or placebo conditions (independent variable) to measure any changes in the intensity of their anxiety (dependent variable).

In a well-designed experimental study , the independent variable is the only important difference between the experimental (e.g., treatment) and control (e.g., placebo) groups.

By changing the independent variable and holding other factors constant, psychologists aim to determine if it causes a change in another variable, called the dependent variable.

For example, in a study investigating the effects of sleep on memory, the amount of sleep (e.g., 4 hours, 8 hours, 12 hours) would be the independent variable, as the researcher might manipulate or categorize it to see its impact on memory recall, which would be the dependent variable.

Dependent Variable

In psychology, the dependent variable is the variable being tested and measured in an experiment and is “dependent” on the independent variable.

In psychology, a dependent variable represents the outcome or results and can change based on the manipulations of the independent variable. Essentially, it’s the presumed effect in a cause-and-effect relationship being studied.

An example of a dependent variable is depression symptoms, which depend on the independent variable (type of therapy).

In an experiment, the researcher looks for the possible effect on the dependent variable that might be caused by changing the independent variable.

For instance, in a study examining the effects of a new study technique on exam performance, the technique would be the independent variable (as it is being introduced or manipulated), while the exam scores would be the dependent variable (as they represent the outcome of interest that’s being measured).

Examples in Research Studies

For example, we might change the type of information (e.g., organized or random) given to participants to see how this might affect the amount of information remembered.

In this example, the type of information is the independent variable (because it changes), and the amount of information remembered is the dependent variable (because this is being measured).

For the following hypotheses, name the IV and the DV.

1. Lack of sleep significantly affects learning in 10-year-old boys.

IV……………………………………………………

DV…………………………………………………..

2. Social class has a significant effect on IQ scores.

DV……………………………………………….…

3. Stressful experiences significantly increase the likelihood of headaches.

4. Time of day has a significant effect on alertness.

Operationalizing Variables

To ensure cause and effect are established, it is important that we identify exactly how the independent and dependent variables will be measured; this is known as operationalizing the variables.

Operational variables (or operationalizing definitions) refer to how you will define and measure a specific variable as it is used in your study. This enables another psychologist to replicate your research and is essential in establishing reliability (achieving consistency in the results).

For example, if we are concerned with the effect of media violence on aggression, then we need to be very clear about what we mean by the different terms. In this case, we must state what we mean by the terms “media violence” and “aggression” as we will study them.

Therefore, you could state that “media violence” is operationally defined (in your experiment) as ‘exposure to a 15-minute film showing scenes of physical assault’; “aggression” is operationally defined as ‘levels of electrical shocks administered to a second ‘participant’ in another room.

In another example, the hypothesis “Young participants will have significantly better memories than older participants” is not operationalized. How do we define “young,” “old,” or “memory”? “Participants aged between 16 – 30 will recall significantly more nouns from a list of twenty than participants aged between 55 – 70” is operationalized.

The key point here is that we have clarified what we mean by the terms as they were studied and measured in our experiment.

If we didn’t do this, it would be very difficult (if not impossible) to compare the findings of different studies to the same behavior.

Operationalization has the advantage of generally providing a clear and objective definition of even complex variables. It also makes it easier for other researchers to replicate a study and check for reliability .

For the following hypotheses, name the IV and the DV and operationalize both variables.

1. Women are more attracted to men without earrings than men with earrings.

I.V._____________________________________________________________

D.V. ____________________________________________________________

Operational definitions:

I.V. ____________________________________________________________

2. People learn more when they study in a quiet versus noisy place.

I.V. _________________________________________________________

D.V. ___________________________________________________________

3. People who exercise regularly sleep better at night.

Can there be more than one independent or dependent variable in a study?

Yes, it is possible to have more than one independent or dependent variable in a study.

In some studies, researchers may want to explore how multiple factors affect the outcome, so they include more than one independent variable.

Similarly, they may measure multiple things to see how they are influenced, resulting in multiple dependent variables. This allows for a more comprehensive understanding of the topic being studied.

What are some ethical considerations related to independent and dependent variables?

Ethical considerations related to independent and dependent variables involve treating participants fairly and protecting their rights.

Researchers must ensure that participants provide informed consent and that their privacy and confidentiality are respected. Additionally, it is important to avoid manipulating independent variables in ways that could cause harm or discomfort to participants.

Researchers should also consider the potential impact of their study on vulnerable populations and ensure that their methods are unbiased and free from discrimination.

Ethical guidelines help ensure that research is conducted responsibly and with respect for the well-being of the participants involved.

Can qualitative data have independent and dependent variables?

Yes, both quantitative and qualitative data can have independent and dependent variables.

In quantitative research, independent variables are usually measured numerically and manipulated to understand their impact on the dependent variable. In qualitative research, independent variables can be qualitative in nature, such as individual experiences, cultural factors, or social contexts, influencing the phenomenon of interest.

The dependent variable, in both cases, is what is being observed or studied to see how it changes in response to the independent variable.

So, regardless of the type of data, researchers analyze the relationship between independent and dependent variables to gain insights into their research questions.

Can the same variable be independent in one study and dependent in another?

Yes, the same variable can be independent in one study and dependent in another.

The classification of a variable as independent or dependent depends on how it is used within a specific study. In one study, a variable might be manipulated or controlled to see its effect on another variable, making it independent.

However, in a different study, that same variable might be the one being measured or observed to understand its relationship with another variable, making it dependent.

The role of a variable as independent or dependent can vary depending on the research question and study design.

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Research Variables

The research variables, of any scientific experiment or research process, are factors that can be manipulated and measured.

This article is a part of the guide:

• Experimental Research
• Pretest-Posttest
• Third Variable
• Research Bias
• Independent Variable

Browse Full Outline

• 1 Experimental Research
• 2.1 Independent Variable
• 2.2 Dependent Variable
• 2.3 Controlled Variables
• 2.4 Third Variable
• 3.1 Control Group
• 3.2 Research Bias
• 3.3.1 Placebo Effect
• 3.3.2 Double Blind Method
• 4.1 Randomized Controlled Trials
• 4.2 Pretest-Posttest
• 4.3 Solomon Four Group
• 4.4 Between Subjects
• 4.5 Within Subject
• 4.6 Repeated Measures
• 4.7 Counterbalanced Measures
• 4.8 Matched Subjects

Any factor that can take on different values is a scientific variable and influences the outcome of experimental research .

Most scientific experiments measure quantifiable factors, such as time or weight, but this is not essential for a component to be classed as a variable.

As an example, most of us have filled in surveys where a researcher asks questions and asks you to rate answers. These responses generally have a numerical range, from ‘1 - Strongly Agree’ through to ‘5 - Strongly Disagree’. This type of measurement allows opinions to be statistically analyzed and evaluated.

Dependent and Independent Variables

The key to designing any experiment is to look at what research variables could affect the outcome.

There are many types of variable but the most important, for the vast majority of research methods, are the independent and dependent variables.

The independent variable is the core of the experiment and is isolated and manipulated by the researcher. The dependent variable is the measurable outcome of this manipulation, the results of the experimental design . For many physical experiments , isolating the independent variable and measuring the dependent is generally easy.

If you designed an experiment to determine how quickly a cup of coffee cools, the manipulated independent variable is time and the dependent measured variable is temperature.

In other fields of science, the variables are often more difficult to determine and an experiment needs a robust design. Operationalization is a useful tool to measure fuzzy concepts which do not have one obvious variable.

The Difficulty of Isolating Variables

In biology , social science and geography, for example, isolating a single independent variable is more difficult and any experimental design must consider this.

For example, in a social research setting, you might wish to compare the effect of different foods upon hyperactivity in children. The initial research and inductive reasoning leads you to postulate that certain foods and additives are a contributor to increased hyperactivity. You decide to create a hypothesis and design an experiment , to establish if there is solid evidence behind the claim.

The type of food is an independent variable, as is the amount eaten, the period of time and the gender and age of the child. All of these factors must be accounted for during the experimental design stage. Randomization and controls are generally used to ensure that only one independent variable is manipulated.

To eradicate some of these research variables and isolate the process, it is essential to use various scientific measurements to nullify or negate them.

For example, if you wanted to isolate the different types of food as the manipulated variable, you should use children of the same age and gender.

The test groups should eat the same amount of the food at the same times and the children should be randomly assigned to groups. This will minimize the physiological differences between children. A control group , acting as a buffer against unknown research variables, might involve some children eating a food type with no known links to hyperactivity.

In this experiment, the dependent variable is the level of hyperactivity, with the resulting statistical tests easily highlighting any correlation . Depending upon the results , you could try to measure a different variable, such as gender, in a follow up experiment.

Converting Research Variables Into Constants

Ensuring that certain research variables are controlled increases the reliability and validity of the experiment, by ensuring that other causal effects are eliminated. This safeguard makes it easier for other researchers to repeat the experiment and comprehensively test the results.

What you are trying to do, in your scientific design, is to change most of the variables into constants, isolating the independent variable. Any scientific research does contain an element of compromise and inbuilt error , but eliminating other variables will ensure that the results are robust and valid .

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Martyn Shuttleworth (Aug 9, 2008). Research Variables. Retrieved Apr 25, 2024 from Explorable.com: https://explorable.com/research-variables

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Types of Variables – A Comprehensive Guide

Published by Carmen Troy at August 14th, 2021 , Revised On October 26, 2023

A variable is any qualitative or quantitative characteristic that can change and have more than one value, such as age, height, weight, gender, etc.

Before conducting research, it’s essential to know what needs to be measured or analysed and choose a suitable statistical test to present your study’s findings. 

In most cases, you can do it by identifying the key issues/variables related to your research’s main topic.

Example:  If you want to test whether the hybridisation of plants harms the health of people. You can use the key variables like agricultural techniques, type of soil, environmental factors, types of pesticides used, the process of hybridisation, type of yield obtained after hybridisation, type of yield without hybridisation, etc.

Variables are broadly categorised into:

• Independent variables
• Dependent variable
• Control variable

Independent Vs. Dependent Vs. Control Variable

The research includes finding ways:

• To change the independent variables.
• To prevent the controlled variables from changing.
• To measure the dependent variables.

Note:  The term dependent and independent is not applicable in  correlational research  as this is not a  controlled experiment.  A researcher doesn’t have control over the variables. The association and between two or more variables are measured. If one variable affects another one, then it’s called the predictor variable and outcome variable.

Example:  Correlation between investment (predictor variable) and profit (outcome variable)

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Types of Variables Based on the Types of Data

A data is referred to as the information and statistics gathered for analysis of a research topic. Data is broadly divided into two categories, such as:

Quantitative/Numerical data  is associated with the aspects of measurement, quantity, and extent. 

Categorial data  is associated with groupings.

A qualitative variable consists of qualitative data, and a quantitative variable consists of a quantitative variable.

Quantitative Variable

The quantitative variable is associated with measurement, quantity, and extent, like how many . It follows the statistical, mathematical, and computational techniques in numerical data such as percentages and statistics. The research is conducted on a large group of population.

Example:  Find out the weight of students of the fifth standard studying in government schools.

The quantitative variable can be further categorised into continuous and discrete.

Categorial Variable

The categorical variable includes measurements that vary in categories such as names but not in terms of rank or degree. It means one level of a categorical variable cannot be considered better or greater than another level. 

Example: Gender, brands, colors, zip codes

The categorical variable is further categorised into three types:

Note:  Sometimes, an ordinal variable also acts as a quantitative variable. Ordinal data has an order, but the intervals between scale points may be uneven.

Example: Numbers on a rating scale represent the reviews’ rank or range from below average to above average. However, it also represents a quantitative variable showing how many stars and how much rating is given.

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Other Types of Variables

It’s important to understand the difference between dependent and independent variables and know whether they are quantitative or categorical to choose the appropriate statistical test.

There are many other types of variables to help you differentiate and understand them.

Also, read a comprehensive guide written about inductive and deductive reasoning .

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Frequently Asked Questions

What are the 10 types of variables in research.

The 10 types of variables in research are:

• Independent
• Confounding
• Categorical
• Extraneous.

What is an independent variable?

An independent variable, often termed the predictor or explanatory variable, is the variable manipulated or categorized in an experiment to observe its effect on another variable, called the dependent variable. It’s the presumed cause in a cause-and-effect relationship, determining if changes in it produce changes in the observed outcome.

What is a variable?

In research, a variable is any attribute, quantity, or characteristic that can be measured or counted. It can take on various values, making it “variable.” Variables can be classified as independent (manipulated), dependent (observed outcome), or control (kept constant). They form the foundation for hypotheses, observations, and data analysis in studies.

What is a dependent variable?

A dependent variable is the outcome or response being studied in an experiment or investigation. It’s what researchers measure to determine the effect of changes in the independent variable. In a cause-and-effect relationship, the dependent variable is presumed to be influenced or caused by the independent variable.

What is a variable in programming?

In programming, a variable is a symbolic name for a storage location that holds data or values. It allows data storage and retrieval for computational operations. Variables have types, like integer or string, determining the nature of data they can hold. They’re fundamental in manipulating and processing information in software.

What is a control variable?

A control variable in research is a factor that’s kept constant to ensure that it doesn’t influence the outcome. By controlling these variables, researchers can isolate the effects of the independent variable on the dependent variable, ensuring that other factors don’t skew the results or introduce bias into the experiment.

What is a controlled variable in science?

In science, a controlled variable is a factor that remains constant throughout an experiment. It ensures that any observed changes in the dependent variable are solely due to the independent variable, not other factors. By keeping controlled variables consistent, researchers can maintain experiment validity and accurately assess cause-and-effect relationships.

How many independent variables should an investigation have?

Ideally, an investigation should have one independent variable to clearly establish cause-and-effect relationships. Manipulating multiple independent variables simultaneously can complicate data interpretation.

However, in advanced research, experiments with multiple independent variables (factorial designs) are used, but they require careful planning to understand interactions between variables.

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Types of Variables in Psychology Research

Examples of Independent and Dependent Variables

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

 James Lacy, MLS, is a fact-checker and researcher.

Dependent and Independent Variables

• Intervening Variables
• Extraneous Variables
• Controlled Variables
• Confounding Variables
• Operationalizing Variables

Frequently Asked Questions

Variables in psychology are things that can be changed or altered, such as a characteristic or value. Variables are generally used in psychology experiments to determine if changes to one thing result in changes to another.

Variables in psychology play a critical role in the research process. By systematically changing some variables in an experiment and measuring what happens as a result, researchers are able to learn more about cause-and-effect relationships.

The two main types of variables in psychology are the independent variable and the dependent variable. Both variables are important in the process of collecting data about psychological phenomena.

This article discusses different types of variables that are used in psychology research. It also covers how to operationalize these variables when conducting experiments.

Students often report problems with identifying the independent and dependent variables in an experiment. While this task can become more difficult as the complexity of an experiment increases, in a psychology experiment:

• The independent variable is the variable that is manipulated by the experimenter. An example of an independent variable in psychology: In an experiment on the impact of sleep deprivation on test performance, sleep deprivation would be the independent variable. The experimenters would have some of the study participants be sleep-deprived while others would be fully rested.
• The dependent variable is the variable that is measured by the experimenter. In the previous example, the scores on the test performance measure would be the dependent variable.

So how do you differentiate between the independent and dependent variables? Start by asking yourself what the experimenter is manipulating. The things that change, either naturally or through direct manipulation from the experimenter, are generally the independent variables. What is being measured? The dependent variable is the one that the experimenter is measuring.

Intervening Variables in Psychology

Intervening variables, also sometimes called intermediate or mediator variables, are factors that play a role in the relationship between two other variables. In the previous example, sleep problems in university students are often influenced by factors such as stress. As a result, stress might be an intervening variable that plays a role in how much sleep people get, which may then influence how well they perform on exams.

Extraneous Variables in Psychology

Independent and dependent variables are not the only variables present in many experiments. In some cases, extraneous variables may also play a role. This type of variable is one that may have an impact on the relationship between the independent and dependent variables.

For example, in our previous example of an experiment on the effects of sleep deprivation on test performance, other factors such as age, gender, and academic background may have an impact on the results. In such cases, the experimenter will note the values of these extraneous variables so any impact can be controlled for.

There are two basic types of extraneous variables:

• Participant variables : These extraneous variables are related to the individual characteristics of each study participant that may impact how they respond. These factors can include background differences, mood, anxiety, intelligence, awareness, and other characteristics that are unique to each person.
• Situational variables : These extraneous variables are related to things in the environment that may impact how each participant responds. For example, if a participant is taking a test in a chilly room, the temperature would be considered an extraneous variable. Some participants may not be affected by the cold, but others might be distracted or annoyed by the temperature of the room.

Other extraneous variables include the following:

• Demand characteristics : Clues in the environment that suggest how a participant should behave
• Experimenter effects : When a researcher unintentionally suggests clues for how a participant should behave

Controlled Variables in Psychology

In many cases, extraneous variables are controlled for by the experimenter. A controlled variable is one that is held constant throughout an experiment.

In the case of participant variables, the experiment might select participants that are the same in background and temperament to ensure that these factors don't interfere with the results. Holding these variables constant is important for an experiment because it allows researchers to be sure that all other variables remain the same across all conditions.  

Using controlled variables means that when changes occur, the researchers can be sure that these changes are due to the manipulation of the independent variable and not caused by changes in other variables.

It is important to also note that a controlled variable is not the same thing as a control group . The control group in a study is the group of participants who do not receive the treatment or change in the independent variable.

All other variables between the control group and experimental group are held constant (i.e., they are controlled). The dependent variable being measured is then compared between the control group and experimental group to see what changes occurred because of the treatment.

Confounding Variables in Psychology

If a variable cannot be controlled for, it becomes what is known as a confounding variable. This type of variable can have an impact on the dependent variable, which can make it difficult to determine if the results are due to the influence of the independent variable, the confounding variable, or an interaction of the two.

Operationalizing Variables in Psychology

An operational definition describes how the variables are measured and defined in the study. Before conducting a psychology experiment , it is essential to create firm operational definitions for both the independent variable and dependent variables.

For example, in our imaginary experiment on the effects of sleep deprivation on test performance, we would need to create very specific operational definitions for our two variables. If our hypothesis is "Students who are sleep deprived will score significantly lower on a test," then we would have a few different concepts to define:

• Students : First, what do we mean by "students?" In our example, let’s define students as participants enrolled in an introductory university-level psychology course.
• Sleep deprivation : Next, we need to operationally define the "sleep deprivation" variable. In our example, let’s say that sleep deprivation refers to those participants who have had less than five hours of sleep the night before the test.
• Test variable : Finally, we need to create an operational definition for the test variable. For this example, the test variable will be defined as a student’s score on a chapter exam in the introductory psychology course.

Once all the variables are operationalized, we're ready to conduct the experiment.

Variables play an important part in psychology research. Manipulating an independent variable and measuring the dependent variable allows researchers to determine if there is a cause-and-effect relationship between them.

A Word From Verywell

Understanding the different types of variables used in psychology research is important if you want to conduct your own psychology experiments. It is also helpful for people who want to better understand what the results of psychology research really mean and become more informed consumers of psychology information .

Independent and dependent variables are used in experimental research. Unlike some other types of research (such as correlational studies ), experiments allow researchers to evaluate cause-and-effect relationships between two variables.

Researchers can use statistical analyses to determine the strength of a relationship between two variables in an experiment. Two of the most common ways to do this are to calculate a p-value or a correlation. The p-value indicates if the results are statistically significant while the correlation can indicate the strength of the relationship.

In an experiment on how sugar affects short-term memory, sugar intake would be the independent variable and scores on a short-term memory task would be the independent variable.

In an experiment looking at how caffeine intake affects test anxiety, the amount of caffeine consumed before a test would be the independent variable and scores on a test anxiety assessment would be the dependent variable.

Just as with other types of research, the independent variable in a cognitive psychology study would be the variable that the researchers manipulate. The specific independent variable would vary depending on the specific study, but it might be focused on some aspect of thinking, memory, attention, language, or decision-making.

American Psychological Association. Operational definition . APA Dictionary of Psychology.

American Psychological Association. Mediator . APA Dictionary of Psychology.

Altun I, Cınar N, Dede C. The contributing factors to poor sleep experiences in according to the university students: A cross-sectional study .  J Res Med Sci . 2012;17(6):557-561. PMID:23626634

Skelly AC, Dettori JR, Brodt ED. Assessing bias: The importance of considering confounding .  Evid Based Spine Care J . 2012;3(1):9-12. doi:10.1055/s-0031-1298595

• Evans, AN & Rooney, BJ. Methods in Psychological Research. Thousand Oaks, CA: SAGE Publications; 2014.
• Kantowitz, BH, Roediger, HL, & Elmes, DG. Experimental Psychology. Stamfort, CT: Cengage Learning; 2015.

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

Variables: Definition, Examples, Types of Variables in Research

What is a Variable?

Within the context of a research investigation, concepts are generally referred to as variables. A variable is, as the name applies, something that varies.

Examples of Variable

These are all examples of variables because each of these properties varies or differs from one individual to another.

• income and expenses,
• family size,
• country of birth,
• capital expenditure,
• class grades,
• blood pressure readings,
• preoperative anxiety levels,
• eye color, and
• vehicle type.

What is Variable in Research?

A variable is any property, characteristic, number, or quantity that increases or decreases over time or can take on different values (as opposed to constants, such as n , that do not vary) in different situations.

When conducting research, experiments often manipulate variables. For example, an experimenter might compare the effectiveness of four types of fertilizers.

In this case, the variable is the ‘type of fertilizers.’ A social scientist may examine the possible effect of early marriage on divorce. Her early marriage is variable.

A business researcher may find it useful to include the dividend in determining the share prices . Here, the dividend is the variable.

Effectiveness, divorce, and share prices are variables because they also vary due to manipulating fertilizers, early marriage, and dividends.

11 Types of Variables in Research

Qualitative variables.

An important distinction between variables is the qualitative and quantitative variables.

Qualitative variables are those that express a qualitative attribute, such as hair color, religion, race, gender, social status, method of payment, and so on. The values of a qualitative variable do not imply a meaningful numerical ordering.

The value of the variable ‘religion’ (Muslim, Hindu.., etc..) differs qualitatively; no ordering of religion is implied. Qualitative variables are sometimes referred to as categorical variables.

For example, the variable sex has two distinct categories: ‘male’ and ‘female.’ Since the values of this variable are expressed in categories, we refer to this as a categorical variable.

Similarly, the place of residence may be categorized as urban and rural and thus is a categorical variable.

Categorical variables may again be described as nominal and ordinal.

Ordinal variables can be logically ordered or ranked higher or lower than another but do not necessarily establish a numeric difference between each category, such as examination grades (A+, A, B+, etc., and clothing size (Extra large, large, medium, small).

Nominal variables are those that can neither be ranked nor logically ordered, such as religion, sex, etc.

A qualitative variable is a characteristic that is not capable of being measured but can be categorized as possessing or not possessing some characteristics.

Quantitative Variables

Quantitative variables, also called numeric variables, are those variables that are measured in terms of numbers. A simple example of a quantitative variable is a person’s age.

Age can take on different values because a person can be 20 years old, 35 years old, and so on. Likewise, family size is a quantitative variable because a family might be comprised of one, two, or three members, and so on.

Each of these properties or characteristics referred to above varies or differs from one individual to another. Note that these variables are expressed in numbers, for which we call quantitative or sometimes numeric variables.

A quantitative variable is one for which the resulting observations are numeric and thus possess a natural ordering or ranking.

Discrete and Continuous Variables

Quantitative variables are again of two types: discrete and continuous.

Variables such as some children in a household or the number of defective items in a box are discrete variables since the possible scores are discrete on the scale.

For example, a household could have three or five children, but not 4.52 children.

Other variables, such as ‘time required to complete an MCQ test’ and ‘waiting time in a queue in front of a bank counter,’ are continuous variables.

The time required in the above examples is a continuous variable, which could be, for example, 1.65 minutes or 1.6584795214 minutes.

Of course, the practicalities of measurement preclude most measured variables from being continuous.

Discrete Variable

A discrete variable, restricted to certain values, usually (but not necessarily) consists of whole numbers, such as the family size and a number of defective items in a box. They are often the results of enumeration or counting.

A few more examples are;

• The number of accidents in the twelve months.
• The number of mobile cards sold in a store within seven days.
• The number of patients admitted to a hospital over a specified period.
• The number of new branches of a bank opened annually during 2001- 2007.
• The number of weekly visits made by health personnel in the last 12 months.

Continuous Variable

A continuous variable may take on an infinite number of intermediate values along a specified interval. Examples are:

• The sugar level in the human body;
• Blood pressure reading;
• Temperature;
• Height or weight of the human body;
• Rate of bank interest;
• Internal rate of return (IRR),
• Earning ratio (ER);
• Current ratio (CR)

No matter how close two observations might be, if the instrument of measurement is precise enough, a third observation can be found, falling between the first two.

A continuous variable generally results from measurement and can assume countless values in the specified range.

Dependent Variables and Independent Variable

In many research settings, two specific classes of variables need to be distinguished from one another: independent variable and dependent variable.

Many research studies aim to reveal and understand the causes of underlying phenomena or problems with the ultimate goal of establishing a causal relationship between them.

Look at the following statements:

• Low intake of food causes underweight.
• Smoking enhances the risk of lung cancer.
• Level of education influences job satisfaction.
• Advertisement helps in sales promotion.
• The drug causes improvement of health problems.
• Nursing intervention causes more rapid recovery.
• Previous job experiences determine the initial salary.
• Blueberries slow down aging.
• The dividend per share determines share prices.

In each of the above queries, we have two independent and dependent variables. In the first example, ‘low intake of food’ is believed to have caused the ‘problem of being underweight.’

It is thus the so-called independent variable. Underweight is the dependent variable because we believe this ‘problem’ (the problem of being underweight) has been caused by ‘the low intake of food’ (the factor).

Similarly, smoking, dividend, and advertisement are all independent variables, and lung cancer, job satisfaction, and sales are dependent variables.

In general, an independent variable is manipulated by the experimenter or researcher, and its effects on the dependent variable are measured.

Independent Variable

The variable that is used to describe or measure the factor that is assumed to cause or at least to influence the problem or outcome is called an independent variable.

The definition implies that the experimenter uses the independent variable to describe or explain its influence or effect of it on the dependent variable.

Variability in the dependent variable is presumed to depend on variability in the independent variable.

Depending on the context, an independent variable is sometimes called a predictor variable, regressor, controlled variable, manipulated variable, explanatory variable, exposure variable (as used in reliability theory), risk factor (as used in medical statistics), feature (as used in machine learning and pattern recognition) or input variable.

The explanatory variable is preferred by some authors over the independent variable when the quantities treated as independent variables may not be statistically independent or independently manipulable by the researcher.

If the independent variable is referred to as an explanatory variable, then the term response variable is preferred by some authors for the dependent variable.

Dependent Variable

The variable used to describe or measure the problem or outcome under study is called a dependent variable.

In a causal relationship, the cause is the independent variable, and the effect is the dependent variable. If we hypothesize that smoking causes lung cancer, ‘smoking’ is the independent variable and cancer the dependent variable.

A business researcher may find it useful to include the dividend in determining the share prices. Here dividend is the independent variable, while the share price is the dependent variable.

The dependent variable usually is the variable the researcher is interested in understanding, explaining, or predicting.

In lung cancer research, the carcinoma is of real interest to the researcher, not smoking behavior per se. The independent variable is the presumed cause of, antecedent to, or influence on the dependent variable.

Depending on the context, a dependent variable is sometimes called a response variable, regressand, predicted variable, measured variable, explained variable, experimental variable, responding variable, outcome variable, output variable, or label.

An explained variable is preferred by some authors over the dependent variable when the quantities treated as dependent variables may not be statistically dependent.

If the dependent variable is referred to as an explained variable, then the term predictor variable is preferred by some authors for the independent variable.

Levels of an Independent Variable

If an experimenter compares an experimental treatment with a control treatment, then the independent variable (a type of treatment) has two levels: experimental and control.

If an experiment were to compare five types of diets, then the independent variables (types of diet) would have five levels.

In general, the number of levels of an independent variable is the number of experimental conditions.

Background Variable

In almost every study, we collect information such as age, sex, educational attainment, socioeconomic status, marital status, religion, place of birth, and the like. These variables are referred to as background variables.

These variables are often related to many independent variables, so they indirectly influence the problem. Hence they are called background variables.

The background variables should be measured if they are important to the study. However, we should try to keep the number of background variables as few as possible in the interest of the economy.

Moderating Variable

In any statement of relationships of variables, it is normally hypothesized that in some way, the independent variable ’causes’ the dependent variable to occur.

In simple relationships, all other variables are extraneous and are ignored.

In actual study situations, such a simple one-to-one relationship needs to be revised to take other variables into account to explain the relationship better.

This emphasizes the need to consider a second independent variable that is expected to have a significant contributory or contingent effect on the originally stated dependent-independent relationship.

Such a variable is termed a moderating variable.

Suppose you are studying the impact of field-based and classroom-based training on the work performance of health and family planning workers. You consider the type of training as the independent variable.

If you are focusing on the relationship between the age of the trainees and work performance, you might use ‘type of training’ as a moderating variable.

Extraneous Variable

Most studies concern the identification of a single independent variable and measuring its effect on the dependent variable.

But still, several variables might conceivably affect our hypothesized independent-dependent variable relationship, thereby distorting the study. These variables are referred to as extraneous variables.

Extraneous variables are not necessarily part of the study. They exert a confounding effect on the dependent-independent relationship and thus need to be eliminated or controlled for.

An example may illustrate the concept of extraneous variables. Suppose we are interested in examining the relationship between the work status of mothers and breastfeeding duration.

It is not unreasonable in this instance to presume that the level of education of mothers as it influences work status might have an impact on breastfeeding duration too.

Education is treated here as an extraneous variable. In any attempt to eliminate or control the effect of this variable, we may consider this variable a confounding variable.

An appropriate way of dealing with confounding variables is to follow the stratification procedure, which involves a separate analysis of the different levels of lies in confounding variables.

For this purpose, one can construct two cross­tables for illiterate mothers and the other for literate mothers.

Suppose we find a similar association between work status and duration of breast­feeding in both the groups of mothers. In that case, we conclude that mothers’ educational level is not a confounding variable.

Intervening Variable

Often an apparent relationship between two variables is caused by a third variable.

For example, variables X and Y may be highly correlated, but only because X causes the third variable, Z, which in turn causes Y. In this case, Z is the intervening variable.

An intervening variable theoretically affects the observed phenomena but cannot be seen, measured, or manipulated directly; its effects can only be inferred from the effects of the independent and moderating variables on the observed phenomena.

We might view motivation or counseling as the intervening variable in the work-status and breastfeeding relationship.

Thus, motive, job satisfaction, responsibility, behavior, and justice are some of the examples of intervening variables.

Suppressor Variable

In many cases, we have good reasons to believe that the variables of interest have a relationship, but our data fail to establish any such relationship. Some hidden factors may suppress the true relationship between the two original variables.

Such a factor is referred to as a suppressor variable because it suppresses the relationship between the other two variables.

The suppressor variable suppresses the relationship by being positively correlated with one of the variables in the relationship and negatively correlated with the other. The true relationship between the two variables will reappear when the suppressor variable is controlled for.

Thus, for example, low age may pull education up but income down. In contrast, a high age may pull income up but education down, effectively canceling the relationship between education and income unless age is controlled for.

4 Relationships Between Variables

In dealing with relationships between variables in research, we observe a variety of dimensions in these relationships.

Positive and Negative Relationship

Symmetrical relationship, causal relationship, linear and non-linear relationship.

Two or more variables may have a positive, negative, or no relationship. In the case of two variables, a positive relationship is one in which both variables vary in the same direction.

However, they are said to have a negative relationship when they vary in opposite directions.

When a change in the other variable does not accompany the change or movement of one variable, we say that the variables in question are unrelated.

For example, if an increase in wage rate accompanies one’s job experience, the relationship between job experience and the wage rate is positive.

If an increase in an individual’s education level decreases his desire for additional children, the relationship is negative or inverse.

If the level of education does not have any bearing on the desire, we say that the variables’ desire for additional children and ‘education’ are unrelated.

Strength of Relationship

Once it has been established that two variables are related, we want to ascertain how strongly they are related.

A common statistic to measure the strength of a relationship is the so-called correlation coefficient symbolized by r. r is a unit-free measure, lying between -1 and +1 inclusive, with zero signifying no linear relationship.

As far as the prediction of one variable from the knowledge of the other variable is concerned, a value of r= +1 means a 100% accuracy in predicting a positive relationship between the two variables, and a value of r = -1 means a 100% accuracy in predicting a negative relationship between the two variables.

So far, we have discussed only symmetrical relationships in which a change in the other variable accompanies a change in either variable.

This relationship does not indicate which variable is the independent variable and which variable is the dependent variable.

In other words, you can label either of the variables as the independent variable.

Such a relationship is a symmetrical  relationship. In an asymmetrical relationship, a change in variable X (say) is accompanied by a change in variable Y, but not vice versa.

The amount of rainfall, for example, will increase productivity, but productivity will not affect the rainfall. This is an asymmetrical relationship.

Similarly, the relationship between smoking and lung cancer would be asymmetrical because smoking could cause cancer, but lung cancer could not cause smoking.

Indicating a relationship between two variables does not automatically ensure that changes in one variable cause changes in another.

It is, however, very difficult to establish the existence of causality between variables. While no one can ever be certain that variable A causes variable B , one can gather some evidence that increases our belief that A leads to B.

In an attempt to do so, we seek the following evidence:

• Is there a relationship between A and B?  When such evidence exists, it indicates a possible causal link between the variables.
• Is the relationship asymmetrical so that a change in A results in B but not vice-versa? In other words, does A occur before B? If we find that B occurs before A, we can have little confidence that A causes.
• Does a change in A result in a change in B regardless of the actions of other factors? Or, is it possible to eliminate other possible causes of B? Can one determine that C, D, and E (say) do not co-vary with B in a way that suggests possible causal connections?

A linear relationship is a straight-line relationship between two variables, where the variables vary at the same rate regardless of whether the values are low, high, or intermediate.

This is in contrast with the non-linear (or curvilinear) relationships, where the rate at which one variable changes in value may differ for different values of the second variable.

Whether a variable is linearly related to the other variable or not can simply be ascertained by plotting the K values against X values.

If the values, when plotted, appear to lie on a straight line, the existence of a linear relationship between X and Y is suggested.

Height and weight almost always have an approximately linear relationship, while age and fertility rates have a non-linear relationship.

Frequently Asked Questions about Variable

What is a variable within the context of a research investigation.

A variable, within the context of a research investigation, refers to concepts that vary. It can be any property, characteristic, number, or quantity that can increase or decrease over time or take on different values.

How is a variable used in research?

In research, a variable is any property or characteristic that can take on different values. Experiments often manipulate variables to compare outcomes. For instance, an experimenter might compare the effectiveness of different types of fertilizers, where the variable is the ‘type of fertilizers.’

What distinguishes qualitative variables from quantitative variables?

Qualitative variables express a qualitative attribute, such as hair color or religion, and do not imply a meaningful numerical ordering. Quantitative variables, on the other hand, are measured in terms of numbers, like a person’s age or family size.

How do discrete and continuous variables differ in terms of quantitative variables?

Discrete variables are restricted to certain values, often whole numbers, resulting from enumeration or counting, like the number of children in a household. Continuous variables can take on an infinite number of intermediate values along a specified interval, such as the time required to complete a test.

What are the roles of independent and dependent variables in research?

In research, the independent variable is manipulated by the researcher to observe its effects on the dependent variable. The independent variable is the presumed cause or influence, while the dependent variable is the outcome or effect that is being measured.

What is a background variable in a study?

Background variables are information collected in a study, such as age, sex, or educational attainment. These variables are often related to many independent variables and indirectly influence the main problem or outcome, hence they are termed background variables.

How does a suppressor variable affect the relationship between two other variables?

A suppressor variable can suppress or hide the true relationship between two other variables. It does this by being positively correlated with one of the variables and negatively correlated with the other. When the suppressor variable is controlled for, the true relationship between the two original variables can be observed.

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Changes in Undergraduate Students’ Self-Efficacy and Outcome Expectancy in an Introductory Statistics Course

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Main Article Content

The exploration of psychological variables that potentially impact college student performance in challenging academic courses can be useful for understanding success in introductory statistics. Although previous research has examined specific beliefs that students hold about their abilities and future outcomes, the current study is novel in its examination of changes in both self-efficacy (SE) and outcome expectancy (OE) in relation to performance over the course of an undergraduate introductory psychology statistics course. These psychological variables—relating to one’s belief about one’s ability to accomplish a task and the anticipated outcomes—may impact student motivation and performance. Students’ SE, OE, and other variables related to statistics performance were measured through a survey administered at the beginning and end of the course. Multivariate logistic regression and McNemar tests were conducted to examine factors that affected changes in SE and OE as the semester progressed. Students with lower scores on the final exam demonstrated a decrease in both high SE and positive OE. However, higher scores on exams earlier in the course were associated with increased odds for high SE but not for positive OE, suggesting that SE is less resilient to course performance. Based on these findings, the authors recommend that statistics instructors identify students at risk for decreasing SE. Instructors can help foster high SE in students struggling academically by connecting the course content to their everyday lives and suggesting strategies to enhance their confidence in their content knowledge and increase their comfort in navigating such a challenging course.

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License .

ORIGINAL RESEARCH article

Chinese college students’ pgd symptoms and their relationship to cognitive variables: a latent profile analysis.

• 1 School of Public Health and Health Management, Fujian Health College, Fuzhou, China
• 2 School of Health, Fujian Medical University, Fuzhou, China

Bereavement is a commonly experienced grief event; however, bereavement can also trigger a number of psychological consequences, such as prolonged grief disorder (PGD). At present, the differences in prolonged grief disorder symptoms (PGD symptoms) among various individuals and how those symptoms relate to cognitive variables are unclear. In the present study, 817 Chinese college students with bereavement experience were selected as participants. Based on the evaluation results of their irrational beliefs, bereavement-related irrational beliefs, basic world assumptions, and PGD symptoms, an individual-centered latent profile analysis was used to divide a group with PGD symptoms into several subgroups and comprehensively examine the relationships between these subgroups and cognitive variables. (1) The severity of PGD symptoms among Chinese college students can be categorized into three subgroups: mild, moderate, and severe. (2) Cognitive variables such as irrational beliefs and basic world assumptions were all found to correlate with the severity of PGD symptoms; bereavement-related irrational beliefs was the variable with the largest correlation. However, for the first time, this study found that different dimensions of basic world assumptions had different directions of correlation, based on the severity of the PGD symptoms. Justice, control, randomness, and self-control had significantly positive correlations. Conversely, benevolence of the world, benevolence of people, and worthiness of the self had significantly negative correlations. These results have important reference value for cognitive behavioral therapy (CBT) treatment and interventions for PGD issues in Chinese college students.

Introduction

Bereavement is one of the most challenging life stress time events that individuals may experience throughout the course of life development. College students are in a transitional stage from adolescence to adulthood, a period of rapid psychological and physiological maturity. Bereavement, a common major crisis event that occurs during this period, inevitably has a significant impact on future educational, life, and occupational outcomes ( Mannarino and Cohen, 2014 ; Andriessen et al., 2019 ). Therefore, the grief-based psychological problems experienced by college students after experiencing bereavement have attracted much attention from Chinese mental health workers ( Li et al., 2017 ; Boelen et al., 2021 ; Huang et al., 2023 ).

Generally speaking, after bereavement, people experience a high-intensity grief reaction in a short period of time, as demonstrated through crying, a sense of heartbreak, and so on ( Kristensen et al., 2012 ; Szuhany et al., 2021 ). Later in life, people manage to adapt to bereavement without suffering as severe and lengthy grief symptoms and gradually begin to cope with life again; however, a considerable number of people have difficulties recovering from the pain of prolonged grief symptoms, and even develop psychiatric complications such as depressive- and anxiety-related symptoms. More seriously, a small number of people develop prolonged grief disorder (PGD) ( Stroebe et al., 2007 ; Prigerson et al., 2009 ; Maercker et al., 2013 ). At present, PGD has been formally included in the International Classification of Diseases, 11th edition (ICD-11: World Health Organization [WHO], 2018 ) and Diagnostic and Statistical Manual of Mental Disorders 5, Text Revision (DSM-5-TR: American Psychiatric Association [APA], 2022 ). PGD manifests as a series of intense grief reactions that persist in a person at least 12 months after bereavement (for children and adolescents, at least six months), with persistent thoughts of and longing for the deceased and a dwelling on thoughts and memories of the deceased acting as core symptoms. These are accompanied by intense emotional distress, severely impairing the bereaved person’s day-to-day functioning. This grief response is not consistent with the social and cultural environment in which they live ( Wortman and Boerner, 2011 ; Silverman and Rubin, 2015 ; Duffy and Wild, 2017 ; Szuhany et al., 2021 ).

What is the psychological mechanism fueling PGD symptoms after loss? At present, the cognitive-behavioral conceptualization model (CBCM) proposed by Boelen et al. (2006) is one such systematic psychological theory. This model holds that when an individual faces loss, the original negative cognition directly leads to a pathological grief reaction, coupled with a negative avoidance strategy. The interaction of these factors makes the grief reaction both lasting and intense, eventually evolving into PGD. In other words, the evolution of PGD symptoms in individuals is the result of the interaction of loss experience, negative cognition, anxiety, and depression, among which negative cognitive variables play the key role in developing prolonged grief symptoms ( Boelen et al., 2006 ; Ehlers, 2006 ). Previous studies have shown that the impact of bereavement on individual cognitive systems mainly includes two aspects: irrational beliefs and basic world assumptions ( Janoff-Bulman, 1989 ; Boelen et al., 2003 ; Rubin et al., 2016 ). First of all, after experiencing bereavement, individuals increase their tendency to engage in general irrational thinking, and subsequently, irrational beliefs regarding bereavement and the self are dominant, including low frustration tolerance and discomfort anxiety, making individuals more prone to PGD symptoms ( Ellis, 2001 ; Ehlers, 2006 ; Nagy and Szamosközi, 2014 ). Secondly, after bereavement, the individual’s basic world assumptions about the self, others, and the external world are damaged. World assumptions mean the individual’s set of basic cognitive schemata, which is the core content of the individual belief system; this includes eight aspects (e.g., benevolence of the world, benevolence of people, randomness, etc.). Basic assumptions of the world are developed by the individual over many years and feature the illusion of invulnerability that is necessary for the individual to perform the activities of daily life ( Janoff-Bulman, 1989 ). After experiencing bereavement, these stable basic world assumptions are broken, resulting in PGD symptoms ( Janoff-Bulman, 1992 ). However, to our knowledge, there has been no quantitative study that systematically explores the relationship between the two cognitive variables of irrational beliefs (defined as individuals’ unrealistic demands that lack an objective basis, including absolute demandingness, awfulizing, low frustration tolerance, and global evaluation) and basic world assumptions (a set of basic cognitive schema that individuals have about the self and world, the content of which can be categorized into three main types: perceived benevolence of the world, meaningfulness of the world, and worthiness of the self) and their relation to PGD symptoms.

In addition, there is as yet no conclusive answer to the question of what specific cognitive variables are significantly associated with PGD symptoms ( Boelen et al., 2003 ; Zhou et al., 2018 ). This uncertainty is most obvious in the cognitive variable of basic world assumptions ( Schwartzberg and Janoff-Bulman, 1991 ; Boelen et al., 2004 ; Currier et al., 2009 ; Schuler and Boals, 2016 ). For example, Boelen et al. (2004) found that the PGD symptoms of Dutch college students had a significant positive correlation with irrational beliefs related to loss and a significant negative relationship with the control and luck dimensions of world assumptions. Currier et al. (2009) found that PGD symptoms in American college students had a significant negative correlation with basic world assumptions, but only the meaning and self-worth dimensions of world assumptions had significant negative correlations. As the research designs of these studies were similar, the inconsistent results between them may be due to two factors. The first is the heterogeneity of PGD symptoms of the bereaved individuals selected for each study. A bereaved population with PGD symptoms can be divided into different subgroups. The nature of this cognitive variable and severity of PGD symptoms will vary among subgroups; however, few previous studies have examined this issue. Moreover, college students reside in schools for long periods of time, and most experience a relatively limited distribution, time, and cause of loss ( Ehlers, 2006 ; Kristensen et al., 2012 ; Smid et al., 2019 ). Thus, it would be worthwhile to explore whether PGD symptoms in college students have specific manifestations. Previous studies have mainly used the variable-centered analysis perspective. Although this perspective can reflect the overall situation of individual PGD symptoms and their relationship to other variables, it is difficult to reflect the characteristics and differences of PGD symptoms among different subgroups. Therefore, individual-centered latent profile analysis (LPA) techniques are needed ( Lanza et al., 2007 ). By identifying the variations shared by individuals with bereavement experience and different PGD symptoms, in the present research, a heterogeneous group was divided into several homogeneous subgroups. The differences in PGD symptom characteristics among the different subgroups were then examined to solve the problem of heterogeneity in PGD symptoms.

Sociocultural factors may also affect the relationship between cognitive variables and PGD distress symptoms because variables such as individuals’ irrational beliefs and basic world assumptions are formed in a specific cultural context and obviously influenced by the norms of the individual’s cultural identity ( Janoff-Bulman, 1992 ; Li et al., 2018 ). College students’ basic world assumptions about the self and world are progressively developed and refined over the course of their life experience accumulation and personal growth ( Janoff-Bulman, 1989 ; Schuler and Boals, 2016 ). Importantly, the pattern of transition of PGD symptoms may also vary across cultures. For example, according to a previous study ( Boelen et al., 2003 ; Bonanno et al., 2005 ), bereavement survivors in China have different patterns of grief symptoms compared with such survivors in the United States. Specifically, in the early stages of grief, Chinese bereavement survivors have stronger grief reactions and worse mental health than do such individuals in the United States. However, 18 months later, bereaved Chinese people were found to report less grief, a lower level of pain, and better mental health than did Americans. In addition, funeral culture, understanding of death, and other cultural issues are also predictors of PGD symptoms ( Currier et al., 2009 ; Zukerman and Korn, 2014 ; Vermunt, 2017 ). Since previous studies on the psychological mechanism prompting PGD symptoms have mainly focused on bereaved people in Western countries, this study was carried out in an Eastern country (China), which has a unique funeral culture (e.g., wearing mourning clothes, sweeping graves during the Qingming Festival, etc.) and concepts related to death (e.g., rebirth, death, death taboos, etc.). Therefore, this study will improve our understanding of the relationship between PGD symptoms and cognitive variables as they operate in the Chinese culture.

In sum, this study selected Chinese college students with bereavement experience as the participants, and measured the cognitive variables (i.e., irrational beliefs and basic world assumptions) and PGD symptoms according to CBCM theory. In addition, considering the event-specific nature of individual irrational beliefs ( Hyland et al., 2013 ), this study examined both general and bereavement-related irrational beliefs in order to more fully assess the cognitive variable of irrational beliefs in college students who have experienced bereavement. On this basis, this study used an individual-centered research method to analyze the latent profiles of PGD symptoms and determine the different latent subgroups of those symptoms in Chinese college students (Aim 1). Additionally this study used multinomial logistic regression analysis to comprehensively investigate the correlation of cognitive variables with the latent subgroups of PGD symptoms (Aim 2). The purpose of this study was to promote a more comprehensive understanding of the relationship between PGD symptoms and cognitive variables in Chinese college students with PGD.

Materials and methods

Participants.

First, 13,965 college students from Fujian Health Vocational and Technical College, Fujian University of Traditional Chinese Medicine, Fujian Agricultural and Forestry University, Fujian Engineering College, Sunshine College, Fujian Jiangxia College, Fujian Preschool Teachers College, Zhangzhou Health Vocational College, Quanzhou Medical College, and Quanzhou Preschool Teachers College were selected as participants. A preliminary questionnaire was distributed on a class basis, and all participants were tested with the four research instruments used in this study. All tested individuals voluntarily participated and provided written informed consent beforehand. Next, on the basis of the above preliminary test and according to the previous screening criteria ( Boelen et al., 2004 ), this study explored the relationship between PGD symptoms and cognitive variables for college students after they experienced bereavement. That is, these individuals had experienced the death of a parent, sibling, relative, or good friend in the past 0.6 to 7.6 years and have not experienced other obvious traumatic event that would be a time of obvious trauma such as rape, robbery, serious traffic accident, earthquake disaster, typhoon disaster, mudslide disaster, landslide, fire, or explosion, among others. A total of 817 college students experiencing bereavement in the past 0.6 to 7.6 years were selected as the final participant set. There were 144 boys and 673 girls in the study, totaling 817 participants; the average age was 18.79 ± 1.51 years (range = 18 to 28 years), and the time of bereavement was 2.99 ± 3.76 years from the test (range = 0.6 to 7.6 years). Additional information is shown in Table 1 .

Table 1. Information about the loss of 817 participants.

All participants in this study completed four measures, including the irrational beliefs scale (IBS), bereavement-related irrational beliefs scale (BRIB), world assumptions scale (WAS), and Inventory of traumatic grief (TGI). Before each test, participants were informed that the purpose was to understand college students’ attitudes toward bereavement and death. Written informed consent was obtained and all participants participated voluntarily. In addition, the order of presentation of the four scales differed by class, so as to avoid the influence of the order of the tests and participants’ response formulas on the results.

Irrational beliefs scale

The IBS was developed by Yang et al. (2007) . It has a total of 22 items and three dimensions: low frustration tolerance (e.g., I can’t control my emotions when something is not going as expected), perfectionism (e.g., Everything I decide to do, I have to do perfectly), and global evaluation (e.g., Asking for help is a sign of weakness). The scale is mainly used to measure the general irrational beliefs of people with emotional disorders. It employs a 5-point Likert scale ranging from “strongly disagree” (0 points) to “strongly agree” (4 points). The higher the score, the higher is the degree of irrational beliefs. Since the present study mainly considered the relationship between the general level of irrational beliefs and PGD symptoms, the total IBS score was used as a variable. The results showed that the reliability and validity indicators of the IBS were good. The alpha coefficient of the total scale was 0.93 and the three sub-dimensions were between 0.87 and 0.93; confirmatory factor analysis showed that the fit indicators of the three-factor structure were good, χ2 = 648.31, df = 152, TLI = 0.91, CFI = 0.94, AIC = 44,751.56, BIC = 45,330.35, SRMR = 0.04, RMSEA = 0.063.

Bereavement-related irrational beliefs scale

The BRIB was developed by Boelen et al. (2004) . It is a single-dimensional scale with eight items, for example: “I would be a worthless person if I took too long to process this loss.” The scale was specifically designed to assess individuals’ irrational beliefs related to bereavement events. It uses a 5-point Likert scale ranging from “strongly disagree” (0 points) to “strongly agree” (4 points), with higher scores indicating higher levels of irrational beliefs related to bereavement. In the present study, the scale was translated first into Chinese, and then translated back by a researcher with an English major background. The Chinese version of the BRIB is the same as the original version in terms of the number of items and response options. The reliability and validity of the BRIB are good, alpha coefficient of the scale is 0.85, and results of confirmatory factor analysis show that the single factor structure fits well, χ2 = 86.19, DF = 17, TLI = 0.97, CFI = 0.98, AIC = 14,796.47, BIC = 119,23.53, SRMR = 0.028, RMSEA = 0.071. A significant positive correlation was found between the BRIB and IBS scores for the 817 college students with bereavement experience: r = 0.57, p < 0.001.

World assumptions scale

The WAS was developed by Janoff-Bulman (1989) . It has 32 items and is divided into three basic world assumptions and eight dimensions, including benevolence of the world assumptions (e.g., There is more good than evil in the world), benevolence of people (e.g., People are naturally unfriendly and unkind), meaningfulness of the world (e.g., Misfortune is least likely to strike worthy, decent people), control (e.g., People’s misfortunes result from mistakes they have made), randomness (e.g., The course of our lives is largely determined by chance), worthiness of self (e.g., I have a low opinion of myself), self-controllability (e.g., I usually behave so as to bring about the greatest good for me), and luck (e.g., I am basically a lucky person). The scale is mainly used to measure the individual’s basic cognition of the world and basic world assumptions. It uses a 5-point Likert score ranging from “strongly disagree” to “strongly agree.” Items 2, 8, 12, 18, and 31 are reverse-scored. The raw scores of the five reverse-scored items must be converted forward. Then, the scores of the items belonging to each dimension are added together as that dimension’s score. The scores of each dimension are then added together to form the total score for the WAS. The higher the WAS score, the higher is the level of world assumptions in the respondent’s belief system (e.g., for benevolence of people, a higher score indicates that a person’s social cognition makes them more inclined to assume that humans living in the world are friendly and kind). Huang and Zhang (2014) revised the Chinese version of the WAS. The Chinese version is consistent with the original scale in terms of the number of items, structure, and response options, and has good reliability and validity indicators. In the present study, for the α coefficient index, the total scale was 0.87 and the eight sub-dimensions were between 0.70 and 0.87. The results of a confirmatory factor analysis showed that the fit index of the second-order eight-factor structure was good, χ2 = 234.57, DF = 74, TLI = 0.96, CFI = 0.97, AIC = 11,851.01, BIC = 12,030.28, SRMR = 0.024, RMSEA = 0.074.

Inventory of traumatic grief

The TGI was developed by Prigerson et al. (1995) . It is a single-dimensional structure containing 19 items (e.g., “I cannot accept the death of the person who died,” “I feel that life is empty without the person who died,” etc.). It is mainly used to measure PGD symptoms such as the degree of separation pain (i.e., pain due to separation) and grief after loss. The scale uses a 5-point Likert scale ranging from “strongly disagree” (0 points) to “strongly agree” (4 points). The total score of the scale is calculated by adding the scores for the 19 items. The higher the score, the more serious are the PGD symptoms. Zhuang and Huang (2014) revised the Chinese version of the TGI for college students with bereavement experience. The revised TGI is consistent with the original scale in terms of the number of items and response options. Factor analysis results supported a single latent structure and had good reliability and validity indicators. In the present study, the alpha coefficient of the Chinese version of the TGI was 0.96. Confirmatory factor analysis of the single latent structure fit the indicators well, χ2 = 706.57, df = 124, TLI = 0.95, CFI = 0.93, AIC = 33,545.14, BIC = 33,945.12, SRMR = 0.05, RMSEA = 0.076. The TGI scores for college students with bereavement experience were significantly and positively correlated with irrational beliefs and bereavement-related irrational beliefs, r 1 = 0.40, p 1 < 0.001, r 2 = 0.62, p 2 < 0.001. Therefore, the measurement attributes of the Chinese version of the TGI in this study met the psychometrics requirements.

Data analysis

Harman’s single-factor test was used to evaluate the common method bias of the research data. The results of an exploratory factor analysis showed that the variance explained by the first factor without a rotation analysis was 24.48%, which was less than the 40% critical value. This indicated that common method bias did not have a significant impact on the results of this study.

For Aim 1, Mplus8.0 software was used to analyze the latent profile of the data ( Lanza et al., 2007 ) and explore the latent subgroups of Chinese college students’ PGD symptoms. Starting with a one-profile model, the number of profiles (or subgroups) was gradually increased and the fit indexes calculated. The measures included the likelihood ratio, Chi-squared test, log likelihood (LL), information evaluation criteria, Akaike information criterion (AIC), Bayesian information criterion (BIC) and adjusted Bayesian information criterion (aBIC). The smaller the value, the better was the model fit effect. The larger the entropy index, the higher was the classification accuracy. If entropy > 0.8, the classification accuracy of the model was more than 90%. The Lo-Mendell-Rubin (LMR) and bootstrapped likelihood ratio test (BLRT) indicated that the K-class model was significantly better than the K − 1 model if the test result was p < 0.05. To ensure the simplicity and interpretability of the model, the fit indicators were integrated to determine the best latent profile for fitting the model ( McClintock et al., 2016 ).

For Aim 2, based on the LPA, the setting “AUXILIARY = X (R3STEP)” in Mplus8.0 and a robust three-step method were used to conduct a multinomial logistic regression analysis, allowing for an examination of the correlation of cognitive variables with the latent profiles of Chinese college students’ PGD symptoms ( Vermunt, 2017 ). In the present study, a multicollinearity test was performed on the 11 predictor variables entered into the regression equation, using the three latent subgroups of PGD symptoms as the outcome variables. The results showed that the tolerance indicator for all predictors ranged from 0.39 to 0.98, and the variance inflate factor (VIF) indicator ranged from 1.02 to 3.15. This showed that there was no significant multi-collinearity for the 11 predictors.

Latent profile analysis of Chinese college students’ PGD symptoms

In order to explore the latent subgroups of PGD symptoms experienced by Chinese college students, the scores of the 19 items measured via the TGI scale were employed as indicators for a latent profile analysis, and the latent profile models for one to six profiles successively established. The fit indicators of the models are shown in Table 2 . As shown therein, the entropy values of all models were greater than 0.9, and the entropy values after Class 2 showed little change. The fit indexes of LL, AIC, BIC, aBIC, and other models showed a decreasing trend with an increase in the number of classes. Both the LMR and BLRT indicators reached significant levels, demonstrating that increasing the number of profiles may improve the model. However, after the BIC index of each profile of model was drawn into a steep slope chart, the decline in the BIC index after the third Class of model became gentle. This meant that the third profile of model was the inflection point of the change-of-fit index. In addition, compared with the fit indices for Models 3 and 4, if the individuals’ responses were divided into four latent subgroups, the probability of the smallest class was 5%, which was not conducive to classification accuracy. At the same time, responses were divided into three latent subgroups. Therefore, considering the simplicity and interpretability of the latent profile model, Model 3 was determined as the best latent profile fit model in this study. The attribution probabilities of the three latent profiles were between 96.90 and 98.90%, meaning that the classification results of the three latent profile models were reliable. See Table 3 for details.

Table 2. Indices of fit for the latent profile model of Chinese college students’ PGD symptoms ( n = 817).

Table 3. Average attribution probabilities for three latent profiles of Chinese college students’ PGD symptoms.

Naming the three latent subgroups of Chinese college students’ PGD symptoms

Based on the results of the latent profile analysis, the mean scores of the three latent subgroups of PGD symptoms for the 19 items of the TGI scale were plotted linearly, as shown in Figure 1 . The three latent subgroups of PGD symptoms did not have intersections among the items, and the morphologic trends of the different subgroups were consistent. In addition, with the three subgroups as the independent variable and total score of the TGI as the dependent variable, the results of a one-way ANOVA showed a significant main effect of the subgroups, F (2, 816) = 1,898.62, p < 0.001, ω p 2 = 0.82. Multiple post hoc comparisons showed that the total score of Class 3 (46.79 ± 9.56) was significantly higher than that of Class 2 (25.24 ± 5.66), and the total score of Class 2 was significantly higher than that of Class 1 (7.32 ± 4.89).

Figure 1. Mean scores for the 19 items representing the three latent subgroups of Chinese college students’ PGD symptoms.

Higher total TGI scores indicated more severe PGD symptoms, and the three latent subgroups were named accordingly in this study. Specifically, in Class 1, the total TGI score was the lowest among the three classes, and in addition, except for Item 4 (longing), which scored higher than 1 (rarely), the scores of all the other items were lower than 1. This suggested that Class 1 had the mildest PGD symptoms, and thus Class 1 was named the mild PGD symptom group; it accounted for 31% of the total. For Class 2, the total TGI scores were higher than for Class 1 but lower than for Class 3. Most items were between 1 (rarely) and 2 (sometimes), such as “reminders” and “bitter,” indicating that the PGD symptoms in Class 2 were at an intermediate level. Therefore, Class 2 was named the group with moderate PGD symptoms; it accounted for 48% of the total group. For Class 3, the total TGI score was the highest among the three classes. At the same time, most of the items were higher than 2 (sometimes), and some even higher than 3 (often). This indicated that the PGD symptoms in Class 3 were at a severe level. Therefore, Class 3 was named the severe PGD symptom group; this group accounted for 21% of the whole.

Relationship between cognitive variables and latent subgroups of PGD symptoms among Chinese college students

In order to comprehensively explore the correlation of relevant cognitive variables with latent subgroups of PGD symptoms among Chinese college students, three of the subgroups were used as outcome variables, and gender, bereavement-related irrational beliefs, irrational beliefs, and underlying world assumptions were included in the 11 factors as predictor variables. A robust three-step method was used for the multinomial logistic regression analysis. ODD-ratio (OR) coefficients were calculated to indicate the magnitude of the probability that college students characterized by a predictor variable might belong to a particular latent subgroup of PGD symptoms (outcome variables), as compared to the reference group ( Johnson and Lebreton, 2016 ). The specific results are shown in Table 4 .

Table 4. Results of the multinomial logistic regression analysis of the influence of cognitive variables on the latent subgroups of Chinese college students’ PGD symptom ( n = 817).

Table 4 shows the relationships among the 11 cognitive variables and three latent subgroups of PGD symptoms. Specifically, in the IBS with Class 1 as the reference group, a one-point increase in score increased the probability of a college student becoming Class 2 by 1.04 times and Class 3 by 1.09 times. With Class 2 as the reference group, a one-point increase in IBS score increased the probability of a college student becoming Class 3 by 1.05 times. For the BRIB and with Class 1 as the reference group, a one-point increase in score increased the probability of a college student becoming Class 2 by 1.58 times and Class 3 by 1.26 times. With Class 2 as the reference group, a one-point increase in BRIB score increased the probability of a college student becoming Class 3 by 1.25 times. However, there were differences in the relationships among the eight basic world assumptions of the WAS to the latent subgroups of PGD grief symptoms. Specifically, in terms of benevolence of the world, a one-point increase in score decreased the probability of a college student becoming Class 2 by 0.88 times and did not significantly change the probability of becoming Class 3, when using Class 1 as the reference group. A one-point increase in score decreased the probability of a college student becoming Class 3 by 1.12 times, when using Class 2 as the reference group. In terms of benevolence of people, using Class 1 as the reference group, a one-point increase in score decreased the probability of college students becoming Class 2 by 0.94 times ( p = 0.07, borderline significant) and Class 3 by 0.76 times. Using Class 2 as the reference group, a one-point increase in score decreased the probability of college students becoming Class 3 by a factor of 0.81. For justice, with Class 1 as the reference group, a one-point increase in score increased the probability of college students becoming Class 2 by 1.12 times and Class 3 by 1.14 times. No other significant differences were found. For control, with Class 1 as the reference group, a one-point increase in score increased the probability of college students becoming Class 2 by 1.09 times and Class 3 by 1.15 times. With Class 2 as the reference group, a one-point increase in score increased the probability of college students becoming Class 3 by 1.05 times. For randomness, with Class 1 as the reference group, a one-point increase in score increased the probability of a college student becoming Class 2 by 1.24 times and Class 3 by 1.07 times. With Class 2 as the reference group, a one-point increase in the randomness score increased the probability of a college student becoming Class 3 by 0.86 times. For self-worth, with Class 1 as the reference group, a one-point increase in score decreased the probability of a college student becoming Class 2 by 0.82 times and Class 3 by 0.66 times. With Class 2 as the reference group, a one-point increase in self-worth decreased the probability of a college student becoming Class 3 by 0.80 times. For self-controllability, with Class 1 as the reference group, a one-point increase in score increased the probability of a college student becoming Class 2 by 1.08 times and Class 3 by 1.12 times. With Class 2 as the reference group, a one-point increase in score decreased the probability of a college student becoming Class 3 by 1.03 times ( p = 0.09, borderline significant). If the controllability score increased by 1 point, college students were 1.03 times more likely to be Class 3. Gender and luck, as measured by the WAS, were not significantly associated with latent subgroups of PGD symptoms.

After synthesizing the results of the aforementioned multinomial logistic regression analysis and referring to previous research ( Johnson and Lebreton, 2016 ), the relationships among the different cognitive variables and latent subgroups of PGD symptoms were generalized. First, the positive and negative coefficients were used to indicate the directions of the relationships between the cognitive variables and latent subgroups of PGD symptoms (positive numbers indicating positive relationships and negative numbers indicating negative relationships). The scores of the two irrational beliefs scales (i.e., IBS and BRIB) were significantly positive related to PGD symptoms; however, there were variations in the nature of the relationships between the different world assumptions and PGD symptoms, with justice, control, randomness, and self-controllability having a significantly positive relationship with PGD symptoms and benevolence of the world, benevolence of people, and self-worth having a significantly negative one. Second, the magnitudes of the effect sizes of the relationships among the cognitive variables and latent subgroups of PGD symptoms were indicated by the magnitudes of the OR coefficient (with larger values indicating closer relationship effects between variables). It was found that the OR coefficient of the BRIB was the largest among all the variables, indicating that its relationship with different subgroups of PGD symptoms was the greatest.

Based on an individual-centered perspective, this study explored the latent subgroups of PGD symptoms experienced by Chinese college students and examined the relationships among cognitive variables (i.e., irrational beliefs and basic world assumptions) and various subgroups of PGD symptoms. The goal was to promote a better understanding of the relationship between PGD symptoms and cognitive variables among Chinese college students.

Latent subgroups and the characteristics of PGD symptoms in Chinese college students

This study found that PGD symptoms among Chinese college students can be divided into three latent subgroups: mild, moderate, and severe. Specifically, the mild PGD symptoms group accounted for 31% of the total, and the total TGI score for this group was the lowest. However, it was still higher than 1 (rarely) for the “longing” symptom, indicating that bereavement does not completely correlate with PGD symptoms, or at least “longing.” It may still be associated with other symptoms of PGD. The moderate PGD symptoms group accounted for 48% of the total. The total TGI score for this group was in the middle of the three groups, and most of the PGD symptoms ranged between 1 (rarely) and 2 (sometimes), indicating that most of the college students had some PGD symptoms after bereavement, especially the four symptoms of “longing,” “anger,” “reminders,” and “bitterness.” The severe PGD symptoms group accounted for 21% of the total. This group had the highest total TGI score, with most of the PGD symptoms higher than 2 (sometimes). Some were even higher than 3 (often), indicating that this group was more maladjusted after bereavement, especially in the following areas: “longing,” “anger,” “reminders,” and “bitterness.” This was especially true for “longing,” “reminders,” and “bitterness.” Although the three subgroups of PGD symptoms among Chinese college students shared some similar characteristics in terms of expression (for example, “longing,” “reminders,” and “bitterness” were PGD symptoms that all three subgroups exhibited); these three subgroups had similar patterns of symptoms. In other words, there was no intersection between the PGD symptoms of these three potential subgroups, and the morphologic trends were relatively consistent. The subgroups mainly significantly differed in terms of total TGI scores. The similarity in PGD symptoms among Chinese college students may be related to the limited distribution of loss events experienced by this group. As described in the introduction, the study group had been living in a school area for a long period of time, and the important influential factors of PGD symptoms (such as the object and cause of the loss experienced) were relatively consistent ( Ehlers, 2006 ; Kristensen et al., 2012 ), likely influencing the PGD symptoms to be similar. This result corresponds with the assumption that PGD is a syndrome created by a number of co-occurring symptoms of persistent grief ( Prigerson et al., 2009 ). The differences in severity of PGD symptoms may be related to differences in students’ cognitive belief systems. To summarize the previous analyses, this study concluded that PGD symptoms among Chinese college students can be divided into three subgroups, and the differences among these subgroups mainly manifest in the overall severity of PGD symptoms.

Cognitive variables as predictors of latent categories of PGD symptoms in Chinese college students

This study found that two cognitive variables related to bereavement (irrational beliefs and basic world assumptions) were significantly correlated to Chinese college students’ PGD symptoms, indicating an association between the severity of college students’ PGD symptoms and cognitive variables. This result supports the Cognitive-Behavioral Conceptualization Model’s expectation from an empirical perspective ( Boelen et al., 2006 ). Because the participants of this study are Chinese college students (of an Asian cultural background), the irrational beliefs related to individual bereavement should be a key factor in the degree of PGD symptoms, thus providing further evidence of the universality of the CBCM’s basic views.

This study found that the two cognitive variables of irrational beliefs and basic world assumptions had a significant correlation with Chinese college students’ PGD symptoms, of which the coefficient of correlation (OR value) between irrational beliefs and PGD symptom severity was the largest. This suggests that bereavement-related irrational thinking may be a primary factor associated with the severity of PGD symptoms. For instance, bereaved individuals may believe that they are unable to cope with life without the deceased (i.e., low frustration tolerance). To some extent, this result supports Ellis’s (2001) view that people can choose to respond to traumatic events (including bereavement) with appropriate or unreasonable emotional and behavioral responses. In other words, irrational thinking is a significant factor in dealing with the impact of traumatic life events ( Ellis, 2001 ). It follows that after experiencing a bereavement event, those individuals who hold irrational beliefs regarding the bereavement event will exhibit additional irrational beliefs or behaviors such as catastrophizing and low frustration, and devaluing beliefs such as “I feel worthless since he/she has passed away.” This type of outcome may be related to the exacerbation of bereavement symptoms and could even produce psychopathological reactions such as PGD symptoms ( Malkinson and Ellis, 2000 ; David et al., 2002 ; DiLorenzo et al., 2007 ). At the same time, previous studies have also found that there is a significant relationship between individual irrational beliefs and other negative symptoms after bereavement, such as depressive- and anxiety-related indicators ( Malkinson and Ellis, 2000 ; Boelen et al., 2003 ; Nagy and Szamosközi, 2014 ).

Secondly, there was also a statistically significant relationship between basic world assumptions and the severity of PGD symptoms. According to Janoff-Bulman (1989) , individuals form a set of basic world assumptions about the self, others, and the world within a specific cultural environment. It is a cognitive schema necessary for people’s daily functioning and provides an unassailable illusion. Bereavement events can lead to the deterioration of the basic world assumptions previously held by individual. For example, previous studies based on variables have found that compared with individuals who have not experienced bereavement, those with bereavement experience showed significant differences in meaningfulness of the world (e.g., justice, control, randomness) and worthiness of the self (e.g., self-worth, self-controllability). If other aspects of their world assumptions have been damaged ( Faschingbauer et al., 1977 ; Schwartzberg and Janoff-Bulman, 1991 ; Boelen et al., 2004 ; Zukerman and Korn, 2014 ; Huang et al., 2023 ), the breakdown of these basic world assumptions will correlate with the individual suffering from PGD symptoms. It was not found that benevolence of the world (e.g., benevolence of people) was damaged by bereavement events. The results of the present research show that the nature of the correlation effect of the eight basic world assumptions on the severity of PGD symptoms was different for different dimensions. This study had findings similar to those of previous studies ( Schwartzberg and Janoff-Bulman, 1991 ; Boelen et al., 2004 ; Zukerman and Korn, 2014 ; Huang et al., 2023 ). Specifically, justice, control, randomness, and self-controllability are positively associated with the severity of PGD symptoms. Thus, the greater the degree to which an individual suffers from these four convenient basic world assumptions, the more severe their PGD symptoms will be. This result, together with those of previous studies, supports the notion that bereavement is associated with the breakdown of two basic world assumptions: the meaningfulness of the world (e.g., justice, control) and worthiness of the self (e.g., self-control). For example, bereaved individuals may perceive life as full of uncertainty and determined by chance and may resort to more irrational self-control. This can cause the individual’s mental representations of the external world, self, and death to appear incongruent or even contradictory to their original assumptions about the world, potentially worsening their PGD symptoms ( Boelen et al., 2006 ). The results further suggest that an individual’s increased vulnerability to these basic world assumptions can exacerbate the severity of PGD symptoms.

Interestingly, this study also found that the three assumptions of benevolence of the world, benevolence of people, and self-worth significantly negatively associated with the severity of PGD symptoms in Chinese college students. In other words, the higher the scores for these three basic world assumptions, the lower the severity of the PGD symptoms, from which we hypothesized that these three basic world assumptions were related to a reduction in the severity of PGD symptoms and could serve as variables protecting against such symptoms. This result has not been found in previous studies on bereaved people in Western countries; such studies did not find that there would be changes in the two dimensions of benevolence of the world and people in terms of individuals’ basic world assumptions after bereavement ( Boelen et al., 2004 ). These two basic world assumptions have not been found to have a significant correlation with PGD symptoms ( Schwartzberg and Janoff-Bulman, 1991 ). Therefore, this study speculates that the relationship between individual basic world assumptions and bereavement events may vary depending on culture, particularly in Eastern cultures. Why did this study find that the three basic world assumptions of benevolence of the world, benevolence of people, and self-worth had significant negative associations with the symptoms of PGD among Chinese college students? This result may be related to funeral etiquette in the Chinese culture. Chinese people hold a unique funeral after someone dies. For example, relatives and friends, fellow villagers, and neighbors will almost spontaneously carry out collective mourning for the bereaved family, send consolation money, and so on, in order to express their condolences and concern, practices unique to Chinese funeral culture. This is also a way to provide social support, making the bereaved feel the respect and care of important individuals and groups and showing them that the “world is full of care” and “human beings are friendly and moral.” Thus, they experience a sense of collective belonging that strengthens the relationship within the social support system such that the bereaved individual can experience it. In such cases it is easier to evaluate oneself positively and experience the power of oneself, and the direction and goals of life become more clear, helping people maintain their sense of the benevolence of the world and enhancing their self-worth ( Zheng et al., 2016 ; Li et al., 2018 ).

Application value, limitations, and future research directions

This study explored the relationship between post-bereavement cognitive variables and PGD symptoms in Chinese college students. The results showed that both irrational beliefs and basic world assumptions of bereaved individuals had a strong connection to the severity of PGD symptoms. These results will be of great value to those pursuing psychological interventions for this cohort.

This study argues that psychological interventions for PGD symptoms in Chinese college students should use cognitive-behavioral therapy (CBT) because the results support the existence of significant associations between cognitive variables and PGD symptoms; this is consistent with the core logic of CBT ( Ellis, 2001 ; Rubin et al., 2016 ). Since among these cognitive variables, bereavement-related irrational beliefs had the highest correlation with PGD symptoms (the largest OR coefficient) ( Duffy and Wild, 2017 ; Boelen et al., 2021 ), this suggests that the irrational beliefs of Chinese PGD may be the primary variable of concern for counselors. According to the CBT model, when intervening on behalf of Chinese college students with PGD symptoms, counselors first need to think adaptively about the irrational beliefs contributing to their symptoms and adjust their tendency to engage in automated irrational thinking. Secondly, it is also necessary to adjust any basic world assumptions related to PGD symptoms, as those assumptions are the core beliefs motivating PGD symptoms. Those suffering from such symptoms should be helped to reorganize their belief systems about themselves and the world. It should be added that in the behavioral therapy stage of CBT, counselors should help them build their internal and interpersonal resources, focusing on the protective correlation effect of the three assumptions of benevolence of the world, benevolence of people, and self-worth. For example, we suggest that that the bereaved actively participate in funeral rituals such as the annual Qingming Festival. Ultimately, this will help them change their negative thoughts and beliefs, reconstruct the belief system about themselves and the world, eliminate emotional problems, and achieve a higher rate of success for interventions related to PGD.

This study does have some limitations that require the attention of future research. First, the TGI was used to measure the symptoms of PGD in Chinese college students. Although the measure can reflect such symptoms in college students, it is still somewhat outdated, and future work should use newer tools to measure the symptoms of this disorder, such as the Prolonged Grief 13-Revised ( Prigerson et al., 2021 ). Secondly, the sample source for the study was made up entirely of college students (whose bereavement experience is limited), and different causes of death will have correlations with distinct cognitive modifications ( Silverman and Rubin, 2015 ). Future research should expand the distribution of samples ( Djelantik et al., 2017 ). Third, for the results of the multicollinearity analysis of the 11 predictor variables, it is important to note that the VIF index value for benevolence of the world (BoW) was 3.147, which is greater than 2.5. BoW is uniquely significant in the Chinese culture because benevolence has for thousands of years been one of the core ideas expressed in Chinese Confucianism, and Chinese benevolence is embodied both by individual Chinese people and the collective Chinese culture. Thus, the important cognitive variable of BoW was not excluded. However, this may have had an impact on the results of the multiple regression analysis, which is a concern. Fourth, this study did not take into account non-cognitive variables that are known to be associated with PGD, so it is impossible to determine if cognitive variables are more important than those that are attachment-related or interpersonal. To gain a better understanding of Chinese college students’ PGD symptoms and their association with cognitive variables, future research should consider both cognitive and attachment-related factors, such as attachment styles ( Sekowski and Prigerson, 2022b ), the quality of the relationship prior to loss ( Sekowski and Prigerson, 2022a ), excessive interpersonal dependency ( Sekowski and Prigerson, 2021 ), and maintaining a connection with the deceased ( Sekowski, 2021 ). Lastly, a cross-sectional research method was used in this study, which has some shortcomings. The relationship between the cognitive variables and symptoms of PGD is not only a state relationship, but also a dynamic process of inner reorganization and balance. Thus, future research should use longitudinal and qualitative research methods. This will better explain the relationship between the cognitive variables and PGD symptoms ( Zhou and Jia, 2021 ), such as the positive correlation of the two world assumptions of benevolence of the world and benevolence of people with PGD symptoms.

Based on the above discussion, this study concluded that the PGD symptoms of Chinese college students can be classified into three subgroups: mild, moderate, and severe. All cognitive variables (e.g., irrational beliefs, basic world assumptions, etc.) have significant relationships with PGD symptoms in Chinese college students, among which bereavement-related irrational beliefs were found to have the largest positive correlation. This study was also the first to find that the nature of the relationship between basic world assumptions and Chinese college students’ PGD symptoms varied across dimensions. Specifically, justice, control, randomness, and self-controllability were significantly positively related to PGD symptoms, whereas benevolence of the world, benevolence of people, and self-worth were significantly negatively associated with such symptoms and may act as variables protecting against them. These results have practical value for CBT-based interventions for PGD-related problems in Chinese college students.

Data availability statement

The original contributions presented in this study are included in this article/supplementary material, further inquiries can be directed to the corresponding authors.

Ethics statement

The studies involving humans were approved by the Biomedical Research Ethics Committee of Fujian Medical University (Nos. 2023-121). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

The idea for this study was conceived and revised the manuscript by FH, ML, and WT. WT and YC collected the data. FH and WT engaged in the analysis and interpretation of the data and wrote the manuscript. All authors contributed to the article, designed the research, reviewed, read, and approved the submitted version of the manuscript.

This work was supported by the Fujian Medical University high-level Talents Start-up Research Fund (XRCZX2022008) and the School of Health at Fujian Medical University. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Acknowledgments

We thank all of the participants for their willingness to participate in the study.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords : PGD, latent profile analysis, irrational beliefs, basic world assumptions, CBT

Citation: Tian W, Cui Y, Liao M and Huang F (2024) Chinese college students’ PGD symptoms and their relationship to cognitive variables: a latent profile analysis. Front. Psychol. 15:1242425. doi: 10.3389/fpsyg.2024.1242425

Received: 24 June 2023; Accepted: 01 April 2024; Published: 23 April 2024.

Reviewed by:

Copyright © 2024 Tian, Cui, Liao and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Fajie Huang, [email protected] ; Meiling Liao, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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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.

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Computer Science > Machine Learning

Title: evolutionary causal discovery with relative impact stratification for interpretable data analysis.

Abstract: This study proposes Evolutionary Causal Discovery (ECD) for causal discovery that tailors response variables, predictor variables, and corresponding operators to research datasets. Utilizing genetic programming for variable relationship parsing, the method proceeds with the Relative Impact Stratification (RIS) algorithm to assess the relative impact of predictor variables on the response variable, facilitating expression simplification and enhancing the interpretability of variable relationships. ECD proposes an expression tree to visualize the RIS results, offering a differentiated depiction of unknown causal relationships compared to conventional causal discovery. The ECD method represents an evolution and augmentation of existing causal discovery methods, providing an interpretable approach for analyzing variable relationships in complex systems, particularly in healthcare settings with Electronic Health Record (EHR) data. Experiments on both synthetic and real-world EHR datasets demonstrate the efficacy of ECD in uncovering patterns and mechanisms among variables, maintaining high accuracy and stability across different noise levels. On the real-world EHR dataset, ECD reveals the intricate relationships between the response variable and other predictive variables, aligning with the results of structural equation modeling and shapley additive explanations analyses.

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Naval Postgraduate School

Where Science Meets the Art of Warfare

NPS Researchers Recognized for Modeling Integrated Deterrence in INDOPACOM Region

MC2 Janiel Adames   |  April 22, 2024

Members of an interdisciplinary team of NPS and Naval War College Monterey researchers were recognized for their efforts to develop a series of detailed models supporting a high-interest operational scenario in the U.S. Indo-Pacific Command region.

In recognition of their efforts to advance future force design and the next-generation Joint Warfighting Concept, several researchers from the Naval Postgraduate School (NPS) were recently honored with the U.S. Navy’s Civilian Service Commendation Medal for the development of quantifiable models that relate operational variables and integrated deterrence in a high-interest operational scenario in the U.S. Indo-Pacific Command (INDOPACOM) region.

The awards recognized the collective efforts and outstanding contributions of Associate Chair Brian Greenshields and Associate Professor Tommy Jamison, both from NPS’ Department of Defense Analysis; Professor of the Practice Jeffrey Kline and Faculty Associates-Research Mary McDonald and Stephen Upton from the Department of Operations Research; Department of Defense Management lecturer Dr. Mitchell McCarthy; and numerous other NPS personnel and students. 

Also honored were two resident faculty members from the Naval War College (NWC) at NPS – Dr. Yvonne Chiu, Associate Professor of Strategy and Policy, and Dr. Jonathan Czarnecki, Professor of Joint Military/Maritime Operations. 

Dr. Andy Hernandez, a retired U.S. Army colonel and current Associate Chair for Operations with the NPS Department of Systems Engineering, serves as lead principal investigator on the project.

Initiated in April 2022 at the behest of then-Vice Adm. Stuart Munsch, director of Joint Force Development (J7) on the Joint Staff, the project aimed to develop detailed models encompassing critical variables pertinent to an operational scenario in the INDOPACOM region.

Specifically, the request was for NPS to develop an independent, multidisciplinary academic approach to examine the effects of operational variables on deterrence and to quantify those relationships. The initial study on military deterrence included a combination of problem structuring techniques, systems analysis, campaign analysis, wargaming, computer simulation and experimentation, political analysis, and thorough regional expertise. More than two dozen faculty members and researchers, along with more than a dozen students, contributed to the research effort.

The interdisciplinary team was comprised of experts from diverse fields, including regional security studies, political science, military operations, systems engineering, and computer science. The team was assembled from NPS and the Naval War College, as well as other government and non-governmental institutions.

Chiu served as team leader for the project’s indications and warning, as well as value modeling efforts, and was one of many significant contributors to the project’s success.

“The value modeling and the simulations models prompted some reconsideration and revision of existing DOD doctrine, and generated actionable strategic, operational, and force design recommendations across the relevant operational variables for Joint Staff J7 and INDOPACOM,” noted Chiu. “This project also demonstrates the use and value of this particular multi-method and multi-disciplinary approach to mission engineering.”

Beyond its immediate impact, the project lays the groundwork for future endeavors, particularly in the realm of economic deterrence. 

“Other DOD units have expressed interest in both the project’s results and its tools and methodology,” said Chiu. “So, there will be applications of the results, tools, and methodology for other DOD research and planning projects.”

The project team’s results were presented to multiple offices, including J7, the Navy’s Warfighting Development team (OPNAV N7), and INDOPACOM’s Strategic Planning and Policy Directorate (J5), from July to October 2023. In February 2024, the team presented results regarding “Posture” as an operational variable during INDOPACOM’s Posture Conference. The discussion led the INDOPACOM J56 to make some decisions in its current approach.

Hernandez emphasized the significance of integrating the team’s results into future concepts. 

“The J7 will use these results to inform Joint Force Design and the next generation of the Joint Warfighting Concept,” said Hernandez. “Additionally, the NPS project results prompted J7 to expand the work. While the initial effort was on the military instrument of national power, the work for FY24 and FY25 will develop economic deterrence options … These FY24 and FY25 efforts can greatly contribute to developing INDOPACOM’s theater engagement plan and deterrence efforts.”

The next phase for the project team is the development of functional economic deterrence options (FEDO). This effort began in February 2024 and is ongoing.

“NPS’ emphasis on STEM education alongside its regional and strategic studies education in its own departments and in the resident NWC-at-NPS program provided a central pool of diverse experts who already converse with each other on national security issues,” said Chiu. “This was a solid foundation on which to build their collaboration and broader coordination with other institutions for this project.” 

The recognition highlights NPS’ pivotal role in advancing defense capabilities and underscores the institution’s commitment to excellence in defense research and education.

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