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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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Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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In research and statistics, a variable is a characteristic or attribute that can take on different values or categories. It represents data points or information that can be measured, observed, or manipulated within a study.

Statistical and experimental analysis aims to explore the relationships between variables. For example, researchers may hypothesize a connection between a particular variable and an outcome, like the association between physical activity levels (an independent variable) and heart health (a dependent variable).

Variables play a crucial role in data analysis . Data sets collected through research typically consist of multiple variables, and the analysis is driven by how these variables are related, how they influence each other, and what patterns emerge from these relationships.

Therefore, as a researcher, your understanding of variables and their manipulation forms the crux of your study.

To help with your understanding, I’ve presented 27 of the most common types of variables below.

Types of Variables

1. quantitative (numerical) variables.

Definition: Quantitative variables, also known as numerical variables, are quantifiable in nature and represented in numbers, allowing the data collected to be measured on a scale or range (Moodie & Johnson, 2021). These variables generally yield data that can be organized, ranked, measured, and subjected to mathematical operations.

Explanation: The values of quantitative variables can either be counted (referred to as discrete variables) or measured (continuous variables). Quantifying data in numerical form allows for a range of statistical analysis techniques to be applied, from calculating averages to finding correlations.

Quantitative Variable Example : Consider a marketing survey where you ask respondents to rate their satisfaction with your product on a scale of 1 to 10. The satisfaction score here represents a quantitative variable. The data can be quantified and used to calculate average satisfaction scores, identify the scope for product improvement, or compare satisfaction levels across different demographic groups.

2. Continuous Variables

Definition: Continuous variables are a subtype of quantitative variables that can have an infinite number of measurements within a specified range. They provide detailed insights based on precise measurements and are often representative on a continuous scale (Christmann & Badgett, 2009).

Explanation: The variable is “continuous” because there are an infinite number of possible values within the chosen range. For instance, variables like height, weight, or time are measured continuously.

Continuous Variable Example : The best real-world example of a continuous variable is time. For instance, the time it takes for a customer service representative to resolve a customer issue can range anywhere from few seconds to several hours, and can accurately be measured down to the second, providing an almost finite set of possible values.

3. Discrete Variables

Definition: Discrete variables are a form of quantitative variable that can only assume a finite number of values. They are typically count-based (Frankfort-Nachmias & Leon-Guerrero, 2006).

Explanation: Discrete variables are commonly used in situations where the “count” or “quantity” is distinctly separate. For instance, the number of children in a family is a common example – you can’t have 2.5 kids.

Discrete Variable Example : The number of times a customer contacts customer service within a month. This is a discrete variable because it can only take a whole number of values – you can’t call customer service 2.5 times.

4. Qualitative (Categorical) Variables

Definition: Qualitative, or categorical variables, are non-numerical data points that categorize or group data entities based on shared features or qualities (Moodie & Johnson, 2021).

Explanation: They are often used in research to classify particular traits, characteristics, or properties of subjects that are not easily quantifiable, such as colors, textures, tastes, or smells.

Qualitative Variable Example : Consider a survey that asks respondents to identify their favorite color from a list of choices. The color preference would be a qualitative variable as it categorizes data into different categories corresponding to different colors.

5. Nominal Variables

Definition: Nominal variables, a subtype of qualitative variables, represent categories without any inherent order or ranking (Norman & Streiner, 2008).

Explanation: Nominal variables are often used to label or categorize particular sets of items or individuals, with no intention of giving numerical value or order. For example, race, gender, or religion.

Nominal Variable Example : For instance, the type of car someone owns (sedan, SUV, truck, etc.) is a nominal variable. Each category is unique and one is not inherently higher, better, or larger than the others.

6. Ordinal Variables

Definition: Ordinal variables are a subtype of categorical (qualitative) variables with a key feature of having a clear, distinct, and meaningful order or ranking to the categories (De Vaus, 2001).

Explanation: Ordinal variables represent categories that can be logically arranged in a specific order or sequence but the difference between categories is unknown or doesn’t matter, such as satisfaction rating scale (unsatisfied, neutral, satisfied).

Ordinal Variable Example : A classic example is asking survey respondents how strongly they agree or disagree with a statement (strongly disagree, disagree, neither agree nor disagree, agree, strongly agree). The answers form an ordinal scale; they can be ranked, but the intervals between responses are not necessarily equal.

7. Dichotomous (Binary) Variables

Definition: Dichotomous or binary variables are a type of categorical variable that consist of only two opposing categories like true/false, yes/no, success/failure, and so on (Adams & McGuire, 2022).

Explanation: Dichotomous variables refer to situations where there can only be two, and just two, possible outcomes – there is no middle ground.

Dichotomous Variable Example : Whether a customer completed a transaction (Yes or No) is a binary variable. Either they completed the purchase (yes) or they did not (no).

8. Ratio Variables

Definition: Ratio variables are the highest level of quantitative variables that contain a zero point or absolute zero, which represents a complete absence of the quantity (Norman & Streiner, 2008).

Explanation: Besides being able to categorize and order units, ratio variables also allow for the relative degree of difference between them to be calculated. For example, income, height, weight, and temperature (in Kelvin) are ratio variables.

Ratio Variable Example : An individual’s annual income is a ratio variable. You can say someone earning $50,000 earns twice as much as someone making $25,000. The zero point in this case would be an income of $0, which indicates that no income is being earned.

9. Interval Variables

Definition: Interval variables are quantitative variables that have equal, predictable differences between values, but they do not have a true zero point (Norman & Streiner, 2008).

Explanation: Interval variables are similar to ratio variables; both provide a clear ordering of categories and have equal intervals between successive values. The primary difference is the absence of an absolute zero.

Interval Variable Example : The classic example of an interval variable is the temperature in Fahrenheit or Celsius. The difference between 20 degrees and 30 degrees is the same as the difference between 70 degrees and 80 degrees, but there isn’t a true zero because the scale doesn’t start from absolute nonexistence of the quantity being measured.

Related: Quantitative Reasoning Examples

10. Dependent Variables

Definition: The dependent variable is the outcome or effect that the researcher wants to study. Its value depends on or is influenced by one or more other variables known as independent variables.

Explanation: In a research study, the dependent variable is the phenomenon or behavior that may be affected by manipulations in the independent variable. It’s what you measure to see if your predictions about the effects of the independent variable are correct.

Dependent Variable Example: Suppose you want to study the impact of exercise frequency on weight loss. In this case, the dependent variable is weight loss, which changes based on how often the subject exercises (the independent variable).

11. Independent Variables

Definition: The independent variable, or the predictor variable, is what the researcher manipulates to test its effect on the dependent variable.

Explanation: The independent variable is presumed to have some effect on the dependent variable in a study. It can often be thought of as the cause in a cause-and-effect relationship.

Independent Variable Example: In a study looking at how different dosages of a medication affect the severity of symptoms, the medication dosage is an independent variable. Researchers will adjust the dosage to see what effect it has on the symptoms (the dependent variable).

See Also: Independent and Dependent Variable Examples

12. Confounding Variables

Definition: Confounding variables—also known as confounders—are variables that might distort, confuse or interfere with the relationship between an independent variable and a dependent variable, leading to a false correlation (Boniface, 2019).

Explanation: Confounders are typically related in some way to both the independent and dependent variables. Because of this, they can create or hide relationships, leading researchers to make inaccurate conclusions about causality.

Confounding Variable Example : If you’re studying the relationship between physical activity and heart health, diet could potentially act as a confounding variable. People who are physically active often also eat healthier diets, which could independently improve heart health [National Heart, Lung, and Blood Institute].

13. Control Variables

Definition: Control variables are variables in a research study that the researcher keeps constant to prevent them from interfering with the relationship between the independent and dependent variables (Sproull, 2002).

Explanation: Control variables allow researchers to isolate the effects of the independent variable on the dependent variable, ensuring that any changes observed are solely due to the manipulation of the independent variable and not an external factor.

Control Variable Example : In a study evaluating the impact of a tutoring program on student performance, some control variables could include the teacher’s experience, the type of test used to measure performance, and the student’s previous grades.

14. Latent Variables

Definition: Latent variables—also referred to as hidden or unobserved variables—are variables that are not directly observed or measured but are inferred from other variables that are observed (measured directly).

Explanation: Latent variables can represent abstract concepts like intelligence, socioeconomic status, or even happiness. They are often used in psychological and sociological research, where certain concepts can’t be measured directly.

Latent Variable Example: In a study on job satisfaction, factors like job stress, financial reward, work-life balance, or relationship with colleagues can be measured directly. However, “job satisfaction” itself is a latent variable as it is inferred from these observed variables.

15. Derived Variables

Definition: Derived variables are variables that are created or developed based on existing variables in a dataset. They involve applying certain calculations or manipulations to one or more variables to create a new one.

Explanation: Derived variables can be created by either transforming a single variable (like taking the square root) or combining multiple variables (computing the ratio of two variables).

Derived Variable Example: In a dataset containing a person’s height and weight, a derived variable could be the Body Mass Index (BMI). The BMI is calculated by dividing weight (in kilograms) by the square of height (in meters).

16. Time-series Variables

Definition: Time-series variables are a set of data points ordered or indexed in time order. They provide a sequence of data points, each associated with a specific instance in time.

Explanation: Time-series variables are often used in statistical models to study trends, analyze patterns over time, make forecasts, and understand underlying causes and characteristics of the trend.

Time-series Variable Example : The quarterly GDP (Gross Domestic Product) data over a period of several years would be an example of a time series variable. Economists use such data to examine economic trends over time.

17. Cross-sectional Variables

Definition: Cross-sectional variables are data collected from many subjects at the same point in time or without regard to differences in time.

Explanation: This type of data provides a “snapshot” of the variables at a specific time. They’re often used in research to compare different population groups at a single point in time.

Cross-sectional Variable Example: A basic example of a set of cross-sectional data could be a national survey that asks respondents about their current employment status. The data captured represents a single point in time and does not track changes in employment over time.

18. Predictor Variables

Definition: A predictor variable—also known as independent or explanatory variable—is a variable that is being manipulated in an experiment or study to see how it influences the dependent or response variable.

Explanation: In a cause-and-effect relationship, the predictor variable is the cause. Its modification allows the researcher to study its effect on the response variable.

Predictor Variable Example : In a study evaluating the impact of studying hours on exam score, the number of studying hours is a predictor variable. Researchers alter the study duration to see its impact on the exam results (response variable).

19. Response Variables

Definition: A response variable—also known as the dependent or outcome variable—is what the researcher observes for any changes in an experiment or study. Its value depends on the predictor or independent variable.

Explanation: The response variable is the “effect” in a cause-and-effect scenario. Any changes occurring to this variable due to the predictor variable are observed and recorded.

Response Variable Example: Continuing from the previous example, the exam score is the response variable. It changes based on the manipulation of the predictor variable, i.e., the number of studying hours.

20. Exogenous Variables

Definition: Exogenous variables are variables that are not affected by other variables in the system but can affect other variables within the same system.

Explanation: In a model, an exogenous variable is considered to be an input, it’s determined outside the model, and its value is simply imposed on the system.

Exogenous Variable Example: In an economic model, the government’s taxation rate may be considered an exogenous variable. The rate is set externally (not determined within the economic model) but impacts variables within the model, such as business profitability.

21. Endogenous Variables

Definition: In contrast, endogenous variables are variables whose value is determined by the functional relationships within the system in an economic or statistical model. They depend on the values of other variables in the model.

Explanation: These are the “output” variables of a system, determined through cause-and-effect relationships within the system.

Endogenous Variable Example: To continue the previous example, business profitability in an economic model may be considered an endogenous variable. It is influenced by several other variables within the model, including the exogenous taxation rate set by the government.

22. Causal Variables

Definition: Causal variables are variables which can directly cause an effect on the outcome or dependent variable. Their value or level determines the value or level of other variables.

Explanation: In a cause-and-effect relationship, a causal variable is the cause. The understanding of causal relationships is the basis of scientific enquiry, allowing researchers to manipulate variables to see the effect.

Causal Variable Example: In a study examining the effect of fertilizer on plant growth, the type or amount of fertilizer used is the causal variable. Changing its type or amount should directly affect the outcome—plant growth.

23. Moderator Variables

Definition: Moderator variables are variables that can affect the strength or direction of the association between the predictor (independent) and response (dependent) variable. They specify when or under what conditions a relationship holds.

Explanation: The role of a moderator is to illustrate “how” or “when” an independent variable’s effect on a dependent variable changes.

Moderator Variable Example: If you are studying the effect of a training program on job performance, a potential moderator variable could be the employee’s education level. The influence of the training program on job performance could depend on the employee’s initial level of education.

24. Mediator Variables

Definition: Mediator variables are variables that account for, or explain, the relationship between an independent variable and a dependent variable, providing an understanding of “why” or “how” an effect occurs.

Explanation: Often, the relationship between an independent and a dependent variable isn’t direct—it’s through a third, intervening, variable known as a mediator variable.

Mediator Variable Example: In a study looking at the relationship between socioeconomic status and academic performance, a mediator variable might be the access to educational resources. Socioeconomic status may influence access to educational resources, which in turn affects academic performance. The relationship between socioeconomic status and academic performance isn’t direct but through access to resources.

25. Extraneous Variables

Definition: Extraneous variables are variables that are not of primary interest to a researcher but might influence the outcome of a study. They can add “noise” to the research data if not controlled.

Explanation: An extraneous variable is anything else that has the potential to influence our dependent variable or confound our results if not kept in check, other than our independent variable.

Extraneous Variable Example : Consider an experiment to test whether temperature influences the rate of a chemical reaction. Potential extraneous variables could include the light level, humidity, or impurities in the chemicals used—each could affect the reaction rate and, thus, should be controlled to ensure valid results.

26. Dummy Variables

Definition: Dummy variables, often used in regression analysis, are artificial variables created to represent an attribute with two or more distinct categories or levels.

Explanation: They are used to turn a qualitative variable into a quantitative one to facilitate mathematical processing. Typically, dummy variables are binary – taking a value of either 0 or 1.

Dummy Variable Example: Consider a dataset that includes a variable “Gender” with categories “male” and “female”. A corresponding dummy variable “IsMale” could be introduced, where males get classified as 1 and females as 0.

27. Composite Variables

Definition: Composite variables are new variables created by combining or grouping two or more variables.

Explanation: Depending upon their complexity, composite variables can help assess concepts that are explicit (e.g., “total score”) or relatively abstract (e.g., “life quality index”).

Composite Variable Example: A “Healthy Living Index” might be created as a composite of multiple variables such as eating habits, physical activity level, sleep quality, and stress level. Each of these variables contributes to the overall “Healthy Living Index”.

Knowing your variables will make you a better researcher. Some you need to keep an eye out for: confounding variables , for instance, always need to be in the backs of our minds. Others you need to think about during study design, matching the research design to the research objectives.

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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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Variables in Research | Types, Definiton & Examples

Introduction

What is a variable, what are the 5 types of variables in research, other variables in research.

Variables are fundamental components of research that allow for the measurement and analysis of data. They can be defined as characteristics or properties that can take on different values. In research design , understanding the types of variables and their roles is crucial for developing hypotheses , designing methods , and interpreting results .

This article outlines the the types of variables in research, including their definitions and examples, to provide a clear understanding of their use and significance in research studies. By categorizing variables into distinct groups based on their roles in research, their types of data, and their relationships with other variables, researchers can more effectively structure their studies and achieve more accurate conclusions.

A variable represents any characteristic, number, or quantity that can be measured or quantified. The term encompasses anything that can vary or change, ranging from simple concepts like age and height to more complex ones like satisfaction levels or economic status. Variables are essential in research as they are the foundational elements that researchers manipulate, measure, or control to gain insights into relationships, causes, and effects within their studies. They enable the framing of research questions, the formulation of hypotheses, and the interpretation of results.

Variables can be categorized based on their role in the study (such as independent and dependent variables ), the type of data they represent (quantitative or categorical), and their relationship to other variables (like confounding or control variables). Understanding what constitutes a variable and the various variable types available is a critical step in designing robust and meaningful research.

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Variables are crucial components in research, serving as the foundation for data collection , analysis , and interpretation . They are attributes or characteristics that can vary among subjects or over time, and understanding their types is essential for any study. Variables can be broadly classified into five main types, each with its distinct characteristics and roles within research.

This classification helps researchers in designing their studies, choosing appropriate measurement techniques, and analyzing their results accurately. The five types of variables include independent variables, dependent variables, categorical variables, continuous variables, and confounding variables. These categories not only facilitate a clearer understanding of the data but also guide the formulation of hypotheses and research methodologies.

Independent variables

Independent variables are foundational to the structure of research, serving as the factors or conditions that researchers manipulate or vary to observe their effects on dependent variables. These variables are considered "independent" because their variation does not depend on other variables within the study. Instead, they are the cause or stimulus that directly influences the outcomes being measured. For example, in an experiment to assess the effectiveness of a new teaching method on student performance, the teaching method applied (traditional vs. innovative) would be the independent variable.

The selection of an independent variable is a critical step in research design, as it directly correlates with the study's objective to determine causality or association. Researchers must clearly define and control these variables to ensure that observed changes in the dependent variable can be attributed to variations in the independent variable, thereby affirming the reliability of the results. In experimental research, the independent variable is what differentiates the control group from the experimental group, thereby setting the stage for meaningful comparison and analysis.

Dependent variables

Dependent variables are the outcomes or effects that researchers aim to explore and understand in their studies. These variables are called "dependent" because their values depend on the changes or variations of the independent variables.

Essentially, they are the responses or results that are measured to assess the impact of the independent variable's manipulation. For instance, in a study investigating the effect of exercise on weight loss, the amount of weight lost would be considered the dependent variable, as it depends on the exercise regimen (the independent variable).

The identification and measurement of the dependent variable are crucial for testing the hypothesis and drawing conclusions from the research. It allows researchers to quantify the effect of the independent variable , providing evidence for causal relationships or associations. In experimental settings, the dependent variable is what is being tested and measured across different groups or conditions, enabling researchers to assess the efficacy or impact of the independent variable's variation.

To ensure accuracy and reliability, the dependent variable must be defined clearly and measured consistently across all participants or observations. This consistency helps in reducing measurement errors and increases the validity of the research findings. By carefully analyzing the dependent variables, researchers can derive meaningful insights from their studies, contributing to the broader knowledge in their field.

Categorical variables

Categorical variables, also known as qualitative variables, represent types or categories that are used to group observations. These variables divide data into distinct groups or categories that lack a numerical value but hold significant meaning in research. Examples of categorical variables include gender (male, female, other), type of vehicle (car, truck, motorcycle), or marital status (single, married, divorced). These categories help researchers organize data into groups for comparison and analysis.

Categorical variables can be further classified into two subtypes: nominal and ordinal. Nominal variables are categories without any inherent order or ranking among them, such as blood type or ethnicity. Ordinal variables, on the other hand, imply a sort of ranking or order among the categories, like levels of satisfaction (high, medium, low) or education level (high school, bachelor's, master's, doctorate).

Understanding and identifying categorical variables is crucial in research as it influences the choice of statistical analysis methods. Since these variables represent categories without numerical significance, researchers employ specific statistical tests designed for a nominal or ordinal variable to draw meaningful conclusions. Properly classifying and analyzing categorical variables allow for the exploration of relationships between different groups within the study, shedding light on patterns and trends that might not be evident with numerical data alone.

Continuous variables

Continuous variables are quantitative variables that can take an infinite number of values within a given range. These variables are measured along a continuum and can represent very precise measurements. Examples of continuous variables include height, weight, temperature, and time. Because they can assume any value within a range, continuous variables allow for detailed analysis and a high degree of accuracy in research findings.

The ability to measure continuous variables at very fine scales makes them invaluable for many types of research, particularly in the natural and social sciences. For instance, in a study examining the effect of temperature on plant growth, temperature would be considered a continuous variable since it can vary across a wide spectrum and be measured to several decimal places.

When dealing with continuous variables, researchers often use methods incorporating a particular statistical test to accommodate a wide range of data points and the potential for infinite divisibility. This includes various forms of regression analysis, correlation, and other techniques suited for modeling and analyzing nuanced relationships between variables. The precision of continuous variables enhances the researcher's ability to detect patterns, trends, and causal relationships within the data, contributing to more robust and detailed conclusions.

Confounding variables

Confounding variables are those that can cause a false association between the independent and dependent variables, potentially leading to incorrect conclusions about the relationship being studied. These are extraneous variables that were not considered in the study design but can influence both the supposed cause and effect, creating a misleading correlation.

Identifying and controlling for a confounding variable is crucial in research to ensure the validity of the findings. This can be achieved through various methods, including randomization, stratification, and statistical control. Randomization helps to evenly distribute confounding variables across study groups, reducing their potential impact. Stratification involves analyzing the data within strata or layers that share common characteristics of the confounder. Statistical control allows researchers to adjust for the effects of confounders in the analysis phase.

Properly addressing confounding variables strengthens the credibility of research outcomes by clarifying the direct relationship between the dependent and independent variables, thus providing more accurate and reliable results.

Beyond the primary categories of variables commonly discussed in research methodology , there exists a diverse range of other variables that play significant roles in the design and analysis of studies. Below is an overview of some of these variables, highlighting their definitions and roles within research studies:

• Discrete variables : A discrete variable is a quantitative variable that represents quantitative data , such as the number of children in a family or the number of cars in a parking lot. Discrete variables can only take on specific values.
• Categorical variables : A categorical variable categorizes subjects or items into groups that do not have a natural numerical order. Categorical data includes nominal variables, like country of origin, and ordinal variables, such as education level.
• Predictor variables : Often used in statistical models, a predictor variable is used to forecast or predict the outcomes of other variables, not necessarily with a causal implication.
• Outcome variables : These variables represent the results or outcomes that researchers aim to explain or predict through their studies. An outcome variable is central to understanding the effects of predictor variables.
• Latent variables : Not directly observable, latent variables are inferred from other, directly measured variables. Examples include psychological constructs like intelligence or socioeconomic status.
• Composite variables : Created by combining multiple variables, composite variables can measure a concept more reliably or simplify the analysis. An example would be a composite happiness index derived from several survey questions .
• Preceding variables : These variables come before other variables in time or sequence, potentially influencing subsequent outcomes. A preceding variable is crucial in longitudinal studies to determine causality or sequences of events.

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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

Type of variable	Definition	Example
Independent Variable (Stimulus)	It is the variable that influences other variables.
Dependent variable (Response)	The dependent variable is the outcome of the influence of the independent variable.	You want to identify “How refined carbohydrates affect the health of human beings?” : refined carbohydrates : the health of human beings You can manipulate the consumption of refined carbs in your human participants and measure how those levels of consuming processed carbohydrates influence human health.
Control Variables	Control variables are variables that are not changed and kept constant throughout the experiment. 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) What data collection best suits your research? Find out by hiring an expert from ResearchProspect today! Despite how challenging the subject may be, we are here to help you. 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. Type of variable Definition Example Continuous Variable A continuous variable is a quantitative variable that can take a value between two specific values. Discrete Variable A discrete variable is a quantitative variable whose attributes are separated from each other.  Literacy rate, gender, and nationality. Scale: Nominal and ordinal. 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: Type of variable Definition Example Dichotomous (Binary) Variable This is the categorical variable with two possible results (Yes/No) Alcoholic (Yes/No) Nominal Variable Nominal Variable can take the value that is not organised in terms of groups, degree, or rank. Ordinal Variable Ordinal Variable can take the value that can be logically ordered or ranked. 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. Not sure which statistical tests to use for your data? Let the experts at researchprospect do the daunting work for you.. Using our approach, we illustrate how to collect data, sample sizes, validity, reliability, credibility, and ethics, so you won’t have to do it all by yourself! 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 . Type of variable Definition Example Confounding variables The confounding variable is a hidden variable that produces an association between two unrelated variables because the hidden variable affects both of them. There is an association between water consumption and cold drink sales. The confounding variable could be the   and compels people to drink a lot of water and a cold drink to reduce heat and thirst caused due to the heat. Latent Variable These are the variables that cannot be observed or measured directly. Self-confidence and motivation cannot be measured directly. Still, they can be interpreted through other variables such as habits, achievements, perception, and lifestyle. Composite variables A composite variable is a combination of multiple variables. It is used to measure multidimensional aspects that are difficult to observe. Entertainment Online education Database management, storage, and retrieval 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. You May Also Like Content analysis is used to identify specific words, patterns, concepts, themes, phrases, or sentences within the content in the recorded communication. A confounding variable can potentially affect both the suspected cause and the suspected effect. Here is all you need to know about accounting for confounding variables in research. Baffled by the concept of reliability and validity? Reliability refers to the consistency of measurement. Validity refers to the accuracy of measurement. USEFUL LINKS LEARNING RESOURCES COMPANY DETAILS How It Works Foundations Write Paper Search form Experiments Anthropology Self-Esteem Social Anxiety 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 . Psychology 101 Flags and Countries Capitals and Countries Martyn Shuttleworth (Aug 9, 2008). Research Variables. 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Whether you are an academic novice, or you simply want to brush up your skills, this book will take your academic writing skills to the next level. Download electronic versions: - Epub for mobiles and tablets - For Kindle here - For iBooks here - PDF version here Save this course for later Don't have time for it all now? No problem, save it as a course and come back to it later. Footer bottom Privacy Policy Subscribe to our RSS Feed Like us on Facebook Follow us on Twitter USC Libraries Research Guides Organizing Your Social Sciences Research Paper Independent and Dependent Variables Purpose of Guide Design Flaws to Avoid Glossary of Research Terms Reading Research Effectively Narrowing a Topic Idea Broadening a Topic Idea Extending the Timeliness of a Topic Idea Academic Writing Style Applying Critical Thinking Choosing a Title Making an Outline Paragraph Development Research Process Video Series Executive Summary The C.A.R.S. Model Background Information The Research Problem/Question Theoretical Framework Citation Tracking Content Alert Services Evaluating Sources Primary Sources Secondary Sources Tiertiary Sources Scholarly vs. Popular Publications Qualitative Methods Quantitative Methods Insiderness Using Non-Textual Elements Limitations of the Study Common Grammar Mistakes Writing Concisely Avoiding Plagiarism Footnotes or Endnotes? Further Readings Generative AI and Writing USC Libraries Tutorials and Other Guides Bibliography 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. << Previous: Design Flaws to Avoid Next: Glossary of Research Terms >> Last Updated: Sep 4, 2024 9:40 AM URL: https://libguides.usc.edu/writingguide Transcription Service for Your Academic Paper Start Transcription now Editing & Proofreading for Your Research Paper Get it proofread now Online Printing & Binding with Free Express Delivery Configure binding now Academic essay overview The writing process Structuring academic essays Types of academic essays Academic writing overview Sentence structure Academic writing process Improving your academic writing Titles and headings APA style overview APA citation & referencing APA structure & sections Citation & referencing Structure and sections APA examples overview Commonly used citations Other examples British English vs. American English Chicago style overview Chicago citation & referencing Chicago structure & sections Chicago style examples Citing sources overview Citation format Citation examples College essay overview Application How to write a college essay Types of college essays Commonly confused words Definitions Dissertation overview Dissertation structure & sections Dissertation writing process Graduate school overview Application & admission Study abroad Master degree Harvard referencing overview Language rules overview Grammatical rules & structures Parts of speech Punctuation Methodology overview Analyzing data Experiments Observations Inductive vs. Deductive Qualitative vs. Quantitative Types of validity Types of reliability Sampling methods Theories & Concepts Types of research studies Types of variables MLA style overview MLA examples MLA citation & referencing MLA structure & sections Plagiarism overview Plagiarism checker Types of plagiarism Printing production overview Research bias overview Types of research bias Example sections Types of research papers Research process overview Problem statement Research proposal Research topic Statistics overview Levels of measurment Frequency distribution Measures of central tendency Measures of variability Hypothesis testing Parameters & test statistics Types of distributions Correlation Effect size Hypothesis testing assumptions Types of ANOVAs Types of chi-square Statistical data Statistical models Spelling mistakes Tips overview Academic writing tips Dissertation tips Sources tips Working with sources overview Evaluating sources Finding sources Including sources Types of sources Your Step to Success Transcription Service for Your Paper Printing & Binding with 3D Live Preview Types of Variables in Research – Definition & Examples How do you like this article cancel reply. Save my name, email, and website in this browser for the next time I comment. A fundamental component in statistical investigations is the methodology you employ in selecting your research variables. The careful selection of appropriate variable types can significantly enhance the robustness of your experimental design . This piece explores the diverse array of variable classifications within the field of statistical research. Additionally, understanding the different types of variables in research can greatly aid in shaping your experimental hypotheses and outcomes. Inhaltsverzeichnis 1 Types of Variables in Research – In a Nutshell 2 Definition: Types of variables in research 3 Types of variables in research – Quantitative vs. Categorical 4 Types of variables in research – Independent vs. Dependent 5 Other useful types of variables in research Types of Variables in Research – In a Nutshell A variable is an attribute of an item of analysis in research. The types of variables in research can be categorized into: independent vs. dependent , or categorical vs. quantitative . The types of variables in research (correlational) can be classified into predictor or outcome variables. Other types of variables in research are confounding variables , latent variables , and composite variables. Definition: Types of variables in research A variable is a trait of an item of analysis in research. Types of variables in research are imperative, as they describe and measure places, people, ideas , or other research objects . There are many types of variables in research. Therefore, you must choose the right types of variables in research for your study. Note that the correct variable will help with your research design , test selection, and result interpretation. In a study testing whether some genders are more stress-tolerant than others, variables you can include are the level of stressors in the study setting, male and female subjects, and productivity levels in the presence of stressors. Also, before choosing which types of variables in research to use, you should know how the various types work and the ideal statistical tests and result interpretations you will use for your study. The key is to determine the type of data the variable contains and the part of the experiment the variable represents. Types of variables in research – Quantitative vs. Categorical Data is the precise extent of a variable in statistical research that you record in a data sheet. It is generally divided into quantitative and categorical classes. Quantitative or numerical data represents amounts, while categorical data represents collections or groupings. The type of data contained in your variable will determine the types of variables in research. For instance, variables consisting of quantitative data are called quantitative variables, while those containing categorical data are called categorical variables. The section below explains these two types of variables in research better. Quantitative variables The scores you record when collecting quantitative data usually represent real values you can add, divide , subtract , or multiply . There are two types of quantitative variables: discrete variables and continuous variables . The table below explains the elements that set apart discrete and continuous types of variables in research: Discrete or integer variables Individual item counts or values • Number of employees in a company • Number of students in a school district Continuous or ratio variables Measurements of non-finite or continuous scores • Age • Weight • Volume • Distance Categorical variables Categorical variables contain data representing groupings. Additionally, the data in categorical variables is sometimes recorded as numbers . However, the numbers represent categories instead of real amounts. There are three categorical types of variables in research: nominal variables, ordinal variables , and binary variables . Here is a tabular summary. Binary/dichotomous variables YES/NO outcomes • Win/lose in a game • Pass/fail in an exam Nominal variables No-rank groups or orders between groups • Colors • Participant name • Brand names Ordinal variables Groups ranked in a particular order • Performance rankings in an exam • Rating scales of survey responses It is worth mentioning that some categorical variables can function as multiple types. For example, in some studies, you can use ordinal variables as quantitative variables if the scales are numerical and not discrete. Data sheet of quantitative and categorical variables A data sheet is where you record the data on the variables in your experiment. In a study of the salt-tolerance levels of various plant species, you can record the data on salt addition and how the plant responds in your datasheet. The key is to gather the information and draw a conclusion over a specific period and filling out a data sheet along the process. Below is an example of a data sheet containing binary, nominal, continuous , and ordinal types of variables in research. A 12 0 - - - A 18 50 - - - B 11 0 - - - B 15 50 - - - C 25 0 - - - C 31 50 - - - Types of variables in research – Independent vs. Dependent The purpose of experiments is to determine how the variables affect each other. As stated in our experiment above, the study aims to find out how the quantity of salt introduce in the water affects the plant’s growth and survival. Therefore, the researcher manipulates the independent variables and measures the dependent variables . Additionally, you may have control variables that you hold constant. The table below summarizes independent variables, dependent variables , and control variables . Independent/ treatment variables The variables you manipulate to affect the experiment outcome The amount of salt added to the water Dependent/ response variables The variable that represents the experiment outcomes The plant’s growth or survival Control variables Variables held constant throughout the study Temperature or light in the experiment room Data sheet of independent and dependent variables In salt-tolerance research, there is one independent variable (salt amount) and three independent variables. All other variables are neither dependent nor independent. Below is a data sheet based on our experiment: Types of variables in correlational research The types of variables in research may differ depending on the study. In correlational research , dependent and independent variables do not apply because the study objective is not to determine the cause-and-effect link between variables. However, in correlational research, one variable may precede the other, as illness leads to death, and not vice versa. In such an instance, the preceding variable, like illness, is the predictor variable, while the other one is the outcome variable. Other useful types of variables in research The key to conducting effective research is to define your types of variables as independent and dependent. Next, you must determine if they are categorical or numerical types of variables in research so you can choose the proper statistical tests for your study. Below are other types of variables in research worth understanding. Confounding variables Hides the actual impact of an alternative variable in your study Pot size and soil type Latent variables Cannot be measured directly Salt tolerance Composite variables Formed by combining multiple variables The health variables combined into a single health score What is the definition for independent and dependent variables? An autonomous or independent variable is the one you believe is the origin of the outcome, while the dependent variable is the one you believe affects the outcome of your study. What are quantitative and categorical variables? Knowing the types of variables in research that you can work with will help you choose the best statistical tests and result representation techniques. It will also help you with your study design. Discrete and continuous variables: What is their difference? Discrete variables are types of variables in research that represent counts, like the quantities of objects. In contrast, continuous variables are types of variables in research that represent measurable quantities like age, volume, and weight. Bachelor Print is the most amazing company ever to print or bind academic work... We use cookies on our website. Some of them are essential, while others help us to improve this website and your experience. 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Datenschutzerklärung Host(s) google.com Cookie Name NID Cookie Laufzeit 6 Monate Privacy Policy Imprint Variables in Quantitative Research: A Beginner's Guide (COUN) Quantitative variables. Because quantitative methodology requires measurement, the concepts being investigated need to be defined in a way that can be measured. Organizational change, reading comprehension, emergency response, or depression are concepts, but they cannot be measured as such. Frequency of organizational change, reading comprehension scores, emergency response time, or types of depression can be measured. They are variables (concepts that can vary). Independent variables (IV). Dependent variables (DV). Sample variables. Extraneous variables. Independent Variables (IV) Independent variables (IV) are those that are suspected of being the cause in a causal relationship. If you are asking a cause and effect question, your IV will be the variable (or variables) that you suspect causes the effect. There are two main sorts of IV—active independent variables and attribute independent variables: Active IV are interventions or conditions that are being applied to the participants. A special tutorial for the third graders, a new therapy for clients, or a new training program being tested on employees would be active IVs. Attribute IV are intrinsic characteristics of the participants that are suspected of causing a result. For example, if you are examining whether gender which is intrinsic to the participants results in higher or lower scores on some skill, gender is an attribute IV. Both types of IV can have what are called levels. For example: In the example above, the active IV special tutorial , receiving the tutorial is one level, and tutorial withheld (control) is a second level. In the same example, being a third grader would be an attribute IV. It could be defined as only one level—being in third grade or you might wish to define it with more than one level, such as first half of third grade and second half of third grade. Indeed, that attribute IV could take many more, for example, if you wished to look at each month of third grade. Independent variables are frequently called different things depending on the nature of the research question. In predictive questions, where a variable is thought to predict another but it is not yet appropriate to ask whether it causes the other, the IV is usually called a predictor or criterion variable rather than an independent variable. Dependent Variables (DV) Dependent variables are variables that depend on or are influenced by the independent variables. They are outcomes or results of the influence of the independent variable. Dependent variables answer the question, "What do I observe happening when I apply the intervention?" The dependent variable receives the intervention. In questions where full clausation is not assumed, such as a predictive question or a question about differences between groups but no manipulation of an IV, the dependent variables are usually called outcome variable s, and the independent variables are usually called the predictor or criterion variables. Sample Variables In some studies, some characteristic of the participants must be measured for some reason, but that characteristic is not the IV or the DV. In this case, these are called sample variables. For example, suppose you are investigating whether amount of sleep affects level of concentration in depressed people. In order to obtain a sample of depressed people, a standard test of depression will be given. So the presence or absence of depression will be a sample variable. That score is not used as an IV or a DV, but simply to get the appropriate people into the sample. When there is no measure of a characteristic of the participants, the characteristic is called a sample characteristic . When the characteristic must be measured, it is called a sample variable . Extraneous Variables Extraneous variables are not of interest to the study, but may influence the dependent variable. For this reason, most quantitative studies attempt to control extraneous variables. The literature should inform you what extraneous variables to account for. For example, in the study of third graders' reading scores, variables such as noise levels in the testing room, the size or lighting or temperature of the room, and whether the children had had a good breakfast might be extraneous variables. There is a special class of extraneous variables called confounding variables. These are variables that can cause the effect we are looking for if they are not controlled for, resulting in a false finding that the IV is effective when it is not. In a study of changes in skill levels in a group of caseworkers after a training program, if the follow-up measure is taken relatively late after the training, the simple effect of practicing the skills might explain improved scores, and the training might be mistakenly thought to be successful when it was not. There are many details about variables not covered in this handout. Please consult any text on research methods for a more comprehensive review. Doc. reference: phd_t2_coun_u02s2_h02_quantvar.html An official website of the United States government The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site. The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely. Publications Account settings Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now . Advanced Search Journal List J Korean Med Sci v.37(16); 2022 Apr 25 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 . Quantitative research questions Quantitative research hypotheses Descriptive research questions Simple hypothesis Comparative research questions Complex hypothesis Relationship research questions Directional hypothesis Non-directional hypothesis Associative hypothesis Causal hypothesis Null hypothesis Alternative hypothesis Working hypothesis Statistical hypothesis Logical hypothesis Hypothesis-testing Qualitative research questions Qualitative research hypotheses Contextual research questions Hypothesis-generating Descriptive research questions Evaluation research questions Explanatory research questions Exploratory research questions Generative research questions Ideological research questions Ethnographic research questions Phenomenological research questions Grounded theory questions Qualitative case study questions 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 . Quantitative research questions Descriptive research question - Measures responses of subjects to variables - Presents variables to measure, analyze, or assess What is the proportion of resident doctors in the hospital who have mastered ultrasonography (response of subjects to a variable) as a diagnostic technique in their clinical training? Comparative research question - Clarifies difference between one group with outcome variable and another group without outcome variable Is there a difference in the reduction of lung metastasis in osteosarcoma patients who received the vitamin D adjunctive therapy (group with outcome variable) compared with osteosarcoma patients who did not receive the vitamin D adjunctive therapy (group without outcome variable)? - Compares the effects of variables How does the vitamin D analogue 22-Oxacalcitriol (variable 1) mimic the antiproliferative activity of 1,25-Dihydroxyvitamin D (variable 2) in osteosarcoma cells? Relationship research question - Defines trends, association, relationships, or interactions between dependent variable and independent variable Is there a relationship between the number of medical student suicide (dependent variable) and the level of medical student stress (independent variable) in Japan during the first wave of the COVID-19 pandemic? 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 . Quantitative research hypotheses Simple hypothesis - Predicts relationship between single dependent variable and single independent variable If the dose of the new medication (single independent variable) is high, blood pressure (single dependent variable) is lowered. Complex hypothesis - Foretells relationship between two or more independent and dependent variables The higher the use of anticancer drugs, radiation therapy, and adjunctive agents (3 independent variables), the higher would be the survival rate (1 dependent variable). Directional hypothesis - Identifies study direction based on theory towards particular outcome to clarify relationship between variables Privately funded research projects will have a larger international scope (study direction) than publicly funded research projects. Non-directional hypothesis - Nature of relationship between two variables or exact study direction is not identified - Does not involve a theory Women and men are different in terms of helpfulness. (Exact study direction is not identified) Associative hypothesis - Describes variable interdependency - Change in one variable causes change in another variable A larger number of people vaccinated against COVID-19 in the region (change in independent variable) will reduce the region’s incidence of COVID-19 infection (change in dependent variable). Causal hypothesis - An effect on dependent variable is predicted from manipulation of independent variable A change into a high-fiber diet (independent variable) will reduce the blood sugar level (dependent variable) of the patient. Null hypothesis - A negative statement indicating no relationship or difference between 2 variables There is no significant difference in the severity of pulmonary metastases between the new drug (variable 1) and the current drug (variable 2). Alternative hypothesis - Following a null hypothesis, an alternative hypothesis predicts a relationship between 2 study variables The new drug (variable 1) is better on average in reducing the level of pain from pulmonary metastasis than the current drug (variable 2). Working hypothesis - A hypothesis that is initially accepted for further research to produce a feasible theory Dairy cows fed with concentrates of different formulations will produce different amounts of milk. Statistical hypothesis - Assumption about the value of population parameter or relationship among several population characteristics - Validity tested by a statistical experiment or analysis The mean recovery rate from COVID-19 infection (value of population parameter) is not significantly different between population 1 and population 2. There is a positive correlation between the level of stress at the workplace and the number of suicides (population characteristics) among working people in Japan. Logical hypothesis - Offers or proposes an explanation with limited or no extensive evidence If healthcare workers provide more educational programs about contraception methods, the number of adolescent pregnancies will be less. Hypothesis-testing (Quantitative hypothesis-testing research) - Quantitative research uses deductive reasoning. - This involves the formation of a hypothesis, collection of data in the investigation of the problem, analysis and use of the data from the investigation, and drawing of conclusions to validate or nullify the hypotheses. 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 research questions Contextual research question - Ask the nature of what already exists - Individuals or groups function to further clarify and understand the natural context of real-world problems What are the experiences of nurses working night shifts in healthcare during the COVID-19 pandemic? (natural context of real-world problems) Descriptive research question - Aims to describe a phenomenon What are the different forms of disrespect and abuse (phenomenon) experienced by Tanzanian women when giving birth in healthcare facilities? Evaluation research question - Examines the effectiveness of existing practice or accepted frameworks How effective are decision aids (effectiveness of existing practice) in helping decide whether to give birth at home or in a healthcare facility? Explanatory research question - Clarifies a previously studied phenomenon and explains why it occurs Why is there an increase in teenage pregnancy (phenomenon) in Tanzania? Exploratory research question - Explores areas that have not been fully investigated to have a deeper understanding of the research problem What factors affect the mental health of medical students (areas that have not yet been fully investigated) during the COVID-19 pandemic? Generative research question - Develops an in-depth understanding of people’s behavior by asking ‘how would’ or ‘what if’ to identify problems and find solutions How would the extensive research experience of the behavior of new staff impact the success of the novel drug initiative? Ideological research question - Aims to advance specific ideas or ideologies of a position Are Japanese nurses who volunteer in remote African hospitals able to promote humanized care of patients (specific ideas or ideologies) in the areas of safe patient environment, respect of patient privacy, and provision of accurate information related to health and care? Ethnographic research question - Clarifies peoples’ nature, activities, their interactions, and the outcomes of their actions in specific settings What are the demographic characteristics, rehabilitative treatments, community interactions, and disease outcomes (nature, activities, their interactions, and the outcomes) of people in China who are suffering from pneumoconiosis? Phenomenological research question - Knows more about the phenomena that have impacted an individual What are the lived experiences of parents who have been living with and caring for children with a diagnosis of autism? (phenomena that have impacted an individual) Grounded theory question - Focuses on social processes asking about what happens and how people interact, or uncovering social relationships and behaviors of groups What are the problems that pregnant adolescents face in terms of social and cultural norms (social processes), and how can these be addressed? Qualitative case study question - Assesses a phenomenon using different sources of data to answer “why” and “how” questions - Considers how the phenomenon is influenced by its contextual situation. How does quitting work and assuming the role of a full-time mother (phenomenon assessed) change the lives of women in Japan? Qualitative research hypotheses Hypothesis-generating (Qualitative hypothesis-generating research) - Qualitative research uses inductive reasoning. - This involves data collection from study participants or the literature regarding a phenomenon of interest, using the collected data to develop a formal hypothesis, and using the formal hypothesis as a framework for testing the hypothesis. - Qualitative exploratory studies explore areas deeper, clarifying subjective experience and allowing formulation of a formal hypothesis potentially testable in a future quantitative approach. 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. Variables Unclear and weak statement (Statement 1) Clear and good statement (Statement 2) Points to avoid Research question Which is more effective between smoke moxibustion and smokeless moxibustion? “Moreover, regarding smoke moxibustion versus smokeless moxibustion, it remains unclear which is more effective, safe, and acceptable to pregnant women, and whether there is any difference in the amount of heat generated.” 1) Vague and unfocused questions 2) Closed questions simply answerable by yes or no 3) Questions requiring a simple choice Hypothesis The smoke moxibustion group will have higher cephalic presentation. “Hypothesis 1. The smoke moxibustion stick group (SM group) and smokeless moxibustion stick group (-SLM group) will have higher rates of cephalic presentation after treatment than the control group. 1) Unverifiable hypotheses Hypothesis 2. The SM group and SLM group will have higher rates of cephalic presentation at birth than the control group. 2) Incompletely stated groups of comparison Hypothesis 3. There will be no significant differences in the well-being of the mother and child among the three groups in terms of the following outcomes: premature birth, premature rupture of membranes (PROM) at < 37 weeks, Apgar score < 7 at 5 min, umbilical cord blood pH < 7.1, admission to neonatal intensive care unit (NICU), and intrauterine fetal death.” 3) Insufficiently described variables or outcomes Research objective To determine which is more effective between smoke moxibustion and smokeless moxibustion. “The specific aims of this pilot study were (a) to compare the effects of smoke moxibustion and smokeless moxibustion treatments with the control group as a possible supplement to ECV for converting breech presentation to cephalic presentation and increasing adherence to the newly obtained cephalic position, and (b) to assess the effects of these treatments on the well-being of the mother and child.” 1) Poor understanding of the research question and hypotheses 2) Insufficient description of population, variables, or study outcomes a These statements were composed for comparison and illustrative purposes only. b These statements are direct quotes from Higashihara and Horiuchi. 16 Variables Unclear and weak statement (Statement 1) Clear and good statement (Statement 2) Points to avoid Research question Does disrespect and abuse (D&A) occur in childbirth in Tanzania? How does disrespect and abuse (D&A) occur and what are the types of physical and psychological abuses observed in midwives’ actual care during facility-based childbirth in urban Tanzania? 1) Ambiguous or oversimplistic questions 2) Questions unverifiable by data collection and analysis Hypothesis Disrespect and abuse (D&A) occur in childbirth in Tanzania. Hypothesis 1: Several types of physical and psychological abuse by midwives in actual care occur during facility-based childbirth in urban Tanzania. 1) Statements simply expressing facts Hypothesis 2: Weak nursing and midwifery management contribute to the D&A of women during facility-based childbirth in urban Tanzania. 2) Insufficiently described concepts or variables Research objective To describe disrespect and abuse (D&A) in childbirth in Tanzania. “This study aimed to describe from actual observations the respectful and disrespectful care received by women from midwives during their labor period in two hospitals in urban Tanzania.” 1) Statements unrelated to the research question and hypotheses 2) Unattainable or unexplorable objectives 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. Elements of Research                                                                                     The purpose of all research is to describe and explain in the world. Variance is simply the difference; that is, variation that occurs naturally in the world or change that we create as a result of a manipulation. Variables are names that are given to the variance we wish to explain. A variable is either a result of some force or is itself the force that causes a change in another variable. In experiments, these are called and variables respectively. When a researcher gives an active drug to one group of people and a placebo , or inactive drug, to another group of people, the independent variable is the drug treatment. Each person's response to the active drug or is called the dependent variable. This could be many things depending upon what the drug is for, such as high blood pressure or muscle pain. Therefore in experiments, a researcher manipulates an independent variable to determine if it causes a change in the dependent variable. As we learned earlier in a descriptive study, variables are not manipulated.  They are observed as they naturally occur and then associations between variables are studied.  In a way, all the variables in descriptive studies are dependent variables because they are studied in relation to all the other variables that exist in the setting where the research is taking place. However, in descriptive studies, variables are not discussed using the terms "independent" or "dependent." Instead, the names of the variables are used when discussing the study.  For example, there is more diabetes in people of Native American heritage than people who come from Eastern Europe.  In a descriptive study, the researcher would examine how diabetes (a variable) is related to a person's genetic heritage (another variable). Variables are important to understand because they are the basic units of the information studied and interpreted in research studies. Researchers carefully analyze and interpret the value(s) of each variable to make sense of how things relate to each other in a descriptive study or what has happened in an experiment.                                                                                                                                               RESEARCH VARIABLES: TYPES, USES AND DEFINITION OF TERMS In book: Research in Education (pp.43-54) Publisher: His Lineage Publishing House University of Ibadan Discover the world's research 25+ million members 160+ million publication pages 2.3+ billion citations Tiffany Angelene Dharsono Marylise Hebrard Ahmed Ali DARAR Susan Sabillo Merry Jean Tiauzon Mylene A Bautista Elsye Mayshelly Jonathan Phelipe Silaban Kapeso Singogo Louis Cohen Lawrence Manion Keith Morrison J O Adeleke O Aderounmu D O Owuamanam A E Uzoagulu Recruit researchers Join for free Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up Once you have brainstormed project topics, narrowed down the list, and reviewed the research related to that narrowed list, select a topic that seems most appealing to you. However, this project topic is not set in stone yet. After you begin working through the project, you may realize that the topic needs to be revised, or even entirely changed to a different topic. The next step is to identify the key variables and the research design. All research projects are based around variables. A variable is the characteristic or attribute of an individual, group, educational system, or the environment that is of interest in a research study. Variables can be straightforward and easy to measure, such as gender, age, or course of study. Other variables are more complex, such as socioeconomic status, academic achievement, or attitude toward school. Variables may also include an aspect of the educational system, such as a specific teaching method or counseling program. Characteristics of the environment may also be variables, such as the amount of school funding or availability of computers. Therefore, once the general research topic has been identified, the researcher should identify the key variables of interest. For example, a researcher is interested in low levels of literacy. Literacy itself is still a broad topic. In most instances, the broad topic and general variables need to be specifically identified. For example, the researcher needs to identify specific variables that define literacy: reading fluency (the ability to read a text out loud), reading comprehension (understanding what is read), vocabulary, interest in reading, etc. If a researcher is interested in motivation, what specific motivation variables are of interest: external motivation, goals, need for achievement, etc? Reading other research studies about your chosen topic will help you better identify the specific variables of interest. The key variables provide focus when writing the Introduction section. The key variables are the major terms to use when searching for research articles for the Literature Review. The key variables are the terms to be operationally defined if an Operational Definition of Terms section is necessary. The key variables provide focus to the Methods section. The Instrument will measure the key variables. These key variables must be directly measured or manipulated for the research study to be valid. Descriptive: Describes the current state of variables. For example, a descriptive study might examine teachers' knowledge of literacy development. This is a descriptive study because it simply describes the current state of teachers' knowledge of literacy development. Causal Comparative: Examines the effect of one variable that cannot be manipulated on other variables. An example would be the effect of gender on examination malpractice. A researcher cannot manipulate a person's gender, so instead males and females are compared on their examination malpractice behavior. Because the variable of interest cannot be manipulated, causal comparative studies (sometimes also called ex post facto) ccompare two groups that differ on the independent variable (e.g., gender) on the dependent variable (e.g., examination malpractice). Thus, the key identifying factor of a causal comparative study is that it compares two or more groups on a different variable. Correlational: Describes the relationship between variables. Correlational studies must examine two variables that have continuous values. For example, academic achievement is a continuous variable because students' scores have a wide range of values - oftentimes from 0 to 100. However, gender is not a continuous variable because there are only two categories that gender can have: male and female. A correlational study might examine the relationship between motivation and academic achievement - both continuous variables. Note that in a correlational design, both variables must be studied within the same group of individuals. In other words, it is acceptable to study the relationship between academic achievement and motivation in students because the two variables (academic achievement and motivation) are in the same group of individuals (students). However, it is extremely difficult to study two variables in two groups of people, such as the relationship between teacher motivation and student achievement. Here, the two variables are compared between two groups: teachers and students. I strongly advise against this latter type of study. Experimental and Quasi-Experimental: Examines the effect of a variable that the researcher manipulates on other variables. An experimental or quasi-experimental study might examine the effect of telling stories on children's literacy skills. In this case, the researcher will "manipulate" the variable of telling stories by placing half of the children in a treatment group that listens to stories and the other half of children in a control group that gets the ordinary literacy instruction. The difference between an experimental design and quasi-experimental design is described in Step 4: Research Design. Descriptive studies are the most simple research design and provide the least amount of information about improving education. Therefore, descriptive studies should only be conducted for first degree and diploma projects. Only in special cases should a Masters thesis be descriptive. Doctoral dissertations should aim for experimental or quasi-experimental studies. The purpose, research questions, and hypotheses will be written about the variables based on the research design. The Instruments will be developed to measure the key variables and the Instruments section in Chapter 3 is written to describe the instruments. The Procedures section describes the treatment for experimental studies and/or how the instrument will be administered. The Method of Data Analysis describes how the data is summarized and tested based on the research questions and hypotheses. Copyright 2012, Katrina A. Korb, All Rights Reserved Have a language expert improve your writing Run a free plagiarism check in 10 minutes, generate accurate citations for free. Knowledge Base Methodology Research Methods | Definitions, Types, Examples Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make. First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question : Qualitative vs. quantitative : Will your data take the form of words or numbers? Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else? Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment? Second, decide how you will analyze the data . For quantitative data, you can use statistical analysis methods to test relationships between variables. For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data. Table of contents Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods. Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research. Qualitative vs. quantitative data Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop. For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data . If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data . Qualitative to broader populations. . Quantitative . You can also take a mixed methods approach , where you use both qualitative and quantitative research methods. Primary vs. secondary research Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies). If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice. Primary . methods. Secondary Descriptive vs. experimental data In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method . In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design . To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect. Descriptive . . Experimental Prevent plagiarism. Run a free check. Research methods for collecting data Research method Primary or secondary? Qualitative or quantitative? When to use Primary Quantitative To test cause-and-effect relationships. Primary Quantitative To understand general characteristics of a population. Interview/focus group Primary Qualitative To gain more in-depth understanding of a topic. Observation Primary Either To understand how something occurs in its natural setting. Secondary Either To situate your research in an existing body of work, or to evaluate trends within a research topic. Either Either To gain an in-depth understanding of a specific group or context, or when you don’t have the resources for a large study. Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis. Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses. Qualitative analysis methods Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected: From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers. Using non-probability sampling methods . Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias . Quantitative analysis methods Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments). You can use quantitative analysis to interpret data that was collected either: During an experiment . Using probability sampling methods . Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers. Research methods for analyzing data Research method Qualitative or quantitative? When to use Quantitative To analyze data collected in a statistically valid manner (e.g. from experiments, surveys, and observations). Meta-analysis Quantitative To statistically analyze the results of a large collection of studies. Can only be applied to studies that collected data in a statistically valid manner. Qualitative To analyze data collected from interviews, , or textual sources. To understand general themes in the data and how they are communicated. Either To analyze large volumes of textual or visual data collected from surveys, literature reviews, or other sources. Can be quantitative (i.e. frequencies of words) or qualitative (i.e. meanings of words). Receive feedback on language, structure, and formatting Professional editors proofread and edit your paper by focusing on: Academic style Vague sentences Style consistency See an example If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples. Chi square test of independence Statistical power Descriptive statistics Degrees of freedom Pearson correlation Null hypothesis Double-blind study Case-control study Research ethics Data collection Hypothesis testing Structured interviews Research bias Hawthorne effect Unconscious bias Recall bias Halo effect Self-serving bias Information bias Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail. In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question . A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students. In statistics, sampling allows you to test a hypothesis about the characteristics of a population. The research methods you use depend on the type of data you need to answer your research question . If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods . If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data. If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods. Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives. Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ). In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section . In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods. 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If only sugarcane bagasse is considered, it represents 7.5% of the country’s electricity supply. Thus, seeking a broader economic vision, the main objective of this research is to analyze the behavior of electricity consumption generated through sugarcane bagasse in Brazil, from 2013 to 2021. To do this, it used a model in dynamic panel using as independent variables the price of electricity, per capita income, sugarcane production, price of ethanol (competing product), total electricity consumption in each state and the level of rainfall. The analysis sought to capture differences in bioelectricity consumption between states considered richer and poorer. After confirming the indication of the absence of a unit root in bioelectricity consumption, the model was estimated using the generalized first difference moment method (FD GMM), which was more robust to the data. The results suggest that the consumption of sugar-energy electricity has been decreasing, on average, 0.82% per year in the period analyzed. However, the research also showed that the increase in electricity prices had a positive effect on the consumption of sugar-alcohol bioelectricity, offsetting the effect of the downward trend in recent years. From a regional point of view, the research also showed a downward trend in consumption for the states considered more developed and a slight upward trend for the poorest regions. This implies that price and income, analyzed together, point to the existence of a systematic effect between income levels on the price elasticity of demand for electricity from sugarcane. The finding is important for those responsible for conducting energy policy as it indicates that different income levels between regions must be taken into account when proposing public policies to contain or encourage electricity consumption from renewable sources. This is a preview of subscription content, log in via an institution to check access. Access this article Subscribe and save. Get 10 units per month Download Article/Chapter or eBook 1 Unit = 1 Article or 1 Chapter Cancel anytime Price includes VAT (Russian Federation) Instant access to the full article PDF. Rent this article via DeepDyve Institutional subscriptions The Brazilian hydroelectric system is made up of four interconnected subsystems (Southeast/Midwest – 19.6%, South – 54.3%, Northeast – 37.7% and North – 33.2%), which together have 200 reservoirs and a maximum monthly storage capacity of 291,231 MW (ONS, 2021b ). The country has two sugarcane crops. The first one, as mentioned in the text, refers to the period from April to November (Central/South region harvest). This is the crop with the highest production and productivity. The harvest in the State of São Paulo, the largest producer of sugarcane and bioelectricity in the country, is concentrated in this period. The second, the Northeast crop, extends from November to April, making the country produce year-round. Authors’ Note. It is important to note that if the technical potential based on the 2019/2020 harvest is considered, the estimated value of generating bioelectricity from sugarcane bagasse would exceed 200,000 GWh/year (according to estimates from Martins et al. ( 2019 ) e Coelho et al. ( 2012 ). In other words, technically, Brazil uses only 11% of its potential. Abid, N., Ahmad, F., Aftab, J., & Razzaq, A. (2023). A blessing or a burden? Assessing the impact of Climate Change Mitigation efforts in Europe using Quantile Regression models. Energy Policy , 178 (113589). https://doi.org/10.1016/j.enpol.2023.113589 Alberini, A., & Filippini, M. (2011). Response of residential electricity demand to price: The effect of measurement error. Energy Economics , 33 (5), 889–895. https://doi.org/10.1016/j.eneco.2011.03.009 Article   Google Scholar   ANA (2019). 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This work was also supported by the Post-Graduate Program in Intellectual Property and Technology Transfer for Innovation (in Portuguese, Programa de Pós-Graduação em Propriedade Intelectual e Transferência de Tecnologia para Inovação – PROFNIT) and PRPGI/IFBA. Author information Authors and affiliations. Energy and Environment, Federal University of Reconcavo of Bahia (UFRB), Centenário Avenue, 697, Feira de Santana, BA, 44042-280, Brazil Luís Oscar Silva Martins & Luciano Sérgio Hocevar Department of Economics, Federal University of Bahia (BA), Federal University of Bahia, 06 Piedade Square, Salvador, Bahia, 40070-010, Brazil Vinícius de Araújo Mendes Federal Institute of Bahia (IFBA), São José St, Santo Amaro, BA, 44200-000, Brazil Marcelo Santana Silva Energy and Environment and Industrial Engineering at BA - Polytechnic College, 02 Teacher Aristides Novis St. Federação Campus, Salvador, Bahia, 40210-630, Brazil Federal University of Bahia (BA), Federal University of Bahia – Polytechnic College - Energy and Gas Laboratory, 02 Teacher Aristides Novis St. Federação Campus, Salvador, Bahia, 40210-630, Brazil Ednildo Andrade Torres You can also search for this author in PubMed   Google Scholar Corresponding author Correspondence to Luís Oscar Silva Martins . 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Provided by the Springer Nature SharedIt content-sharing initiative Electricity Bioelectricity Sugarcane bagasse Econometric modeling GMM estimator Find a journal Publish with us Track your research 255 IMAGES Types of variables in scientific research The research main variables classification 10 Types of Variables in Research: Definitions and Examples 27 Types of Variables in Research and Statistics (2024) Types of Research Variable in Research with Example How To Find Variables In A Research Study VIDEO Variables in Psychological Research CHARACTERISTICS OF EXPERIMENTAL RESEARCH Research Variables Operationalization of research variables Types of variables in research|Controlled & extragenous variables|Intervening & moderating variables Variables in Research: Applied Linguistics COMMENTS Types of Variables in Research & Statistics Types of Variables in Research & Statistics | Examples Variables in Research Here are some of the main disadvantages of using variables in research: Simplification : Variables may oversimplify the complexity of real-world phenomena. 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If only sugarcane bagasse is considered, it represents 7.5% of the country's electricity supply. Thus, seeking a broader economic vision, the main objective of this research is to analyze the behavior of electricity consumption generated through sugarcane bagasse in Brazil, from 2013 to 2021. To do this, it ... essay homework paper phd project resume review speech thesis