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## Variables in Research – Definition, Types and Examples

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## Variables in Research

Definition:

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

## Types of Variables in Research

Types of Variables in Research are as follows:

## Independent Variable

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

## Dependent Variable

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

## Confounding Variable

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

## Mediating Variable

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

## Moderator Variable

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

## Control Variable

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

## Continuous Variable

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

## Categorical Variable

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

## Discrete Variable

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

## Dummy Variable

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

## Extraneous Variable

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

## Latent Variable

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

## Moderator-mediator Variable

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

## Variables Analysis Methods

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

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

## Examples of Variables

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

## Applications of Variables

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

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

## Purpose of Variables

Variables serve several purposes in research, including:

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

## Characteristics of Variables

Characteristics of Variables are as follows:

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

## Advantages of Variables

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

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

## Disadvantages of Variables

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

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

## About the author

## Muhammad Hassan

Researcher, Academic Writer, Web developer

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Chris Drew (PhD)

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.

Pros | Cons |
---|---|

They provide a precise measure, allow for a higher level of measurement, and can be manipulated statistically for inferential analysis. The resulting data is objective and consistent (Moodie & Johnson, 2021). | can be time-consuming and costly. Secondly, important context or explanation may be lost when data is purely numerical (Katz, 2006). |

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.

Pros | Cons |
---|---|

They give a higher level of detail, useful in determining precise measurements, and allow for complex statistical analysis (Christmann & Badgett, 2009). | They can easily lead to information overload due to granularity (Allen, 2017). The representation and interpretation of results may also be more complex. |

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.

Pros | Cons |
---|---|

They are easier to comprehend and simpler to analyze, as they provide direct and countable insight (Frankfort-Nachmias & Leon-Guerrero, 2006). | They might lack in-depth information because they cannot provide the granularity that continuous variables offer (Privitera, 2022). |

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.

Pros | Cons |
---|---|

Essences or characteristics that cannot be measured numerically can be captured. They provide richer, subjective, and explanatory data (Moodie & Johnson, 2021). | The analysis might be challenging because these variables cannot be subjected to mathematical calculations or operations (Creswell & Creswell, 2018). |

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.

Pros | Cons |
---|---|

They are simple to understand and effective in segregating data into clearly defined, mutually exclusive categories (Norman & Streiner, 2008). | They can often be overly simplistic, leading to a loss of data differentiation and information (Katz, 2006). They also do not provide any directionality or order. |

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).

Pros | Cons |
---|---|

.Ordinal variables allow categorization of data that also reflect some sort of ranking or order, allowing more nuanced insights from your data (De Vaus, 2001). | .It becomes challenging during data analysis due to the unequal intervals (Katz, 2006). Differences between the adjacent categories are unknown and not measurable. |

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.

Pros | Cons |
---|---|

Dichotomous variables simplify analysis. They are particularly useful for “yes/no” questions, which can be coded into a numerical format for statistical analysis (Coolidge, 2012). | Dichotomous variables might , losing valuable information by reducing them to just two categories (Adams & McGuire, 2022). |

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.

Pros | Cons |
---|---|

Having an inherent zero value allows for a broad range of statistical analysis that involves ratios (Norman & Streiner, 2008). It provides a larger volume of information than any other variable type. | Ratio variables may give results that do not actually reflect the reality if zero does not exist (De Vaus, 2001). |

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.

Pros | Cons |
---|---|

Interval variables allow for more complex statistical analyses as they can accommodate a range of mathematical operations like addition and subtraction (Norman & Streiner, 2008). | They restrict the ability to measure the ratio of categories since there’s no true zero (Babbie, Halley & Zaino, 2007). |

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.

Pros | Cons |
---|---|

It provides the results for the research question. Without a dependent variable, it would be impossible to draw conclusions from the conducted experiment or study. | It’s not always straightforward to isolate the impact of independent variables on the dependent variable, especially when multiple independent variables are influencing the results. |

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.

Pros | Cons |
---|---|

Manipulating the independent variable allows researchers to observe changes it causes in the dependent variable, aiding in understanding causal relationships in the data. | It can be challenging to isolate the impact of a single independent variable when multiple factors may influence the dependent variable. |

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.

Pros | Cons |
---|---|

Identifying potential confounders during study design can help optimize the process and to the conclusions drawn (Knapp, 2017). | Confounders can introduce bias and affect the validity of a study. If overlooked, they can lead to about correlations or cause-and-effect relationships (Bonidace, 2019). |

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.

Pros | Cons |
---|---|

Control variables increase the reliability of experiments, ensure a fair comparison between groups, and support the validity of the conclusions (Sproull, 2002). | Misidentification or non-consideration of control variables might affect the outcome of the experiment, leading to biased results (Bonidace, 2019). |

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.

Pros | Cons |
---|---|

Latent variables can help capture unseen factors and give insight into the underlying constructs affecting observable behaviors. | Inferring the values of latent variables can involve complex statistical methods and assumptions. Also, there might be several ways to interpret the values of latent variables, potentially impacting the validity and consistency of findings. |

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).

Pros | Cons |
---|---|

Derived variables can reduce complexity, extract more relevant information, and create new insights from existing data. | They require careful creation as any errors in the genesis of the original variables will impact the derived variable. Also, the process of deriving variables needs to be adequately documented to ensure replicability and avoid misunderstanding. |

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.

Pros | Cons |
---|---|

Time series variables allow for the exploration of causal relationships, testing of theories, and forecasting of future values based on established patterns. | They can be difficult to work with due to issues like seasonality, irregular intervals, autocorrelation, or non-stationarity. Often, additional statistical techniques- such as decomposition, differencing, or transformations- may need to be employed. |

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.

Pros | Cons |
---|---|

Cross-sectional data can be relatively easy and quick to collect. They are useful for examining the relationship between different variables at a given point in time. | Cross-sectional data does not provide any information about causality or the sequence of events. It’s also susceptive to “snapshot bias” since it does not take into account changes over 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.

Pros | Cons |
---|---|

Predictor variables establish cause-and-effect relationships and allow for the prediction of outcomes for the response variable. | It can be challenging to isolate a single predictor variable’s impact when multiple predictor variables are involved, leading to potential interaction effects. |

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.

Pros | Cons |
---|---|

The response variable supplies the results for the research question, offering crucial insights into the study. | It may be influenced by several predictor variables making it difficult to isolate the effect of one specific predictor. |

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.

Pros | Cons |
---|---|

Exogenous variables are often used as control variables in experimental studies, making them essential for creating cause-and-effect relationships. | The relationship between exogenous variables and the dependent variable can be complex and challenging to identify precisely. |

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.

Pros | Cons |
---|---|

Endogenous variables play a significant role in understanding complex systems’ dynamics and aid in developing nuanced mathematical or statistical models. | It can be difficult to untangle the causal relationships and influences surrounding endogenous variables. |

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.

Pros | Cons |
---|---|

Identifying and understanding causal variables can lead to practical interventions as it offers the opportunity to control or change the outcome. | Confusion can arise between correlation and causation. Just because two variables move together doesn’t necessarily mean that one causes the other to move. |

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.

Pros | Cons |
---|---|

The identification of the moderator variables can provide a more nuanced understanding of the relationship between independent and dependent variables. | It’s often challenging to identify potential moderators and require experimental design to appropriately assess their impact. |

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.

Pros | Cons |
---|---|

The identification of mediators can enhance the understanding of underlying processes or mechanisms that explain why an effect exists. | The establishment of mediation effects requires strong and complex modeling techniques, and it may be difficult to establish temporal precedence, a prerequisite for mediation. |

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.

Pros | Cons |
---|---|

The identification and control of extraneous variables can improve the validity of the study’s conclusions by minimizing potential sources of bias. | These variables can confuse the outcome of a study if not adequately observed, measured, and controlled. |

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.

Pros | Cons |
---|---|

Using dummy variables allows the modelling of categorical or nominal variables in regression equations, which can only handle numerical values. | Creating too many dummy variables—known as the “dummy variable trap”—can lead to multicollinearity in regression models, making the results hard to interpret. |

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”).

Pros | Cons |
---|---|

They can simplify analysis by reducing the number of variables considered and may help in handling multicollinearity in statistical models. | The creation of composite variables requires careful consideration of the underlying variables that make up the composite. It might be hard to interpret and requires an understanding of the individual variables. |

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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- Types of Variables in Research | Definitions & Examples

## Types of Variables in Research | Definitions & Examples

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

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

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

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

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

## Table of contents

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

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

- Quantitative data represents amounts.
- Categorical data represents groupings.

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

## Quantitative variables

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

Type of variable | What does the data represent? | Examples |
---|---|---|

Discrete variables (aka integer variables) | Counts of individual items or values. | |

Continuous variables (aka ratio variables) | Measurements of continuous or non-finite values. |

## Categorical variables

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

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

Type of variable | What does the data represent? | Examples |
---|---|---|

Binary variables (aka dichotomous variables) | Yes/no outcomes. | |

Nominal variables | Groups with no rank or order between them. | |

Ordinal variables | Groups that are ranked in a specific order. |

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

## Example data sheet

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

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

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

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

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

Type of variable | Definition | Example (salt tolerance experiment) |
---|---|---|

Independent variables (aka treatment variables) | Variables you manipulate in order to affect the outcome of an experiment. | The amount of salt added to each plant’s water. |

Dependent variables (aka response variables) | Variables that represent the outcome of the experiment. | Any measurement of plant health and growth: in this case, plant height and wilting. |

Control variables | Variables that are held constant throughout the experiment. | The temperature and light in the room the plants are kept in, and the volume of water given to each plant. |

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

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

## What about correlational research?

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

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

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

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

Type of variable | Definition | Example (salt tolerance experiment) |
---|---|---|

A variable that hides the true effect of another variable in your experiment. This can happen when another variable is closely related to a variable you are interested in, but you haven’t controlled it in your experiment. | Pot size and soil type might affect plant survival as much as or more than salt additions. In an experiment, you would control these potential confounders by holding them constant. | |

Latent variables | A variable that can’t be directly measured, but that you represent via a proxy. | Salt tolerance in plants cannot be measured directly, but can be inferred from measurements of plant health in our salt-addition experiment. |

Composite variables | A variable that is made by combining multiple variables in an experiment. These variables are created when you analyse data, not when you measure it. | The three plant-health variables could be combined into a single plant-health score to make it easier to present your findings. |

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

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

Discrete and continuous variables are two types of quantitative variables :

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

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

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

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

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

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## 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

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Types of Variables in Research & Statistics | Examples. Published on September 19, 2022 by Rebecca Bevans. Revised on June 21, 2023. In statistical research, a variable is defined as an attribute of an object of study. Choosing which variables to measure is central to good ...

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.

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.

Types of Variables in Research | Definitions & Examples. Published on 19 September 2022 by Rebecca Bevans. Revised on 28 November 2022. In statistical research, a variable is defined as an attribute of an object of study. Choosing which variables to measure is central to good experimental design.

By understanding the types of variables and choosing tests that are appropriate to the data, individuals can draw appropriate conclusions and promote their work for an application. Variables. To determine which statistical design is appropriate for the data and research plan, one must first examine the scales of each measurement. Multiple types ...

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.

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.

Types. Discrete and continuous. Binary, nominal and ordinal. Researchers can further categorize quantitative variables into discrete or continuous types of variables: Discrete: Any numerical variables you can realistically count, such as the coins in your wallet or the money in your savings account.

The Role of Variables in Research. In scientific research, variables serve several key functions: Define Relationships: Variables allow researchers to investigate the relationships between different factors and characteristics, providing insights into the underlying mechanisms that drive phenomena and outcomes. Establish Comparisons: By manipulating and comparing variables, scientists can ...

Experimental and Non-Experimental Research. Experimental research: In experimental research, the aim is to manipulate an independent variable(s) and then examine the effect that this change has on a dependent variable(s).Since it is possible to manipulate the independent variable(s), experimental research has the advantage of enabling a researcher to identify a cause and effect between variables.

Within research, especially scientific research, variables form the foundation of studies, as researchers are often interested in how one variable impacts another, ... 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, ...

Compare the independent variable and dependent variable in research. See other types of variables in research, including confounding and extraneous variables. Updated: 11/21/2023 ...

A variable can collect either qualitative or quantitative data. A variable differing in quantity is called a quantitative variable (e.g., weight of a group of patients), whereas a variable differing in quality is called a qualitative variable (e.g., the Fitzpatrick skin type)

Generally, the independent variable goes on the x-axis (horizontal) and the dependent variable on the y-axis (vertical). The type of visualization you use depends on the variable types in your research questions: A bar chart is ideal when you have a categorical independent variable.

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 ...

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

30 Other Variable Types Used in Experiments. This is by no means a comprehensive list, as the list of all variable types would be difficult to document in one place. Below are many of the common and some less common variable types used in scientific experiments and statistical studies. Included is a brief overview of what that variable type ...

TYPES OF RESEARCH VARIABLES. When undertaking research, there are 4 basic types of variables to consider and define: 1, 5-7. Independent variable: A variable that is believed to be the cause of some of the observed effect or association and one that is directly manipulated by the researcher during the study or experiment.

Step 1: Consider your aims and approach. Step 2: Choose a type of research design. Step 3: Identify your population and sampling method. Step 4: Choose your data collection methods. Step 5: Plan your data collection procedures. Step 6: Decide on your data analysis strategies. Other interesting articles.

There are three main types of research variables: independent variables, dependent variables, and control variables. Figure: Types of Research Variables. Independent Variables (IV): An independent variable is a variable that is manipulated or controlled by the researcher in order to observe its effect on the dependent variable.

The purpose of research is to describe and explain variance in the world, that is, variance that. occurs naturally in the world or chang e that we create due to manipulation. Variables are ...

Closely related to the understanding of what a variable is, is the idea of definition of terms. This chapter explores the use of variables in research, types of variables and the definition of terms, so as to help some of the students who have a problem identifying and clarifying the variables they are working on in their project work.

That's why research methods like surveys, observational studies, case studies, and other descriptive types of research are necessary: They all provide that sweet, sweet zero-party data for your team. ... Uncontrolled variables: In descriptive research, none of the variables are impacted by the team doing the research in any way. (Doing so ...

Research over the past decade had linked changes in the gut microbiome to the development of type 2 diabetes, but scientists had not been able to draw significant conclusions because of those studies' small size and varied design. "The microbiome is highly variable across different geographic locations and racial and ethnic groups.

The EDGAR database provides free public access to corporate information, allowing you to research a public company's financial information and operations by reviewing the filings the company makes with the SEC. You can also research information provided by mutual funds (including money market funds), exchange-traded funds (ETFs), and variable annuities.

Regular: 21.24%-26.24% Variable Business ideas Pet walker: Pet walkers allow pet owners to work without worrying about whether the dogs are getting the exercise they need.

Advertised Annual Percentage Yield (APY) is variable and accurate as of April 11, 2024. Rates are subject to change at any time before or after account opening. Min. to earn

But bank rates, which are variable, may not be as attractive once the Federal Reserve starts lowering interest rates, Brachman says. Most banks raised their rates slowly as the central bank hiked ...

You can also create a mixed methods research design that has elements of both. Descriptive research vs experimental research. Descriptive research gathers data without controlling any variables, while experimental research manipulates and controls variables to determine cause and effect.

The research team found that the Framingham Offspring Risk Score underestimated type 2 diabetes risk for non-Hispanic Black patients, but overestimated risk for their white counterparts. The ARIC Model and PRT overestimated risk for both groups, but to a greater extent for white patients.