**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.

**Contents**show

## 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 |
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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). | Collecting quantitative data 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 |
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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 |
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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 |
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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 |
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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 |
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.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 |
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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 oversimplify complex issues, 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 |
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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 |
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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 |
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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 |
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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 |
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Identifying potential confounders during study design can help optimize the process and lend more credibility to the conclusions drawn (Knapp, 2017). | Confounders can introduce bias and affect the validity of a study. If overlooked, they can lead to incorrect assumptions 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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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 |
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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”.

## Conclusion

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.

## References

Adams, K. A., & McGuire, E. K. (2022). *Research Methods, Statistics, and Applications*. SAGE Publications.

Allen, M. (2017). The SAGE Encyclopedia of Communication Research Methods (Vol. 1). New York: SAGE Publications.

Babbie, E., Halley, F., & Zaino, J. (2007). *Adventures in Social Research: Data Analysis Using SPSS 14.0 and 15.0 for Windows* (6th ed.). New York: SAGE Publications.

Boniface, D. R. (2019). *Experiment Design and Statistical Methods For Behavioural and Social Research*. CRC Press. ISBN: 9781351449298.

Christmann, E. P., & Badgett, J. L. (2009).* Interpreting Assessment Data: Statistical Techniques You Can Use. *New York: NSTA Press.

Coolidge, F. L. (2012). *Statistics: A Gentle Introduction* (3rd ed.). SAGE Publications.

Creswell, J. W., & Creswell, J. D. (2018). *Research Design: Qualitative, Quantitative, and Mixed Methods Approaches*. New York: SAGE Publications.

De Vaus, D. A. (2001). *Research Design in Social Research*. New York: SAGE Publications.

Katz, M. (2006)*. Study Design and Statistical Analysis: A Practical Guide for Clinicians*. Cambridge: Cambridge University Press.

Knapp, H. (2017). *Intermediate Statistics Using SPSS.* SAGE Publications.

Moodie, P. F., & Johnson, D. E. (2021). Applied Regression and ANOVA Using SAS. CRC Press.

Norman, G. R., & Streiner, D. L. (2008). *Biostatistics: The Bare Essentials*. New York: B.C. Decker.

Privitera, G. J. (2022). *Research Methods for the Behavioral Sciences*. New Jersey: SAGE Publications.

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]