In research and statistics, variables are essential elements that represent characteristics or attributes that can be measured or observed. Variables can take on different values and are used to describe and analyze relationships, patterns, and trends in data. Understanding the types of variables is crucial for designing experiments, collecting data, and performing statistical analyses.
Types of data: Quantitative vs categorical variables
Data is a specific measurement or value recorded for a particular variable in a study or experiment. It is the information collected and entered into a data sheet for analysis. Generally, data can be classified into two main categories: quantitative data and categorical data.
- Quantitative data refers to numerical measurements that represent quantities or amounts. These values can be counted or measured, and they provide information about the magnitude or size of a variable. Examples of quantitative data include height, weight, temperature, and test scores.
- Categorical data represents characteristics or attributes that can be divided into distinct groups or categories. This type of data does not convey any quantitative information but rather describes qualities or memberships. Examples of categorical data include gender, eye color, marital status, and geographical location.
When a variable contains quantitative data, it is referred to as a quantitative variable.
Conversely, when a variable contains categorical data, it is called a categorical variable. These variables are used to represent and analyze groupings or classifications of data points.
Quantitative variables
Quantitative variables are used to represent numerical data that can be measured or counted. When you collect quantitative data, the recorded numbers correspond to actual quantities or magnitudes.
These values can be manipulated mathematically, allowing you to perform operations such as addition, subtraction, multiplication, and division. Quantitative variables can be further classified into two subtypes: discrete and continuous.
Discrete vs continuous variables
Discrete variables are quantitative variables that can only take on specific, distinct values, often whole numbers or integers. They typically involve counts of individual items or values. For example, the number of students in a classroom or the number of cars in a parking lot are discrete variables.
Continuous variables, on the other hand, are quantitative variables that can take on any value within a specified range. They are measured on a continuous scale and can be fractional or decimal values. Examples of continuous variables include height, weight, time, and temperature.
| Type of Variable | What does the data represent? | Examples |
| Discrete variables (integer variables) | Counts of individual items or values | – Number of students in a class – Number of different tree species in a forest – Number of siblings in a family – Number of correct answers on a test – Number of defective products in a batch – Number of customers served per day – Number of books in a library |
| Continuous variables (ratio variables) | Measurements that can take on any value within a specific range | – Height of individuals in a population – Time taken to complete a task – Temperature readings – Weight of a person – Distance traveled by a vehicle – Concentration of a chemical solution – Rainfall amount in millimeters – Duration of a movie in minutes |
Categorical variables
Categorical variables are used to represent data that can be divided into distinct groups or categories. These variables do not convey quantitative information but rather describe qualities or attributes. Although categorical variables may sometimes be recorded as numbers, these numbers serve as labels for different categories rather than representing actual quantities.
There are three main types of categorical variables: binary, nominal, and ordinal.
Binary vs nominal vs ordinal variables
- Binary variables, also known as dichotomous variables, have only two possible categories or outcomes. They often represent the presence or absence of a characteristic or a yes/no situation.
- Nominal variables are categorical variables that have two or more categories with no inherent order or ranking. The categories are mutually exclusive and exhaustive, meaning that each data point can only belong to one category, and all possible categories are represented.
- Ordinal variables are categorical variables that have two or more categories with a natural order or ranking, but the differences between categories are not necessarily equal or measurable. The categories have a meaningful sequence or hierarchy.
| Type of variable | What does the data represent? | Examples |
| Binary variables (dichotomous variables) | Presence or absence of a characteristic; two possible outcomes | – Gender (male or female) – Smoking status (smoker or non-smoker) – Pass/fail in an exam |
| Nominal variables | Distinct categories with no inherent order or ranking | – Eye color (blue, brown, green) – Car brands (Toyota, Ford, Honda) – Nationality (American, British, Chinese) |
| Ordinal variables | Categories with a natural order or ranking, but not necessarily equal intervals | – Educational level (high school, bachelor’s degree, master’s degree, Ph.D.) – Likert scale ratings (strongly disagree, disagree, neutral, agree, strongly agree) – Socioeconomic status (low, middle, high) |
Example data sheet
| Participant ID | Gender | Age | Height (cm) | Smoking Status | Education Level |
| 001 | Male | 25 | 175.3 | Non-smoker | Bachelor’s |
| 002 | Female | 32 | 162.8 | Smoker | Master’s |
| 003 | Male | 41 | 180.1 | Non-smoker | High School |
| … | … | … | … | … | … |
Parts of the experiment: Independent vs dependent variables
In experimental research, the primary goal is to investigate the impact of one variable on another. To conduct the experiment, you deliberately change the independent variable, also known as the presumed cause or predictor variable. Then, you observe and measure the dependent variable, also referred to as the presumed effect or outcome variable.
To ensure that your focus remains on the experimental treatment, you will likely maintain other variables at a constant level. These variables, known as control variables, are kept unchanged throughout the experiment. By controlling these variables, you can isolate the effect of the independent variable on the dependent variable and minimize the influence of other factors that could potentially confound the results.
In experimental research, variables are classified based on their role in the study design. The main types of variables in this context are independent, dependent, and control variables.
Independent vs dependent vs control variables
- Independent variables are the variables that are manipulated or changed by the researcher to observe their effect on the dependent variables. They are the presumed cause or predictor variables.
- Dependent variables are the variables that are measured or observed to determine the effect of the independent variables. They are the presumed effect or outcome variables.
- Control variables are variables that are kept constant throughout the experiment to minimize their influence on the dependent variables. By controlling these variables, researchers can isolate the effect of the independent variables on the dependent variables.
| Type of variable | Definition | Example (study time and exam scores) |
| Independent variables (treatment variables) | Variables that are manipulated or changed by the researcher to observe their effect on the dependent variables | – Amount of study time (e.g., 1 hour, 2 hours, 3 hours per day) |
| Dependent variables (response variables) | Variables that are measured or observed to determine the effect of the independent variables | – Exam scores (e.g., percentage correct, letter grade) |
| Control variables | Variables that are kept constant throughout the experiment to minimize their influence on the dependent variables | – Difficulty level of the exam – Subject matter covered in the exam – Testing environment (e.g., classroom, time of day) – Student’s prior knowledge of the subject – Student’s age, gender, or other demographic factors |
In this example, the researcher manipulates the amount of study time (independent variable) to investigate its effect on exam scores (dependent variable). The control variables are kept constant to isolate the relationship between study time and exam performance.
Example data sheet
| Treatment (Salt Concentration) | Plant ID | Initial Height (cm) | Final Height (cm) | Leaf Area (cm²) | Biomass (g) |
| 0 mM (Control) | 1 | 10.2 | 25.6 | 48.3 | 12.5 |
| 0 mM (Control) | 2 | 9.8 | 24.9 | 45.7 | 11.9 |
| 50 mM | 3 | 10.5 | 22.1 | 40.2 | 10.3 |
| 50 mM | 4 | 9.6 | 21.4 | 38.9 | 9.7 |
| 100 mM | 5 | 10.1 | 18.7 | 33.5 | 8.2 |
| 100 mM | 6 | 9.9 | 19.2 | 35.1 | 8.6 |
| … | … | … | … | … | … |
What about correlational research?
In correlational research, the goal is to examine the relationship between variables without manipulating them or establishing a cause-and-effect relationship. Therefore, the terms “dependent” and “independent” variables are not applicable in this context.
However, there are situations where one variable clearly precedes the other in time or logical sequence. In such cases, the variable that comes first is referred to as the predictor variable, while the variable that follows is called the outcome variable.
For instance, consider the relationship between rainfall and mud formation. In this example, rainfall occurs before the formation of mud, and it is logical to assume that rainfall contributes to the creation of muddy conditions. Therefore, rainfall would be considered the predictor variable, as it precedes and potentially influences the presence of mud. Consequently, mud would be the outcome variable, as it is the result or consequence of the rainfall.
Note: Although the predictor variable precedes the outcome variable, correlational research does not establish causality. The presence of a correlation between two variables does not necessarily mean that one variable directly causes the other.
Other common types of variables
After identifying your independent and dependent variables and classifying them as either categorical or quantitative, you will be well-equipped to select the appropriate statistical test for your research.
However, there are several other ways to describe variables that can aid in interpreting your research findings.
| Type of variable | Definition | Example |
| Confounding variables | Variables that are related to both the independent and dependent variables, potentially influencing the relationship between them | In a study examining the relationship between coffee consumption and heart disease, age might be a confounding variable because it is related to both coffee consumption and the risk of heart disease |
| Latent variables | Variables that cannot be directly observed or measured but are inferred from other observable variables | In a study of job satisfaction, latent variables such as work motivation or organizational commitment might be inferred from observable behaviors or responses to survey questions |
| Composite variables | Variables that are created by combining two or more individual variables to represent a more complex concept or construct | In a study of physical fitness, a composite variable called “overall fitness score” might be created by combining measures of cardiovascular endurance, muscular strength, flexibility, and body composition |