A fundamental component in statistical investigations is the methodology you employ in selecting your research variables. The careful selection of appropriate variable types can significantly enhance the robustness of your experimental design. This piece explores the diverse array of variable classifications within the field of statistical research. Additionally, understanding the different types of variables in research can greatly aid in shaping your experimental hypotheses and outcomes.
Inhaltsverzeichnis
Types of Variables in Research – In a Nutshell
- A variable is an attribute of an item of analysis in research.
- The types of variables in research can be categorized into: independent vs. dependent, or categorical vs. quantitative.
- The types of variables in research (correlational) can be classified into predictor or outcome variables.
- Other types of variables in research are confounding variables, latent variables, and composite variables.
Definition: Types of variables in research
A variable is a trait of an item of analysis in research. Types of variables in research are imperative, as they describe and measure places, people, ideas, or other research objects. There are many types of variables in research. Therefore, you must choose the right types of variables in research for your study.
Note that the correct variable will help with your research design, test selection, and result interpretation.
Example
In a study testing whether some genders are more stress-tolerant than others, variables you can include are the level of stressors in the study setting, male and female subjects, and productivity levels in the presence of stressors.
Also, before choosing which types of variables in research to use, you should know how the various types work and the ideal statistical tests and result interpretations you will use for your study. The key is to determine the type of data the variable contains and the part of the experiment the variable represents.
Types of variables in research – Quantitative vs. Categorical
Data is the precise extent of a variable in statistical research that you record in a data sheet. It is generally divided into quantitative and categorical classes.
Quantitative or numerical data represents amounts, while categorical data represents collections or groupings.
The type of data contained in your variable will determine the types of variables in research. For instance, variables consisting of quantitative data are called quantitative variables, while those containing categorical data are called categorical variables. The section below explains these two types of variables in research better.
Quantitative variables
The scores you record when collecting quantitative data usually represent real values you can add, divide, subtract, or multiply. There are two types of quantitative variables: discrete variables and continuous variables.
The table below explains the elements that set apart discrete and continuous types of variables in research:
| Quantitative variable types | The data they represent | Examples |
| Discrete or integer variables | Individual item counts or values | • Number of employees in a company • Number of students in a school district |
| Continuous or ratio variables | Measurements of non-finite or continuous scores | • Age • Weight • Volume • Distance |
Categorical variables
Categorical variables contain data representing groupings. Additionally, the data in categorical variables is sometimes recorded as numbers. However, the numbers represent categories instead of real amounts.
There are three categorical types of variables in research: nominal variables, ordinal variables, and binary variables. Here is a tabular summary.
| Type of categorical variable | What its data represents | Examples |
| Binary/dichotomous variables | YES/NO outcomes | • Win/lose in a game • Pass/fail in an exam |
| Nominal variables | No-rank groups or orders between groups | • Colors • Participant name • Brand names |
| Ordinal variables | Groups ranked in a particular order | • Performance rankings in an exam • Rating scales of survey responses |
It is worth mentioning that some categorical variables can function as multiple types. For example, in some studies, you can use ordinal variables as quantitative variables if the scales are numerical and not discrete.
Data sheet of quantitative and categorical variables
A data sheet is where you record the data on the variables in your experiment.
Example
In a study of the salt-tolerance levels of various plant species, you can record the data on salt addition and how the plant responds in your datasheet.
The key is to gather the information and draw a conclusion over a specific period and filling out a data sheet along the process.
Below is an example of a data sheet containing binary, nominal, continuous, and ordinal types of variables in research.
| Plant species | Starting height (cm) | (INDEPENDENT VARIABLE) Amount of salt added (mg/L) | (DEPENDENT VARIABLE) Height increase (Current – starting height in cm) | (DEPENDENT VARIABLE) Wilting rate (0-5 rate) | (DEPENDENT VARIABLE) Outcome (0 for dead and 1 for survived) |
| A | 12 | 0 | - | - | - |
| A | 18 | 50 | - | - | - |
| B | 11 | 0 | - | - | - |
| B | 15 | 50 | - | - | - |
| C | 25 | 0 | - | - | - |
| C | 31 | 50 | - | - | - |
Types of variables in research – Independent vs. Dependent
The purpose of experiments is to determine how the variables affect each other. As stated in our experiment above, the study aims to find out how the quantity of salt introduce in the water affects the plant’s growth and survival.
Therefore, the researcher manipulates the independent variables and measures the dependent variables. Additionally, you may have control variables that you hold constant.
The table below summarizes independent variables, dependent variables, and control variables.
| Types of variables in research | Explanation | Example (based on our example) |
| Independent/ treatment variables | The variables you manipulate to affect the experiment outcome | The amount of salt added to the water |
| Dependent/ response variables | The variable that represents the experiment outcomes | The plant’s growth or survival |
| Control variables | Variables held constant throughout the study | Temperature or light in the experiment room |
Data sheet of independent and dependent variables
Example
In salt-tolerance research, there is one independent variable (salt amount) and three independent variables. All other variables are neither dependent nor independent.
Below is a data sheet based on our experiment:
| Plant species | Starting height (cm) | (INDEPENDENT VARIABLE) Amount of salt added (mg/L) | (DEPENDENT VARIABLE) Height increase (Current – starting height in cm) | (DEPENDENT VARIABLE) Wilting rate (0-5 rate) | (DEPENDENT VARIABLE) Outcome (0 for dead and 1 for survived) |
| A | 12 | 0 | - | - | - |
| A | 18 | 50 | - | - | - |
| B | 11 | 0 | - | - | - |
| B | 15 | 50 | - | - | - |
| C | 25 | 0 | - | - | - |
| C | 31 | 50 | - | - | - |
Types of variables in correlational research
The types of variables in research may differ depending on the study.
Example
In correlational research, dependent and independent variables do not apply because the study objective is not to determine the cause-and-effect link between variables.
However, in correlational research, one variable may precede the other, as illness leads to death, and not vice versa. In such an instance, the preceding variable, like illness, is the predictor variable, while the other one is the outcome variable.
Other useful types of variables in research
The key to conducting effective research is to define your types of variables as independent and dependent. Next, you must determine if they are categorical or numerical types of variables in research so you can choose the proper statistical tests for your study.
Below are other types of variables in research worth understanding.
| Types of variables in research | Explanation | Example (as per our example) |
| Confounding variables | Hides the actual impact of an alternative variable in your study | Pot size and soil type |
| Latent variables | Cannot be measured directly | Salt tolerance |
| Composite variables | Formed by combining multiple variables | The health variables combined into a single health score1 |
FAQs
An autonomous or independent variable is the one you believe is the origin of the outcome, while the dependent variable is the one you believe affects the outcome of your study.
Knowing the types of variables in research that you can work with will help you choose the best statistical tests and result representation techniques. It will also help you with your study design.
Discrete variables are types of variables in research that represent counts, like the quantities of objects. In contrast, continuous variables are types of variables in research that represent measurable quantities like age, volume, and weight.