In research, variables are the characteristics or factors that can change or be manipulated. The two main types of variables are independent variables and dependent variables. They are manipulated to investigate cause-and-effect relationships.
The independent variable is the presumed cause, and its value is not influenced by other variables in the study. On the other hand, the dependent variable is the presumed effect, and its value changes in response to variations in the independent variable.
Example: Independent and dependent variables
Let’s consider a simple example to illustrate the concept of independent and dependent variables. Suppose you want to study the effect of fertilizer on plant growth. In this case:
The independent variable (the cause) is the amount of fertilizer applied to the plants.
The dependent variable (the effect) is the plant growth, which is measured by the height of the plants.
You would manipulate the independent variable by applying different amounts of fertilizer to different groups of plants and then measure the resulting plant growth (the dependent variable) to determine the effect of fertilizer on plant growth.
What is an independent variable?
An independent variable, also known as a predictor variable or explanatory variable, is a variable that is manipulated or changed by the researcher in an experimental study to observe its effect on the dependent variable. In other words, it is the presumed cause in a cause-and-effect relationship.
Types of independent variables
The 2 different types of independent variables are:
Experimental independent variables
When you conduct experiments, you directly manipulate the independent variables to observe their impact on your dependent variable. You typically apply the independent variable at different levels or conditions to determine how the outcomes vary.
In some cases, you may choose to apply only two levels of the independent variable. This approach allows you to determine whether the independent variable has any effect on your dependent variable at all. By comparing the results between the two levels, you can establish if there is a significant difference in the outcomes.
Example: Independent variable levels
In a study investigating the effect of a new medication on blood pressure, your independent variable could be the treatment condition with two levels: the medication group and the placebo group. By comparing the blood pressure measurements (your dependent variable) between these two groups, you can determine if the medication has any effect on blood pressure.
Alternatively, you may opt to apply multiple levels of the independent variable to gain a more comprehensive understanding of how it affects your dependent variable. By using several different levels or conditions, you can observe patterns, trends, or dose-response relationships.
In a true experiment, you randomly assign participants to different levels or conditions of the independent variable. This random assignment helps control for participant characteristics, ensuring they don’t influence the experimental results. As a result, you can be confident that any changes in the dependent variable are solely due to the manipulation of the independent variable.
Subject variables
Subject variables are participant characteristics that cannot be manipulated by researchers, such as gender identity, ethnicity, race, income, and education. These variables are treated as independent variables in social research. Since they are pre-existing characteristics, it’s not possible to randomly assign them to participants.
Instead, you can design a study that compares outcomes between groups of participants with different characteristics, known as a quasi-experimental design. Keep in mind that research methods using non-random assignment are susceptible to biases like selection bias and sampling bias.
Example: Quasi-experimental design
Suppose you want to investigate the relationship between income level and job satisfaction. In this case, income level is a subject variable that cannot be randomly assigned to participants. You can’t randomly assign people to different income levels because it is a pre-existing characteristic.
To conduct this study using a quasi-experimental design, you would:
- Identify groups of participants with different income levels (e.g., low, medium, and high income).
- Measure job satisfaction (the dependent variable) for each group using a survey or questionnaire.
- Compare job satisfaction scores between the different income groups to determine if there is a relationship between income level and job satisfaction.
In this quasi-experimental design, income level is treated as the independent variable, and job satisfaction is the dependent variable. However, because participants are not randomly assigned to income levels, there may be other factors (such as education, age, or occupation) that differ between the groups and could influence job satisfaction.
What is a dependent variable?
A dependent variable, also known as a response variable or outcome variable, is a variable that is measured or observed to determine the effect of the independent variable. It is the presumed effect in a cause-and-effect relationship. The dependent variable “depends” on the independent variable, meaning that its value or behavior is influenced by the independent variable.
Identifying independent vs. dependent variables
When trying to determine which variable is the independent variable and which is the dependent variable in a research study, there are a few key things to consider:
Recognizing independent variables
When designing a study, researchers have control over the independent variables and can decide on the different levels or conditions they want to investigate.
- Independent variables are the variables that are manipulated or changed by the researcher.
- They are the presumed cause in a cause-and-effect relationship.
- They are typically listed first in the research question or hypothesis.
Recognizing dependent variables
Researchers do not manipulate dependent variables; instead, they record and analyze any changes that occur as a result of manipulating the independent variable.
- Dependent variables are the variables that are measured or observed to determine the effect of the independent variable.
- They are the presumed effect in a cause-and-effect relationship.
- They are typically listed after the independent variable in the research question or hypothesis.
Independent and dependent variables in research
Independent and dependent variables are commonly used in experimental and quasi-experimental research designs. These types of research aim to investigate cause-and-effect relationships between variables by manipulating the independent variable and measuring the resulting changes in the dependent variable.
| Research question | Independent variable | Dependent variable(s) |
| How does the amount of sleep affect reaction time? | Amount of sleep | Reaction time |
| Does the type of music played in a store influence customer purchasing behavior? | Type of music played | Customer purchasing behavior |
| How does the level of education impact annual income? | Level of education | Annual income |
| What is the effect of different exercise routines on weight loss? | Type of exercise routine | Weight loss |
| Does the use of a specific study technique improve test scores? | Study technique used | Test scores |
| How does the amount of fertilizer applied affect plant growth? | Amount of fertilizer | Plant height, leaf size, biomass |
| Does the color of packaging influence consumer product perception? | Color of packaging | Consumer perception ratings |
| How does the duration of exposure to a learning task impact memory retention? | Exposure duration | Memory retention scores |
| What is the effect of different leadership styles on employee job satisfaction? | Leadership style | Employee job satisfaction ratings |
| Does the presence of distractions during a task affect task completion time? | Presence of distractions | Task completion time |
Visualizing independent and dependent variables
When presenting the findings of quantitative research, it’s recommended to use visual aids such as charts or graphs to effectively communicate the results. The standard convention is to place the independent variable on the x-axis (horizontal axis) and the dependent variable on the y-axis (vertical axis).
The choice of visualization depends on the types of variables involved in your research question:
- Bar charts: When your independent variable is categorical (nominal or ordinal), a bar chart is the most suitable choice. Bar charts allow you to compare the values of the dependent variable across different categories of the independent variable.
- Scatter plots or line graphs: When both your independent and dependent variables are quantitative (interval or ratio), a scatter plot or line graph is the best way to visualize the relationship between them.
Scatter plots display individual data points, with each point representing the values of the independent and dependent variables for a single observation.
Line graphs connect these points to emphasize patterns or trends over a continuous range of the independent variable.
Example: Results visualization
Research Question: How does the type of fertilizer affect the average plant height?
Independent Variable (Categorical): Type of Fertilizer (Fertilizer A, Fertilizer B, Fertilizer C)
Dependent Variable (Quantitative): Average Plant Height (cm)
