What Is an Independent vs. Dependent Variable in Research?
By Indeed Editorial Team
Published November 12, 2022
The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.
Variables represent the different aspects of a research study, and they may be independent or dependent. Learning to identify the difference between a dependent and independent variable is essential to conducting and analyzing a controlled experiment. If you plan on a career in data analytics, statistics, or science, understanding variable management is an essential skill. In this article, we examine the difference between an independent vs. dependent variable, detail how they interact, and provide examples of these variables in ecological, marketing, academic, and psychological studies.
What is an independent vs. dependent variable?
The difference between an independent vs. dependent variable is how that they relate to the overall study. Variables either control, respond to, or are independent of, a research initiative. While a study can use dependent or independent analyses to gather as much data as possible, both variables are important because they can impact the results of a study. Though each of the of variables has significant value to researchers, they are inherently different.
A dependent variable changes as a direct result of changes to other parts of the study. From heat-increasing cell replication to drought-slowing plant growth, certain variable rely on the change in other variables. This shows their dependency. In effect, the result depends on changes within the study. An example of a dependent variable is crop yield, where it relies on the amount of sunshine and rain. The dependent variables in a study are easy to identify because they change as a result of another change.
Dependent variables have several names, depending on the field of study. When a variable alters due to another variable's change, it's usually called a response variable. Left-hand variables occur left from the centre during a regression analysis. The similar component is that a dependent variable is subject to chance.
An independent variable does not see an impact due to changes to the other variables within a study. Though an independent variable can change, according to the decision of the research scientist, its change doesn't rely on another variable. When a separate variable changes within the study, it doesn't change the independent variables. For example, with crop yield, the sunshine is an independent variable because the growth of the plants don't alter whether it's a sunny or rainy day.
There are two main types of independent variables. The first is experimental, where the researcher can directly manipulate the value of the variable. The second is subject variable, where the researcher can't manipulate the values, but they allow the analyst to categorize data.
Identifying independent and dependent variables
The differences between independent and dependent variables are easy to identify because they are separate aspects of a study, and most research projects include both. To assess the variable type, consider the following differences:
Consider whether you have the ability to control the variable in question. If you have the direct ability to manipulate a variable, then it is dependent. By relying on the researcher instead of depending on the other variables in the study, you can identify a dependent variable. Conversely, if the variable is outside of your control, then it's independent of the other aspects of the studies.
Change doesn't connote an independent or dependent variable. It can help you assess which type to classify it as. If a variable's value changes when another variable's value changes, then it's dependent. Because it relies on external change and responds to it, the variable is dependent. Conversely, if a variable changes without responding to a primary change, then you can assess it as an independent variable. This type can change, but it's the root cause of the change rather than a response to something else.
If you remain unsure as to how to classify a variable, you can perform a test on it to determine how to classify it. Simply adjust one of the variables in your control. Observe whether that causes a change to a different variable. If there is no variable change that this causes, the work is with independent variables. When change occurs to a secondary variable due to the change you make to the control, you can declare it as a dependent variable.
Examples of variables in research
The following examples represent the way that variables, independent and dependent, relate within research studies. You can use these as a point of reference when determining the type of variables in the calculation:
Ecological research study
Here's an example of independent and dependent variables for an ecological research study:
To commence a housing development in a new area, scientists perform studies on the land to determine the impact of change to the environment. It considers the quality of the air, water, and the wildlife in the area. To perform this study, the control is the housing development that already occurred in a similar area. It determines that the quality of the water and air, and the quantity of wildlife in the area are dependent variables.
The independent variable is the development itself because the wildlife, water, and air can change and it doesn't affect the process. To test this, the scientist measures the amount of oxygen, pollutants, and wildlife in the area before development. It then compares those values to the calculations at the site that the company already developed. Using this information, it determines that the development, an independent variable, is likely to cause changes to the dependent variables.
After the development finishes, the scientist returns to test whether the independent variable caused the predicted changes. The results are in line with the previous development's results, with small changes to the air, water, and wildlife in the area. It establishes the independent variable because the change in the environment didn't alter the building. The dependent variables all induced changes.
Related: How to Become an Ecologist
Marketing research study
Here's an example of independent and dependent variables for a market research study:
Registered Marketing is an agency that aims to improve the effect of its marketing intentions. To assess its success, the company wants to determine the effectiveness of a campaign with rural and urban demographics. The company has this information from its client lists, and can classify each of its customers as either city- or town-dwelling.
Registered marketing assesses the data and determines that over 70% of its successful marketing responses come from rural areas. Using this information, the company begins to target more rural audiences. It focuses the content of the marketing accordingly to appeal to the other demographics of the rural market. In this situation, the location of the customers is an independent variable because it's no indication of whether the client responds to the marketing material. The rate of response for each urban and rural settings are dependent variables because they rely on the appeal of the marketing.
Psychological research study
Here's an example of independent and dependent variables for a psychological research study:
A clinical psychologist performs an assessment on general life satisfaction and the hours an individual works during a week. While the study acknowledges the external factors that can impact attitude, such as wage, job responsibilities, and others, this specific analysis measures the time related to life satisfaction. To assess this, the psychologist issues a survey to a thousand working individuals. It asks the participant to confirm the number of hours they work in a week. It also asks for a self-assessment of life satisfaction using a scale between one and five.
The study reveals a non-linear data pattern, where ascending hours of work proportionately increase with life satisfaction, until the time reaches 40 hours per week. After this, there is a diminishing return on life satisfaction. In this situation, the independent variable is the hours an individual works because these remain constant regardless of satisfaction. Conversely, the life satisfaction is a dependent variable that changes according to the amount of time an individual works per week. This clear cause and effect between the dependent and independent variables can help employers and human resources managers make scheduling decisions.
Academic research study
Here's an example of independent and dependent variables for an academic study:
Learning for Life is a company that plans to conduct a study on the proportion of rest relative to test performance. To do this, it surveys the students at a local university. It inquires as to the numbers of hours spent studying, the amount of time spent resting, and the resulting grades for the class. The researchers compile the data and find a direct correlation between the amount of rest and the grades a student earns. There is also a direct correlation with the amount of study time.
Both have positive interactions with the academic results, meaning the test score is a dependent variable of rest and study. As nothing impacts either the rest or the hours of study, these two are independent variables. Researchers can use this information to optimize the study and rest time suggested to students. It allows those who create the curriculum schedule to optimize the grades in their classes according to how much rest opportunity and study materials they provide.
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