So, regardless of the type of data, researchers analyze the relationship between independent and dependent variables to gain insights into their research questions. In some studies, researchers may want to explore how multiple factors affect the outcome, so they include more than one independent variable. It’s considered the cause or factor that drives change, allowing psychologists to observe how it influences behavior, emotions, or other dependent variables in an experimental setting. Essentially, it’s the presumed cause in cause-and-effect relationships being studied. It is used when both the independent and dependent variables are categorical.

Everyday Examples

In quantitative research, independent variables are usually measured numerically and manipulated to understand their impact on the dependent variable. In qualitative research, independent variables can be qualitative in nature, such as individual experiences, cultural factors, or social contexts, influencing the phenomenon of interest. Researchers conduct experiments to understand the cause-and-effect relationships between various entities. In such experiments, the entities whose values change are called variables. These variables describe the relationships among various factors and help in drawing conclusions in experiments.

Continuous dependent variables

The business could also alter the independent variable by instead evaluating how work hours or low morale influence worker productivity. Here are additional dependent variables examples you might find helpful. We’ve already highlighted several tangible examples of dependent variables.

What Is an Independent Variable?

Operationalization has the advantage of generally providing a clear and objective definition of even complex variables. It also makes it easier for other researchers to replicate a study and check for reliability. In a study that observes the impact of income level on consumer habits and spending, “income” is the predictor variable.

Independent and Dependent Variables

  1. In the simplest terms, an independent variable is the cause, and the dependent variable is the effect.
  2. If you’re conducting a study like this, you must ask which variable affects the other variables.
  3. Here, the dependent variable could be the scores on the PHQ-9 assessment tool, which provisionally diagnoses depression.
  4. The independent variable is usually applied at different levels to see how the outcomes differ.
  5. It’s called ‘independent’ because it’s not influenced by any other variables in the study.

The stories of dependent variables continue to unfold, and the adventure of learning and discovery is boundless. As we conclude our exploration of dependent variables, we leave with a sense of wonder and curiosity, equipped with the knowledge to observe, question, and explore the world around us. In the dance of variables, the dependent variable is the one that responds. When something is tweaked, adjusted, or altered (that’s the independent variable), the dependent variable is what shows the effect of those changes.

So, if the experiment is trying to see how one variable affects another, the variable that is being affected is the dependent variable. The dependent variable is called “dependent” because it is thought to depend, in some way, on the variations of the independent variable. However, in a different study, that same variable might be the one being measured or observed to understand its relationship with another variable, making it dependent. The classification of a variable as independent or dependent depends on how it is used within a specific study.

A dog food company promises that their food will increase dental health. They look at the dog food that their patients eat and document their dental health over a few years, only to find no evidence to support the brand’s claim. The dog food is the variable that is easy to manipulate, so it’s the independent variable.

When you take data in an experiment, the dependent variable is the one being measured. For example, it’s common for treatment-based studies to have some subjects receive a certain treatment while others receive no treatment at all (often called a sham or placebo treatment). In this case, the treatment is an independent variable because it is the one being manipulated or changed. The independent variable is “independent” because the experimenters are free to vary it as they need.

Of course, these types of studies often have more than one independent variable. It’s important to account for other factors that can influence your dependent variable. Although they serve the same function in any type of study, independent variables can look different depending on whether you’re conducting an experiment or surveying and observing.

The independent variable is usually placed on the X-axis and the dependent variable on the Y-axis. The role of dependent variables in shaping and grounding modern-day research experiments is undeniably important. If the value of an independent variable changes at any time, that change happens at the researcher’s discretion, not because of other variables. True to its name, an independent variable stands alone, and other variables don’t change or affect it. For example, the dependent variable is easy to pick out in an experiment examining the effects of sleeping on test results.

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

In a well-designed experimental study, the independent variable is the only important difference between the experimental (e.g., treatment) and control (e.g., placebo) groups. In research, the independent variable is manipulated to observe its effect, while the dependent annualized salary variable is the measured outcome. Essentially, the independent variable is the presumed cause, and the dependent variable is the observed effect. Independent variables can also be differentiated into broad categories such as qualitative and quantitative variables.

Krystal lives in Dallas, Texas with her husband, child, and basset hound. This doesn’t really make sense (unless you can’t sleep because you are worried you failed a test, but that would be a different experiment). You’ll often use t tests or ANOVAs to analyse your data and answer your research questions.

Some examples of variables include age, gender, race, income, weight, etc. These famous studies and experiments spotlight the pivotal role of dependent variables in scientific discovery. They illustrate how observing and measuring dependent variables have expanded our knowledge, led to breakthroughs, and addressed fundamental questions about the natural and social world. For example, you might be curious if a person’s level of education affects their health later in life.