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GuidesResearch methodsWhat is a dependent variable?

What is a dependent variable?

Last updated

4 March 2023

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Dovetail Editorial Team

Admit it. The mere mention of the term "dependent variables" evokes vague memories of your math and science classes back in high school. If you're a science buff, you likely enjoyed those classes a lot. 

Fast forward to today, and that knowledge could've come in handy—except you don't remember the nitty-gritty of it all. Fret not; we've got you covered.

At the heart of every scientific experiment lies the dependent variable, and we cannot overstate its importance in understanding cause-and-effect relationships. 

In this definitive guide, we'll look at dependent variables, how they differ from their independent counterparts, how to choose one, examples, and everything in between. 

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First things first, what is a variable?

A variable is an entity that can assume different values. In the simplest of terms, we can consider anything that can vary as a variable. 

For instance, height is a variable because we can assign a person's height a value. Other variables include income, age, country of birth, test scores, and so on.

What exactly is a dependent variable?

Now, back to our topic of the day. A dependent variable varies when other factors influence it. Specifically, it changes as a result of the independent variable's influence. 

In an experimental study, the dependent variable is typically the one you're interested in measuring or monitoring to determine whether or not other variables affect it. 

For example, the dependent variable is easy to pick out in an experiment examining the effects of sleeping on test results. Researchers are trying to determine if changes in the independent variable (sleeping) result in considerable changes to the dependent variable (the test results).

In statistics, dependent variables use a few other names, including:

  • Outcome variables because you observe and measure them by changing independent variables

  • Response variables because they respond to changes in other variables

  • Left-hand-side variables because they appear on the left side of the equals sign in a regression equation

  • Y-variables because 'Y' usually represents them on a graph

Is it possible to define dependent variables in the context of cause-and-effect relationships? Absolutely! That's precisely why this phenomenon exists in the first place. 

While the independent variable is the "cause," the dependent variable is the "effect"—the affected variable. 

Naturally, you're itching to learn the difference between dependent and independent variables. Luckily for you, that's next.

Dependent vs. independent variables: How are they different?

Let's first understand what an independent variable is. True to its name, an independent variable stands alone, and other variables don’t change or affect it. 

If the value of an independent variable changes at any time, that change happens at the researcher's discretion, not because of other variables. 

Typically, the researcher determines the independent variable. Its value is clear and well-known right at the beginning of the experiment, unlike the dependent variable. Those values only become clear after the experiment's conclusion.

Comprehending the difference between dependent and independent variables is vital for any research. Thankfully, getting it right the first time isn't difficult. 

The quickest way is to place both variables in the sentence below in a logical way:

"The IV causes changes to the DV. It is not possible that DV could cause any changes to IV."

Here's how that would reflect in our above example:

"Sleeping causes changes to test results. It is not possible that test results could cause any changes to sleeping."

When altering the independent variable during an experiment, your goal is to track and measure the changes it causes to dependent variables. Remember that changes in the dependent variable can only occur due to independent variable manipulation. 

To better understand the nuanced differences between dependent and independent variables, let's explore a few examples:

Example 1: What is the effect of green tea on blood pressure?

Independent variable: The amount of green tea consumed

Dependent variable: Blood pressure

Example 2: How does employee productivity affect business growth?

Independent variable: Hours spent doing productive work

Dependent variable: Business growth

Example 3: What is the impact of economic change on customer behavior?

Independent variable: Individual changes in the economy

Dependent variable: Customer behavior

On a broader level, here's what makes dependent and independent variables fundamentally different:

Dependent variables:

  • Depend on other variables

  • May change due to other variables

  • Are always the ones you’re measuring

Independent variables:

  • Stand on their own

  • Never change due to other variables

  • Undergo manipulation

How to choose a good dependent variable

Pinpointing a good dependent variable is more complex than it sounds. You're often contending with several above-par variables, leaving you spoilt for choice. Other times, the research context is way too complex and gives nothing away. 

Fortunately for you, we've formulated a set of questions to streamline your selection process.

How stable is the variable?

A dependent variable is only half as good as the stability and consistency of its output. A high-quality variable yields the same outcome irrespective of how often you repeat the experiment. 

To arrive at accurate conclusions, you must maintain the same conditions, experimental manipulations, and participants from start to finish.

How complex is your study?

Choosing a dependent variable without first considering the complexity of your study is a recipe for failure. Some studies require more than just a single variable of either type. 

You must do your due diligence early in the process to ensure your final results are accurate and conclusive. 

You might also have a situation where you want to find out how changes in one independent variable impact a couple of dependent variables. In that case, it's crucial to pinpoint all of them correctly from the get-go.

For instance, say you want to investigate how low employee morale affects productivity. 

Obviously, the dependent variable here is productivity, while low employee morale is the independent variable. Upon further scrutiny, you'll realize there's an opportunity to test for a few more dependent variables, including employee turnover and profitability. 

So, it all boils down to how complex you want your study to be.

Is it possible to operationalize the variable?

In research, operationalization refers to the ability to measure a variable. A dependent variable is only good enough if you can measure it easily, accurately, and without hiccups.

In measuring individual test results, you may use the standard error of measurement (SEm). 

If measuring blood pressure, you could use a digital blood pressure monitor. SEm will tell you how much the repeated measures of the same person on the same digital pressure monitor tend to be spread around the person’s “true” score.

Pitfalls to keep an eye on

We hate to break it to you, but dependent and independent variables aren't the only variables that may influence the outcome of your experiment. Several others can, too. 

Here are a few to be aware of:

Confounding variables

You can’t account for a confounding variable in a scientific experiment. It acts as an external force that can quickly change the effect of dependent and independent research variables, often yielding outcomes that differ completely from reality.

For example, a confounding variable may be responsible for the correlation between weight loss and weight loss. We’d expect that the more you exercise, the more likely you will lose weight.

However, a confounding variable may be eating habits: The more people eat, the more weight they gain, regardless of exercise.

It's best to account for confounding variables before your study starts to prevent them from wreaking havoc. Matching, restriction, and randomization are all reliable methods for keeping these wayward variables in check.

Extraneous variables

Sometimes, it's impossible to control a confounding variable. When that happens, it automatically becomes an extraneous variable.

One way to control extraneous variables is through elimination. Control by elimination means removing potential extraneous variables by holding them constant in all experimental conditions. Otherwise, you may draw inaccurate conclusions about the relationships between the independent and dependent variables.

Examples of Dependent Variables

We've already highlighted several tangible examples of dependent variables. For clarity's sake, let's go a step further.

Here are additional dependent variables examples you might find helpful.

In organizations

A business wants to find out how the color of the office decor affects worker productivity. 

In this case, worker productivity would be the dependent variable, and the color of the office would be the independent variable. The business could also alter the independent variable by instead evaluating how work hours or low morale influence worker productivity.

In the workplace

A researcher wants to determine if giving workers more control over their extra shifts leads to increased job satisfaction. 

In an experiment, one group of employees gets to pick up shifts freely and without restriction, while the other group enjoys little freedom. Job satisfaction is the dependent variable in this example.

In psychology research

A researcher intends to investigate the effects of alcohol on the brain. 

Here, the dependent variable could be the scores on the PHQ-9 assessment tool, which provisionally diagnoses depression. The independent variable might be the amount of alcohol a participant ingests.

Of course, dependent variable examples abound. We couldn't possibly exhaust all of them. But with the information and slew of examples in this piece, you should be well-positioned to make your next experiment a resounding success.

Final words

The role of dependent variables in shaping and grounding modern-day research experiments is undeniably important. 

Alongside independent variables, dependent variables make it easy for researchers and organizations to uncover the true impact of events. This speeds up the formulation of real and tangible solutions.

FAQs

What are the three types of variables?

An experimental study has three types of variables:

  • Independent variable

  • Dependent variable

  • Controlled variable

A dependent variable is the one a researcher tests to get its values. 

An independent variable is what the researcher changes to test the dependent variable. 

The variable that the scientist intentionally holds constant throughout the research is a controlled variable. While it may not be part of the experiment, it's important because it can affect the results.

Is the control group the same as the dependent variable?

No. The control group serves as the standard of comparison in a specific experiment. In other words, this group isn't part of the actual experiment.

The opposite of a control group is an experimental group.

Meanwhile, the dependent variable is the factor that may change as a result of independent variable manipulation.

How do you identify a dependent variable?

The quickest way to identify a dependent variable is to ask yourself these three questions:

  • Does it depend on another variable in the experiment?

  • Does it change due to other variables?

  • Is it the one you’re measuring?

If your answer to all these questions is yes, that's a dependent variable.

If not, reexamine the above criteria to see if it’s an independent variable instead.

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