What is a predictive statement that relates an independent variable to a dependent variable?

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Answered By: APUS Librarians Last Updated: Apr 15, 2022 Views: 306173
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What is an independent variable?
Types of independent variables
Experimental variables
Subject variables
What is a dependent variable?
Identifying independent vs. dependent variables
Recognizing independent variables
Recognizing dependent variables
Independent and dependent variables in research
Visualizing independent and dependent variables
Frequently asked questions about independent and dependent variables

A hypothesis is an educated guess or prediction of what will happen. In science, a hypothesis proposes a relationship between factors called variables. A good hypothesis relates an independent variable and a dependent variable. The effect on the dependent variable depends on or is determined by what happens when you change the independent variable. While you could consider any prediction of an outcome to be a type of hypothesis, a good hypothesis is one you can test using the scientific method. In other words, you want to propose a hypothesis to use as the basis for an experiment.

A good experimental hypothesis can be written as an if, then statement to establish cause and effect on the variables. If you make a change to the independent variable, then the dependent variable will respond. Here's an example of a hypothesis:

If you increase the duration of light, (then) corn plants will grow more each day.

The hypothesis establishes two variables, length of light exposure, and the rate of plant growth. An experiment could be designed to test whether the rate of growth depends on the duration of light. The duration of light is the independent variable, which you can control in an experiment. The rate of plant growth is the dependent variable, which you can measure and record as data in an experiment.

When you have an idea for a hypothesis, it may help to write it out in several different ways. Review your choices and select a hypothesis that accurately describes what you are testing.

Does the hypothesis relate an independent and dependent variable? Can you identify the variables?
Can you test the hypothesis? In other words, could you design an experiment that would allow you to establish or disprove a relationship between the variables?
Would your experiment be safe and ethical?
Is there a simpler or more precise way to state the hypothesis? If so, rewrite it.

It's not wrong or bad if the hypothesis is not supported or is incorrect. Actually, this outcome may tell you more about a relationship between the variables than if the hypothesis is supported. You may intentionally write your hypothesis as a null hypothesis or no-difference hypothesis to establish a relationship between the variables.

For example, the hypothesis:

The rate of corn plant growth does not depend on the duration of light.

This can be tested by exposing corn plants to different length "days" and measuring the rate of plant growth. A statistical test can be applied to measure how well the data support the hypothesis. If the hypothesis is not supported, then you have evidence of a relationship between the variables. It's easier to establish cause and effect by testing whether "no effect" is found. Alternatively, if the null hypothesis is supported, then you have shown the variables are not related. Either way, your experiment is a success.

Need more examples of how to write a hypothesis? Here you go:

If you turn out all the lights, you will fall asleep faster. (Think: How would you test it?)
If you drop different objects, they will fall at the same rate.
If you eat only fast food, then you will gain weight.
If you use cruise control, then your car will get better gas mileage.
If you apply a top coat, then your manicure will last longer.
If you turn the lights on and off rapidly, then the bulb will burn out faster.

Answered By: APUS Librarians Last Updated: Apr 15, 2022 Views: 306173

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In research, variables are any characteristics that can take on different values, such as height, age, temperature, or test scores.

Researchers often manipulate or measure independent and dependent variables in studies to test cause-and-effect relationships.

The independent variable is the cause. Its value is independent of other variables in your study.
The dependent variable is the effect. Its value depends on changes in the independent variable.

Example: Independent and dependent variablesYou design a study to test whether changes in room temperature have an effect on math test scores.

Your independent variable is the temperature of the room. You vary the room temperature by making it cooler for half the participants, and warmer for the other half.

Your dependent variable is math test scores. You measure the math skills of all participants using a standardized test and check whether they differ based on room temperature.

What is an independent variable?

An independent variable is the variable you manipulate or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

Independent variables are also called:

Explanatory variables (they explain an event or outcome)
Predictor variables (they can be used to predict the value of a dependent variable)
Right-hand-side variables (they appear on the right-hand side of a regression equation).

These terms are especially used in statistics, where you estimate the extent to which an independent variable change can explain or predict changes in the dependent variable.

Types of independent variables

There are two main types of independent variables.

Experimental independent variables can be directly manipulated by researchers.
Subject variables cannot be manipulated by researchers, but they can be used to group research subjects categorically.

Experimental variables

In experiments, you manipulate independent variables directly to see how they affect your dependent variable. The independent variable is usually applied at different levels to see how the outcomes differ.

You can apply just two levels in order to find out if an independent variable has an effect at all.

You can also apply multiple levels to find out how the independent variable affects the dependent variable.

Example: Independent variable levelsYou are studying the impact of a new medication on the blood pressure of patients with hypertension. Your independent variable is the treatment that you directly vary between groups.

You have three independent variable levels, and each group gets a different level of treatment.

You randomly assign your patients to one of the three groups:

A low-dose experimental group
A high-dose experimental group
A placebo group

A true experiment requires you to randomly assign different levels of an independent variable to your participants.

Random assignment helps you control participant characteristics, so that they don’t affect your experimental results. This helps you to have confidence that your dependent variable results come solely from the independent variable manipulation.

Subject variables

Subject variables are characteristics that vary across participants, and they can’t be manipulated by researchers. For example, gender identity, ethnicity, race, income, and education are all important subject variables that social researchers treat as independent variables.

It’s not possible to randomly assign these to participants, since these are characteristics of already existing groups. Instead, you can create a research design where you compare the outcomes of groups of participants with characteristics. This is a quasi-experimental design because there’s no random assignment. Note that any research methods that use non-random assignment are at risk for research biases like selection bias and sampling bias.

Example: Quasi-experimental designYou study whether gender identity affects neural responses to infant cries.

Your independent variable is a subject variable, namely the gender identity of the participants. You have three groups: men, women and other.

Your dependent variable is the brain activity response to hearing infant cries. You record brain activity with fMRI scans when participants hear infant cries without their awareness.

After collecting data, you check for statistically significant differences between the groups. You find some and conclude that gender identity influences brain responses to infant cries.

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What is a dependent variable?

A dependent variable is the variable that changes as a result of the independent variable manipulation. It’s the outcome you’re interested in measuring, and it “depends” on your independent variable.

In statistics, dependent variables are also called:

Response variables (they respond to a change in another variable)
Outcome variables (they represent the outcome you want to measure)
Left-hand-side variables (they appear on the left-hand side of a regression equation)

The dependent variable is what you record after you’ve manipulated the independent variable. You use this measurement data to check whether and to what extent your independent variable influences the dependent variable by conducting statistical analyses.

Based on your findings, you can estimate the degree to which your independent variable variation drives changes in your dependent variable. You can also predict how much your dependent variable will change as a result of variation in the independent variable.

Identifying independent vs. dependent variables

Distinguishing between independent and dependent variables can be tricky when designing a complex study or reading an academic paper.

A dependent variable from one study can be the independent variable in another study, so it’s important to pay attention to research design.

Here are some tips for identifying each variable type.

Recognizing independent variables

Use this list of questions to check whether you’re dealing with an independent variable:

Is the variable manipulated, controlled, or used as a subject grouping method by the researcher?
Does this variable come before the other variable in time?
Is the researcher trying to understand whether or how this variable affects another variable?

Recognizing dependent variables

Check whether you’re dealing with a dependent variable:

Is this variable measured as an outcome of the study?
Is this variable dependent on another variable in the study?
Does this variable get measured only after other variables are altered?

Independent and dependent variables in research

Independent and dependent variables are generally used in experimental and quasi-experimental research.

Here are some examples of research questions and corresponding independent and dependent variables.

Research question	Independent variable	Dependent variable(s)
Do tomatoes grow fastest under fluorescent, incandescent, or natural light?	Type of light the tomato plant is grown under	The rate of growth of the tomato plant
What is the effect of intermittent fasting on blood sugar levels?	Presence or absence of intermittent fasting
Is medical marijuana effective for pain reduction in people with chronic pain?	Presence or absence of medical marijuana use	Frequency of pain Intensity of pain
To what extent does remote working increase job satisfaction?	Type of work environment (remote or in office)	Job satisfaction self-reports

For experimental data, you analyze your results by generating descriptive statistics and visualizing your findings. Then, you select an appropriate statistical test to test your hypothesis.

The type of test is determined by:

You’ll often use t tests or ANOVAs to analyze your data and answer your research questions.

Visualizing independent and dependent variables

In quantitative research, it’s good practice to use charts or graphs to visualize the results of studies. Generally, the independent variable goes on the x-axis (horizontal) and the dependent variable on the y-axis (vertical).

The type of visualization you use depends on the variable types in your research questions:

A bar chart is ideal when you have a categorical independent variable.
A scatter plot or line graph is best when your independent and dependent variables are both quantitative.

Example: Results visualizationYou collect data on blood pressure before and after treatment for all participants over a period of 2 months.

To inspect your data, you place your independent variable of treatment level on the x-axis and the dependent variable of blood pressure on the y-axis.

You plot bars for each treatment group before and after the treatment to show the difference in blood pressure.

Based on your results, you note that the placebo and low-dose groups show little difference in blood pressure, while the high-dose group sees substantial improvements.

Frequently asked questions about independent and dependent variables

What’s the definition of an independent variable?

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

Independent variables are also called:

Explanatory variables (they explain an event or outcome)
Predictor variables (they can be used to predict the value of a dependent variable)
Right-hand-side variables (they appear on the right-hand side of a regression equation).

What’s the definition of a dependent variable?

A dependent variable is what changes as a result of the independent variable manipulation in experiments. It’s what you’re interested in measuring, and it “depends” on your independent variable.

In statistics, dependent variables are also called:

Response variables (they respond to a change in another variable)
Outcome variables (they represent the outcome you want to measure)
Left-hand-side variables (they appear on the left-hand side of a regression equation)

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Bhandari, P. (2022, November 25). Independent vs. Dependent Variables | Definition & Examples. Scribbr. Retrieved December 2, 2022, from https://www.scribbr.com/methodology/independent-and-dependent-variables/