Changing (independent variable) affects the value of (dependent variable). For example, a researcher might change the amount of water they provide to a certain plant to observe how it affects the growth rate of the plant. The independent variable (\(x\)) is the number of hours Ethan what is bank reconciliation definition, examples, and process works each visit. The dependent variable (\(y\)) is the amount, in dollars, Ethan earns for each visit. The independent variable (\(x\)) is the number of hours Svetlana tutors each session. The dependent variable (\(y\)) is the amount, in dollars, Svetlana earns for each session.

• You can apply just two levels in order to find out if an independent variable has an effect at all.
• In experiments, you manipulate independent variables directly to see how they affect your dependent variable.
• A change in the independent variable directly causes a change in the dependent variable.
• Yes, but including more than one of either type requires multiple research questions.

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. X is independent because of the x axis and y axis on a
coordinate plane. You need to find the marginal densities and show that the joint is the product of the marginals in order to show that they are independent. This website is using a security service to protect itself from online attacks.

Sometimes, even if their influence is not of direct interest, independent variables may be included for other reasons, such as to account for their potential confounding effect. The independent and dependent variables may be viewed in terms of cause and effect. If the independent variable is changed, then an effect is seen in the dependent variable. Remember, the values of both variables may change in an experiment and are recorded.

Hence, here the height of the boy is shown on the y-axis, whereas x-axis indicates the age. The dependence of the former on the latter is being examined by the statistical models. So, here in this article, we are going to discuss some important points of difference between independent and dependent variable. Thus, we know that we must have the independent and dependent variables switched around. In an experiment, an experimenter is interested in seeing how the dependent variable changes as a result of the independent being changed or manipulated in some way.

If you have the variables in the wrong order, the sentence won’t make sense. The independent and dependent variables are the two key variables in a science experiment. You have to be the one to change the popcorn and fertilizer brands in Experiments 1 and 2, and the ocean temperature in Experiment 3 cannot be significantly changed by other factors. Changes to each of these independent variables cause the dependent variables to change in the experiments. The independent variable (sometimes known as the manipulated variable) is the variable whose change isn’t affected by any other variable in the experiment. Either the scientist has to change the independent variable herself or it changes on its own; nothing else in the experiment affects or changes it.

## Independent vs. Dependent Variables Definition & Examples

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. The target variable is used in supervised learning algorithms but not in unsupervised learning.

You can also think of the independent variable as the cause and the dependent variable as the effect. You can use this typical form to determine the independent and dependent variables from the title of the study. X is the independent variable and Y is the dependent variable – the outcome, and Z is the type of subjects represented. An example of a dependent variable is how tall you are at different ages. The dependent variable (height) depends on the independent variable (age).

Generally, the independent variable goes on the x-axis (horizontal) and the dependent variable on the y-axis (vertical). A true experiment requires you to randomly assign different levels of an independent variable to your participants. You can also apply multiple levels to find out how the independent variable affects the dependent variable. For example, a scientist wants to see if the brightness of light has any effect on a moth being attracted to the light. How the moth reacts to the different light levels (distance to light source) would be the dependent variable. As the experimenter changes the independent variable, the effect on the dependent variable is observed and recorded.

Observational and some quasi-experimental studies lack active interventions – their independent variables are not specifically imposed by the investigators. While these studies cannot tell us whether one variable causes changes, they can tell us how strong a relationship exists between variables. Some non-experimental studies also have independent variables, but they may not be determined or manipulated by the investigators. The convention is to use the independent variable as the x-axis and the dependent variable as the y-axis.

Christine graduated from Michigan State University with degrees in Environmental Biology and Geography and received her Master’s from Duke University. In high school she scored in the 99th percentile on the SAT and was named a National Merit Finalist. Yes, but including more than one of either type requires multiple research questions. You’ll often use t tests or ANOVAs to analyze your data and answer your research questions. Compare your paper to billions of pages and articles with Scribbr’s Turnitin-powered plagiarism checker. While one group gets the placebo, the other group gets the medication that is intended to have therapeutic value.

## Independent vs Dependent Variable Key Takeaways

Equations, some functions, it is the case that several variables can be used as the independent variable. If you’re serious about the question, eventually you’ll want to learn the implicit function theorem which sorts out this question in considerable generality. To ensure the internal validity of an experiment, you should only change one independent variable at a time. 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. In quantitative research, it’s good practice to use charts or graphs to visualize the results of studies.

## Where Do You Put Independent and Dependent Variables on Graphs?

On the other hand, the value of a dependent variable is determined by some input, or independent variable. Dependent variables therefore represent the output value of a function, and are commonly denoted as y, or f(x). For example, a study may compare test performance between men and women; so gender would be the independent variable. However, since investigators didn’t determine or specify which individuals would be men and which would be women (!), it is not considered to be an active independent variable.

Both the independent variable and dependent variable are examined in an experiment using the scientific method, so it’s important to know what they are and how to use them. Here are the definitions for independent and dependent variables, examples of each variable, and the explanation for how to graph them. If you didn’t have any constant variables, you wouldn’t be able to tell if the independent variable was what was really affecting the dependent variable.

## Formula Review

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Independent and dependent variables are generally used in experimental and quasi-experimental research. A dependent variable from one study can be the independent variable in another study, so it’s important to pay attention to research design. 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. 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.