A business wants to determine if giving employees more control over how to do their work leads to increased job satisfaction. In an experiment, one group of workers is given a great deal of input in how they perform their work, while the other group is not. The amount of input the workers have over their work is the independent variable in this example. A researcher wants to determine if the color of an office has any effect on worker productivity. In an experiment, one group of workers performs a task in a yellow room while another performs the same task in a blue room. In this example, the type of information is the independent variable (because it changes), and the amount of information remembered is the dependent variable (because this is being measured).
Operationalizing Variables
If both groups had no significant difference in their recovery rates, that means the pill was not effective against cough.
Importance in Scientific Research
These variables are manipulated or controlled by the researcher to observe their effect on the dependent variable. Examples of controlled independent variables include the type of treatment or therapy given, the dosage of a medication, or the amount of exposure to a stimulus. Of the two, it is always the dependent variable whose variation is being studied, by altering inputs, also known as regressors in a statistical context.
Psychology Resources
In research, a variable is any characteristic, number, or quantity that can be measured or counted in experimental investigations. One is called the dependent variable, and the other is the independent variable. The beauty of independent variables lies in their ability to unlock new knowledge and insights, guiding us to discoveries that improve our lives and the world around us. By watching how changes in one thing (like the amount of rain) affect something else (like the height of grass), you can identify the independent variable.
Independent Variable Examples
Choosing the right statistical test (for example, ANOVA analysis) is crucial in any research. In this scenario, the variables are the treatments (i.e. the pill or the placebo) and the recovery rates of the patients. The treatment variable is the independent variable whereas the recovery rate variable is the dependent variable. In other cases, multiple levels of the IV may be used to look at the range of effects that the variable may have. The dependent variable, in both cases, is what is being observed or studied to see how it changes in response to the independent variable. In psychology, the independent variable is the variable the experimenter manipulates or changes and is assumed to directly affect the dependent variable.
- Below are the key differences when looking at an independent variable vs. dependent variable.
- This method is used to compare the means of two groups for a continuous dependent variable.
- In quantitative research, independent variables are usually measured numerically and manipulated to understand their impact on the dependent variable.
- An independent variable is defines as the variable that is changed or controlled in a scientific experiment.
- 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.
Reviewing Past Studies
The role of a variable as independent or dependent can vary depending on the research question and study design. If you think back to the last science class you took, you probably remember a lot of discussion surrounding variables. In fact, this concept is widespread and applied to many different areas of life, but it has the same fundamental meaning. The weather can be “variable”, meaning that it changes quite often, and the same can be said of personalities and moods. By introducing a new “variable” into a situation, such as inviting your new in-laws over for Christmas, you are expecting the outcome to be different than if they were not in attendance.
At the outset of an experiment, it is important for researchers to operationally define the independent variable. An operational definition describes exactly what the independent variable is and how it is measured. Doing this helps ensure that the experiments know exactly what they are looking at or manipulating, allowing them to measure it and determine if it is the IV that is causing changes in the DV.
This method is used to examine the relationship between a dependent variable and one or more independent variables. Linear regression is a common type of regression analysis that can be used to predict the value of the dependent variable based on the value of one or more independent variables. Examples of discrete independent variables include the number of siblings, the number of children in a family, and the number of pets owned. These variables are categorical or nominal in nature and represent a group or category. Examples of categorical independent variables include gender, ethnicity, marital status, and educational level.
In an experiment, any variable that can be attributed a value without attributing a value to any other variable is called an independent variable. Models and experiments test the effects that the independent variables have on the dependent variables. 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. By reviewing what scientists and researchers have done before, you can learn how they identified independent variables in their work. In experiments, even if measured time isn’t the variable, it may relate to duration or intensity. An independent variable is defines as the variable that is changed or controlled in a scientific experiment.
It allows scientists to explore relationships, unravel patterns, and unearth the secrets hidden within the fabric of our universe. There are of course other types of variables, and different ways to manipulate them called “schedules of reinforcement,” but we won’t get into that too much here. Once upon a time, in a world thirsty for understanding, people observed the stars, the seas, and everything in between, seeking to unlock the mysteries of the universe.
It represents the cause or reason for an outcome.Independent variables are the variables that the experimenter changes to test their dependent variable. A change in the independent variable directly causes a change in the dependent variable. Sometimes varying the independent variables will result in changes in the dependent variables. In other cases, researchers might find that changes in the independent variables have no effect on the variables that are being measured. As mentioned above, independent and dependent variables are the two key components of an experiment. Quite simply, the independent variable is the state, condition or experimental element that is controlled and manipulated by the experimenter.
ManipulationWhen researchers manipulate the independent variable, they are orchestrating a symphony of cause and effect. They’re adjusting the strings, the brass, the percussion, observing how each change influences the melody—the dependent variable. If the dependent and independent variables are plotted on a graph, the x-axis would be the independent variable and the y-axis would be the dependent variable. You can remember this using the DRY MIX acronym, where DRY means dependent or responsive variable is on the y-axis, while MIX means the manipulated or independent variable is on the x-axis.
These variables are continuous in nature and can take any value on a continuous scale. Examples of continuous independent variables include age, height, weight, temperature, and blood pressure. In an experiment on the effects of the type of diet on weight loss, for example, researchers might look at several different types of diet. Each type of diet that the experimenters look at would be a different level of the independent variable while weight loss would always be the dependent variable. For example, in an experiment looking at the effects of studying on test scores, studying would be the independent variable. Researchers are trying to determine if changes to the independent variable (studying) result in significant changes to the dependent variable (the test results).
Continuing with the given example, we may want to keep the age and weight ranges of the subjects from both groups (those taking the real pill and those taking the placebo) the same. The efficacy of a treatment may depend on the age and the weight of the patient taking the treatment. And so when the age and weight are kept the same for both groups, then, the experimenters can make valid conclusions that otherwise would lead to bias and false claims.
