Educators are interested in whether participating in after-school math tutoring can increase scores on standardized math exams. In an experiment, one group of students attends an after-school tutoring session twice a week while another group of students does not receive this additional assistance. In this case, participation in after-school math tutoring is the independent variable. A researcher wants what is the difference between public companies and public sector 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 a scientific experiment, you’ll ultimately be changing or controlling the independent variable and measuring the effect on the dependent variable. In research, the independent variable is manipulated to observe its effect, while the dependent variable is the measured outcome. Essentially, the independent variable is the presumed cause, and the dependent variable is the observed effect.
- (Ref. 4) In this case, the type of treatment is the independent variable and the amount of blood glucose is the variable that depends on the type of treatment.
- Also, controlling the extraneous variables in an experiment is important to come up with more precise conclusions based on the empirical data.
- Below are the key differences when looking at an independent variable vs. dependent variable.
Can there be more than one independent or dependent variable in a study?
Researchers want to determine if a new type of treatment will lead to a reduction in anxiety for patients living with social phobia. In an experiment, some volunteers receive the new treatment, another group receives a different treatment, and a third group receives no treatment. 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.
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Any information here should not be considered absolutely correct, complete, and up-to-date. Views expressed here do not necessarily reflect those of Biology Online, its staff, or its partners. By Kendra Cherry, MSEdKendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the « Everything Psychology Book. » Ethical guidelines help ensure that research is conducted responsibly and with respect for the well-being of the participants involved.
Overview: Variables In Research
The key point here is that we have clarified what we mean by the terms as they were studied and measured in our experiment. A one-way ANOVA example is when you want to test if there is a significant difference in crop yields between the three different fertilizer mixtures on the crop fields. A two-way ANOVA example is when apart from the fertilizer mixture you also want to determine if the crop yield will also vary significantly between different strains. If both groups had no significant difference in their recovery rates, that means the pill was not effective against cough. If the patients who were taking the real drug were able to recover significantly faster than the patients taking the placebo, that means the pill was effective in treating cough.
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. These variables are discrete in nature and can only take on specific values. 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.
To preclude the “placebo” effect — wherein the patient apparently feels better after taking the placebo pill, the patients were not informed if the pill they were taking was real or the placebo. Then, the recovery rates of both groups (i.e. the patients taking the placebo and those taking the real pill) were monitored. 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).
The independent variable is the presumed cause in an experiment or study, while the dependent variable is the presumed effect or outcome. The relationship between the independent variable and the dependent variable is often analyzed using statistical methods to determine the strength and direction of the relationship. An independent variable is a variable in a functional relation wherein the value is not affected by other variables.
Independent Variables vs Other Variables
It can be used to test the effect of a binary independent variable on a continuous dependent variable. This method is used to compare the means of two or more groups for a continuous dependent variable. ANOVA can be used to test the effect of a categorical independent variable on a continuous dependent variable. While the independent variable is the “cause”, the dependent variable is the “effect” – or rather, the affected variable. In other words, the dependent variable is the variable that is assumed to change as a result of a change in the independent variable.
The simplest way to understand a variable is as any characteristic or attribute that can experience change or vary over time or context – hence the name “variable”. For example, the dosage of a particular medicine could be classified as a variable, as the amount can vary (i.e., a higher dose or a lower dose). Similarly, gender, age or ethnicity could be considered demographic variables, because each person varies in these respects. The classification of a variable as independent or dependent depends on how it is used within a specific study. In one study, a variable might be manipulated or controlled to see its effect on another variable, making it independent.
If you’re new to the world of research, especially scientific research, you’re bound to run into the concept of variables, sooner or later. If you’re feeling a little confused, don’t worry – you’re not the only one! Independent variables, dependent variables, confounding variables – it’s a lot of jargon. In this post, we’ll unpack the terminology surrounding research variables using straightforward language and loads of examples.
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 dancolestaxes com independent variable in this example.
In an experiment, the researcher looks for the possible effect on the dependent variable that might be caused by changing the independent variable. The purpose of an independent variable is to manipulate or control it in order to observe its effect on the dependent variable. In other words, the independent variable is the variable that is being tested or studied to see if it has an effect on the dependent variable. The independent variable and the dependent variable are the two main variables in a science experiment. Below is the definition of an independent variable and a look at how you might use it.