News

Terminology aside though, the most important takeaway is that independent variables are assumed to be the “cause” in any cause-effect relationship. As you can imagine, these types of variables are of major interest to researchers, as many studies seek to understand the causal factors behind a phenomenon. These types of studies also assume some causality between independent and dependent variables, but it’s not always clear. So, if you go this route, you need to be cautious in terms of how you describe the impact and causality between variables and be sure to acknowledge any limitations in your own research. In scientific studies, researchers will typically pay very close attention to the dependent variable (or variables), carefully measuring any changes in response to hypothesised independent variables. This can be tricky in practice, as it’s not always easy to reliably measure specific phenomena or outcomes – or to be certain that household employment taxes the actual cause of the change is in fact the independent variable.

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 non operating income example formula not receive this additional assistance. In this case, participation in after-school math tutoring is the independent variable. 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.

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. For example, you want to know if taking your indoor plants outside will make them grow faster than making them stay inside near the window. So, you take a group of indoor plants outside and leave them there for about three hours daily. If you notice a significant change in plant growth that means you may need to give them a daily dose of sunshine for at least three hours each day for better growth.

1. They’re also known as hidden or underlying variables, and what makes them rather tricky is that they can’t be directly observed or measured.
2. In one study, a variable might be manipulated or controlled to see its effect on another variable, making it independent.
3. For instance, if you wanted to study the effect of stress on academic performance, then coping strategies might act as a mediating factor by influencing both stress levels and academic performance simultaneously.
4. Terminology aside though, the most important takeaway is that independent variables are assumed to be the “cause” in any cause-effect relationship.
5. Then, social media use is categorized into low, medium, and high, which are a total of three levels.

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.

Independent Variable and dependent variable Analysis Methods

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.

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.

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.

Can there be more than one independent or dependent variable in a study?

The independent variable may be called the “controlled variable” because it is the one that is changed or controlled. This is different from the “control variable,” which is variable that is held constant so it won’t influence the outcome of the experiment. The independent variable always changes in an experiment, even if there is just a control and an experimental group. The dependent variable may or may not change in response to the independent variable. In the example regarding sleep and student test scores, the data might show no change in test scores, no matter how much sleep students get (although this outcome seems unlikely). The point is that a researcher knows the values of the independent variable.

Examples in Research Studies

In other cases, researchers might find that changes in the independent variables have no effect on the variables that are being measured. The independent variable is the variable that is controlled or changed in a scientific experiment to test its effect on the dependent variable. It doesn’t depend on another variable and isn’t changed by any factors an experimenter is trying to measure. The independent variable is denoted by the letter x in an experiment or graph. The independent variable is the factor the researcher changes or controls in an experiment.

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.

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.

Independent Variable – Definition, Types and Examples

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.

Practice Identifying the Independent Variable

The treatment variable is the independent variable whereas the recovery rate variable is the dependent variable. It’s important to note that while moderators can have an influence on outcomes, they don’t necessarily cause them; rather they modify or “moderate” existing relationships between other variables. This means that it’s possible for two different groups with similar characteristics, but different levels of moderation, to experience very different results from the same experiment or study design.

The treatment variable may be further altered by varying the dosages, the route of administration, the timing, or the duration. The results are monitored and recorded by identifying or measuring physiological, morphological, or behavioral modifications following the treatment. If you write out the variables in a sentence that shows cause and effect, the independent variable causes the effect on the dependent variable. If you have the variables in the wrong order, the sentence won’t make sense. The independent variables in a particular experiment all depend on the hypothesis and what the experimenters are investigating. Researchers are interested in investigating the effects of the independent variable on other variables, which are known as dependent variables (DV).