A change in the independent variable directly causes a change in the dependent variable. It should be noted that in some experiments there are other variables present apart from the independent and the dependent variables. Extraneous variables, for example, are the variables that also have an impact on the relationship between the independent and the dependent variables.
In modeling and statistics
Essentially, it’s the presumed cause in cause-and-effect relationships being studied. Although you might not think of these small, daily occurrences as “experiments”, every decision in life can be compared to a scientific study! However, what you may not remember from your science class is the difference between certain variable types.
Can the same variable be independent in one study and dependent in another?
- In the world of scientific research, there’s no shortage of variable types, some of which have multiple names and some of which overlap with each other.
- The role of a variable as independent or dependent can vary depending on the research question and study design.
- 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 independent variable is one that the researchers either manipulate (such as the amount of something) or that already exists but is not dependent upon other variables (such as the age of the participants). 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). The independent variable (IV) in psychology is the characteristic of an experiment that is manipulated or changed by researchers, not by other variables in the experiment.
Confounding Variable – Definition, Method and…
An independent variable is a variable in a functional relation wherein the value is not affected by other variables. That is in contrast to a dependent variable that is influenced by other variables. The independent variable meaning in an experiment is the variable that is to be manipulated and observed. In an independent variable psychology experiment, for instance, it refers to the factor that influences the value of the variable that depends on it. In other words, just because two variables have a relationship doesn’t mean that it’s a causal relationship – they may just happen to vary together. For example, you could find a correlation between the number of people who own a certain brand of car and the number of people who have a certain type of job.
Levels of Independent Variable
To this day, this is the standard system that remains in use across most of the sciences, including mathematics. 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. 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). By Kendra Cherry, MSEdKendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the “Everything Psychology Book.” Without going beyond the scope of this article, this deeper definition of a variable has led to incredible modern advancements in engineering, economics and mathematics, among many others.
For example, in a study examining the effect of post-secondary education on lifetime earnings, some extraneous variables might be gender, ethnicity, social class, genetics, intelligence, age, and so forth. A variable is extraneous only when it can be assumed (or shown) to influence the dependent variable. This effect is called confounding or omitted variable bias; in these situations, design changes and/or controlling for a variable statistical control is necessary. As we mentioned earlier, one of the major challenges in identifying and measuring causal relationships is that it’s difficult to isolate the impact of variables other than the independent variable. Simply put, there’s always a risk that there are factors beyond the ones you’re specifically looking at that might be impacting the results of your study. So, to minimise the risk of this, researchers will attempt (as best possible) to hold other variables constant.
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. This is similar to the mathematical concept of variables, in that an independent variable is a known quantity, and a dependent variable is an unknown quantity.
In the world of scientific research, there’s no shortage of variable types, some of which have multiple names and some of which overlap with each other. In this post, we’ve covered some of the popular ones, but remember that this is not an exhaustive list. Operational variables (or operationalizing definitions) refer to how you will define and measure a specific variable as it is used in your study.
In some studies, researchers may want to explore how multiple factors affect the outcome, so they include more than one independent variable. 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.
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.
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. Some common examples of confounding variables include demographic factors such as gender, ethnicity, socioeconomic status, age, education level, and health status.
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. This type of hypothesis is constructed to state the independent variable followed by the predicted impact on the dependent variable.
In psychology, the independent variable is the variable the experimenter manipulates or changes and is assumed to directly affect 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.
Researchers must ensure that participants provide informed consent and that their privacy and confidentiality are respected. Additionally, it is important to avoid manipulating independent variables in ways that could cause harm or discomfort to participants. Similarly, they may measure multiple things to see how they are influenced, resulting in multiple dependent variables.
For example, air pollution could confound the impact of the variables of interest in a study investigating health outcomes. 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. 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.
In the above, x is the independent variable because it is the variable that we control. As such, it is common to characterize the independent variable as the input of a function, while the dependent variable is the output. An independent variable is a type of variable that is used in mathematics, statistics, and the experimental sciences. It is the variable that is manipulated in order to determine whether it has an effect on the dependent variable.
If the experimenter cannot control an extraneous variable, then, this variable is referred to as a confounding variable. (Ref. 2) As the name implies, the presence of a confounding variable will confound the results. It may be due to the independent variable or to a confounding variable, and therefore the result will likely be inconclusive. Mediating variables also help researchers understand how different factors interact with each other to influence outcomes. 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.