What is an independent factor in an experiment?
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In analytical health research there are generally two types of variables. Independent variables are what we expect will influence dependent variables. A Dependent variable is what happens as a result of the independent variable. For example, if we want to explore whether high concentrations of vehicle exhaust impact incidence of asthma in children, vehicle exhaust is the independent variable while asthma is the dependent variable. Show
A confounding variable, or confounder, affects the relationship between the independent and dependent variables. A confounding variable in the example of car exhaust and asthma would be differential exposure to other factors that increase respiratory issues, like cigarette smoke or particulates from factories. Because it would be unethical to expose a randomized group of people to high levels of vehicle exhaust,[1] a study comparing two populations with differential exposure to vehicle exhaust would rely on a natural experiment, or a situation in which this already occurs due to factors unrelated to the researchers. In this natural experiment, a community living near higher concentrations of car exhaust may also live near factories that pollute or have higher rates of smoking. When running a study or analyzing statistics, researchers try to remove or account for as many of the confounding variables as possible in their study design or analysis. Confounding variables lead to bias, or a factor that may cause an estimate to differ from the true population value. Bias is a systematic error in study design, subject recruitment, data collection, or analysis that results in a mistaken estimate of the true population parameter.[2] Although there are many types of bias, two common types are selection bias and information bias. Selection bias occurs when the procedures used to select subjects and others factors that influence participation in the study produce a result that is different from what would have been obtained if all members of the target population were included in the study.[2] For example, an online website that rates the quality of primary care physicians based on patients’ input may produce ratings that suffer from selection bias. This is because individuals that had a particularly bad (or good) experience with the physician may be more likely to go to the website and provide a rating. Information bias refers to a “systematic error due to inaccurate measurement or classification of disease, exposure, or other variables.”[3] Recall bias, a type of information bias, occurs when study participants do not remember the information they report accurately or completely. The subject of confounding and bias relates to a larger discussion of the relationship between correlation and causation. Although two variables may be correlated, this does not imply that there is a causal relationship between them. One way to determine whether a relationship between variables is causal is based on three criteria for research design: temporal precedence meaning that the hypothesized cause happens before the measured effect; covariation of the cause and effect meaning that there is an established relationship between the two variables regardless of causation; and a lack of plausible alternative explanations. Plausible alternative explanations are other factors that may cause the dependent variable under observation.[4]. These alternative explanations are closely related to the concept of internal validity. [1]Trochim, W.M.K. “Establishing Cause and Effect.” Research Methods Knowledge Base, 10/20/2006. Web 1/24/2017. Previous Section Next Section What Is an Independent Variable?Independent VariableThe 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. 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). In general, experiments have these three types of variables: independent, dependent,
and controlled. Identifying the Independent VariableIf you are having trouble identifying the independent variables of an experiment, there are some questions that may help:
Researchers are interested in investigating the effects of the independent variable on other variables, which are known as dependent variables (DV). 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). Below are the key differences when looking at an independent variable vs. dependent variable. Independent Variable
Dependent Variable
TypesThere can be all different types of independent variables. The independent variables in a particular experiment all depend on the hypothesis and what the experimenters are investigating. Independent variables also have different levels. In some experiments, there may only be one level of an IV. In other cases, multiple levels of the IV may be used to look at the range of effects that the variable may have. 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. To understand this concept, it's helpful to take a look at the independent variable in research examples. In OrganizationsA 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 color of the office is the independent variable. In the WorkplaceA 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. In Educational ResearchEducators 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. In Mental Health ResearchResearchers 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. The independent variable in this example is the type of therapy. ImpactSometimes 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. 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. Choosing an Independent VariableIf you are designing an experiment, here are a few tips for choosing an independent variable (or variables):
Potential PitfallsIt is also important to be aware that there may be other variables that might influence the results of an experiment. Two other kinds of variables that might influence the outcome include:
Extraneous variables can also include demand characteristics (which are clues about how the participants should respond) and experimenter effects (which is when the researchers accidentally provide clues about how a participant will respond). Verywell Mind uses only high-quality sources, including peer-reviewed studies, to support the facts within our articles. Read our editorial process to learn more about how we fact-check and keep our content accurate, reliable, and trustworthy.
Additional Reading By Kendra Cherry
Thanks for your feedback! How do you know if a variable is independent or dependent?An easy way to think of independent and dependent variables is, when you're conducting an experiment, the independent variable is what you change, and the dependent variable is what changes because of that. You can also think of the independent variable as the cause and the dependent variable as the effect.
How do you find the independent variable in an experiment?The easiest way to identify which variable in your experiment is the Independent Variable (IV) and which one is the Dependent Variable (DV) is by putting both the variables in the sentence below in a way that makes sense. “The IV causes a change in the DV. It is not possible that DV could cause any change in IV.”
Is factor the independent variable?Independent variables are also known as predictors, factors, treatment variables, explanatory variables, input variables, x-variables, and right-hand variables—because they appear on the right side of the equals sign in a regression equation. In notation, statisticians commonly denote them using Xs.
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