Researchers design an experiment so that they can observe or measure if changes to one thing cause something else to vary in a repeatable way. The things that are changing in an experiment are called variables. A variable is any factor, trait, or condition that can exist in differing amounts or types. An experiment usually has three kinds of variables: independent, dependent, and controlled. Any factor, trait, or condition that can exist in differing amounts or types.
Experiments contain different types of variables; below, we will present you with some of the main types and their definitions, then finish by giving an example containing all variable types.
Types of experimental variables:
Independent variables (IV): These are the factors or conditions that you manipulate in an experiment. Your hypothesis is that this variable causes a direct effect on the dependent variable.
Dependent variables (DV): These are the factor that you observe or measure. As you vary your independent variable you watch what happens to your dependent variable.
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Examples of Independent and Dependent Variables in Experiments
In an experiment, the researcher is looking for a possible effect on the dependent variable that might be caused by changing the independent variable. Below are overviews of three experiments, each with their independent and dependent variables identified.
Experiment 1: You want to figure out which brand of microwave popcorn pops the most kernels so you can get the most value for your money. You test different brands of popcorn to see which bag pops the most popcorn kernels.
Independent Variable: Brand of popcorn bag (It's the independent variable because you are actually deciding the popcorn bag brands)
Dependent Variable: Number of kernels popped (This is the dependent variable because it's what you measure for each popcorn brand)
Experiment 2: You want to see which type of fertilizer helps plants grow fastest, so you add a different brand of fertilizer to each plant and see how tall they grow.
Independent Variable: Type of fertilizer given to the plant
Dependent Variable: Plant height
Experiment 3: You're interested in how rising sea temperatures impact algae life, so you design an experiment that measures the number of algae in a sample of water taken from a specific ocean site under varying temperatures.
Independent Variable: Ocean temperature
Dependent Variable: The number of algae in the sample
For each of the independent variables above, it's clear that they can't be changed by other variables in the experiment. You have to be the one to change the popcorn and fertilizer brands in Experiments 1 and 2, and the ocean temperature in Experiment 3 cannot be significantly changed by other factors. Changes to each of these independent variables cause the dependent variables to change in the experiments.
Other types Variables
Extraneous variable: An extraneous variable is any extra factor that may influence the outcome of an experiment, even though it is not the focus of the experiment. Ideally, these variables won’t affect the conclusions drawn from the results as a careful experimental design should equally spread influence across your test conditions and stimuli. Nevertheless, extraneous variables should always be considered and controlled when possible as they may introduce unwanted variation in your data. In this case, you need to tweak your design and procedure to be able to keep the variation constant or find a strategy to monitor its influence (constant or controlled variables). All experiments have extraneous variables. Here are some examples of different types of extraneous variables:
aspects of the environment where the data collection will take place, e.g., room temperature, background noise level, light levels;
differences in participant characteristics (participant variables); and
test operator or experimenter behavior during the test, i.e., their instructions to the test participants are not consistent or they give unintentional clues of the goal of the experiment to the participants.
Controlled (or constant) variables: Are extraneous variables that you manage to keep constant or controlled for during the course of the experiment, as they may have an effect on your dependent variables as well.
Participant variables: Participant variables can be defined as the different individual characteristics that may impact how a participant responds in an experiment. Examples of participant variables include gender, age, ethnicity, socioeconomic status, literacy status, mood, clinical diagnosis, etc.
Stimulus variables: These are specific features of your stimulus or group of stimuli that are part of the context in which the behavior occurs. These are often an expression of or a subset of your independent variables and covariates. Examples include the number of items, item category, stimulus crowdedness, color, brightness, contrast, etc.
Another important defining characteristic of an experiment is to control bias. Bias happens when something else outside the experiment affects the dependent variable. For example, if the independent variable is the location on the page of our call to action, then it is important to keep constant other features of the call-to-action, such as the button size, the color, the label, even the font. Otherwise, we can’t be sure whether any change we measure is really due to location and not due to one of these other variables.