Final answer:
In experiments, the independent variable is manipulated to observe effects on the dependent variable. Control variables remain constant, while confounding variables are mitigated to ensure accuracy. Examples include studying the effect of age on insurance premiums and the size of tableware on food intake.
Step-by-step explanation:
Understanding experimental design is crucial when interpreting the results of a study. In each experiment or observational study, there are typically different types of variables that are of interest.
- The dependent variable is what is being measured or observed in an experiment. It is thought to be affected by changes in the independent variable.
- The independent variable is what the experimenter manipulates or changes to see if it has an effect on the dependent variable.
- Control variables are constants during the experiment; they are not changed because they can affect the outcome of the experiment.
- Confounding variables are factors other than the independent variable that may cause a result and must be controlled for to avoid skewed results.
Examples:
- In an experiment where researchers look at how age affects insurance premiums, age is the independent variable while the insurance premium is the dependent variable.
- When studying how the size of tableware influences food intake among college students, the size of the tableware is the explanatory variable or the independent variable, and the amount of food eaten is the response variable or the dependent variable.
A sociological study might examine how gender (independent variable) affects the rate of income (dependent variable), or how one's religion (independent variable) affects family size (dependent variable).
To determine causation between two variables, researchers must control for confounding variables and carefully manipulate only the independent variable.