Group Design

Between-groups design randomly assigns different participants to each experimental condition, so each person experiences only one level of the independent variable. Repeated-measures design assigns the same participants to all conditions, meaning each person completes every level. Repeated-measures automatically controls for individual differences but risks order effects, which counterbalancing can mitigate.

Random assignment ensures all participants have an equal chance of being assigned to experimental or control groups, making systematic differences between groups unlikely. This allows researchers to assume any observed differences result from the independent variable manipulation rather than preexisting characteristics. With sufficiently large samples, random assignment distributes potential confounds equally across groups.

The independent variable is manipulated by the experimenter and represents the treatment or condition being tested. The dependent variable is what the researcher measures to assess the independent variable's effect. The dependent variable depends on the independent variable, answering the question: what effect does the independent variable have on the dependent variable?

Counterbalancing ensures all possible orders of experimental conditions occur across participants, preventing order effects from biasing results. In a repeated-measures study, participants might perform differently based on condition sequence rather than the independent variable itself. By having half the participants experience conditions in one order and half in the reverse order, counterbalancing controls for this confound.

Operationalization is a precise description of how variables will be measured, ensuring clarity and reproducibility. For example, defining violent behavior as only physical acts like kicking or punching versus including verbal aggression. Clear operational definitions allow other researchers to understand exactly what was measured and replicate the study accurately.

Random sampling gives every population member an equal chance of selection, creating a sample that reflects the larger population's characteristics. When samples are large enough and representative, researchers can generalize findings to the broader population without bias. This approach ensures percentages of demographic characteristics in the sample closely match those in the target population.

Confounding variables are unknown factors that could influence results independently of the independent variable. Random assignment distributes potential confounds equally across experimental and control groups, preventing them from systematically affecting one group more than another. In repeated-measures designs, using the same participants automatically controls for individual difference confounds.