copy: Chapter 10: Using Between-Subjects and Within-Subjects Experimental Designs (copy)

Random Sampling

Choosing participants from a population to enhance external validity.

Random Assignment

Assigning participants randomly to groups to enhance internal validity.

Quasi-experiment

An experiment where subjects are not randomly assigned.

Between-Subjects Design

Experimental design where different groups are exposed to different levels of the independent variable.

Within-Subjects Design

Experimental design where the same subjects are exposed to all levels of the independent variable.

Single-Subject Design

Experimental design focusing on the behavior of an individual subject.

Error Variance

Variability in the dependent variable due to extraneous variables.

Extraneous Variables

Variables not controlled in a study that can affect results.

Subject Variables

Differences between individuals that can introduce variability in the results.

Practice Effect

Improvement in performance resulting from repeated testing.

Randomized Two-Group Design

A between-subjects design where subjects are randomly assigned to two groups.

Parametric Design

Experimental design systematically varying the independent variable across levels.

Nonparametric Design

Experimental design using categories rather than amounts to define independent variable levels.

Multiple Control Group Design

Single-factor design including two or more control groups.

Matched-Groups Design

Between-subjects design where paired subjects are randomly assigned across groups.

Matched-Pairs Design

Two-group matched design with similar subjects paired together.

Carryover Effect

Alteration in behavior from prior exposure to one level of an independent variable.

Counterbalancing

Technique to balance order effects in within-subject designs.

Factorial Design

Experimental design combining every level of one independent variable with every level of others.

Main Effect

Independent effect of one independent variable in a factorial design.

Interaction

When the effect of one independent variable changes over levels of another in a factorial design.

Simple Main Effect

Effect of one factor at a specific level of another in a factorial ANOVA.

Higher-Order Factorial Design

Design with more than two independent variables.

Dependent Samples t-Test

Statistical method to compare means of two related groups.

Caffeine Concentration Study

Example of a within-subjects design comparing concentration levels after different caffeine doses.

Control Group

Group in an experiment that does not receive treatment for comparison.

Treatment Group

Group in an experiment that receives the treatment being tested.

Variables

Factors that can change and affect outcomes in an experiment.

Independent Variable

The variable that is manipulated to observe its effect on the dependent variable.

Dependent Variable

The outcome measured in an experiment to assess the effect of the independent variable.

Control for Extraneous Variables

Taking steps to minimize the impact of variables not being studied.

Causation vs Correlation

The distinction between determining if one event causes changes in another versus merely a relationship.

Within-Subjects Variance

Variation within a single group across different treatments or conditions.

Sample Size Impact

Larger sample sizes can reduce error variance and increase the reliability of results.

Statistical Significance

A mathematical measure that suggests the likelihood that the observed results happened by chance.