Factorial Designs Lecture Notes

These flashcards cover key concepts related to factorial designs, including their definitions, benefits, potential issues, and analysis methods.

What is a factorial design?

A design that tests the effects of more than one independent variable, typically simultaneously.

What are main effects in research?

The effect of an independent variable on a dependent variable without regard to other independent variables.

What is an interaction effect?

When the effect of one independent variable depends on the level of another independent variable.

Why are factorial designs used?

They allow testing multiple independent variables at once and exploring dependencies of effects.

What is orthogonality in factorial designs?

Independence of effects achieved by ensuring independent variables are not confounded with each other.

What is a crossed design in experimental research?

A design wherein each level of an independent variable is paired with each level of every other independent variable.

What is one of the main benefits of factorial designs?

Increased power to detect effects by reducing unexplained variance in outcomes.

What is a potential issue with factorial designs?

Increased complexity which introduces more potential sources of error in a study.

What are order effects in the context of factorial designs?

The possibility of interacting effects due to the order in which independent variables are applied.

How does the effect size of interactions compare to main effects?

Interactions are typically much smaller than main effects, requiring larger sample sizes to detect.

What is the purpose of analyzing factorial designs with Chi-squared tests?

To analyze data where all variables are measured at the nominal level.

What type of analysis is used when IVs are nominal and DV is an interval or ratio?

Factorial ANOVA, which tests for main effects and interactions simultaneously.

What does multiple regression analyze in factorial designs?

How well a set of independent variables predicts some outcome, particularly using moderation analysis for interactions.

What is the relationship between efficiency and factorial designs?

Factorial designs improve research efficiency by allowing multiple effects to be studied in a single study, reducing the need for separate studies.

What can result from confounding variables in factorial designs?

Accurate assessment of main effects and interactions becomes impossible, obscuring results.