Factorial Designs Lecture

These flashcards cover key concepts related to factorial designs, including definitions, types of effects, analysis methods, and associated issues.

Factorial Design

A design that tests the effects of more than one independent variable, typically at the same time.

Main Effects

The effect of an independent variable on a dependent variable without considering the influence of other independent variables.

Interactions

When the effect of one independent variable depends on the level of one or more other independent variables.

Orthogonality

A principle where independent variables are independent of one another, allowing clear assessment of their effects.

Crossed Designs

Designs wherein each level of independent variables is paired with each level of every other independent variable.

Increased Power

The enhancement in detecting effects due to reduced unexplained variance in outcomes.

Chi-squared Test

A statistical method used to analyze nominal data in factorial designs.

ANOVA

A method for analyzing the effects of one or more independent variables on a dependent variable, especially when the independent variables are nominal.

Multiple Regression

An analysis that assesses how well a set of variables predicts an outcome, allowing for examination of interactions.

Effect Size

A measure of the strength of the relationship between variables, with interaction effects often being smaller than main effects.

Order Effects

Possible confounding that arises when independent variables are not applied simultaneously.

Variables vs. Constructs

The distinction between variables that can be manipulated or measured in research and the underlying constructs that they are intended to represent.

Measured vs. Manipulated Variables

The differentiation between variables that are observed versus those directly controlled by the researcher.

Interaction Effect Size

The size of the effect found in interactions, typically smaller than main effects, requiring larger samples to detect.

Interaction Example

A scenario where the effect of one treatment depends on another, such as therapy and type of medication on depression.

Factors of Complexity

The increased number of variables in factorial designs, introducing more potential sources of error.

Control for Variables

The ability to isolate the effects of one independent variable while examining another.