PSYC3010 EXAM

Interaction

Effect of one variable changes with another variable.

Boundary Conditions

Conditions under which effects vary or reverse.

Factorial Design

Examines effects of multiple independent variables simultaneously.

Main Effects

Individual effects of each factor on the dependent variable.

Two-Way Design

Design with two independent variables affecting a DV.

Levels of Factors

Different conditions or categories within each factor.

Economical Design

More efficient use of participants in experiments.

Generalisability

Effect remains consistent across levels of another factor.

Conditional Effect

Effect depends on the level of another factor.

Research Questions

Questions addressed in factorial design experiments.

Main Effect of Factor A

Overall effect of Factor A on the DV.

Main Effect of Factor B

Overall effect of Factor B on the DV.

Interaction Effect (A x B)

Effect of Factor A varies by levels of Factor B.

Significant Interaction

Indicates main effect interpretation depends on another factor.

Marginal Means

Average scores at each level of a factor.

Two-Way ANOVA

Statistical test for main and interaction effects.

Simple Effects

Effects examined at specific levels of another factor.

Independent Variables (IVs)

Variables manipulated to observe effects on the DV.

Dependent Variable (DV)

Outcome measured in response to IV manipulation.

Comprehensive Understanding

Deeper insight into complex variable relationships.

Power of Detection

Ability to identify true effects with sufficient sample size.

Nuance in Effects

Subtle variations in how effects manifest.

Experimental Conditions

Specific setups under which experiments are conducted.

Cell Means

Averages of scores at each factor level.

Main Effect

Overall effect of one independent variable on the DV.

Line Graphs

Used to plot two-way interactions visually.

Main Effect Identification

Average across cell means to detect differences.

Interaction Identification

Non-parallel lines indicate an interaction between factors.

Simple Effect Identification

Differences along a single line or vertical differences.

Fully Between-Participants Design

Different groups for each level of independent variables.

Between-Groups Variance

Variance due to differences between participant groups.

Treatment Variance

Variance attributed to different treatment conditions.

Research Question for One-Way ANOVA

Tests mean differences across levels of one IV.

Independent Variable (IV)

Factor manipulated to observe effects on the DV.

Factorial Design Notation

Expressed as A x B for two factors.

Levels of a Factor

Different conditions or categories of an independent variable.

Converging Lines

Indicate that one factor's effect changes with another.

Diverging Lines

Show that the effect of one factor increases with another.

Crossing Lines

Suggest a complex interaction between factors.

Overall Trend

Average height of lines indicates main effects.

Vertical Separation

Distance between lines indicates main effects presence.

One-way ANOVA

Statistical test for comparing means across groups.

Within-groups variance

Random variation within each group, unexplained by IV.

SStreatment

Sum of squared differences between group means and grand mean.

SSerror

Sum of squared differences within groups from their means.

MStreatment

Variance index among treatment means, calculated from SStreatment.

MSerror

Variance index within groups, calculated from SSerror.

F ratio

Ratio of between-groups variance to within-groups variance.

Factor A x Factor B Interaction

Effect of one factor varies by levels of another factor.

Total variance

Overall variability in dependent variable scores.

Variance due to Factor A

Variance attributed to the main effect of Factor A.

Variance due to Factor B

Variance attributed to the main effect of Factor B.

Interaction Effect A x B

Variance due to interaction between Factors A and B.

Degrees of freedom

Number of independent values in a statistical calculation.

Random error

Unmeasured influences causing variability in scores.

Treatment effect

Impact of independent variable on dependent variable scores.

Conceptual model

Framework explaining participant scores in ANOVA.

Systematic variance

Variance explained by the treatment or independent variable.

Unexplained variance

Variance not accounted for by the independent variable.

Pooled within-cell variance

Combined variance from multiple groups in ANOVA.

ANOVA

Analysis of variance, comparing means across groups.

SS for main effect

Variability due to a specific main factor's influence.

SS for interaction

Variability from combined effects of factors.

SS error

Unexplained variability within each group or cell.

Significant F test (Factor A)

Indicates significant differences in Factor A's levels.

Significant F test (Factor B)

Indicates significant differences in Factor B's levels.

Significant F test (A x B interaction)

Shows Factor A's effect varies across Factor B's levels.

Omnibus test

Detects overall differences among factor levels.

Population Normality

Assumes group populations are normally distributed.

Homogeneity of variance

Assumes equal variances across group populations.

Independence of observations

No repeated measures from the same participant.

Continuous DV

Dependent variable measured on continuous scale.

Follow-up tests

Identify specific differences after omnibus tests.

Simple effect

Effect of one factor at specific level of another.

Mean Square (MS)

Average of squared deviations from the mean.

MS error

Mean square of the unexplained variability.

Preliminary partitioning of variance

Initial breakdown of variance in ANOVA.

Statistical significance

Indicates likelihood that results are not due to chance.

Omnibus F-test

Initial test assessing overall significance across groups.

Significant Main Effect

Indicates differences among marginal means of factors.

Pairwise Comparisons

Comparing two means at a time for significance.

Linear Contrasts

Comparing one mean or set of means to another.

Significant t-test

Indicates significant difference between two marginal means.

Dependent Variable

Outcome measured in an experiment or study.

Type I Error

Incorrectly rejecting a true null hypothesis.

Simple Comparison

Further tests needed after significant F-test for simple effects.

Hypotheses Testing

Process of determining the validity of a hypothesis.

Control Group

Group not exposed to treatment, used for comparison.

Experimental Groups

Groups exposed to different treatments in a study.

Weights in Linear Contrasts

Values defining specific comparisons in mean analysis.

Significance of Simple Effects

Determines if one factor's effect varies across levels.

Maximum Simple Effects

Determined by the number of levels in the other factor.

F-test for Simple Effect

Tests significance among cell means for one factor.

Significant Simple Effect

Requires follow-up if more than two levels exist.

Simple Comparisons

Compare means within a specific level of another factor.

Pairwise Simple Comparisons

Compare two cell means at one factor level.

Linear Simple Contrasts

Compare specific means within one factor level.

Repartitioning Variance

Focuses on variability of one factor within another.

Simple Effects of Factor A

Variance of Factor A analyzed at each level of Factor B.

Simple Effects of Factor B

Variance of Factor B analyzed at each level of Factor A.