Psych 10 Final Exam Study Guide Flashcards

Vocabulary flashcards for Psych 10 Final Exam Study Guide.

ANOVA (Analysis of Variance)

Statistical method used to compare means of three or more groups to determine if at least one differs significantly.

Factor

Independent variable used to sort data into groups for comparison in ANOVA.

Level

Category or condition within a factor (e.g., 3 types of diets = 3 levels).

One-way vs. Two-way ANOVA

One-way ANOVA analyzes the effect of one factor; two-way ANOVA examines the effects of two factors and their interaction.

Between-subjects vs. Within-subjects Factor

Between-subjects factor assigns different participants to each level; within-subjects means the same participants experience all levels.

Assumptions of a One-Way ANOVA

Independence of observations, Normal distribution of residuals, Homogeneity of variances (equal variances across groups).

Post-hoc Comparisons

Tests that identify which group means differ after a significant ANOVA result.

Univariate Statistics

Analysis involving only one dependent variable.

Multivariate Statistics

Analysis involving multiple dependent variables simultaneously.

Main Effect

The effect of one independent variable on the dependent variable, averaged across levels of other variables.

Interaction Effect

Occurs when the effect of one factor depends on the level of another factor.

Collapse Over a Variable

Averaging across the levels of a variable to analyze the main effect of another variable.

Main Effect Mean

The average score for a level of one factor, across all other factors.

Cell Mean

The average score in a specific combination of two or more factor levels in a factorial design.

Complete Factorial Design

A design where all possible combinations of factor levels are tested.

Correlation Coefficient

A value (r) from -1 to +1 that shows the strength and direction of a linear relationship between two variables.

Positive Relationship

As one increases, so does the other

Negative Relationship

As one increases, the other decreases.

Regression Line

A straight line that best fits data points, showing the predicted values of Y from X.

Linear Relationship

Straight-line relationship.

Curvilinear Relationship

Curved (e.g., U-shaped) relationship.

Pearson Correlation

Measures linear relationship (interval/ratio data).

Spearman Correlation

Measures rank-order (ordinal) relationships.

Restriction of Range Problem

It reduces variability and can underestimate the true correlation.

Purpose of Regression

To predict the value of a dependent variable based on the value of one or more independent variables.

Y’ in Regression

The predicted value of the dependent variable (Y).

Predictor Variable

Independent variable (X).

Criterion Variable

Dependent variable (Y).

Linear Regression Equation

Y’ = a + bX, where a = intercept and b = slope.

Slope

Change in Y for a one-unit increase in X.

Y Intercept

The predicted value of Y when X = 0.

Variance of the Y Scores Around Y’

It refers to the spread (error) of actual Y values around predicted Y values.

Standard Error of Estimate

The average distance between actual Y values and predicted Y’ values; measures accuracy of predictions.

Homoscedasticity

Equal spread of residuals across X.

Heteroscedasticity

Unequal spread of residuals across X.