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PSYC 2030 Study Guide - Midterm Guide: Intraclass Correlation, Correlation And Dependence, Regression Analysis

Course Code
PSYC 2030
Rebecca Jubis
Study Guide

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Factorial design: any study with >1 IV
Ex: 2 x 3 x 4 = 3 IVs, 2 levels on 1st, 3 lvls on 2nd, and 4 levels on 3rd
Factorial matrix:
oLevels: apply to a single IVs
oConditions: all the possible IV variations added together (ex: 2x3x4)
Main effect: effect of a single IV, compare all levels of one factor vs another
oCalculate row /column means
Interactions: when effect of one IV depends on level of another IV
oCan occur without a main effect present
oCan trump main effect so that the main effects are actually misleading if reported
as is.
Hint: when 1 condition varies far from all others, with all others being
oOn graph: when lines are not parallel, much easier to see than bar graphs, thus
recommended over bar graphs when interaction is suspected (even if IV is
mixed design: at least 1 b/w subject variable, and 1 w/i subjects
oCounterbalancing required for the w/i variable
oNot required if examining “trials” in learning studies, which actually looks at
changes with trials thus have to be in same order.
P x E: both manipulated and subject variables
oCan draw causality if main effect occurs only for E but not P and no interaction
oATI designs (aptitude treatment interaction)
Mixed PxE: if the manipulated variable is a w/i variable (thus study has b/w and w/i, as
well as subject and manipulated), the subject variable MUST be a b/w
o1 factor = 1F; 2 factor = 3Fs (2 + interaction); 3 factor = 7 Fs
oSimple effects analysis: comparing each levels of one factor to each levels of
Correlational design
Positive: both variables change in same direction
Negative: inverse relationship
Pearson’s r (coefficient of correlation) for interval/ratio scale,
or^2: portion of variability in one variable that can be accounted for by variability in
the other.
Spearman’s rho for ordinal data (rankings)
Scatterplots: would fail to see complex “curved line” relationships
Restriction of range: weakens correlation because only included data within a certain
Regression analysis: making predictions based on correlation
oRegression line / best fitting line: distance between each point and the line are at
Y = a + bX, where X is the predictor variable and Y is the criterion (to be
calculated by a given value of X)
oShould only be used for subjects within range of values that the correlation is
based on
Does NOT hold constants thus no conclusions
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