FactorAnalysis

# Factors may correlate as high as 428 if factors are

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Unformatted text preview: ors may correlate as high as .428. If factors are used in predictors in regression analysis, their moderate correlation would create a problem of MULTICOLLINEARITY! FA solutions are supposed to minimize issues with multicollinearity, not create them! Final Model Final .428 Factor 1 i1 i2 i3 i4 Factor 2 i5 i6 i7 i8 i9 i10 Issues with this model: 1 – Hypothesized 1 factor, obtained 2 factors 2 – 3 of 10 items are cross-loaders 3 – Two factors have a moderate correlation of .428 which would create problem of multicollinearity if used as two predictors in regression analysis RECOMMENDATION: REJECT THIS MODEL!! Forced Factor Solution Forced Use only after rejection of initial solution Force the number of factors expected Interpret Forced Solution Interpret Note: There are still 2 Eigen Values above 1.0. However in forced solution, only one factor is solved for (b’c that was specified) Forced Solution: Keep all items except for los1. More sensible than initial solution. Uncorrelated factors: Orthogonal (Varimax) Rotation Orthogonal Expect 2 or more uncorrelated factors, Expect want rotation to be orthogonal (means independent) independent) In SPSS, click Varimax Rotation Example: 20-item scale measuring Example: Extraversion and Neuroticism Traits Extraversion Initial 4-factor solution Initial Forced 2-Factor Solution Forced To interpret this solution, label each item as one of the following: Clean-loader Cross-loader Non-loader Criterion: <.4 Factor Analys...
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