0 greater determine of factors determine count of

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Unformatted text preview: han 1.0 greater Determine # of Factors: Determine Count # of Eigen Values >1 Count Factor Analysis (FA) Factor Step 4: Examine Factor Loadings Variance accounted for in an individual item Variance by a given factor by Each item has a loading on each retained Each factor factor Rule of Thumb: >.40 is acceptable to keep the Rule .40 item with that factor item Potential problem: Sometimes items crossload .40 or higher on 2 or more factors Examine Factor Loadings Examine 1 Factor Solution: No rotation necessary Interpret Component Matrix Strong items: > .4 2-Factor Rotated Solutions 2-Factor 2+ factor solution allows for factor rotation Specify rotation type prior to running FA Orthogonal (Varimax) rotation does not allow factors to correlate factors Oblique (Direct Oblimin) rotation allows factors to correlate and estimates correlation factors Rotation rules: Few items (<5): Select Varimax in SPSS More items(>6): Select Direct Oblimin in SPSS 2-Factor Rotated Solutions 2-Factor Interpreting factor loadings (>.4): First solution (not rotated): Maximizes variance explained in the items relative Maximizes to the first factor only to The factor with the largest Eigen Value Loadings found in Component Matrix Output Rotated Solution: Rotated Variance explained is distributed among the retained factors retained Loadings found in last Matrix Output table >5 items: Specify Oblique Rotation Specify Specify Oblique Rotation Specify Determine # of Factors Determine Direct Oblimin Rotation: Direct Interpret Structure Matrix Solution shows 7 Clean-loading items 3 Cross-loading items Estimate Correlation among Factors among If we were to accept this solution, the resulting fact...
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