Posted by
cynicalflyer on
Aug 26, 2014; 3:48pm
URL: http://spssx-discussion.165.s1.nabble.com/Principal-components-analysis-regression-tp5727080.html
Here's my conundrum-
Independent Variable: I have a survey of 50 states indicating the amount of control the state board of education has in 31 areas answered on a three point scale (1 = total control; 2 = partial control; 3 = no control). I have a solid theoretical underpinning for all 31, or to be more precise, the literature review found evidence for all 31 as being important (Study X found items 1, 4 and 7; Study Y found items 2, 9, and 11, etc.)
Dependent variable: % of students graduating HS within 4 years.
1) In SPSS Analyze -> Dimension Reduction -> Factor
2) Descriptives: Initial Solution
3) Extraction: Method = Principal components; Analyze = Correlation matrix; Display = Unrotated factor solution and Scree plot; Extract: Based on Eigenvalue greater than 1; Maximum Iterations for Convergence = 25
4) Rotation: Method = Varimax; Display = Rotated Solution and Loading Plots; Maximum Iterations for Convergence = 25
5) Scores: Save as variables; Method = Regression; Display factor score coefficient matrix
6) Options: Exclude cases listwise; Suppress small coefficients [with] absolutely value below .10
The result are 9 saved columns (FAC1_1, FAC1_2, FAC1_3...FAC1_9) in the SPSS sheet.
The Total Variance Explained -> Rotation Sums of Squared Loadings indicates that the first 5 of these explain 51.51% of the variance.
Should I then go back into SPSS run a linear regression (Analyze -> Regression -> Linear) with the Dependent Variable % of students graduating HS within 4 years and the Independent Variables being FAC1_1, FAC1_2, FAC1_3, FAC1_4, and FAC1_5?
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