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||||<50% style="TEXT-ALIGN: center"> '''Factor Loading''' || '''Sample Size needed for significance'''|| ||||<50% style="VERTICAL-ALIGN: top; TEXT-ALIGN: center"> 0.30 || 350 || ||||<50% style="VERTICAL-ALIGN: top; TEXT-ALIGN: center"> 0.35 || 250 || ||||<50% style="VERTICAL-ALIGN: top; TEXT-ALIGN: center"> 0.40 || 200 || ||||<50% style="VERTICAL-ALIGN: top; TEXT-ALIGN: center"> 0.45 || 150 || ||||<50% style="VERTICAL-ALIGN: top; TEXT-ALIGN: center"> 0.50 || 120 || ||||<50% style="VERTICAL-ALIGN: top; TEXT-ALIGN: center"> 0.55 || 100 || ||||<50% style="VERTICAL-ALIGN: top; TEXT-ALIGN: center"> 0.60 || 85 || ||||<50% style="VERTICAL-ALIGN: top; TEXT-ALIGN: center"> 0.65 || 70 || ||||<50% style="VERTICAL-ALIGN: top; TEXT-ALIGN: center"> 0.70 || 60 || ||||<50% style="VERTICAL-ALIGN: top; TEXT-ALIGN: center"> 0.75 || 50 || |
When SPSS performs a principal components analysis using a direct oblimin rotation it produces three matrices of loadings. A component matrix of initial unrotated loadings, a structure matrix of item-factor correlations and a pattern matrix of standardised regression coefficients between each factor and each item. For reporting purposes http://lists.asu.edu/cgi-bin/wa?A2=ind0312&L=aera-d&T=0&F=&S=&P=7624 suggests using the pattern matrix, in this situation, as it partials out the effects of other items on each factor.
Factor Loading |
Sample Size needed for significance |
|
0.30 |
350 |
|
0.35 |
250 |
|
0.40 |
200 |
|
0.45 |
150 |
|
0.50 |
120 |
|
0.55 |
100 |
|
0.60 |
85 |
|
0.65 |
70 |
|
0.70 |
60 |
|
0.75 |
50 |