Multiple-Factor Analysis to Identify Underlying Dimensions of Multiple Indicators of Quality: Rated As Useful in Making Program Quality-Evaluation Decisions By Administrators in Florida's Community Colleges

Cover Multiple-Factor Analysis to Identify Underlying Dimensions of Multiple Indicators of Quality: Rated As Useful in Making Program Quality-Evaluation Decisions By Administrators in Florida's Community Colleges
Multiple-Factor Analysis to Identify Underlying Dimensions of Multiple Indicators of Quality: Rated As Useful in Making Program Quality-Evaluation Decisions By Administrators in Florida's Community Colleges
Steuart, Thomas Albert
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These mean ratings ranged from 1.36 to 2.13.
The Pearson product-moment correlation coefficients for the inter- correlations of the 108 program characteristics, based upon the ratings by all respondents (N = 450) with missing values for any program charac- teristic set equal to the mean rating for that program characteristic, are presented in Appendix F. The Pearson product-moment correlation co- efficients for the intercorrelations of the 108 program characteristics, based upon the ratings by
...respondents with no missing responses (N = 315), 58 59 are presented in Appendix G. These two sets of correlation coefficients were used in the factor analysis.
Factor Analysis Results The iterated principal axes factor-analytic method as applied to both sets of correlation coefficients resulted in a solution with 21 principal axes. The principal axes solution based upon N = 450 is pre- sented in Appendix H with the final communal ity estimates and eigenval- ues. The principal axes solution based upon N = 315 is presented in Appendix I with the final communal ity estimates and eigenvalues.


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