Haves, Halves, and Have-Nots: School Libraries and Student Achievement in California Page: 160
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Component Matrix for Factor Analysis of Library Variables, Grade 11 U.S. History
Variable Factor 1a
Hours Open .88
Collection Size .82
Total Staff Hours .92
Total Services .90
Total Technology .92
Note: Extraction method was principal component analysis.
a. Only one component was extracted.
Results of the hierarchical regression were similar to those with eleventh grade English
Language Arts CST scores. Entering community and school and community factors in the first
step, followed by all three factors in the second step, produced an adjusted R2 of .56 (F(3, 931) =
395.47, p <.001) for the prediction of eleventh grade U.S. History CST scores. Assumptions of
linearity, normality and multicollinearity were met in tests outlined in the previous chapter.
Unlike other regressions performed, however, Beta weights indicated that the library factor was
the strongest predictor of U.S. History CST scores (.48), followed by community (.47) and
school (.16) factors. The addition of the library factor produced a AR2 of .21; 21% of the variance
in U.S. History Scores was accounted for by the library factor. See Table 63 for unstandardized
and standardized betas and standard error.
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Achterman, Douglas L. Haves, Halves, and Have-Nots: School Libraries and Student Achievement in California, dissertation, December 2008; Denton, Texas. (digital.library.unt.edu/ark:/67531/metadc9800/m1/174/: accessed June 25, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; .