Haves, Halves, and Have-Nots: School Libraries and Student Achievement in California Page: 149
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lowest communality and loading values of the remaining variables. The remaining three
variables accounted for 62.97% of the variance. Table 50 displays the final factor loadings.
Component Matrix for Factor Analysis of Library Variables, Grade 4
Variable Factor la
Total Staff Hours .71
Total Services .83
Total Technology .83
Note: Extraction method was principal component analysis.
a. Only one component was extracted.
The school and community factors were then entered into the first step of a hierarchical
multiple regression, followed by the library factor added in the second step. Assumptions of
linearity, normality and multicollinearity were met in tests outlined in the previous chapter.
Together, the school, community and library factors produced an adjusted R2 of .70 (F(3, 3452)
2619.63, p <.001) for the prediction of English Language Arts CST scores. The library factor
produced a AR2 of just .002. Beta weights indicated that the strongest predictor was the
community factor (.80), followed by school (.23) and library factors(.04). See Table 51 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/m1/163/: accessed May 29, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; .