Haves, Halves, and Have-Nots: School Libraries and Student Achievement in California Page: 152
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Rotated Component Matrix for Factor Analysis of School and Community Variables, Grade 8
Variable Factor 1 Factor 2
Average Parent Education -.83 .18
Free and Reduced Lunch .89 -.22
Ethnicity -.88 -.09
Percentage English Learners .86 .06
Average Teacher Salary -.05 .987
Note: Extraction method was principal component analysis. Rotation Method: Varimax with Kaiser Normalization.
Rotation converged in 3 iterations.
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 .64 (F(3, 1143)
688.44, p <.001) for the prediction of English Language Arts CST scores. Beta weights
indicated that the strongest predictor was the community factor (-.76), followed by school (.17)
and library factors(.13). The library factor produced a AR2 of .02, meaning that it accounted for
2% of the variance in 8th grade English Language Arts CST scores. See Table 54 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/m1/166/: accessed May 23, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; .