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The first portion of Table 5 presents the fixed effects of the multilevel logit regression model.
The second portion of Table 4 presents random effects of the multilevel logit regression model.
Multilevel logit regression model was used to examine the combined influences of the affective factors, home environment, and school environment on the probabilities of students being readers with high proficiency and readers with low proficiency.
Multilevel logit regression analysis was chosen to address the research questions, given that the dependent variable was a categorical variable with three categories readers with high proficiency, average readers, and readers with low proficiency (Hox [1995]).
We also particularly wanted to compare the strength of this relationship between good and poor readers directly by testing, through use of a multilevel logit regression model, the extent to which these variables predicted the likelihood of the students in the sample being good or poor readers.
Table 2 reports results from multilevel logit regression.
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Estimates for the multilevel logit regressions are reported in Table 3. Model 1 includes only individual level factors (age, gender, marital status, education, employment status).
Therefore, multilevel multinomial logit regression models will be used to examine the cross-sectional associations between physical activity and the various sociodemographic, psychological, social, environmental and areal level factors, and these models will be extended to also account for the correlation in observations arising from the longitudinal nature of the design.
Table 4 Multi-level logit regressions.
Multilevel mixed effects logit regression analyses were performed using SAS version 9.3 (SAS Institute Inc., Cary, NC).
To ease the reading of the results, we also report odds ratios for multilevel mixed effects logit regression analyses as a different way to describe the information.
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