| dc.description.abstract | The purpose of this study was to investigate the factors that affect student performance in
primary schools in Kenya. Hierarchical linear modelling (HLM) was used to statistically
analyze a data structure where students (level-I) were nested within schools (level-2). Of
specific interest was the relationship between student's score (level-I outcome variable)
and SES, sex and mother's education of the students (level-I predictor variable) and the
student-teacher ratio (level-2 predictor variable). Model testing proceeded in 4 phases:
unconstrained (null) model, random intercepts model, means-as-outcome model, and
intercepts- and slopes-as-outcomes model. The intercept-only model revealed an ICC of
.132. Thus, 13% of the variance in scores is between schools and 87% of the variance in
scores is between students within a given school. Because variance existed at both levels
of the data structure, predictor variables were' individually added at each level. The
random-regression coefficients model was tested using the explanatory variables at
student level and all the regression coefficients were statistically significant. Next, the
means-as-outcomes model added student-teacher ratio as a level-2 predictor variable. The
regression coefficient relating student-teacher ratio to score was positive and statistically
significant. Scores were higher in schools with more student-teacher ratio. Finally, the
intercepts model and slopes-as-outcomes model were simultaneously tested with all
predictor variables tested in the model to test the presence of any interactions between
predictor variables. The cross-level interactions indicated that student-teacher ratio is a
moderator variable. | en |
