Hierarchical Linear Models: Applications and Data Analysis MethodsHierarchical Linear Models launches a new Sage series, Advanced Quantitative Techniques in the Social Sciences. This introductory text explicates the theory and use of hierarchical linear models (HLM) through rich, illustrative examples and lucid explanations. The presentation remains reasonably nontechnical by focusing on three general research purposes - improved estimation of effects within an individual unit, estimating and testing hypotheses about cross-level effects, and partitioning of variance and covariance components among levels. This innovative volume describes use of both two and three level models in organizational research, studies of individual development and meta-analysis applications, and concludes with a formal derivation of the statistical methods used in the book. |
Contents
Introduction | 1 |
The Logic of Hierarchical Linear Models | 8 |
Locaton of | 28 |
Copyright | |
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ACADEMIC BACKGROUND applications assumed assumption average bias Bryk Chapter classroom computed correlation covariance deviance statistic empirical Bayes estimates example Fixed Effect Coefficient grand mean growth rate hierarchical analysis hierarchical linear model hierarchical model High School individual growth inferences initial status intercept and slope learning rate least squares Level Level-1 coefficients Level-1 model Level-2 predictors Level-2 units likelihood-ratio test math achievement matrix maximum likelihood mean achievement measure multivariate multivariate normally distributed nonrandomly varying normally distributed null hypothesis OLS estimates organizational outcome person-level point estimates predicted Quadratic Growth random-coefficient regression model Raudenbush regression coefficients reliability residual variance school effects school means SCHOOL POVERTY school-level SECTOR shrinkage slopes-as-outcomes social class Specifically standard deviation standard error statistics student-level Table three-level model two-level unconditional model variables variance components variance explained variance-covariance components variation zero σ²