## Hierarchical linear models: applications and data analysis methodsMuch social and behavioral research involves hierarchical data structures. The effects of school characteristics on students, how differences in government policies from country to country influence demographic relations within them, and how individuals exposed to different environmental conditions develop over time are a few examples. This introductory text explicates the theory and use of hierarchical linear models through rich illustrative examples and lucid explanations. |

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### Contents

Introduction | 1 |

The Logic of Hierarchical Linear Models | 9 |

Principles of Estimation and Hypothesis Testing | 32 |

Copyright | |

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ACADEMIC BACKGROUND applications assume assumption average Bryk Chapter classroom computed correlation covariance deviance statistic dispersion empirical Bayes estimates Equation example Fixed Effect Coefficient formulation grand mean growth rate hierarchical analysis hierarchical linear model hierarchical model hypothesis tests individual growth inferences initial status intercept and slope learning rate least squares Level Level-1 model Level-2 predictors Level-2 units likelihood-ratio test math achievement matrix mean achievement measure multivariate nonrandomly varying normally distributed null hypothesis OLS estimates One-Way ANOVA organizational outcome person-level point estimates predicted 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 error statistics student-level Table teacher three-level model tion two-level unconditional model variables variance components variance-covariance components variance-covariance matrix variances and covariances variation zero