The classic text in multilevel analysis, dealing with everything you need to know, has been hugely revised and added to and is supported by all the software. A must-have for modellers.

Preface second editionPreface to first editionIntroductionMultilevel analysis Probability modelsThis book Prerequisites NotationMultilevel Theories, Multi-Stage Sampling and Multilevel ModelsDependence as a nuisance Dependence as an interesting phenomenon Macro-level, micro-level, and cross-level relations GlommaryStatistical Treatment of Clustered DataAggregation Disaggregation The intraclass correlation Within-group and between group variance Testing for group differences Design effects in two-stage samples Reliability of aggregated variables Within-and between group relations Regressions Correlations Estimation of within-and between-group correlations Combination of within-group evidence GlommaryThe Random Intercept ModelTerminology and notation A regression model: fixed effects onlyVariable intercepts: fixed or random parameters? When to use random coefficient models Definition of the random intercept model More explanatory variables Within-and between-group regressions Parameter estimation ¿Estimating¿ random group effects: posterior means Posterior confidence intervals Three-level random intercept models GlommaryThe Hierarchical Linear Model Random slopes Heteroscedasticity Do not force ?01 to be 0! Interpretation of random slope variancesExplanation of random intercepts and slopes Cross-level interaction effects A general formulation of fixed and random parts Specification of random slope models Centering variables with random slopes? Estimation Three or more levels GlommaryTesting and Model SpecificationTests for fixed parameters Multiparameter tests for fixed effects Deviance tests More powerful tests for variance parameters Other tests for parameters in the random part Confidence intervals for parameters in the random part Model specification Working upward from level one Joint consideration of level-one and level-two variables Concluding remarks on model specification GlommaryHow Much Does the Model Explain?Explained variance Negative values of R2? Definition of the proportion of explained variance in two-level models Explained variance in three-level models Explained variance in models with random slopesComponents of variance Random intercept models Random slope modelsGlommaryHeteroscedasticityHeteroscedasticity at level one Linear variance functions Quadratic variance functionsHeteroscedasticity at level twoGlommaryMissing DataGeneral issues for missing data Implications for designMissing values of the dependent variableFull maximum likelihood Imputation The imputation method Putting together the multiple results Multiple imputations by chained equations Choice of the imputation model GlommaryAssumptions of the Hierarchical Linear ModelAssumptions of the hierarchical linear model Following the logic of the hierarchical linear model Include contextual effects Check whether variables have random effects Explained variance Specification of the fixed part Specification of the random part Testing for heteroscedasticity What to do in case of heteroscedasticity Inspection of level-one residuals Residuals at level two Influence of level-two units More general distributional assumptions GlommaryDesigning Multilevel StudiesSome introductory notes on powerEstimating a population meanMeasurement of subjects Estimating association between variables Cross-level interaction effects Allocating treatment to groups or individualsExploring the variance structure The intraclass correlation Variance parametersGlommaryOther Methods and ModelsBayesian inference Sandwich estimators for standard errors Latent class modelsGlommaryImperfect HierarchiesA two-level model with a crossed random factor Crossed random effects in three-level models Multiple membership models Multiple membership multiple classification modelsGlommarySurvey WeightsModel-based and design-based inference Descriptive and analytic use of surveysTwo kinds of weights Choosing between model-based and design-based analysis Inclusion probabilities and two-level weights Exploring the informativeness of the sampling designExample: Metacognitive strategies as measured in the PISA study Sampling design Model-based analysis of data divided into parts Inclusion of weights in the model How to assign weights in multilevel models Appendix. Matrix expressions for the single-level estimatorsGlommaryLongitudinal DataFixed occasions The compound symmetry models Random slopes The fully multivariate model Multivariate regression analysis Explained varianceVariable occasion designs Populations of curves Random functions Explaining the functions 27415.2.4 Changing covariates Autocorrelated residualsGlommaryMultivariate Multilevel ModelsWhy analyze multiple dependent variables simultaneously? The multivariate random intercept model Multivariate random slope models GlommaryDiscrete Dependent VariablesHierarchical generalized linear models Introduction to multilevel logistic regression Heterogeneous proportions The logit function: Log-odds The empty model The random intercept model Estimation Aggregation Further topics on multilevel logistic regression Random slope model Representation as a threshold model Residual intraclass correlation coefficient Explained variance Consequences of adding effects to the model Ordered categorical variables Multilevel event history analysis Multilevel Poisson regressionGlommarySoftware Special software for multilevel modeling HLM MLwiN The MIXOR suite and SuperMixModules in general-purpose software packages SAS procedures VARCOMP, MIXED, GLIMMIX, and NLMIXED R Stata SPSS, commands VARCOMP and MIXEDOther multilevel software PinT Optimal Design MLPowSim Mplus Latent Gold REALCOM WinBUGSReferencesIndex