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1、ContentsPrefaceAcknowledgementsGlossaryNotationChapter 1 1.1 Multilevel data1.2 School effectiveness1.3 Sample survey methods1.4 Repeated measures data1.5 Event history models1.6 Discrete response data1.7 Multivariate models1.8 Nonlinear models1.9 Measurement errors1.10 Random cross classifications1
2、.11 Structural equation models1.12 Levels of aggregation and ecological fallacies1.13 Causality1.14 A caveatChapter 22.1 The 2-level model and basic notation2.2 The 2-level model2.3 Parameter estimation for the variance components model2.4 The general 2-level model including random coefficients2.5 E
3、stimation for the multilevel model2.6 Other estimation procedures2.7 Residuals2.8 The adequacy of Ordinary Least Squares estimates.2.9 A 2-level example using longitudinal educational achievement data2.9.1 Checking model assumptions2.9.2 Checking for influential units2.10 Higher level explanatory va
4、riables and compositional effects2.11 Hypothesis testing and confidence intervals2.11.1 Fixed parameters2.11.2 Random parameters2.11.3 ResidualsAppendix 2.1 The general structure and estimation for a multilevel modelAppendix 2.2 Multilevel residuals estimationAppendix 2.3 The EM algorithmAppendix 2.
5、4 Markov Chain Monte Carlo (MCMC) estimationChapter 33.1 Complex variance structures3.1.1 Variances for subgroups defined at level 13.1.2 Variance as a function of predicted value3.1.3 Variances for subgroups defined at higher levels3.2 A 3-level complex variation model3.3 Parameter Constraints3.4 W
6、eighting units3.5 Robust, Jacknife and Bootstrap Uncertainty Estimates3.6 Aggregate level analyses3.7 Meta AnalysisChapter 44.1 Multivariate Multilevel models4.2 The basic 2-level multivariate model4.3 Rotation Designs4.4 A rotation design example using Science test scores4.5 Principal Components an
7、alysis4.6 Multiple Discriminant analysis4.7 Other ProceduresChapter 55.1 Nonlinear models5.2Nonlinear functions of linear components5.8 Estimating population means5.4 Nonlinear functions for variances and covariances5.5 Examples of nonlinear growth and nonlinear level 1 variance5.6 Multivariate Nonl
8、inear ModelsAppendix 5.1 Nonlinear model estimation5.1.1 Modelling linear components5.1.2 Modelling variances and covariances as nonlinear functions5.1.3 Likelihood valuesChapter 66.1 Models for repeated measures6.2 A 2-level repeated measures model6.3 A polynomial model example for adolescent growt
9、h and the prediction of adult height6.4 Modelling an autocorrelation structure at level 16.5 A growth model with autocorrelated residuals6.6 Multivariate repeated measures models6.7 Scaling across time6.8 Cross-over designsChapter 77.1 Models for discrete response data7.2 Proportions as responses7.3
10、 An example from a survey of voting behaviour7.4 Models for multiple response categories7.5 An example of voting behaviour with multiple responses7.6 Models for counts7.7 Ordered responses7.8 Mixed discrete - continuous response modelsAppendix 7.1 Differentials for some discrete response modelsChapt
11、er 88.1 Random cross classifications8.2 A basic cross classified model8.3 Examination results for a cross classification of schools8.4 Computational considerations8.5 Interactions in cross classifications8.6 Level 1 cross classifications8.7 Cross-unit membership models8.8 Multivariate cross classifi
12、ed modelsAppendix 8.1 Random cross classified data structuresChapter 99.9 Event history models9.2 Censoring9.3 Hazard based models in continuous time9.4 Parametric proportional hazard models9.5 The semiparametric Cox model9.6 Tied observations9.7 Repeated measures proportional hazard models9.8 Examp
13、le using birth interval data9.9 The discrete time (piecewise) proportional hazards model9.10 Log duration modelsChapter 1010.1 Errors of measurement10.2 Measurement errors in level 1 variables10.3 Measurement errors in higher level variables10. 4 A 2-level example with measurement error at both leve
14、ls.10.5 Multivariate responses10. 6 Nonlinear models10.7 Measurement errors for discrete explanatory variablesAppendix 10.1 Measurement errors10.1.1 The Basic 2-level Model10.1.2 Parameter estimation10.1.3 Random coefficients for explanatory variables measured with error10.1.4 Nonlinear modelsChapter 1111.1 Software for multilevel analysis11.2 Design issues11.3 Missing data11.4 Creating a completed data set11. 5 Multiple imputation and error corrections11.6 Discrete variables with missing data11.7 An
