Multilevel analysis : an introduction to basic and advanced multilevel modeling /: an introduction to basic and advanced multilevel modeling. (2011)

• Record Type: Book
• Further Information: Note: Tom A.B. Snijders, Roel J. Bosker.
• Other Names: Snijders, T. A. B; Bosker, R. J (Roel J.)
• Contents: Preface second edition; Preface to first edition; Introduction; Multilevel analysis; Probability models; This book; Prerequisites; Notation; Multilevel Theories, Multi-Stage Sampling and Multilevel Models; Dependence as a nuisance; Dependence as an interesting phenomenon; Macro-level, micro-level, and cross-level relations; Glommary; Statistical Treatment of Clustered Data; Aggregation; 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; Glommary; The Random Intercept Model; Terminology and notation; A regression model: fixed effects only; Variable 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 <br />Three-level random intercept models; Glommary; The Hierarchical Linear Model; Random slopes; Heteroscedasticity; Do not force ?01 to be 0!; Interpretation of random slope variances; Explanation 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; Glommary; Testing and Model Specification; Tests 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; Glommary; How 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 slopes; Components of variance; Random intercept models; Random slope models; Glommary; Heteroscedasticity; Heteroscedasticity at level one; Linear variance functions; Quadratic variance functions; Heteroscedasticity at level two; Glommary; Missing Data; General issues for missing data; Implications for design; Missing values of the dependent variable; Full maximum likelihood; Imputation; The imputation method; Putting together the multiple results; Multiple imputations by chained equations; Choice of the imputation model; Glommary; Assumptions of the Hierarchical Linear Model; Assumptions 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; Glommary; Designing Multilevel Studies; Some introductory notes on power; Estimating a population mean; Measurement of subjects; Estimating association between variables; Cross-level interaction effects; Allocating treatment to groups or individuals; Exploring the variance structure; The intraclass correlation; Variance parameters; Glommary; Other Methods and Models; Bayesian inference; Sandwich estimators for standard errors; Latent class models; Glommary; Imperfect Hierarchies; A two-level model with a crossed random factor; Crossed random effects in three-level models; Multiple membership models; Multiple membership multiple classification models; Glommary; Survey Weights; Model-based and design-based inference; Descriptive and analytic use of surveys; Two kinds of weights; Choosing between model-based and design-based analysis; Inclusion probabilities and two-level weights; Exploring the informativeness of the sampling design; Example: 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 estimators; Glommary; Longitudinal Data; Fixed occasions; The compound symmetry models; Random slopes; The fully multivariate model; Multivariate regression analysis; Explained variance; Variable occasion designs; Populations of curves; Random functions; Explaining the functions 27415.2.4; Changing covariates; Autocorrelated residuals; Glommary; Multivariate Multilevel Models; Why analyze multiple dependent variables simultaneously?; The multivariate random intercept model; Multivariate random slope models; Glommary; Discrete Dependent Variables; Hierarchical generalized linear models; Introduction to multilevel logistic regression; Heterogeneous proportions; The logit function: Log-odds; The empty model; The random intercept model; Estimation; Aggregation <br />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 regression; Glommary; Software; Special software for multilevel modeling; HLM; MLwiN; The MIXOR suite and SuperMix; Modules in general-purpose software packages; SAS procedures VARCOMP, MIXED, GLIMMIX, and NLMIXED; R; Stata; SPSS, commands VARCOMP and MIXED; Other multilevel software; PinT; Optimal Design; MLPowSim; Mplus; Latent Gold; REALCOM; WinBUGS; References; Index;
• Edition: 2nd ed
• Publisher Details: London : SAGE Publications Ltd
• Publication Date: 2011
• Extent: 1 online resource (368 pages)
• Subjects: 519.535; Multivariate analysis; Mathematical models
• Languages: English
• ISBNs: 9781446254332
• Related ISBNs: 144625433X
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.