<p>Multilevel Analysis :Techniques and Applications </p><p>这是一本不可多得的多水平模型好书,作者Joop Hox 是著名的多水平模型专家,本书从理论和几种软件的应用方面进行了详细的阐述,希望对大家的学习和科研有所帮助!</p><p>格式是WinDJView的.<br/><br/>Utrecht University, the Netherlands <br/>2002 Mahwah, New Jersey London <br/></p><p>Contents <br/></p><p>Preface ix <br/>1. Introduction to multilevel analysis 1 <br/>1.1. Why do we need special multilevel analysis techniques? 5 <br/>1.2. Multilevel theories 7 <br/>1.3. Models described in this book 8 <br/>2. The basic two-level regression model: introduction 11 <br/>2.1 Example 11 <br/>2.2 An extended example 15 <br/>2.3 Inspecting residuals 22 <br/>2.3.1 Examples of residuals plots 22 <br/>2.3.2 Examining slope variation: OLS and shrinkage estimators 27 <br/>2.4 Three- and more-level regression models 30 <br/>2.4.1 Multiple-level models 30 <br/>2.4.2 Intraclass-correlations in three-level models 31 <br/>2.5A note about notation and software 33 <br/>2.5.1 Notation 33 <br/>2.5.2 Software 34 <br/>3. Estimation and hypothesis testing in multilevel regression 37 <br/>3.1 Which estimation method? 3 7 <br/>3.1.1 Maximum Likelihood 37 <br/>3.1.2 Generalized Least Squares 39 <br/>3.1.3 Generalized Estimating Equations 40 <br/>3.1.4 Bootstrapping 40 <br/>3.1.5 Bayesian methods 41 <br/>3.2 Significance testing and confidence intervals 42 <br/>3.2.1 Testing regression coefficients and variance components 42 <br/>3.2.2 Comparing nested models 43 <br/>3.2.3 Comparing non-nested models 45 <br/>3.3 Contrasts and constraints 46 <br/>vi Contents <br/>4. Some Important Methodological and Statistical Issues 49 <br/>4.1 Analysis strategy 49 <br/>4.2 Centering and standardizing explanatory variables 54 <br/>4.3 Interpreting interactions 58 <br/>4.4 How much variance is explained? 63 <br/>5. Analyzing Longitudinal Data 73 <br/>5.1 Fixed and varying occasions 74 <br/>5.2 Example with fixed occasions 75 <br/>5.3 Example with varying occasions 86 <br/>5.4 Advantages of multilevel analysis for longitudinal data 93 <br/>5.5 Some statistical issues in longitudinal analysis 93 <br/>5.5.1 Investigating and analyzing patterns of change 93 <br/>5.5.2 Handling missing data and panel dropout 95 <br/>5.6 Complex covariance structures 96 <br/>5.7 Some software issues 102 <br/>6. The logistic model for dichotomous data and proportions 103 <br/>6.1 Generalized linear models 103 <br/>6.2 Multilevel generalized linear models 107 <br/>6.2.1 Estimating generalized multilevel models 108 <br/>6.3 Example: analyzing dichotomous data 110 <br/>6.4 Example: analyzing proportions 112 <br/>6.5 Analyzing event counts and other nonlinear models 122 <br/>7. Cross-classified multilevel models 123 <br/>7.1 Example of cross-classified data: <br/>pupils nested within (primary and secondary schools) 124 <br/>7.2 Example of cross-classified data: <br/>(sociometric ratings) in small groups 128 <br/>7.3 Statistical and computational issues 136 <br/>8. The multilevel approach to meta-analysis 139 <br/>8.1 Meta-analysis and multilevel modeling 139 <br/>8.2 The variance-known model 141 <br/>8.3 Example and comparison with classical meta-analysis 145 <br/>8.3.1 Classical meta-analysis 146 <br/>8.3.2 Multilevel meta-analysis 147 <br/>8.4 Correcting for artifacts 149 <br/>8.5 Statistical and software issues 153 <br/>Appendix 154 <br/>Contents vii <br/>9. Multivariate multilevel regression models 157 <br/>9.1 The basic model 158 <br/>9.2 Example of multivariate multilevel analysis: <br/>multiple response variables 161 <br/>9.3 Example of multivariate multilevel analysis: <br/>measuring group characteristics 168 <br/>10. Sample sizes and power analysis in multilevel regression 173 <br/>10.1 Sample size and accuracy of estimates 173 <br/>10.1.1 Accuracy of fixed parameters and their standard errors 173 <br/>10.1.2 Accuracy of random parameters and their standard errors 174 <br/>10.1.3 Accuracy and sample size 174 <br/>10.1.4 Accuracy and sample size <br/>with proportions and dichotomous data 175 <br/>10.2 Estimating power in multilevel regression designs 177 <br/>10.2.1 Post hoc power analysis 179 <br/>10.2.2 A priori power analysis: general issues 180 <br/>10.2.3 A priori power analysis: <br/>the intercept variance and intraclass correlation 184 <br/>10.2.4 A priori power analysis: <br/>designing an intervention study 186 <br/>10.2.5 A general procedure for power analysis 194 <br/>11. Advanced methods for estimation and testing 197 <br/>11.1 The profile likelihood method 199 <br/>11.2 Robust standard errors 200 <br/>11.3 Bootstrapping estimates and standard errors 203 <br/>11.3.1 A simple example of bootstrapping 205 <br/>11.3.2 Bootstrapping multilevel regression models 207 <br/>11.3.3 An example of the bootstrap 208 <br/>11.4 Bayesian estimation methods 211 <br/>11.4.1 Simulating the posterior distribution 213 <br/>11.4.2 An example of Bayesian estimation using MlwiN: <br/>the estrone data 214 <br/>11.4.3 An example of Bayesian estimation using MlwiN: <br/>the popularity data 220 <br/>11.4.4 Some remarks on Bayesian estimation methods 223 <br/>viii Contents <br/>12. Multilevel factor models 225 <br/>12.1 Decomposing multilevel variables 227 <br/>12.2 Muthen's pseudobalanced approach 228 <br/>12.2.1 An example of pseudobalanced multilevel factor analysis 232 <br/>12.2.2 Goodness of fit using the pseudobalanced approach 238 <br/>12.3 Direct estimation of the covariances at each level: <br/>the multivariate multilevel approach 242 <br/>12.4 Standardizing parameter estimates in <br/>multilevel structural equation modeling 248 <br/>13. Multilevel path models 251 <br/>13.1 Example of a multilevel path analysis 252 <br/>13.2 Statistical and software issues in multilevel factor and path models 259 <br/>14. Latent curve models 263 <br/>14.1 Example of latent curve modeling 266 <br/>14.2 A brief comparison of multilevel regression analysis <br/>and latent curve modeling of longitudinal data 273 <br/>Appendix: Data and stories 275 <br/>References 281 <br/>Author index 293 <br/>Subject index 299 <br/><br/></p>
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