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multilevel and longitudinal modeling using stata Third Edition Volume 1.pdf
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版本：第三版第一卷
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说明：班上有本书的第一版和第二版，今年刚出了第三班，包括两卷：连续变量卷和类别变量卷。第三版无论在内容和举例上都较一、二版有很大改进，看到有同学需要，此处先上传第一卷。由于本人需要买些数据，所以先设置下价格，相对于书的价值而言，本人设置的价格还是合理的，谢谢大家赞助。该书简介见stata官网，相应练习数据也可从官网下载。http://stata.com/bookstore/multi ... nal-modeling-stata/。
目录：
Table of contentsList of Tables List of Figures Preface Multilevel and longitudinal models: When and why? I Preliminaries 1 Review of linear regression 1.1 Introduction 1.2 Is there gender discrimination in faculty salaries? 1.3 Independent-samples t test 1.4 One-way analysis of variance 1.5 Simple linear regression 1.6 Dummy variables 1.7 Multiple linear regression 1.8 Interactions 1.9 Dummy variables for more than two groups 1.10 Other types of interactions
1.10.1 Interaction between dummy variables 1.10.2 Interaction between continuous covariates
1.11 Nonlinear effects 1.12 Residual diagnostics 1.13 Causal and noncausal interpretations of regression coefficients
1.13.1 Regression as conditional expectation 1.13.2 Regression as structural model
1.14 Summary and further reading 1.15 Exercises
II Two-level models 2 Variance-components models 2.1 Introduction 2.2 How reliable are peak-expiratory-flow measurements? 2.3 Inspecting within-subject dependence 2.4 The variance-components model
2.4.1 Model specification 2.4.2 Path diagram 2.4.3 Between-subject heterogeneity 2.4.4 Within-subject dependence
Intraclass correlation Intraclass correlation versus Pearson correlation
2.5 Estimation using Stata
2.5.1 Data preparation: Reshaping to long form 2.5.2 Using xtreg 2.5.3 Using xtmixed
2.6 Hypothesis tests and confidence intervals
2.6.1 Hypothesis test and confidence interval for the population mean 2.6.2 Hypothesis test and confidence interval for the between-cluster variance
Likelihood-ratio test F test Confidence intervals
2.7 Model as data-generating mechanism 2.8 Fixed versus random effects 2.9 Crossed versus nested effects 2.10 Parameter estimation
2.10.1 Model assumptions
Mean structure and covariance structure Distributional assumptions
2.10.2 Different estimation methods 2.10.3 Inference for β
Estimate and standard error: Balanced case Estimate: Unbalanced case
2.11 Assigning values to the random intercepts
2.11.1 Maximum “likelihood” estimation
Implementation via OLS regression Implementation via the mean total residual
2.11.2 Empirical Bayes prediction 2.11.3 Empirical Bayes standard errors
Comparative standard errors Diagnostic standard errors
2.12 Summary and further reading 2.13 Exercises
3 Random-intercept models with covariates 3.1 Introduction 3.2 Does smoking during pregnancy affect birthweight?
3.2.1 Data structure and descriptive statistics
3.3 The linear random-intercept model with covariates
3.3.1 Model specification 3.3.2 Model assumptions 3.3.3 Mean structure 3.3.4 Residual variance and intraclass correlation 3.3.5 Graphical illustration of random-intercept model
3.4 Estimation using Stata
3.4.1 Using xtreg 3.4.2 Using xtmixed
3.5 Coefficients of determination or variance explained 3.6 Hypothesis tests and confidence intervals
3.6.1 Hypothesis tests for regression coefficients
Hypothesis tests for individual regression coefficients Joint hypothesis tests for several regression coefficients
3.6.2 Predicted means and confidence intervals 3.6.3 Hypothesis test for random-intercept variance
3.7 Between and within effects of level-1 covariates
3.7.1 Between-mother effects 3.7.2 Within-mother effects 3.7.3 Relations among estimators 3.7.4 Level-2 endogeneity and cluster-level confounding 3.7.5 Allowing for different within and between effects 3.7.6 Hausman endogeneity test
3.8 Fixed versus random effects revisited 3.9 Assigning values to random effects: Residual diagnostics 3.10 More on statistical inference
3.10.1 Overview of estimation methods 3.10.2 Consequences of using standard regression modeling for clustered data 3.10.3 Power and sample-size determination
3.11 Summary and further reading 3.12 Exercises
4 Random-coefficient models 4.1 Introduction 4.2 How effective are different schools? 4.3 Separate linear regressions for each school 4.4 Specification and interpretation of a random-coefficient model
4.4.1 Specification of a random-coefficient model 4.4.2 Interpretation of the random-effects variances and covariances
4.5 Estimation using xtmixed
4.5.1 Random-intercept model 4.5.2 Random-coefficient model
4.6 Testing the slope variance 4.7 Interpretation of estimates 4.8 Assigning values to the random intercepts and slopes
4.8.1 Maximum “likelihood” estimation 4.8.2 Empirical Bayes prediction 4.8.3 Model visualization 4.8.4 Residual diagnostics 4.8.5 Inferences for individual schools
4.9 Two-stage model formulation 4.10 Some warnings about random-coefficient models
4.10.1 Meaningful specification 4.10.2 Many random coefficients 4.10.3 Convergence problems 4.10.4 Lack of identification
4.11 Summary and further reading 4.12 Exercises
III Models for longitudinal and panel data Introduction to models for longitudinal and panel data (part III) 5 Subject-specific effects and dynamic models 5.1 Introduction 5.2 Conventional random-intercept model 5.3 Random-intercept models accommodating endogenous covariates
5.3.1 Consistent estimation of effects of endogenous time-varying covariates 5.3.2 Consistent estimation of effects of endogenous time-varying and endogenous time-constant covariates
5.4 Fixed-intercept model
5.4.1 Using xtreg or regress with a differencing operator 5.4.2 Using anova
5.5 Random-coefficient model 5.6 Fixed-coefficient model 5.7 Lagged-response or dynamic models
5.7.1 Conventional lagged-response model 5.7.2 Lagged-response model with subject-specific intercepts
5.8 Missing data and dropout
5.8.1 Maximum likelihood estimation under MAR: A simulation
5.9 Summary and further reading 5.10 Exercises
6 Marginal models 6.1 Introduction 6.2 Mean structure 6.3 Covariance structures
6.3.1 Unstructured covariance matrix 6.3.2 Random-intercept or compound symmetric/exchangeable structure 6.3.3 Random-coefficient structure 6.3.4 Autoregressive and exponential structures 6.3.5 Moving-average residual structure 6.3.6 Banded and Toeplitz structures
6.4 Hybrid and complex marginal models
6.4.1 Random effects and correlated level-1 residuals 6.4.2 Heteroskedastic level-1 residuals over occasions 6.4.3 Heteroskedastic level-1 residuals over groups 6.4.4 Different covariance matrices over groups
6.5 Comparing the fit of marginal models 6.6 Generalized estimating equations (GEE) 6.7 Marginal modeling with few units and many occasions
6.7.1 Is a highly organized labor market beneficial for economic growth? 6.7.2 Marginal modeling for long panels 6.7.3 Fitting marginal models for long panels in Stata
6.8 Summary and further reading 6.9 Exercises
7 Growth-curve models 7.1 Introduction 7.2 How do children grow?
7.2.1 Observed growth trajectories
7.3 Models for nonlinear growth
7.3.1 Polynomial models
Fitting the models Predicting the mean trajectory Predicting trajectories for individual children
7.3.2 Piecewise linear models
Fitting the models Predicting the mean trajectory
7.4 Two-stage model formulation 7.5 Heteroskedasticity
7.5.1 Heteroskedasticity at level 1 7.5.2 Heteroskedasticity at level 2
7.6 How does reading improve from kindergarten through third grade? 7.7 Growth-curve model as a structural equation model
7.7.1 Estimation using sem 7.7.2 Estimation using xtmixed
7.8 Summary and further reading 7.9 Exercises
IV Models with nested and crossed random effects 8 Higher-level models with nested random effects 8.1 Introduction 8.2 Do peak-expiratory-flow measurements vary between methods within subjects? 8.3 Inspecting sources of variability 8.4 Three-level variance-components models 8.5 Different types of intraclass correlation 8.6 Estimation using xtmixed 8.7 Empirical Bayes prediction 8.8 Testing variance components 8.9 Crossed versus nested random effects revisited 8.10 Does nutrition affect cognitive development of Kenyan children? 8.11 Describing and plotting three-level data
8.11.1 Data structure and missing data 8.11.2 Level-1 variables 8.11.3 Level-2 variables 8.11.4 Level-3 variables 8.11.5 Plotting growth trajectories
8.12 Three-level random-intercept model
8.12.1 Model specification: Reduced form 8.12.2 Model specification: Three-stage formulation 8.12.3 Estimation using xtmixed
8.13 Three-level random-coefficient models
8.13.1 Random coefficient at the child level 8.13.2 Random coefficient at the child and school levels
8.14 Residual diagnostics and predictions 8.15 Summary and further reading 8.16 Exercises
9 Crossed random effects 9.1 Introduction 9.2 How does investment depend on expected profit and capital stock? 9.3 A two-way error-components model
9.3.1 Model specification 9.3.2 Residual variances, covariances, and intraclass correlations
Longitudinal correlations Cross-sectional correlations
9.3.3 Estimation using xtmixed 9.3.4 Prediction
9.4 How much do primary and secondary schools affect attainment at age 16? 9.5 Data structure 9.6 Additive crossed random-effects model
9.6.1 Specification 9.6.2 Estimation using xtmixed
9.7 Crossed random-effects model with random interaction
9.7.1 Model specification 9.7.2 Intraclass correlations 9.7.3 Estimation using xtmixed 9.7.4 Testing variance components 9.7.5 Some diagnostics
9.8 A trick requiring fewer random effects 9.9 Summary and further reading 9.10 Exercises
A Useful Stata commands References Author index (pdf) Subject index (pdf)
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