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Multilevel and Longitudinal Modeling Using Stata, Volume I: Continuous Responses, Third Edition 3rd New edition [Pehme köide]

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(London School of Economics, UK), (University of California, Berkeley, USA)
  • Formaat: Paperback / softback, 514 pages, kaal: 1043 g
  • Ilmumisaeg: 02-Apr-2012
  • Kirjastus: Stata Press
  • ISBN-10: 159718103X
  • ISBN-13: 9781597181037
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Multilevel and Longitudinal Modeling Using Stata, Volume I: Continuous Responses, Third Edition 3rd New editionSuurem pilt
  • Formaat: Paperback / softback, 514 pages, kaal: 1043 g
  • Ilmumisaeg: 02-Apr-2012
  • Kirjastus: Stata Press
  • ISBN-10: 159718103X
  • ISBN-13: 9781597181037
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781597181037.html
Märksõnad:

Volume I is devoted to continuous Gaussian linear mixed models and has nine chapters. The chapters are organized in four parts. The first part provides a review of the methods of linear regression. The second part provides an in-depth coverage of the two-level models, the simplest extensions of a linear regression model. The mixed-model foundation and the in-depth coverage of the mixed-model principles provided in volume I for continuous outcomes, make it straightforward to transition to generalized linear mixed models for noncontinuous outcomes described in volume II.

List of Tables xvii
List of Figures xix
Preface xxv
Multilevel and longitudinal models: When and why? 1(8)
I Preliminaries 9(62)
1 Review of linear regression
11(60)
1.1 Introduction
11(1)
1.2 Is there gender discrimination in faculty salaries?
11(1)
1.3 Independent-samples t test
12(5)
1.4 One-way analysis of variance
17(2)
1.5 Simple linear regression
19(8)
1.6 Dummy variables
27(3)
1.7 Multiple linear regression
30(6)
1.8 Interactions
36(6)
1.9 Dummy variables for more than two groups
42(6)
1.10 Other types of interactions
48(4)
1.10.1 Interaction between dummy variables
48(2)
1.10.2 Interaction between continuous covariates
50(2)
1.11 Nonlinear effects
52(2)
1.12 Residual diagnostics
54(2)
1.13 Causal and noncausal interpretations of regression coefficients
56(3)
1.13.1 Regression as conditional expectation
56(1)
1.13.2 Regression as structural model
57(2)
1.14 Summary and further reading
59(1)
1.15 Exercises
60(11)
II Two-level models 71(154)
2 Variance-components models
73(50)
2.1 Introduction
73(1)
2.2 How reliable are peak-expiratory-flow measurements?
74(1)
2.3 Inspecting within-subject dependence
75(2)
2.4 The variance-components model
77(5)
2.4.1 Model specification
77(1)
2.4.2 Path diagram
78(1)
2.4.3 Between-subject heterogeneity
79(1)
2.4.4 Within-subject dependence
80(2)
Intraclass correlation
80(1)
Intraclass correlation versus Pearson correlation
81(1)
2.5 Estimation using Stata
82(5)
2.5.1 Data preparation: Reshaping to long form
83
2.5.2 Using xtreg
81(4)
2.5.3 Using xtmixed
85(2)
2.6 Hypothesis tests and confidence intervals
87(6)
2.6.1 Hypothesis test and confidence interval for the population mean
87(1)
2.6.2 Hypothesis test and confidence interval for the between-cluster variance
88(11)
Likelihood-ratio test
88(1)
Score test
89(3)
F test
92(1)
Confidence intervals
92(1)
2.7 Model as data-generating mechanism
93(2)
2.8 Fixed versus random effects
95(2)
2.9 Crossed versus nested effects
97(2)
2.10 Parameter estimation
99(7)
2.10.1 Model assumptions
99(2)
Mean structure and covariance structure
100(1)
Distributional assumptions
101(1)
2.10.2 Different estimation methods
101(2)
2.10.3 Inference for β
103(3)
Estimate and standard error: Balanced case
103(2)
Estimate: Unbalanced case
105(1)
2.11 Assigning values to the random intercepts
106(9)
2.11.1 Maximum "likelihood" estimation
106
Implementation via OLS regression
107(1)
Implementation via the mean total residual
108
2.11.2 Empirical Bayes prediction
100(13)
2.11.3 Empirical Bayes standard errors
113(10)
Comparative standard errors
113(1)
Diagnostic standard errors
114(1)
2.12 Summary and further reading
115(1)
2.13 Exercises
116(7)
3 Random-intercept models with covariates
123(58)
3.1 introduction
123(1)
3.2 Does smoking during pregnancy affect birthweight?
123(4)
3.2.1 Data structure and descriptive statistics
125(2)
3.3 The linear random-intercept model with covariates
127(4)
3.3.1 Model specification
127(1)
3.3.2 Model assumptions
128(2)
3.3.3 Mean structure
130(1)
3.3.4 Residual variance and intraclass correlation
130(1)
3.3.5 Graphical illustration of random-intercept model
131(1)
3.4 Estimation using Stata
131(3)
3.4.1 Using xtreg
132(1)
3.4.2 Using xtmixed
133(1)
3.5 Coefficients of determination or variance explained
134(4)
3.6 Hypothesis tests and confidence intervals
138(4)
3.6.1 Hypothesis tests for regression coefficients
138(2)
Hypothesis tests for individual regression coefficients
138(1)
Joint hypothesis tests for several regression coefficients
139(1)
3.6.2 Predicted means and confidence intervals
140(2)
3.6.3 Hypothesis test for random-intercept variance
142(1)
3.7 Between and within effects of level-1 covariates
142(16)
3.7.1 Between-mother effects
143(2)
3.7.2 Within-mother effects
145(2)
3.7.3 Relations among estimators
147(2)
3.7.4 Level-2 endogeneity and cluster-level confounding
149(3)
3.7.5 Allowing for different within and between effects
152(5)
3.7.6 Hausman endogeneity test
157(1)
3.8 Fixed versus random effects revisited
158(2)
3.9 Assigning values to random effects: Residual diagnostics
160(4)
3.10 More on statistical inference
164(7)
3.10.1 Overview of estimation methods
164(3)
3.10.2 Consequences of using standard regression modeling for clustered data
167(1)
3.10.3 Power and sample-size determination
168(3)
3.11 Summary and further reading
171(1)
3.12 Exercises
172(9)
4 Random-coefficient models
181(44)
4.1 Introduction
181(1)
4.2 How effective are different schools?
181(1)
4.3 Separate linear regressions for each school
182(6)
4.4 Specification and interpretation of a random-coefficient model
188(6)
4.4.1 Specification of a random-coefficient model
188(3)
4.4.2 Interpretation of the random-effects variances and co-variances
191(3)
4.5 Estimation using xtmixed
194(3)
4.5.1 Random-intercept model
194(2)
4.5.2 Random-coefficient model
196(1)
4.6 Testing the slope variance
197(1)
4.7 Interpretation of estimates
198(2)
4.8 Assigning values to the random intercepts arid slopes
200(10)
4.8.1 Maximum "likelihood" estimation
200(1)
4.8.2 Empirical Bayes prediction
201(2)
4.8.3 Model visualization
203(1)
4.8.4 Residual diagnostics
204(3)
4.8.5 Inferences for individual schools
207(3)
4.9 Two-stage model formulation
210(3)
4.10 Some warnings about random-coefficient models
213(2)
4.10.1 Meaningful specification
213(1)
4.10.2 Many random coefficients
213(1)
4.10.3 Convergence problems
214(1)
4.10.4 Lack of identification
214(1)
4.11 Summary and further reading
215(1)
4.12 Exercises
216(9)
III Models for longitudinal and panel data 225(158)
Introduction to models for longitudinal and panel data (part III)
227(20)
5 Subject-specific effects and dynamic models
247(46)
5.1 Introduction
247(1)
5.2 Conventional random-intercept model
248(2)
5.3 Random-intercept models accommodating endogenous covariates
250(7)
5.3.1 Consistent estimation of effects of endogenous time-varying covariates
250(3)
5.3.2 Consistent estimation of effects of endogenous time-varying and endogenous time-constant covariates
253(4)
5.4 Fixed-intercept model
257(8)
5.4.1 Using xtreg or regress with a differencing operator
259(3)
5.4.2 Using anova
262(3)
5.5 Random-coefficient model
265(2)
5.6 Fixed-coefficient model
267(2)
5.7 Lagged-response or dynamic models
269(9)
5.7.1 Conventional lagged-response model
269(4)
5.7.2 Lagged-response model with subject-specific intercepts
273(5)
5.8 Missing data and dropout
278(4)
5.8.1 Maximum likelihood estimation under MAR: A simulation
279(3)
5.9 Summary and further reading
282(1)
5.10 Exercises
283(10)
6 Marginal models
293(50)
6.1 Introduction
293(1)
6.2 Mean structure
293(1)
6.3 Covariance structures
294(22)
6.3.1 Unstructured covariance matrix
298(5)
6.3.2 Random-intercept or compound symmetric/exchangeable structure
303(2)
6.3.3 Random-coefficient structure
305(3)
6.3.4 Autoregressive and exponential structures
308(3)
6.3.5 Moving-average residual structure
311(2)
6.3.6 Banded and Toeplitz structures
313(3)
6.4 Hybrid and complex marginal models
316(6)
6.4.1 Random effects and correlated residuals
316(1)
6.4.2 Heteroskedastic level-1 residuals over occasions
317(1)
6.4.3 Heteroskedastic level-1 residuals over groups
318(3)
6.4.4 Different covariance matrices over groups
321(1)
6.5 Comparing the fit of marginal models
322(3)
6.6 Generalized estimating equations (GEE)
325(2)
6.7 Marginal modeling with few units and many occasions
327(5)
6.7.1 Is a highly organized labor market beneficial for economic growth'?
328(1)
6.7.2 Marginal modeling for long panels
329(1)
6.7.3 Fitting marginal models for long panels in Stata
329(3)
6.8 Summary and further reading
332(1)
6.9 Exercises
333(10)
7 Growth-curve models
343(40)
7.1 Introduction
343(1)
7.2 How do children grow?
343(2)
7.2.1 Observed growth trajectories
344(1)
7.3 Models for nonlinear growth
345(13)
7.3.1 Polynomial models
345(8)
Fitting the models
346(3)
Predicting the mean trajectory
349(2)
Predicting trajectories for individual children
351(2)
7.3.2 Piecewise linear models
353(7)
Fitting the models
354(3)
Predicting the mean trajectory
357(1)
7.4 Two-stage model formulation
358(2)
7.5 Heteroskedasticity
360(4)
7.5.1 Heteroskedasticity at level 1
360(2)
7.5.2 Heteroskedasticity at level 2
362(2)
7.6 How does reading improve from kindergarten through third grade?
364(1)
7.7 Growth-curve model as a structural equation model
364(11)
7.7.1 Estimation using sem
366(5)
7.7.2 Estimation using xtmixed
371(4)
7.8 Summary and further reading
375(1)
7.9 Exercises
376(7)
IV Models with nested and crossed random effects 383(88)
8 Higher-level models with nested random effects
385(48)
8.1 Introduction
385(1)
8.2 Do peak-expiratory-flow measurements vary between methods within subjects?
386(2)
8.3 Inspecting sources of variability
388(1)
8.4 Three-level variance-components models
389(3)
8.5 Different types of intraclass correlation
392(1)
8.6 Estimation using xtmixed
393(1)
8.7 Empirical Bayes prediction
394(1)
8.8 Testing variance components
395(2)
8.9 Crossed versus nested random effects revisited
397(2)
8.10 Does nutrition affect cognitive development of Kenyan children?
399(1)
8.11 Describing and plotting three-level data
400(5)
8.11.1 Data structure and missing data
400(1)
8.11.2 Level-1 variables
401(1)
8.11.3 Level-2 variables
402(1)
8.11.4 Level-3 variables
403(1)
8.11.5 Plotting growth trajectories
404(1)
8.12 Three-level random-intercept model
405(4)
8.12.1 Model specification: Reduced form
405(1)
8.12.2 Model specification: Three-stage formulation
405(1)
8.12.3 Estimation using xtmixed
406(3)
8.13 Three-level random-coefficient models
409(4)
8.13.1 Random coefficient at the child level
409(2)
8.13.2 Random coefficient at the child and school levels
411(2)
8.14 Residual diagnostics and predictions
413(5)
8.15 Summary and further reading
418(1)
8.16 Exercises
419(14)
9 Crossed random effects
433(38)
9.1 Introduction
433(1)
9.2 How does investment depend on expected profit and capital stock?
434(1)
9.3 A two-way error-components model
435(8)
9.3.1 Model specification
435(1)
9.3.2 Residual variances, covariances, and intraclass correlations
436(1)
Longitudinal correlations
436(1)
Cross-sectional correlations
436(1)
9.3.3 Estimation using xtmixed
437(4)
9.3.4 Prediction
441(2)
9.4 How much do primary and secondary schools affect attainment at age 16?
443(1)
9.5 Data structure
444(2)
9.6 Additive crossed random-effects model
446(2)
9.6.1 Specification
446(1)
9.6.2 Estimation using xtmixed
447(1)
9.7 Crossed random-effects model with random interaction
448(8)
9.7.1 Model specification
448(1)
9.7.2 Intraclass correlations
448(1)
9.7.3 Estimation using xtmixed
449(2)
9.7.4 Testing variance components
451(2)
9.7.5 Some diagnostics
453(3)
9.8 A trick requiring fewer random effects
456(3)
9.9 Summary and further reading
459(1)
9.10 Exercises
460(11)
A Useful Stata commands 471(2)
References 473(12)
Author index 485(6)
Subject index 491