Handbook of Multilevel Analysis

Handbook of Multilevel Analysis

von: Jan Deleeuw, Erik Meijer

Springer-Verlag, 2007

ISBN: 9780387731865 , 494 Seiten

Format: PDF, OL

Kopierschutz: Wasserzeichen

Windows PC,Mac OSX Apple iPad, Android Tablet PC's Online-Lesen für: Windows PC,Mac OSX,Linux

Preis: 178,49 EUR

  • Stochastic Control in Insurance
    Nonsmooth Vector Functions and Continuous Optimization
    Geometric Group Theory - Geneva and Barcelona Conferences
    Multiscale Modeling - A Bayesian Perspective
    Logica Universalis - Towards a General Theory of Logic
    Expounding the Mathematical Seed. Vol. 2: The Supplements - A Translation of Bh?skara I on the Mathematical Chapter of the ?ryabhat?ya
  • All of Nonparametric Statistics
    High Performance Computing on Vector Systems 2007
    Standard Monomial Theory - Invariant Theoretic Approach
    Mathematical Survey Lectures 1943-2004
    Stochastic Global Optimization
    Algebraic Theory of Locally Nilpotent Derivations
 

Mehr zum Inhalt

Handbook of Multilevel Analysis


 

Foreword

5

Contents

9

List of Contributors

11

1 Introduction to Multilevel Analysis

14

1.1 History

14

1.2 Application Areas

16

1.3 Chapter Outline

18

1.4 Models

19

1.5 Loss Functions

32

1.6 Techniques and Algorithms

37

1.7 Software

60

1.8 Sampling Weights

61

1.9 A School Effects Example

67

1.10 Final Remarks

72

Appendix

73

References

81

2 Bayesian Multilevel Analysis and MCMC

89

2.1 Introduction

89

2.2 The Need for Simulation-Based Bayesian Computation

103

2.3 Markov Chain Monte Carlo (MCMC) Methods

106

2.4 MCMC Diagnostics

133

2.5 The Case Study Revisited

139

References

147

3 Diagnostic Checks for Multilevel Models

152

3.1 Specification of the Two-Level Model

152

3.2 Model Checks Within the Framework of the Hierarchical Linear Model

153

3.3 Residuals

160

3.4 Influence Diagnostics of Higher-Level Units

170

3.5 Simulation-Based Assessment of Model Specification

171

3.6 Non-linear Transformations in the Fixed Part

172

3.7 Polynomial Model

173

3.8 Regression Spline Model

173

3.9 Smoothing Spline Model

175

3.10 Example: Effect of IQ on a Language Test

180

3.11 Extensions

182

References

183

4 Optimal Designs for Multilevel Studies

187

4.1 Introduction

187

4.2 Optimality and Power

189

4.3 Optimal Designs for Experiments

191

4.4 Optimal Experimental Designs for Models with Covariates

196

4.5 Optimal Experimental Designs for Multilevel Logistic Models

198

4.6 Optimal Experimental Designs for Longitudinal Data

200

4.7 Optimal Designs for Surveys

203

4.8 Optimal Designs for Variance Parameters

207

4.9 Robustness of Optimal Designs

208

4.10 Concluding Remarks

210

References

211

5 Many Small Groups

216

5.1 Introduction

216

5.2 The Model

219

5.3 Some Applications

224

5.4 Validity of Statistical Inferences: Linear-Normal Case

233

5.5 Validity of Statistical Inferences: Non-Linear Link Functions

239

References

243

6 Multilevel Models for Ordinal and Nominal Variables

246

6.1 Introduction

246

6.2 Multilevel Logistic Regression Model

247

6.3 Multilevel Proportional Odds Model

252

6.4 Multilevel Nominal Response Models

259

6.5 Computational Issues

262

6.6 Intraclass Correlation

264

6.7 Heterogeneous Variance Terms

265

6.8 Health Services Research Example

267

6.9 Discussion

275

References

277

7 Multilevel and Related Models for Longitudinal Data

284

7.1 Introduction

284

7.2 Models with Unit-Specific Intercepts

286

7.3 Models with Unit-Specific Intercepts and Slopes

292

7.4 Models with Correlated Residual Errors

297

7.5 Models with Lagged Responses

299

7.6 Marginal Approaches

301

7.7 Concluding Remarks

304

References

304

8 Non-Hierarchical Multilevel Models

309

8.1 Introduction

309

8.2 Cross-Classified Models

309

8.3 Multiple Membership Models

328

8.4 Combining Multiple Membership and Cross- Classified Structures in a Single Model

335

8.5 Consequences of Ignoring Non-Hierarchical Structures

340

References

341

9 Multilevel Generalized Linear Models

343

9.1 Introduction

343

9.2 Extending Multilevel Models

345

9.3 Approaches to Estimation

354

9.4 Infant and Child Mortality in Kenya

366

9.5 Summary and Conclusions

378

References

379

10 Missing Data

385

10.1 Background and Generalities

385

10.2 Models for Missing Values

389

10.3 EM Algorithm and Multiple Imputation

391

10.4 Multiple Imputation

393

10.5 Missing Values in Multilevel Data

395

10.6 Other Applications of EM and MI

402

10.7 Summary

405

References

406

11 Resampling Multilevel Models

408

11.1 Introduction

408

11.2 Model, ML Estimation, and Assumptions

410

11.3 General Theory of Bootstrap and Jackknife

412

11.4 Bootstrapping Two-Level Models

417

11.5 Jackknifing Two-Level Models

427

11.6 Software

430

11.7 Empirical Evidence

431

11.8 Extensions

434

References

436

12 Multilevel Structural Equation Modeling

441

12.1 Introduction

441

12.2 A General Two-Level Structural Equation Model

442

12.3 Maximum Likelihood for General Means and Covariance Structures

445

12.4 Fit Statistics and Hypothesis Testing

450

12.5 A Simple Illustration

451

12.6 Practical Applications

454

12.7 Conclusion and Discussion

461

Appendix

464

References

482

Author Index

485

Subject Index

490