简介
""Dr. Hox is a master at presenting sophisticated statistical ideas and models in very pragmatic way... There have been many developments in the area of multilevel structural equation modeling and (Pox's] book is the only multilevel one that covers this important area...The additional chapters... make the book more... appealing... I would definitely use Hox's book... [and] recommend it to my colleagues."-Donald Hedeker, University of Illinois at Chicago, USA" ""The second edition offers a simplistic yet in-depth coverage of difficult material. It follows closely the style and approach of the highly successful first edition. The [book] also incorporates many of the latest developments that have emerged over the past few years in the field."-George A. Marcoulides, University of California, Riverside, Quantitative Methodology Series Editor" ""This book continues to be one of the most readable texts on multilevel analysis. Hox does a masterful job of making the complex palatable.
This book is a great addition for the practitioner and methodologist alike."-J. Kyle Roberts, Southern Methodist University, USA" ""The writing style is unquestionably a strength of this book particularly when compared to competing books... Without question I would adopt the revised version and recommend it to others. The.., changes... strengthen an already effective book."-Dick Carpenter, University of Colorado, USA" "This practical introduction helps readers apply multilevel techniques to their research. Noted as an accessible introduction, the book also includes advanced extensions, making it useful as both an introduction and as a reference to students, researchers, and methodologists. Basic models and examples are discussed in non-technical terms with an emphasis on understanding the methodological and statistical issues involved in using these models. The estimation and interpretation of multilevel models is demonstrated using realistic examples from various disciplines.
For example, readers will find data sets on stress in hospitals, GPA scores, survey responses, street safety, epilepsy, divorce, and sociometric scores, to name a few. The data sets are available on the website in SPSS, HLM, MLwiN, LISREL and/or Mplus files. Readers are introduced to both the multilevel regression model and multilevel structural models." "Highlights of the second edition include:" "-Two new chaptersone on multilevel models for ordinal and count data (Ch. 7) and another on multilevel survival analysis (Ch. 8)." "-Thoroughly updated chapters on multilevel structural equation modeling that reflect the enormous technical progress of the last few years." "-The addition of some simpler examples to help the novice, whilst the more complex examples that combine more than one problem have been retained." "-A new section on multivariate meta-analysis (Ch.
11)." "-Expanded discussions of covariance structures across time and analyzing longitudinal data where no trend is expected." "-Expanded chapter on the logistic model for dichotomous data and proportions with new estimation methods." "-An updated website at v ww..000hox.net/ with data sets for all the text examples and up-to-date screen shots and PowerPoint slides for instructors." "Ideal for introductory courses on multilevel modeling and/or ones that introduce this topic in some detail taught in a variety of disciplines including: psychology, education, sociology, the health sciences, and business, the advanced extensions also make this a favorite resource for researchers and methodologists in these disciplines. A basic understanding of ANOVA and multiple regression is assumed. The section on multilevel structural equation models assumes a basic understanding of SEM."--BOOK JACKET.
目录
Table Of Contents:
Preface viii
1. Introduction to Multilevel Analysis 1(10)
1.1 Aggregation and disaggregation 2(2)
1.2 Why do we need special multilevel analysis techniques? 4(3)
1.3 Multilevel theories 7(1)
1.4 Models described in this book 8(3)
2. The Basic Two-Level Regression Model 11(29)
2.1 Example 11(5)
2.2 An extended example 16(7)
2.3 Inspecting residuals 23(9)
2.4 Three- and more-level regression models 32(4)
2.5 A note about notation and software 36(4)
3. Estimation and Hypothesis Testing in Multilevel Regression 40(14)
3.1 Which estimation method? 40(5)
3.2 Significance testing and confidence intervals 45(6)
3.3 Contrasts and constraints 51(3)
4. Some Important Methodological and Statistical Issues 54(25)
4.1 Analysis strategy 54(5)
4.2 Centering and standardizing explanatory variables 59(4)
4.3 Interpreting interactions 63(5)
4.4 Group mean centering 68(1)
4.5 How much variance is explained? 69(10)
5. Analyzing Longitudinal Data 79(33)
5.1 Fixed and varying occasions 80(1)
5.2 Example with fixed occasions 81(12)
5.3 Example with varying occasions 93(5)
5.4 Advantages of multilevel analysis for longitudinal data 98(1)
5.5 Complex covariance structures 99(5)
5.6 Statistical issues in longitudinal analysis 104(7)
5.7 Software issues 111(1)
6. The Multilevel Generalized Linear Model for Dichotomous Data and Proportions 112(29)
6.1 Generalized linear models 112(5)
6.2 Multilevel generalized linear models 117(4)
6.3 Example: Analyzing dichotomous data 121(2)
6.4 Example: Analyzing proportions 123(10)
6.5 The ever changing latent scale: Comparing coefficients and variances 133(6)
6.6 Interpretation and software issues 139(2)
7. The Multilevel Generalized Linear Model for Categorical and Count Data 141(18)
7.1 Ordered categorical data 141(10)
7.2 Count data 151(6)
7.3 The ever changing latent scale, again 157(2)
8. Multilevel Survival Analysis 159(12)
8.1 Survival analysis 159(4)
8.2 Multilevel survival analysis 163(6)
8.3 Multilevel ordinal survival analysis 169(2)
9. Cross-Classified Multilevel Models 171(17)
9.1 Example of cross-classified data: Pupils nested within (primary and secondary schools) 173(4)
9.2 Example of cross-classified data: (Sociometric ratings) in small groups 177(8)
9.3 Statistical and computational issues 185(3)
10. Multivariate Multilevel Regression Models 188(17)
10.1 The multivariate model 189(3)
10.2 Example of multivariate multilevel analysis: Multiple response variables 192(5)
10.3 Example of multivariate multilevel analysis: Measuring group characteristics 197(8)
11. The Multilevel Approach to Meta-Analysis 205(28)
11.1 Meta-analysis and multilevel modeling 205(2)
11.2 The variance-known model 207(4)
11.3 Example and comparison with classical meta-analysis 211(6)
11.4 Correcting for artifacts 217(4)
11.5 Multivariate meta-analysis 221(7)
11.6 Statistical and software issues 228(2)
Appendix 230(3)
12. Sample Sizes and Power Analysis in Multilevel Regression 233(24)
12.1 Sample size and accuracy of estimates 233(4)
12.2 Estimating power in multilevel regression designs 237(20)
13. Advanced Issues in Estimation and Testing 257(31)
13.1 The profile likelihood method 259(1)
13.2 Robust standard errors 260(4)
13.3 Multilevel bootstrapping 264(7)
13.4 Bayesian estimation methods 271(17)
14. Multilevel Factor Models 288(24)
14.1 The within and between approach 290(7)
14.2 Full maximum likelihood estimation 297(2)
14.3 An example of multilevel factor analysis 299(6)
14.4 Standardizing estimates in multilevel Structural equation modeling 305(1)
14.5 Goodness of fit in multilevel structural equation modeling 306(3)
14.6 Notation and software 309(3)
15. Multilevel Path Models 312(13)
15.1 Example of a multilevel path analysis 312(8)
15.2 Statistical and software issues in multilevel factor and path models 320(3)
Appendix 323(2)
16. Latent Curve Models 325(12)
16.1 Example of latent curve modeling 328(7)
16.2 A comparison of multilevel regression analysis and latent curve modeling 335(2)
References 337(15)
Appendix A: Data and Stories 352(8)
Appendix B: Aggregating and Disaggregating 360(3)
Appendix C: Recoiling Categorical Data 363(3)
Appendix D: Constructing Orthogonal Polynomials 366(3)
Author Index 369(7)
Subject Index 376
Preface viii
1. Introduction to Multilevel Analysis 1(10)
1.1 Aggregation and disaggregation 2(2)
1.2 Why do we need special multilevel analysis techniques? 4(3)
1.3 Multilevel theories 7(1)
1.4 Models described in this book 8(3)
2. The Basic Two-Level Regression Model 11(29)
2.1 Example 11(5)
2.2 An extended example 16(7)
2.3 Inspecting residuals 23(9)
2.4 Three- and more-level regression models 32(4)
2.5 A note about notation and software 36(4)
3. Estimation and Hypothesis Testing in Multilevel Regression 40(14)
3.1 Which estimation method? 40(5)
3.2 Significance testing and confidence intervals 45(6)
3.3 Contrasts and constraints 51(3)
4. Some Important Methodological and Statistical Issues 54(25)
4.1 Analysis strategy 54(5)
4.2 Centering and standardizing explanatory variables 59(4)
4.3 Interpreting interactions 63(5)
4.4 Group mean centering 68(1)
4.5 How much variance is explained? 69(10)
5. Analyzing Longitudinal Data 79(33)
5.1 Fixed and varying occasions 80(1)
5.2 Example with fixed occasions 81(12)
5.3 Example with varying occasions 93(5)
5.4 Advantages of multilevel analysis for longitudinal data 98(1)
5.5 Complex covariance structures 99(5)
5.6 Statistical issues in longitudinal analysis 104(7)
5.7 Software issues 111(1)
6. The Multilevel Generalized Linear Model for Dichotomous Data and Proportions 112(29)
6.1 Generalized linear models 112(5)
6.2 Multilevel generalized linear models 117(4)
6.3 Example: Analyzing dichotomous data 121(2)
6.4 Example: Analyzing proportions 123(10)
6.5 The ever changing latent scale: Comparing coefficients and variances 133(6)
6.6 Interpretation and software issues 139(2)
7. The Multilevel Generalized Linear Model for Categorical and Count Data 141(18)
7.1 Ordered categorical data 141(10)
7.2 Count data 151(6)
7.3 The ever changing latent scale, again 157(2)
8. Multilevel Survival Analysis 159(12)
8.1 Survival analysis 159(4)
8.2 Multilevel survival analysis 163(6)
8.3 Multilevel ordinal survival analysis 169(2)
9. Cross-Classified Multilevel Models 171(17)
9.1 Example of cross-classified data: Pupils nested within (primary and secondary schools) 173(4)
9.2 Example of cross-classified data: (Sociometric ratings) in small groups 177(8)
9.3 Statistical and computational issues 185(3)
10. Multivariate Multilevel Regression Models 188(17)
10.1 The multivariate model 189(3)
10.2 Example of multivariate multilevel analysis: Multiple response variables 192(5)
10.3 Example of multivariate multilevel analysis: Measuring group characteristics 197(8)
11. The Multilevel Approach to Meta-Analysis 205(28)
11.1 Meta-analysis and multilevel modeling 205(2)
11.2 The variance-known model 207(4)
11.3 Example and comparison with classical meta-analysis 211(6)
11.4 Correcting for artifacts 217(4)
11.5 Multivariate meta-analysis 221(7)
11.6 Statistical and software issues 228(2)
Appendix 230(3)
12. Sample Sizes and Power Analysis in Multilevel Regression 233(24)
12.1 Sample size and accuracy of estimates 233(4)
12.2 Estimating power in multilevel regression designs 237(20)
13. Advanced Issues in Estimation and Testing 257(31)
13.1 The profile likelihood method 259(1)
13.2 Robust standard errors 260(4)
13.3 Multilevel bootstrapping 264(7)
13.4 Bayesian estimation methods 271(17)
14. Multilevel Factor Models 288(24)
14.1 The within and between approach 290(7)
14.2 Full maximum likelihood estimation 297(2)
14.3 An example of multilevel factor analysis 299(6)
14.4 Standardizing estimates in multilevel Structural equation modeling 305(1)
14.5 Goodness of fit in multilevel structural equation modeling 306(3)
14.6 Notation and software 309(3)
15. Multilevel Path Models 312(13)
15.1 Example of a multilevel path analysis 312(8)
15.2 Statistical and software issues in multilevel factor and path models 320(3)
Appendix 323(2)
16. Latent Curve Models 325(12)
16.1 Example of latent curve modeling 328(7)
16.2 A comparison of multilevel regression analysis and latent curve modeling 335(2)
References 337(15)
Appendix A: Data and Stories 352(8)
Appendix B: Aggregating and Disaggregating 360(3)
Appendix C: Recoiling Categorical Data 363(3)
Appendix D: Constructing Orthogonal Polynomials 366(3)
Author Index 369(7)
Subject Index 376
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