Multilevel analysis : techniques and applications / Joop J. Hox, Mirjam Moerbeek, Rens van de Schoot.

Format:

Book

Author/Creator:

Hox, J. J., author.

Moerbeek, Mirjam, 1973- author.

Schoot, Rens van de, author.

Contributor:

ProQuest ebook central.

Series:

Quantitative methodology series

Language:

English

Subjects (All):

Social sciences--Statistical methods.

Social sciences.

Analysis of variance.

Regression analysis.

Physical Description:

1 online resource (pages cm.)

Edition:

Third edition.

Place of Publication:

New York, NY : Routledge, 2017.

System Details:

text file

Summary:

Applauded for its clarity, this accessible introduction helps readers apply multilevel techniques to their research. The book also includes advanced extensions, making it useful as both an introduction for students and as a reference for researchers. Basic models and examples are discussed in nontechnical 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 including psychology, education, public health, and sociology. Readers are introduced to a general framework on multilevel modeling which covers both observed and latent variables in the same model, while most other books focus on observed variables. In addition, Bayesian estimation is introduced and applied using accessible software. Book jacket.

Contents:

1 Introduction to Multilevel Analysis 1

1.1 Aggregation and Disaggregation 2

1.2 Why Do We Need Special Multilevel Analysis Techniques? 4

1.3 Multilevel Theories 6

1.4 Estimation and Software 7

2 The Basic Two-Level Regression Model 8

2.1 Example 8

2.2 An Extended Example 13

2.3 Three- and More Level Regression Models 19

2.4 Notation and Software 23

3 Estimation and Hypothesis Testing in Multilevel Regression 27

3.1 Which Estimation Method? 27

3.2 Bayesian Methods 30

3.3 Bootstrapping 32

3.4 Significance Testing and Model Comparison 33

3.5 Software 40

4 Some Important Methodological and Statistical Issues 41

4.1 Analysis Strategy 42

4.2 Centering and Standardizing Explanatory Variables 46

4.3 Interpreting Interactions 52

4.4 How Much Variance Is Explained? 57

4.5 Multilevel Mediation and Higher-Level Outcomes 64

4.6 Missing Data in Multilevel Analysis 65

4.7 Software 69

5 Analyzing Longitudinal Data 71

5.1 Introduction 71

5.2 Fixed and Varying Occasions 72

5.3 Example with Fixed Occasions 73

5.4 Example with Varying Occasions 84

5.5 Advantages of Multilevel Analysis for Longitudinal Data 88

5.6 Complex Covariance Structures 89

5.7 Statistical Issues in Longitudinal Analysis 94

5.8 Software 102

6 The Multilevel Generalized Linear Model for Dichotomous Data and Proportions 103

6.1 Generalized Linear Models 103

6.2 Multilevel Generalized Linear Models 107

6.3 Example: Analyzing Dichotomous Data 111

6.4 Example: Analyzing Proportions 113

6.5 The Ever-Changing Latent Scale: Comparing Coefficients and Explained Variances 121

6.6 Interpretation 128

6.7 Software 128

7 The Multilevel Generalized Linear Model for Categorical and Count Data 130

7.1 Ordered Categorical Data 130

7.2 Count Data 139

7.3 Explained Variance in Ordered Categorical and Count Data 146

7.4 The Ever-Changing Latent Scale, Again 147

7.5 Software 147

8 Multilevel Survival Analysis 148

8.1 Survival Analysis 148

8.2 Multilevel Survival Analysis 153

8.3 Multilevel Ordinal Survival Analysis 158

8.4 Software 160

9 Cross-Classified Multilevel Models 161

9.1 Introduction 161

9.2 Example of Cross-Classified Data: Pupils Nested Within (Primary and Secondary Schools) 163

9.3 Example of Cross-Classified Data: (Sociometric Ratings) in Small Groups 165

9.4 Software 172

10 Multivariate Multilevel Regression Models 173

10.1 The Multivariate Model 174

10.2 Example of Multivariate Multilevel Analysis: Multiple Response Variables 176

10.3 Example of Multivariate Multilevel Analysis: Measuring Group Characteristics 181

11 The Multilevel Approach to Meta-Analysis 189

11.1 Meta-Analysis and Multilevel Modeling 189

11.2 The Variance-Known Model 191

11.3 Example and Comparison with Classical Meta-Analysis 195

11.4 Correcting for Artifacts 201

11.5 Multivariate Meta-Analysis 204

11.6 Software 209

12 Sample Sizes and Power Analysis in Multilevel Regression 212

12.1 Sample Size and Accuracy of Estimates 212

12.2 Power Analysis 219

12.3 Methods for Randomized Controlled Trials 221

12.4 Methods for Observational Studies 229

12.5 Methods for Meta-Analysis 230

12.6 Software for Power Analysis 233

13 Assumptions and Robust Estimation Methods 235

13.1 Introduction 235

13.2 Example Data and Some Issues with Non-Normality 236

13.3 Checking Assumptions: Inspecting Residuals 238

13.4 The Profile Likelihood Method 246

13.5 Robust Standard Errors 247

13.6 Multilevel Bootstrapping 250

13.7 Bayesian Estimation Methods 255

13.8 Software 267

114 Multilevel Factor Models 269

14.1 Introduction 269

14.2 The Within and Between Approach 271

14.3 Full Maximum Likelihood Estimation 272

14.4 An Example of Multilevel Factor Analysis 274

14.5 Standardizing Estimates in Multilevel Structural Equation Modeling 278

14.6 Goodness of Fit in Multilevel Structural Equation Modeling 279

14.7 Software 282

15 Multilevel Path Models 284

15.1 Example of a Multilevel Path Analysis 284

15.2 Statistical and Software Issues 291

16 Latent Curve Models 294

16.1 Introduction 294

16.2 Example of Latent Curve Modeling 297

16.3 A Comparison of Multilevel Regression Analysis and Latent Curve Modeling 303

16.4 Software 304.

Notes:

Includes bibliographical references and index.

Electronic reproduction. Ann Arbor, MI Available via World Wide Web.

Description based on print version record.

ISBN:

9781317308676

1317308670

Publisher Number:

99989058177

Access Restriction:

Restricted for use by site license.