An introduction to multilevel modeling techniques: MLM and SEM approaches using Mplus
Gespeichert in:
| Beteiligte Personen: | , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
New York ; London
Routledge
2015
|
| Ausgabe: | Third Edition |
| Schriftenreihe: | Quantitative methodology series
|
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028001812&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | xviii, 440 S. Diagramme |
| ISBN: | 9781848725515 9781848725522 |
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| 100 | 1 | |a Heck, Ronald H. |e Verfasser |0 (DE-588)14277250X |4 aut | |
| 245 | 1 | 0 | |a An introduction to multilevel modeling techniques |b MLM and SEM approaches using Mplus |c Ronald H. Heck ; Scott L. Thomas |
| 250 | |a Third Edition | ||
| 264 | 1 | |a New York ; London |b Routledge |c 2015 | |
| 300 | |a xviii, 440 S. |b Diagramme | ||
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| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-028001812 | |
Datensatz im Suchindex
| _version_ | 1819274461241671680 |
|---|---|
| adam_text | CONTENTS
Preface
1 Introduction
Chapter Objectives 1
Introduction 1
Providing a Conceptual Overview 2
Analysis of Multilevel Data Structures 5
Contrasting Linear Models 6
Univariate Analysis 9
Multiple Regression 10
Analysis of Variance 10
Multivariate Analysis 11
Multivariate Analysis of Variance 11
Structural Equation Modeling 13
Multilevel Data Structures 15
Multilevel Multivariate îodel 17
Multilevel Structural Model 18
Summary 20
References 21
Getting Started With Multilevel Analysis
Chapter Objectives 23
Introduction 23
From Single-Level to Multilevel Analysis 25
Summarizing Some Differences 29
Developing a General Multilevel Modeling Strategy
vl Contents
Step 1: Partitioning the Variance in an Outcome 33
Step 2: Adding Level-1 Predictors to Explain Intercept Variability 3 7
Step 3: Specifying Level-2 Predictors to Explain Intercept Variability 38
Step 4: Examining Possible Variation in Slopes 40
Step 5: Adding Predictors to Explain Variation in Slopes 41
Specifying Random Effects at Level 2 43
Methods for Estimating Model Parameters 44
Maximum Likelihood Estimation 45
Full Information ML 48
Model Convergence 51
Considerations for ML Estimation 52
Other Model Estimation Approaches in Mplus 54
H/LS Estimation 55
Bayesian Estimation 56
A Comparison of Estimation Approaches With Small
Numbers of Level-2 Units 57
Summary 60
References 62
3 Multilevel Regression Models
Chapter Objectives 67
Introduction 67
Overview of Multilevel Regression Models 69
Building a Model to Explain Employee Morale 70
Model l: One-Way ANOVA model 74
Model I Statements 75
Model 1 Output 77
Model 2: Lc el-t Random-Intercept Model 79
Model 2 Statements 81
Model 2 Output 82
Model 3: Specifying a Level-1 Random Slope 83
Model 3 Statements 83
Model 3 Output 84
Model 4: Explaining Variation in the Level-2 Intercept and Slope 85
Model 4 Statements 85
Model 4 Output 86
Centering Predictors 87
Centering Predictors in Models With Random Slopes 91
Summary 93
References 94
4 Extending the Two-Level Regression Model
Chapter Objectives 97
Introduction 97
Contents vil
Three-Level Univariate Model 98
Developing a Three-Level Univariate Model (99
Research Questions 100
Data 100 ‘
Model 1:Null (No Predictors) Model 101
Model 1 Statements 101
Model 1 Output 102
Model 2: Defining Predictors at Each Level 103
* * ՛-,. _b
Grand-Mean Centering 103
Model 2 Statements 105
Model 2: Grand-Mean Centered Output 105
■ *·■■■ ■·. 1 v*i*
Group-Mean Centering 107
Model 2 Statements 107
Model 2: Group-Mean Centered Output 108
Model 3: Does the Slope Vary Randomly Across Schools? 109
Model 3 Statements 110
Model 3 Output 111
Model 4: Developing a Model to Explain Variability in Slopes 111
Model 4 Statements 112
Model 4 Output 112
Defining Path Models 113
Single-Level Path Model 114
Multilevel Path Model 115
Model 1: Two-Level Model With Multivariate Outcomes 117
1Model 1 Statements 119
Model 1 Output 120
Model 2: Specifying a Mediating Variable Between Groups 122
Model 2 Statements 123
Model 2 Output 124
Model 3: Revised Model Removing Nonsignificant Paths 127
Examining an Indirect Effect 128
Model 3 Statements 128
Model 3 Output 129
Final R-Square Estimates 129
Summary 131
References 131
5 Defining Multilevel Latent Variables 133
Chapter Objectives 133
Introduction 133
Latent Variables 135
The Measurement Model 136
Structural Model 139
Proposing a CFA Model 140
vill Contents
Model Identification 143
Model Fit Indices 145
Model 1: Examining a Single-Level CFA Model 148
Model 1 Output 149
Model 2: Freeing an Error Covariance 153
Model 2 Output 153
Extending the Generalizability of a Model 154
Multilevel Measurement Models 155
Multilevel Factor Variance Components 158
Estimating ML-CFA Models 159
Model 3: Defining a Two-Level CFA Model 162
Examining the Fit Indices 166
Examining the Model Parameters 161
Model 4: Applying Equality Constraints on Factor Loadings 168
Model 4 Output 169
Standardized Estimates 171
Comparing Model 3 and Model 4 172
Extending the CFA Model to Three Levels 174
Model 5: Invariant Loadings at Levels 1 and 2 174
Model 5 Fit Indices 175
Model 6: Including Equality Constraints at Level 3 176
Model 6 Fit Indices 176
Model 7: Restricting Errors to Zero at Level 2 177
Mode! 7 Fit Indices 177
Comparing Modeh 6 and 7 177
Model 7 Parameter Estimates 178
Summary 179
References 179
6 Multilevel Structural Equation Models
Chapter Objectives 183
Introduction 183
Multilevel Models With Latent Variables and Covariates 184
Model 1: Two-Level CFA With Observed Predictors 185
Mode! 1 Statements 187
Model 1 Output 189
Mode! 2: Specifying a Random Level-1 Slope 198
Model 2 Statements 199
Model 2 Output 200
Model 3: Speajytng Female as Having Within- and Between-Group
Components 200
Model 3 Statements 200
Model 3 Output 202
Model 4: Adding a Limit Factor Bctuven Groups 202
Contents lx
Model 4 Statements 205/ ?
Model 4 Output 2061 I f
Model 5: Testing an Indirect Effect 206
Model 5 Statements 209
Model 5 Output 210
Model 6: Adding a Relationship Between the Latent Outcomes 211
Model 6 Statements 211 3 ՝
Model 6 Output 212 ** w
Model 7: Specifying a Reciprocal Relationship Between Outcomes 213
Model 7 Statements 216
Model 7 Output 218 if
Summary 219
References 220
7 Methods for Examining Individual and
Organizational Change 221
Chapter Objectives 221
Introduction 221
Analyzing Longitudinal Data 223
Repeated-Measures A NOVA 223
Growth Modeling and Other Approaches 224
Random-Coefficients Growth Modeling 225
Defining the Level-1 Model 226
Defining the Level-2 Model 228
Extending the Model to Examine Changes Between Organizations 229
Defining the Level-3 Model 229
Examining Changes in Institutions’ Graduation Rates 229
Model 1: Within-Individuals (Level-1) Model 231
Between-Individuals (Level-2) Model 232
Coding the Time Variable 232
Model 1 Statements 234
TECH1 Specification Output 235
Model 1 Output 236
Model 2: Explaining Differences in Random Growth Parameters Between
Institutions 238
Model 2 Statements 238
TECH1 Output 239
XIodel 2 Output 240
Other Types of Random-Coefficients Models 241
Examining Individual Change With SEM 241
Intercept and Slope (IS) and Level and Shape (LS) Models 242
Defining the Latent Curve Model 244
TJ։e Measurement Model 244
Tlte Structural Model 246
x Contents
Model 1: Specifying the IS Model 247
Model I: IS Model Statements 247
Model 2: Specifying the LS Model 248
Model 2: LS Model Statements 249
Model Identification 249
Model I IS Output 250
Model 2 LS Output 252
Comparing the Fit of the IS and LS Models 253
Nested Models 254
Model 3: Adding Covariates to the IS Model 255
Model 3 Statements 256
Model 3 Output 256
Extending the Latent Curve Model 256
Multilevel Latent Curve Analysis 258
Examining Variables That Influence Student Growth in Math 258
Data and Variables 259
Defining the Proposed Model 259
Model Statements 259
Model Output 261
Developing a Piecewise Growth Model 262
Specifying the Pieceunse Latent Curve Model 264
Model 1 Statements 265
Model 1 Output 266
Imposing Equality Constraints 266
Model 2: Adding the Covariates 268
Model 2 Statements 268
Model 2 Output 268
Summary 269
References 271
8 Multilevel Models With Categorical Variables
Chapter Objectives 273
Introduction 273
Multilevel Models With Categorical Observed Outcomes 278
Specifying Models for Binary, Ordinal, and Nominal Outcomes 278
Binary Oi/frome 278
Logit Link Function 279
Probit Link Function 281
Ordinal Outcome 283
Ordered Probit Model 286
Unordered Categorical (Nominal) Outcome 287
Mplus Latent Response Formulation 288
Explaining Student Persistence 290
Binary· Outcome 291
Contents xl
Model 2 Statements 292
Ordinal Outcome 294
Estimating Probabilities From Probit Coefficients * 294
Estimating Probabilities From Logit Coefficients 296
Adding Level-1 and Level-2 Predictors 296
Model Statements 297
Examining a Cross-Level Interaction 300
Model Statemen ts 301
Count Data 303
Building a Level-1 and Level-2 Model 306
Model Statements 306
Level-1 and Level-2 Model Output 307
Negative Binomial Results 308
Multilevel CFA With Ordinal Observed Indicators 310
Developing a CFA Model 312
Model Statements 315
Model Output 317
Summary 320
References 321
9 Multilevel Mixture Models
Chapter Objectives 323
Introduction 323
Defining Latent Classes 324
An Example Latent Profile Analysis 328
Model Statements 330
Model Output 330
Examining Heterogeneity in Intercepts 331
Model Statements 333
Model Output 335
Investigating Latent Classes for Random Slopes at Level 2 339
A lodel Statemen ts 341
Model Output 342
Alternative Model Specification 344
Defining a Two-Level Mixture Model for Math 345
Model Statements 349
Model Output 350
Model Modifications 352
Two-Leiel CFA Mixture Model With Continuous Indicators 352
Model Statements 354
Model Output 355
Latent Growth Mixture Models 357
Examining Latent Classes in Students’ Growth in Science 359
Model Statements 360
323
xll Contents
Model Output 361
Two-Level LGMM 364
Model Statements 366
Model Output 361
Summary 370
References 370
10 Data Considerations in Examining Multilevel Models 373
Chapter Objectives 373
Introduction 373
Complex Samples, Design Effects, and Sample Weights 373
An Example Using Multilevel Weights 379
Model Statements 379
Model Output 382
Parameter Bias and Statistical Power 384
Bias 384
Power 385
An Example 386
Anticipated Effect Size and Power 389
Mplus Monte Carlo Study 393
Model Statements 395
Model Output 396
Design Complexity 398
Missing Data 399
Missing Data at Level 2 404
Model Statements 404
Initial Summary Output 405
Imputation File 406
XIodel Estimates 407
Model Output 407
Concluding Thoughts 410
References 413
Glossary
Author Index
Subject Index
417
429
433
|
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| id | DE-604.BV042568212 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-20T17:14:46Z |
| institution | BVB |
| isbn | 9781848725515 9781848725522 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-028001812 |
| oclc_num | 904466636 |
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| physical | xviii, 440 S. Diagramme |
| publishDate | 2015 |
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| spellingShingle | Heck, Ronald H. Thomas, Scott L. An introduction to multilevel modeling techniques MLM and SEM approaches using Mplus Multiniveau-analyse gtt Mathematisches Modell Sozialwissenschaften Social sciences Mathematical models Social sciences Research Mathematical models Sozialwissenschaften (DE-588)4055916-6 gnd Mathematisches Modell (DE-588)4114528-8 gnd |
| subject_GND | (DE-588)4055916-6 (DE-588)4114528-8 |
| title | An introduction to multilevel modeling techniques MLM and SEM approaches using Mplus |
| title_auth | An introduction to multilevel modeling techniques MLM and SEM approaches using Mplus |
| title_exact_search | An introduction to multilevel modeling techniques MLM and SEM approaches using Mplus |
| title_full | An introduction to multilevel modeling techniques MLM and SEM approaches using Mplus Ronald H. Heck ; Scott L. Thomas |
| title_fullStr | An introduction to multilevel modeling techniques MLM and SEM approaches using Mplus Ronald H. Heck ; Scott L. Thomas |
| title_full_unstemmed | An introduction to multilevel modeling techniques MLM and SEM approaches using Mplus Ronald H. Heck ; Scott L. Thomas |
| title_short | An introduction to multilevel modeling techniques |
| title_sort | an introduction to multilevel modeling techniques mlm and sem approaches using mplus |
| title_sub | MLM and SEM approaches using Mplus |
| topic | Multiniveau-analyse gtt Mathematisches Modell Sozialwissenschaften Social sciences Mathematical models Social sciences Research Mathematical models Sozialwissenschaften (DE-588)4055916-6 gnd Mathematisches Modell (DE-588)4114528-8 gnd |
| topic_facet | Multiniveau-analyse Mathematisches Modell Sozialwissenschaften Social sciences Mathematical models Social sciences Research Mathematical models |
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