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What is included with this book?
Preface | p. xiii |
About the Editor | p. xv |
About the Contributors | p. xvii |
Guide | p. 1 |
Fundamentals of Hierarchical Linear and Multilevel Modeling | p. 3 |
Introduction | p. 3 |
Why Use Linear Mixed/Hierarchical Linear? Multilevel Modeling? | p. 5 |
Types of Linear Mixed Models | p. 7 |
Generalized Linear Mixed Models | p. 12 |
Repeated Measures, Longitudinal and Growth Models | p. 18 |
Repeated Measures | p. 18 |
Longitudinal and Growth Models | p. 19 |
Multivariate Models | p. 20 |
Cross-Classified Models | p. 21 |
Summary | p. 23 |
Preparing to Analyze Multilevel Data | p. 27 |
Testing if Linear Mixed Modeling Is Needed for One's Data | p. 27 |
Types of Estimation | p. 28 |
Converging on a Solution in Linear Mixed Modeling | p. 33 |
Meeting Other Assumptions of Linear Mixed Modeling | p. 36 |
Covariance Structure Types | p. 40 |
Selecting the Best Covariance Structure Assumption | p. 44 |
Comparing Model Goodness of Fit With Information Theory Measures | p. 44 |
Comparing Models With Likelihood Ratio Tests | p. 45 |
Effect Size in Linear Mixed Modeling | p. 47 |
Summary | p. 48 |
Introductory Guide to HLM With HLM 7 Software | p. 55 |
HLM Software | p. 55 |
Entering Data Into HLM 7 | p. 56 |
Input Method 1: Separate Files for Each Level | p. 56 |
Input Method 2: Using a Single Statistics Program Data File | p. 57 |
Making the MDM File | p. 57 |
The Null Model in HLM 7 | p. 61 |
A Random Coefficients Regression Model in HLM 7 | p. 67 |
Homogenous and Heterogeneous Full Random Coefficients Models | p. 72 |
Three-Level Hierarchical Linear Models | p. 81 |
Model A | p. 84 |
Model B | p. 85 |
Model C | p. 87 |
Graphics in HLM 7 | p. 92 |
Summary | p. 95 |
Introductory Guide to HLM With SAS Software | p. 97 |
Entering Data Into SAS | p. 97 |
Direct Data Entry Using VIEWTABLE | p. 97 |
Data Entry Using the SAS Import Wizard | p. 99 |
Data Entry Using SAS Commands | p. 100 |
The Null Model in SAS PROC MIXED | p. 101 |
A Random Coefficients Regression Model in SAS 9.2 | p. 104 |
A Full Random Coefficients Model | p. 106 |
Three-Level Hierarchical Linear Models | p. 110 |
Model A | p. 111 |
Model B | p. 112 |
Model C | p. 115 |
Summary | p. 118 |
Introductory Guide to HLM With SPSS Software | p. 121 |
The Null Model in SPSS | p. 121 |
A Random Coefficients Regression Model in SPSS 19 | p. 128 |
A Full Random Coefficients Model | p. 133 |
Three-Level Hierarchical Linear Models | p. 137 |
Model A | p. 137 |
Model B | p. 139 |
Model C | p. 141 |
Summary | p. 146 |
Introductory And Intermediate Applications | p. 147 |
A Random Intercepts Model of Part-Time Employment and Standardized Testing Using SPSS | p. 149 |
The Null Linear Mixed Model | p. 150 |
Interclass Correlation Coefficient (ICC) | p. 151 |
One-Way ANCOVA With Random Effects | p. 152 |
Sample | p. 152 |
Software and Procedure | p. 153 |
Analyzing the Data | p. 153 |
Output and Analysis | p. 156 |
Traditional Ordinary Least Squares (OLS) Approach | p. 156 |
Linear Mixed Model (LMM) Approach | p. 158 |
Conclusion | p. 162 |
Sample Write-Up | p. 163 |
A Random Intercept Regression Model Using HLM: Cohort Analysis of a Mathematics Curriculum for Mathematically Promising Students | p. 167 |
Sample | p. 169 |
Software and Procedure | p. 171 |
Analyzing the Data | p. 171 |
Output and Analysis | p. 175 |
Concluding Results | p. 180 |
Summary | p. 181 |
Random Coefficients Modeling With HLM: Assessment Practices and the Achievement Gap in Schools | p. 183 |
Statistical Formulations | p. 185 |
An Application of the RC Model: Assessment Practices and the Achievement Gap in Schools | p. 187 |
Sample | p. 188 |
Software and Procedure | p. 190 |
Analyzing the Data | p. 191 |
Output and Analysis | p. 193 |
Conclusion | p. 199 |
Baseline Model | p. 199 |
Student Model | p. 200 |
School Model | p. 201 |
Emotional Reactivity to Daily Stressors Using a Random Coefficients Model With SAS PROC Mixed: A Repeated Measures Analysis | p. 205 |
Sample and Procedure | p. 206 |
Measures | p. 206 |
Equations | p. 207 |
SAS Commands | p. 208 |
Structural Specification | p. 208 |
Model Specification | p. 209 |
Unconditional Model Output | p. 210 |
Interpretation of Unconditional Model Results | p. 212 |
Random Coefficients Regression Model | p. 212 |
Random Coefficients Regression Output | p. 213 |
Interpretation of Random Coefficients Regression Results | p. 217 |
Conclusion | p. 217 |
Hierachical Linear Modeling of Growth Curve Trajectories Using HLM | p. 219 |
The Challenges Posed by Longitudinal Data | p. 219 |
The Hierarchical Modeling Approach to Longitudinal Data | p. 221 |
Application: Growth Trajectories of U.S. Country Robbery Rates | p. 224 |
Exploratory Analyses | p. 225 |
Estimation of the Linear Hierachical Model | p. 226 |
Modeling the Variability of the Level 1 Coefficients | p. 232 |
Residual Analysis | p. 236 |
Estimating a Model for Counts | p. 239 |
Assessment of the Methods | p. 243 |
A Piecewise Growth Model Using HLM 7 to Examine Change in Teaching Practices Following a Science Teacher Professional Development Intervention | p. 249 |
Sample | p. 250 |
Software and Procedure | p. 252 |
Analyzing the Data | p. 254 |
Preparing the Data | p. 254 |
HLM Data Analyses | p. 255 |
Output and Analysis | p. 257 |
Examination of Time | p. 257 |
School as a Level 2 Predictor | p. 262 |
Alternative Error Covariance Structures | p. 264 |
Conclusion | p. 269 |
Discussion of Results | p. 269 |
Limitations of the Study | p. 270 |
Studying Reaction to Repeated Life Events With Discontinuous Change Models Using HLM | p. 273 |
Sample | p. 276 |
Software and Procedure | p. 277 |
Analyzing the Data | p. 277 |
Preparing the Data | p. 278 |
Analytic Model | p. 279 |
Output and Analysis | p. 283 |
Conclusion | p. 287 |
A Cross-Classified Multilevel Model for First-Year College Natural Science Performance Using SAS | p. 291 |
Sample | p. 292 |
Predictors | p. 293 |
Software and Procedure | p. 294 |
Analyzing the Data | p. 297 |
Evaluating Residual Variability Due to the Cross-Classified Levels | p. 297 |
Specifying a Covariance Structure | p. 299 |
Building the Student-Level Model | p. 299 |
Building the College- and High School-Level Models | p. 300 |
Evaluating Model Fit | p. 300 |
Output and Analysis | p. 301 |
Evaluating Residual Variability Due to the Cross-Classified Levels | p. 301 |
Specifying a Covariance Structure | p. 302 |
Building the Student-Level Model | p. 303 |
Evaluating Model Fit | p. 305 |
Evaluating Residual Variability in the Final Model | p. 305 |
Conclusion | p. 306 |
Interpreting Fixed Parameter Estimates | p. 306 |
Cross-Classified Multilevel Models Using Stata: How Important Are Schools and Neighborhoods for Students' Educational Attainment? | p. 311 |
Sample | p. 312 |
Software and Procedure | p. 315 |
Analyzing the Data | p. 316 |
Output and Analysis | p. 319 |
Conclusion | p. 330 |
Predicting Future Events From Longitudinal Data With Multivariate Hierarchical Models and Bayes' Theorem Using SAS | p. 333 |
Sample | p. 336 |
Software and Procedure | p. 337 |
Analyzing the Data | p. 344 |
Output and Analysis | p. 344 |
Conclusion | p. 350 |
Author Index | p. 353 |
Subject Index | p. 357 |
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