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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