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Preface | |
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About the Editor | |
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About the Contributors | |
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Guide | |
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Fundamentals of Hierarchical Linear and Multilevel Modeling | |
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Introduction | |
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Why Use Linear Mixed/Hierarchical Linear? Multilevel Modeling? | |
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Types of Linear Mixed Models | |
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Generalized Linear Mixed Models | |
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Repeated Measures, Longitudinal and Growth Models | |
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Repeated Measures | |
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Longitudinal and Growth Models | |
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Multivariate Models | |
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Cross-Classified Models | |
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Summary | |
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Preparing to Analyze Multilevel Data | |
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Testing if Linear Mixed Modeling Is Needed for One's Data | |
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Types of Estimation | |
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Converging on a Solution in Linear Mixed Modeling | |
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Meeting Other Assumptions of Linear Mixed Modeling | |
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Covariance Structure Types | |
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Selecting the Best Covariance Structure Assumption | |
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Comparing Model Goodness of Fit With Information Theory Measures | |
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Comparing Models With Likelihood Ratio Tests | |
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Effect Size in Linear Mixed Modeling | |
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Summary | |
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Introductory Guide to HLM With HLM 7 Software | |
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HLM Software | |
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Entering Data Into HLM 7 | |
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Input Method 1: Separate Files for Each Level | |
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Input Method 2: Using a Single Statistics Program Data File | |
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Making the MDM File | |
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The Null Model in HLM 7 | |
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A Random Coefficients Regression Model in HLM 7 | |
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Homogenous and Heterogeneous Full Random Coefficients Models | |
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Three-Level Hierarchical Linear Models | |
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Model A | |
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Model B | |
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Model C | |
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Graphics in HLM 7 | |
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Summary | |
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Introductory Guide to HLM With SAS Software | |
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Entering Data Into SAS | |
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Direct Data Entry Using VIEWTABLE | |
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Data Entry Using the SAS Import Wizard | |
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Data Entry Using SAS Commands | |
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The Null Model in SAS PROC MIXED | |
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A Random Coefficients Regression Model in SAS 9.2 | |
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A Full Random Coefficients Model | |
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Three-Level Hierarchical Linear Models | |
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Model A | |
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Model B | |
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Model C | |
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Summary | |
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Introductory Guide to HLM With SPSS Software | |
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The Null Model in SPSS | |
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A Random Coefficients Regression Model in SPSS 19 | |
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A Full Random Coefficients Model | |
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Three-Level Hierarchical Linear Models | |
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Model A | |
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Model B | |
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Model C | |
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Summary | |
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Introductory And Intermediate Applications | |
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A Random Intercepts Model of Part-Time Employment and Standardized Testing Using SPSS | |
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The Null Linear Mixed Model | |
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Interclass Correlation Coefficient (ICC) | |
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One-Way ANCOVA With Random Effects | |
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Sample | |
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Software and Procedure | |
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Analyzing the Data | |
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Output and Analysis | |
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Traditional Ordinary Least Squares (OLS) Approach | |
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Linear Mixed Model (LMM) Approach | |
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Conclusion | |
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Sample Write-Up | |
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A Random Intercept Regression Model Using HLM: Cohort Analysis of a Mathematics Curriculum for Mathematically Promising Students | |
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Sample | |
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Software and Procedure | |
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Analyzing the Data | |
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Output and Analysis | |
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Concluding Results | |
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Summary | |
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Random Coefficients Modeling With HLM: Assessment Practices and the Achievement Gap in Schools | |
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Statistical Formulations | |
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An Application of the RC Model: Assessment Practices and the Achievement Gap in Schools | |
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Sample | |
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Software and Procedure | |
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Analyzing the Data | |
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Output and Analysis | |
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Conclusion | |
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Baseline Model | |
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Student Model | |
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School Model | |
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Emotional Reactivity to Daily Stressors Using a Random Coefficients Model With SAS PROC Mixed: A Repeated Measures Analysis | |
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Sample and Procedure | |
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Measures | |
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Equations | |
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SAS Commands | |
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Structural Specification | |
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Model Specification | |
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Unconditional Model Output | |
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Interpretation of Unconditional Model Results | |
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Random Coefficients Regression Model | |
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Random Coefficients Regression Output | |
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Interpretation of Random Coefficients Regression Results | |
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Conclusion | |
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Hierachical Linear Modeling of Growth Curve Trajectories Using HLM | |
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The Challenges Posed by Longitudinal Data | |
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The Hierarchical Modeling Approach to Longitudinal Data | |
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Application: Growth Trajectories of U.S. Country Robbery Rates | |
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Exploratory Analyses | |
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Estimation of the Linear Hierachical Model | |
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Modeling the Variability of the Level 1 Coefficients | |
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Residual Analysis | |
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Estimating a Model for Counts | |
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Assessment of the Methods | |
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A Piecewise Growth Model Using HLM 7 to Examine Change in Teaching Practices Following a Science Teacher Professional Development Intervention | |
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Sample | |
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Software and Procedure | |
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Analyzing the Data | |
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Preparing the Data | |
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HLM Data Analyses | |
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Output and Analysis | |
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Examination of Time | |
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School as a Level 2 Predictor | |
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Alternative Error Covariance Structures | |
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Conclusion | |
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Discussion of Results | |
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Limitations of the Study | |
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Studying Reaction to Repeated Life Events With Discontinuous Change Models Using HLM | |
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Sample | |
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Software and Procedure | |
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Analyzing the Data | |
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Preparing the Data | |
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Analytic Model | |
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Output and Analysis | |
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Conclusion | |
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A Cross-Classified Multilevel Model for First-Year College Natural Science Performance Using SAS | |
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Sample | |
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Predictors | |
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Software and Procedure | |
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Analyzing the Data | |
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Evaluating Residual Variability Due to the Cross-Classified Levels | |
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Specifying a Covariance Structure | |
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Building the Student-Level Model | |
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Building the College- and High School-Level Models | |
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Evaluating Model Fit | |
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Output and Analysis | |
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Evaluating Residual Variability Due to the Cross-Classified Levels | |
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Specifying a Covariance Structure | |
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Building the Student-Level Model | |
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Evaluating Model Fit | |
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Evaluating Residual Variability in the Final Model | |
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Conclusion | |
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Interpreting Fixed Parameter Estimates | |
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Cross-Classified Multilevel Models Using Stata: How Important Are Schools and Neighborhoods for Students' Educational Attainment? | |
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Sample | |
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Software and Procedure | |
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Analyzing the Data | |
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Output and Analysis | |
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Conclusion | |
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Predicting Future Events From Longitudinal Data With Multivariate Hierarchical Models and Bayes' Theorem Using SAS | |
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Sample | |
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Software and Procedure | |
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Analyzing the Data | |
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Output and Analysis | |
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Conclusion | |
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Author Index | |
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Subject Index | |