Hierarchical Linear Modeling Guide and Applications

ISBN-10: 1412998859
ISBN-13: 9781412998857
Edition: 2013
Authors: G. David Garson
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Description: This book provides a brief, easy-to-read guide to implementing hierarchical linear modeling using three leading software platforms, followed by a set of original how-to applications articles following a standardard instructional format. The "guide"  More...

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Book details

List price: $79.00
Copyright year: 2013
Publisher: SAGE Publications, Incorporated
Publication date: 4/10/2012
Binding: Paperback
Pages: 392
Size: 7.25" wide x 8.75" long x 0.75" tall
Weight: 1.716
Language: English

This book provides a brief, easy-to-read guide to implementing hierarchical linear modeling using three leading software platforms, followed by a set of original how-to applications articles following a standardard instructional format. The "guide" portion consists of five chapters by the editor, providing an overview of HLM, discussion of methodological assumptions, and parallel worked model examples in SPSS, SAS, and HLM software. The "applications" portion consists of ten contributions in which authors provide step by step presentations of how HLM is implemented and reported for introductory to intermediate applications.

G. David Garson is full professor of public administration at North Carolina State University, where he teaches courses on advanced research methodology, geographic information systems, information technology, and American government. In 1995 he was recipient of the Donald Campbell Award from the Policy Studies Organization, American Political Science Association, for outstanding contributions to policy research methodology and in 1997 of the Aaron Wildavsky Book Award from the same organization. In 1999 he won the Okidata Instructional Web Award from the Computers and Multimedia Section of the American Political Science Association, in 2002 received an NCSU Award for Innovative Excellence in Teaching and Learning with Technology, and in 2003 received an award "For Outstanding Teaching in Political Science" from the American Political Science Association and the National Political Science Honor Society, Pi Sigma Alpha. In 2008 the NCSU Public Administration Program was named in the top 10 PA schools in the nation in information systems management. Prof. Garson is editor of and contributor to Handbook of Public Information Systems, Third Edition (2010); Handbook of Research on Public Information Technology (2008) and is author, coauthor, editor, or coeditor of 30 other books and author or coauthor of over 50 articles. He has also created award-winning American Government computer simulations, CD-ROMs, and for the last 29 years he has also served as editor of the Social Science Computer Review and is on the editorial board of four additional journals. His widely-cited online textbook, Statnotes: Topics in Multivariate Analysis (2006-2012), is used by over a million people a year. Professor Garson received his undergraduate degree in political science from Princeton University (1965) and his doctoral degree in government from Harvard University (1969).

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

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