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E-raamat: Hierarchical Linear Modeling: Guide and Applications

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  • Ilmumisaeg: 10-Apr-2012
  • Kirjastus: SAGE Publications Inc
  • Keel: eng
  • ISBN-13: 9781452280981
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  • Formaat: PDF+DRM
  • Ilmumisaeg: 10-Apr-2012
  • Kirjastus: SAGE Publications Inc
  • Keel: eng
  • ISBN-13: 9781452280981

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Hierarchical Linear Modeling 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" application articles following a standardized instructional format. The Guide portion consists of five chapters that provide 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.

"The book covers the three most widely accessible statistical programs for multilevel modeling rather than just focusing on one. . . . An excellent tool for researchers who are beginning to learn multilevel modeling, as well as a great resource for experienced researchers who want to learn a different statistical program for multilevel models." Debbie L. Hahs-Vaughn, University of Central Florida

"The intelligent use of the examples helps explain both the conceptual framework of HLM and its basic individual applications."Luis L. Cabo, Mercyhurst College

Arvustused

"The book covers the three most widely accessible statistical programs for multilevel modeling rather than just focusing on one. It will be an excellent tool for researchers who are beginning to learn multilevel modeling as well as a great resource for experienced researchers who want to learn a different statistical program for multilevel models. -- Debbie L. Hahs-Vaughn This books covers the usage and outputs of the three main statistical packages for the application of HLM, and the intelligent use of the examples help explain both the conceptual framework of HLM and its basic individual applications. -- Luis L. Cabo

Preface xiii
About the Editor xv
About the Contributors xvii
PART I GUIDE
1(146)
1 Fundamentals of Hierarchical Linear and Multilevel Modeling
3(24)
G. David Garson
Introduction
3(2)
Why Use Linear Mixed/Hierarchical Linear/Multilevel Modeling?
5(2)
Types of Linear Mixed Models
7(5)
Generalized Linear Mixed Models
12(6)
Repeated Measures, Longitudinal and Growth Models
18(2)
Repeated Measures
18(1)
Longitudinal and Growth Models
19(1)
Multivariate Models
20(1)
Cross-Classified Models
21(2)
Summary
23(4)
2 Preparing to Analyze Multilevel Data
27(28)
G. David Garson
Testing if Linear Mixed Modeling Is Needed for One's Data
27(1)
Types of Estimation
28(5)
Converging on a Solution in Linear Mixed Modeling
33(3)
Meeting Other Assumptions of Linear Mixed Modeling
36(4)
Covariance Structure Types
40(4)
Selecting the Best Covariance Structure Assumption
44(1)
Comparing Model Goodness of Fit With Information Theory Measures
44(1)
Comparing Models With Likelihood Ratio Tests
45(2)
Effect Size in Linear Mixed Modeling
47(1)
Summary
48(7)
3 Introductory Guide to HLM With HLM 7 Software
55(42)
G. David Garson
HLM Software
55(1)
Entering Data Into HLM 7
56(5)
Input Method 1 Separate Files for Each Level
56(1)
Input Method 2 Using a Single Statistics Program Data File
57(1)
Making the MDM File
57(4)
The Null Model in HLM 7
61(6)
A Random Coefficients Regression Model in HLM 7
67(5)
Homogenous and Heterogeneous Full Random Coefficients Models
72(9)
Three-Level Hierarchical Linear Models
81(11)
Model A
84(1)
Model B
85(2)
Model C
87(5)
Graphics in HLM 7
92(3)
Summary
95(2)
4 Introductory Guide to HLM With SAS Software
97(24)
G. David Garson
Entering Data Into SAS
97(4)
Direct Data Entry Using VIEWTABLE
97(2)
Data Entry Using the SAS Import Wizard
99(1)
Data Entry Using SAS Commands
100(1)
The Null Model in SAS PROC MIXED
101(3)
A Random Coefficients Regression Model in SAS 9.2
104(2)
A Full Random Coefficients Model
106(4)
Three-Level Hierarchical Linear Models
110(8)
Model A
111(1)
Model B
112(3)
Model C
115(3)
Summary
118(3)
5 Introductory Guide to HLM With SPSS Software
121(26)
G. David Garson
The Null Model in SPSS
121(7)
A Random Coefficients Regression Model in SPSS 19
128(5)
A Full Random Coefficients Model
133(4)
Three-Level Hierarchical Linear Models
137(9)
Model A
137(2)
Model B
139(2)
Model C
141(5)
Summary
146(1)
PART II INTRODUCTORY AND INTERMEDIATE APPLICATIONS
147(206)
6 A Random Intercepts Model of Part-Time Employment and Standardized Testing Using SPSS
149(18)
Forrest C. Lane
Kim F. Nimon
J. Kyle Roberts
The Null Linear Mixed Model
150(1)
Interclass Correlation Coefficient (ICC)
151(1)
One-Way ANCOVA With Random Effects
152(1)
Sample
152(1)
Software and Procedure
153(1)
Analyzing the Data
153(3)
Output and Analysis
156(2)
Traditional Ordinary Least Squares (OLS) Approach
156(2)
Linear Mixed Model (LMM) Approach
158(4)
Conclusion
162(1)
Sample Write-Up
163(4)
7 A Random Intercept Regression Model Using HLM: Cohort Analysis of a Mathematics Curriculum for Mathematically Promising Students
167(16)
Carissa L. Shafto
Jill L. Adelson
Sample
169(2)
Software and Procedure
171(1)
Analyzing the Data
171(4)
Output and Analysis
175(5)
Concluding Results
180(1)
Summary
181(2)
8 Random Coefficients Modeling With HLM: Assessment Practices and the Achievement Gap in Schools
183(22)
Gregory J. Palardy
Statistical Formulations
185(2)
An Application of the RC Model: Assessment Practices and the Achievement Gap in Schools
187(1)
Sample
188(2)
Software and Procedure
190(1)
Analyzing the Data
191(2)
Output and Analysis
193(6)
Conclusion
199(6)
Baseline Model
199(1)
Student Model
200(1)
School Model
201(4)
9 Emotional Reactivity to Daily Stressors Using a Random Coefficients Model With SAS PROC MIXED: A Repeated Measures Analysis
205(14)
Shevaun D. Neupert
Sample and Procedure
206(1)
Measures
206(1)
Equations
207(1)
SAS Commands
208(1)
Structural Specification
208(1)
Model Specification
209(1)
Unconditional Model Output
210(2)
Interpretation of Unconditional Model Results
212(1)
Random Coefficients Regression Model
212(1)
Random Coefficients Regression Output
213(4)
Interpretation of Random Coefficients Regression Results
217(1)
Conclusion
217(2)
10 Hierarchical Linear Modeling of Growth Curve Trajectories Using HLM
219(30)
David F. Greenberg
Julie A. Phillips
The Challenges Posed by Longitudinal Data
219(2)
The Hierarchical Modeling Approach to Longitudinal Data
221(3)
Application: Growth Trajectories of U.S. County Robbery Rates
224(19)
Exploratory Analyses
225(1)
Estimation of the Linear Hierarchical Model
226(6)
Modeling the Variability of the Level I Coefficients
232(4)
Residual Analysis
236(3)
Estimating a Model for Counts
239(4)
Assessment of the Methods
243(6)
11 A Piecewise Growth Model Using HLM 7 to Examine Change in Teaching Practices Following a Science Teacher Professional Development Intervention
249(24)
Jaime L. Maerten-Rivera
Sample
250(2)
Software and Procedure
252(2)
Analyzing the Data
254(3)
Preparing the Data
254(1)
HLM Data Analyses
255(2)
Output and Analysis
257(5)
Examination of Time
257(5)
School as a Level 2 Predictor
262(2)
Alternative Error Covariance Structures
264(5)
Conclusion
269(4)
Discussion of Results
269(1)
Limitations of the Study
270(3)
12 Studying Reaction to Repeated Life Events With Discontinuous Change Models Using HLM
273(18)
Maike Luhmann
Michael Eid
Sample
276(1)
Software and Procedure
277(1)
Analyzing the Data
277(6)
Preparing the Data
278(1)
Analytic Model
279(4)
Output and Analysis
283(4)
Conclusion
287(4)
13 A Cross-Classified Multilevel Model for First-Year College Natural Science Performance Using SAS
291(20)
Brian F. Patterson
Sample
292(2)
Predictors
293(1)
Software and Procedure
294(3)
Analyzing the Data
297(4)
Evaluating Residual Variability Due to the Cross-Classified Levels
297(2)
Specifying a Covariance Structure
299(1)
Building the Student-Level Model
299(1)
Building the College- and High School---Level Models
300(1)
Evaluating Model Fit
300(1)
Output and Analysis
301(5)
Evaluating Residual Variability Due to the Cross-Classified Levels
301(1)
Specifying a Covariance Structure
302(1)
Building the Student-Level Model
303(2)
Evaluating Model Fit
305(1)
Evaluating Residual Variability in the Final Model
305(1)
Conclusion
306(5)
Interpreting Fixed Parameter Estimates
306(5)
14 Cross-Classified Multilevel Models Using Stata: How Important Are Schools and Neighborhoods for Students' Educational Attainment?
311(22)
George Leckie
Sample
312(3)
Software and Procedure
315(1)
Analyzing the Data
316(3)
Output and Analysis
319(11)
Conclusion
330(3)
15 Predicting Future Events From Longitudinal Data With Multivariate Hierarchical Models and Bayes' Theorem Using SAS
333(20)
Larry J. Brant
Shan L. Sheng
Sample
336(1)
Software and Procedure
337(7)
Analyzing the Data
344(1)
Output and Analysis
344(6)
Conclusion
350(3)
Author Index 353(4)
Subject Index 357
G. David Garson is a full professor of public administration at North Carolina State University, where he teaches courses on advanced research methodology, geographic information systems, information technology, e-government, 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), Patriotic Information Systems:  Privacy, Access, and Security Issues of Bush Information Policy (2008), Modern Public Information Technology Systems (2007), and author of Public Information Technology and E-Governance: Managing the Virtual State (2006), editor of Public Information Systems: Policy and Management Issues (2003), coeditor of Digital Government: Principles and Practices (2003), coauthor of Crime Mapping (2003), author of Guide to Writing Quantitative Papers, Theses, and Dissertations (Dekker, 2001), editor of Social Dimensions of Information Technology (2000),  Information Technology and Computer Applications in Public Administration: Issues and Trends (1999) and is author of Neural Network Analysis for Social Scientists (1998), Computer Technology and Social Issues (1995), Geographic Databases and Analytic Mapping (1992), and is author, coauthor, editor, or coeditor of 17 other books and author or coauthor of over 50 articles. He has also created award-winning American Government computer simulations, CD-ROMs, and six web sites for Prentice-Hall and Simon & Schuster (1995-1999).  For the last 28 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 (2006-2009), is used by over 1.5 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).