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E-raamat: Multilevel Modeling: Applications in STATA(R), IBM(R) SPSS(R), SAS(R), R, & HLM(TM)

(North Carolina State University, USA)
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  • Ilmumisaeg: 31-Jul-2019
  • Kirjastus: SAGE Publications Inc
  • Keel: eng
  • ISBN-13: 9781544319308
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  • Formaat: PDF+DRM
  • Ilmumisaeg: 31-Jul-2019
  • Kirjastus: SAGE Publications Inc
  • Keel: eng
  • ISBN-13: 9781544319308

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Multilevel Modeling: Applications in STATA®, IBM® SPSS®, SAS®, R & HLM™ provides a gentle, hands-on illustration of the most common types of multilevel modeling software, offering instructors multiple software resources for their students and an applications-based foundation for teaching multilevel modeling in the social sciences. Author G. David Garson’s step-by-step instructions for the software walk readers through each package. The instructions for the different platforms allow students to get a running start using the package with which they are most familiar while the instructor can start teaching the concepts of multilevel modeling right away. Instructors will find this text serves as both a comprehensive resource for their students and a foundation for their teaching alike.

Arvustused

"The practical and hands-on approach in addition to using several software make this book appealing to a wide range of readers." -- Amin Mousavi "This is a solid treatment of MLMs which illustrates implementation across all major MLM software." -- J.M. Pogodzinski "This text effectively balances depth, complexity, and readability of a number of challenging topics related to multilevel modeling. The wealth of examples in many different software environments are fantastic." -- Michael Broda

Preface xv
Acknowledgments xvii
About the Author xix
Chapter 1 Introduction to Multilevel Modeling
1(20)
Overview
1(2)
What Multilevel Modeling Does
3(1)
The Importance of Multilevel Theory
4(1)
Types of Multilevel Data
5(1)
Common Types of Multilevel Model
6(4)
The Null Unconditional Random Intercept Model
6(2)
The Conditional Random Intercept Model
8(1)
The Conditional Random Coefficients Model
9(1)
The Random Intercept Regression Model
9(1)
The Random Intercept ANCOVA Model
9(1)
The Random Coefficients ANCOVA Model
10(1)
Mediation and Moderation Models in Multilevel Analysis
10(2)
Alternative Statistical Packages
12(1)
Multilevel Modeling Versus GEE
13(2)
Summary
15(1)
Glossary
16(2)
Challenge Questions With Answers
18(3)
Chapter 2 Assumptions of Multilevel Modeling
21(36)
About This
Chapter
21(1)
Overview
21(1)
Model Specification
22(1)
Construct Operationalization and Validation
23(1)
Random Sampling
24(1)
Sample Size
25(4)
Balanced and Unbalanced Designs
29(1)
Data Level
30(1)
Using Ordinal Items as Continuous
30(1)
Linearity and Nonlinearity
31(1)
Independence
32(1)
Recursivity
32(1)
Missing Data
33(1)
Outliers
34(1)
Centered and Standardized Data
34(5)
Centering
34(3)
Standardization
37(2)
Longitudinal Time Values
39(1)
Multicollinearity
39(1)
Dealing With Multicollinearity
40(1)
Homogeneity of Error Variance
40(3)
Normally Distributed Residuals
43(2)
Normal Distribution of Variables
45(1)
Normal Distribution of Random Effects
45(1)
Convergence
45(2)
Dealing With Failure to Converge
46(1)
Covariance Structure Assumptions
47(3)
Overview
47(1)
Random Effects and Repeated Measures
48(1)
Variance Components Covariance Structure
48(1)
Diagonal Covariance Structure
48(1)
The Unstructured Covariance Structure
49(1)
Choosing a Covariance Structure Assumption
49(1)
Software Defaults for Covariance Structure
50(1)
Summary
50(2)
Glossary
52(2)
Challenge Questions With Answers
54(3)
Chapter 3 The Null Model
57(44)
Overview
57(1)
Testing the Need for Multilevel Modeling
58(2)
Overview
58(1)
The Intraclass Correlation Coefficient (ICC)
59(1)
Variance Components/ICC Test Results vs. ANOVA Results
59(1)
Likelihood Ratio Tests
60(2)
Partition of Variance Components
62(1)
Examples
62(33)
Overview
62(1)
The Null Model in SPSS
63(7)
The Null Model in Stata
70(3)
The Null Model in SAS
73(5)
The Null Model in HLM 7
78(13)
The Null Model in R
91(4)
Summary
95(1)
Glossary
96(1)
Challenge Questions With Answers
97(4)
Chapter 4 Estimating Multilevel Models
101(22)
Fixed and Random Effects
101(2)
Why Not Just Use OLS Regression?
103(1)
Why Not Just Use GLM (ANOVA)?
104(1)
Types of Estimation
104(7)
Maximum Likelihood Estimation
105(1)
Restricted Maximum Likelihood Estimation
106(1)
Full Information Maximum Likelihood Estimation
106(1)
Other Estimation Methods
107(1)
Software Estimation Defaults
108(3)
Robust and Cluster-Robust Standard Errors
111(7)
Statistics Package Support for Robust Estimation
114(1)
Examples
114(4)
Summary
118(1)
Glossary
119(2)
Challenge Questions With Answers
121(2)
Chapter 5 Goodness of Fit and Effect Size in Multilevel Models
123(20)
Overview
123(1)
Goodness of Fit Measures and Tests
124(6)
Likelihood Ratio Tests
124(1)
Information Criteria Measures
124(4)
Summary
128(1)
Manual Computation of AIC and BIC
128(1)
Software Support for Model Fit
128(2)
Effect Size Measures
130(7)
Overview
130(1)
Proportional Reduction in Variance
130(2)
Partition of Variance Components
132(1)
Intraclass Correlation (ICC)
133(1)
R2 for Fixed Effects in the Full Model
133(1)
Pseudo-R2 Measures
134(1)
Cohen's d and GMAd
135(1)
Other Measures
136(1)
Effect Size and Endogeneity
137(1)
Summary
138(1)
Glossary
139(1)
Challenge Questions With Answers
140(3)
Chapter 6 The Two-Level Random Intercept Model
143(48)
Overview
143(1)
Data
143(1)
Model
144(1)
SPSS
144(15)
Overview
144(1)
Input
145(6)
Output
151(8)
Stata
159(5)
Overview
159(1)
Input
159(2)
Output
161(3)
SAS
164(5)
Overview
164(1)
Input
164(1)
Output
165(4)
HLM 7
169(8)
Overview
169(1)
Input
170(3)
Output
173(4)
R
177(7)
Overview
177(1)
Input
177(1)
Output
178(6)
Summary
184(2)
Glossary
186(1)
Challenge Questions With Answers
187(4)
Chapter 7 The Two-Level Random Coefficients Model
191(42)
Overview
191(2)
Data
191(1)
Model
191(2)
SPSS
193(7)
Overview
193(1)
Input
194(1)
Output
195(5)
Stata
200(6)
Overview
200(1)
Input
200(1)
Output
201(5)
SAS
206(6)
Overview
206(1)
Input
206(2)
Output
208(4)
HLM 7
212(5)
Overview
212(1)
Input
212(2)
Output
214(3)
R
217(8)
Overview
217(1)
Input
217(1)
Output
218(7)
Significance (p) Values for Variance Components
225(1)
Summary
226(1)
Glossary
227(1)
Challenge Questions With Answers
228(5)
Chapter 8 The Three-Level Unconditional Random Intercept Model with Longitudinal Data
233(32)
Overview
233(4)
Data
234(1)
Model
235(2)
Longitudinal Versus Repeated Measures Models
237(1)
SPSS
237(5)
Overview
237(1)
Input
238(1)
Output
239(3)
Stata
242(4)
Overview
242(1)
Input
242(1)
Output
243(3)
SAS
246(5)
Overview
246(1)
Input
246(1)
Output
247(4)
HLM 7
251(5)
Overview
251(1)
Input
251(1)
Output
252(4)
R
256(4)
Overview
256(1)
Input
256(2)
Output
258(2)
Summary
260(1)
Glossary
261(1)
Challenge Questions With Answers
262(3)
Chapter 9 Repeated Measures and Heterogeneous Variance Models
265(40)
Overview
265(10)
Data
265(1)
Alternative Ways to Model Time
266(6)
Repeated Measures and Heterogeneous Variances
272(1)
Repeated Variance Components vs. Residual Variance Components
272(2)
The Model
274(1)
SPSS
275(4)
Overview
275(1)
Input
275(1)
Output
276(3)
SAS
279(5)
Overview
279(1)
Input
279(1)
Output
280(4)
Stata
284(5)
Overview
284(1)
Input
284(2)
Output
286(3)
R
289(4)
Overview
289(1)
Input
289(1)
Output
290(3)
HLM 7
293(6)
Overview
293(2)
Input
295(3)
Output
298(1)
Summary
299(2)
Glossary
301(1)
Challenge Questions With Answers
302(3)
Chapter 10 Residual and Influence Analysis for a Three-Level RC Model
305(80)
About This
Chapter
305(1)
Overview
305(1)
Why Residual Analysis?
306(1)
Data
307(1)
Model
308(1)
Model Diagnostics
309(10)
Overview
309(1)
Types of Residuals
310(1)
Types of Residual Plots
311(2)
Types of Influence, Leverage, and Distance Measures
313(3)
Statistical Package Support
316(2)
Handling Outliers
318(1)
SAS
319(19)
Overview
319(1)
Input
319(4)
Output
323(3)
Residual Analysis
326(5)
Influence Analysis
331(6)
Saving and Printing Outliers
337(1)
Stata
338(13)
Overview
338(1)
Input
339(1)
Output
340(3)
Residual Analysis
343(3)
Influence Analysis
346(2)
Saving and Printing Outliers
348(3)
SPSS
351(8)
Overview
351(1)
Input
351(2)
Output
353(1)
Residual Analysis
354(4)
Influence Analysis
358(1)
Saving and Printing Outliers
358(1)
HLM 7
359(6)
Overview
359(1)
Input
359(2)
Output
361(1)
Residual Analysis
362(3)
Influence Analysis
365(1)
Saving and Printing Outliers
365(1)
R
365(13)
Overview
365(1)
Input
365(1)
Output
366(3)
Residual Analysis
369(4)
Influence Analysis
373(3)
Saving Residual Outliers in R
376(2)
Summary
378(2)
Glossary
380(1)
Challenge Questions With Answers
381(4)
Chapter 11 Cross-Classified Linear Mixed Models
385(52)
Overview
385(2)
Data
387(1)
Model
388(1)
Research Purpose
389(1)
Stata
389(9)
Overview
389(1)
Null Random Effects Models
389(1)
Null Additive Cross-Classified Model
390(4)
Additive Cross-Classified Model With Covariates
394(2)
Nonadditive Cross-Classified Model With Level Interaction
396(2)
SPSS
398(10)
Overview
398(3)
Null Random Effects Models
401(2)
Null Additive Cross-Classified Model
403(1)
Additive Cross-Classified Model With Covariates
404(3)
Nonadditive Cross-Classified Model With Level Interaction
407(1)
SAS
408(6)
Overview
408(1)
Null Random Effects Models
409(1)
Null Additive Cross-Classified Model
410(1)
Additive Cross-Classified Model With Covariates
411(2)
Nonadditive Cross-Classified Model With Level Interaction
413(1)
HLM 7
414(11)
Overview
414(4)
Null Random Effects Models
418(1)
Null Additive Cross-Classified Model
419(2)
Additive Cross-Classified Model With Covariates
421(4)
Nonadditive Cross-Classified Model With Level Interaction
425(1)
R
425(7)
Overview
425(1)
Null Random Effects Models
426(2)
Null Additive Cross-Classified Model
428(1)
Additive Cross-Classified Model With Covariates
429(1)
Nonadditive Cross-Classified Model With Level Interaction
430(2)
Summary
432(1)
Glossary
433(1)
Challenge Questions With Answers
434(3)
Chapter 12 Generalized Linear Mixed Models
437(44)
Overview
437(2)
Estimation Methods
439(1)
Data
440(2)
Model
442(1)
Stata
443(5)
Overview
443(2)
Input
445(1)
Output
446(2)
SAS
448(5)
Overview
448(2)
Input
450(1)
Output
450(3)
SPSS
453(9)
Overview
453(2)
Input
455(4)
Output
459(3)
HLM 7
462(9)
Overview
462(3)
Input
465(2)
Output
467(4)
R
471(5)
Overview
471(1)
Input
472(1)
Output
473(3)
Summary
476(1)
Glossary
477(1)
Challenge Questions With Answers
478(3)
Appendix 1 Data Used in Examples. Refers to Student Companion Website 481(6)
Appendix 2 Reporting Multilevel Results 487(6)
References 493(10)
Index 503
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).