Muutke küpsiste eelistusi

Multilevel and Longitudinal Modeling with IBM SPSS [Pehme köide]

4.35/5 (25 hinnangut Goodreads-ist)
(University of Hawaii, Manoa), (University of Hawaii, Manoa), (University of Vermont, USA)
  • Formaat: Paperback / softback, 360 pages, kõrgus x laius: 279x216 mm, kaal: 856 g
  • Sari: Quantitative Methodology Series
  • Ilmumisaeg: 20-May-2010
  • Kirjastus: Routledge Academic
  • ISBN-10: 1848728638
  • ISBN-13: 9781848728639
Teised raamatud teemal:
  • Pehme köide
  • Hind: 50,49 €*
  • * saadame teile pakkumise kasutatud raamatule, mille hind võib erineda kodulehel olevast hinnast
  • See raamat on trükist otsas, kuid me saadame teile pakkumise kasutatud raamatule.
  • Kogus:
  • Lisa ostukorvi
  • Tasuta tarne
  • Lisa soovinimekirja
Multilevel and Longitudinal Modeling with IBM SPSSSuurem pilt
  • Formaat: Paperback / softback, 360 pages, kõrgus x laius: 279x216 mm, kaal: 856 g
  • Sari: Quantitative Methodology Series
  • Ilmumisaeg: 20-May-2010
  • Kirjastus: Routledge Academic
  • ISBN-10: 1848728638
  • ISBN-13: 9781848728639
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781848728639.html
Märksõnad:
This is the first book to demonstrate how to use the multilevel and longitudinal modeling techniques available in IBM SPSS Version 18. The authors tap the power of SPSSs Mixed Models routine to provide an elegant and accessible approach to these models. Readers who have learned statistics using this software will no longer have to adapt to a new program to conduct quality multilevel and longitudinal analyses. Annotated screen shots with all of the key output provide readers with a step-by-step understanding of each technique as they are shown how to navigate through the program. Diagnostic tools, data management issues, and related graphics are introduced throughout. SPSS commands show the flow of the menu structure and how to facilitate model building. Annotated syntax is also available for those who prefer this approach. Most chapters feature an extended example illustrating the logic of model development. These examples show readers the context and rationale of the research questions and the steps around which the analyses are structured. The data used in the text and syntax examples are available at http://www.psypress.com/multilevel-modeling-techniques/ .



The book opens with the conceptual and methodological issues associated with multilevel and longitudinal modeling, followed by a discussion of SPSS data management techniques which facilitate working with multilevel, longitudinal, and/or cross-classified data sets. The next few chapters introduce the basics of multilevel modeling, how to develop a multilevel model, and trouble-shooting techniques for common programming and modeling problems along with potential solutions. Models for investigating individual and organizational change are developed in chapters 5 and 6, followed by models with multivariate outcomes in chapter 7. Chapter 8 illustrates SPSSs facility for examining models with cross-classified data structures. The book concludes with thoughts about ways to expand on the various multilevel and longitudinal modeling techniques introduced and issues to keep in mind in conducting multilevel analyses.



Ideal as a supplementary text for graduate level courses on multilevel, longitudinal, latent variable modeling, multivariate statistics, and/or advanced quantitative techniques taught in departments of psychology, business, education, health, and sociology, this books practical approach will also appeal to researchers in these fields. The book provides an excellent supplement to Heck & Thomass An Introduction to Multilevel Modeling Techniques, 2nd Edition; however, it can also be used with any multilevel and/or longitudinal modeling book or as a stand-alone text.

Arvustused

"The book is written in a logical way with real example studies for each multilevel model. The language is clear and the analysis is accurate. The authors have a talent in making complicated multilevel models more attractive and user friendly. I found the book to be enjoyable. ... The book is mostly suitable for a graduate level course, and it is also a good reference for those who want to learn multilevel modeling using SPSS." - Xing Liu, Education Department, Eastern Connecticut State University, USA, in the International Journal of Research & Method in Education



"With its thorough coverage of the statistical underpinnings of multilevel modeling and the detailed step-by-step instructions on how to analyze data with IBM SPSS, this text is a gold mine for graduate instruction!" -Laura M. Stapleton, University of Maryland, Baltimore County, USA



"This text has both depth and breadth of coverage with material that is accessible and transparent to the novice but at the same time comprehensive for the experienced researcher. It is one of those rare texts that is thorough in both the how-tos of the software and the concepts. It is a key multilevel text that any multilevel researcher will not want to be without." - Debbie L. Hahs-Vaughn, University of Central Florida, USA



"This book is a timely and valuable addition. Multilevel modeling is now becoming much more accessible to practitioners, many of whom use SPSS for other analyses. Therefore, a book like this [ is] a great resource I would purchase the book and require it for my courses... It is a unique contribution to the field... I wish I had thought of writing it first!" - Dick Carpenter, University of Colorado, Colorado Springs, USA



"The first on the market to explain and illustrate how these multilevel models can be analyzed using the popular SPSS program." - George Marcoulides, University of California, Riverside, USA

Preface xi
Introduction to Multilevel and Longitudinal Modeling With IBM SPSS
1(20)
Our Intent
2(1)
Analysis of Multilevel Data Structures
3(6)
Partitioning Variation in an Outcome
6(1)
What SPSS Can and Cannot Do
7(2)
Developing a General Multilevel Modeling Strategy
9(8)
Illustrating the Steps in Investigating a Proposed Model
9(1)
One-Way ANOVA (No Predictors) Model
10(1)
Analyze a Level 1 Model With Fixed Predictors
11(1)
Add the Level 2 Explanatory Variables
12(1)
Examine Whether a Particular Slope Coefficient Varies Between Groups
13(1)
Adding Cross-Level Interactions to Explain Variation in the Slope
14(2)
Syntax Versus SPSS Menu Command Formulation
16(1)
Model Estimation and Other Typical Multilevel Modeling Issues
17(2)
Sample Size
18(1)
Power
18(1)
Differences Between Multilevel Software Programs
19(1)
A Note About Standardized and Unstandardized Coefficients
19(1)
Summary
20(1)
Preparing and Examining the Data for Multilevel Analyses
21(40)
Data Requirements
21(1)
File Layout
22(2)
Getting Familiar With Basic SPSS Data Commands
24(21)
Recode: Creating a New Variable Through Recoding
24(5)
Compute: Creating a New Variable That is a Function of Some Other Variable
29(1)
Match Files: Combining Data From Separate SPSS Files
30(6)
Aggregate: Collapsing Data Within Level 2 Units
36(2)
Varstocases: Vertical Versus Horizontal Data Structures
38(6)
Using "Rank" to Recode the Level 1 or Level 2 Data for Nested Models
44(1)
Creating an Identifier Variable
45(6)
Creating an Individual-Level Identifier Using Compute
45(2)
Creating a Group-Level Identifier Using Rank Cases
47(2)
Creating a Within-Group-Level Identifier Using Rank Cases
49(2)
Centering
51(7)
Grand-Mean Centering
53(1)
Group-Mean Centering
54(4)
Checking the Data
58(1)
A Note About Model Building
58(1)
Summary
59(2)
Defining a Basic Two-Level Multilevel Regression Model
61(50)
From Single-Level to Multilevel Analysis
61(2)
Building a Two-Level Model
63(9)
Research Questions
63(1)
The Data
64(1)
Graphing the Relationship Between SES and Math Test Scores With SPSS Menu Commands
65(5)
Graphing the Subgroup Relationships Between SES and Math Test Scores With SPSS Menu Commands
70(2)
Building a Multilevel Model With SPSS Mixed
72(38)
Examining Variance Components Using the Null Model
73(1)
Defining the Null Model With SPSS Menu Commands
74(4)
Interpreting the Output From the Null Model
78(2)
Building the Individual-Level (or Level 1) Random Intercept Model
80(1)
Defining the Level 1 Random Intercept Model with SPSS Menu Commands
81(2)
Interpreting the Output From Model 1
83(3)
Building the Group-Level (or Level 2) Random Intercept Model
86(1)
Defining the Group-Level Random Intercept Model With SPSS Menu Commands
87(2)
Interpreting the Output From Model 2
89(2)
Defining the Public School Variable as a Covariate Using SPSS Menu Commands
91(2)
Adding a Randomly Varying Slope (the Random Slope and Intercept Model)
93(2)
Defining the Random Slope and Intercept Model With SPSS Menu Commands
95(2)
Interpreting the Output From Model 3
97(1)
Explaining Variability in the Random Slope (More Complex Random Slopes and Intercept Models)
98(1)
Defining More Complex Random Slope and Intercept Models With SPSS Menu Commands
99(5)
Interpreting the Output From Model 4
104(2)
Graphing SES-Achievement Relationships in High- and Low-Achieving Schools With SPSS Menu Commands
106(4)
Summary
110(1)
Three-Level Univariate Regression Models
111(30)
Three-Level Univariate Model
111(10)
Research Questions
111(1)
The Data
112(1)
Defining the Three-Level Multilevel Model
112(1)
Centering Predictors and Interactions
113(2)
The Null Model (No Predictors)
115(1)
Defining the Null Model (No Predictors) With SPSS Menu Commands
115(5)
Interpreting the Output From the Null Model
120(1)
Defining Predictors at Each Level
121(19)
Defining Model 1 (Predictors at Each Level) With SPSS Menu Commands
122(2)
Interpreting the Output From Model 1
124(1)
Group-Mean Centering
125(1)
Defining Model 2 With SPSS Menu Commands
125(2)
Interpreting the Output From Model 2
127(1)
Covariance Estimates
128(1)
Does the Slope Vary Randomly Across Classrooms and Schools?
129(1)
Defining Model 3 With SPSS Menu Commands
130(2)
Interpreting the Output From Model 3
132(1)
Developing an Interaction Term
133(1)
Preliminary Investigation of the Interaction
133(1)
Examining a Level 2 Interaction
134(1)
Defining Model 4 With SPSS Menu Commands
135(3)
Interpreting the Output From Model 4
138(1)
Comparing the Fit of Successive Models
139(1)
Summary
140(1)
Examining Individual Change With Repeated Measures Data
141(48)
An Example Study
141(19)
Research Questions
142(1)
Data
142(1)
Univariate or Multivariate Approach
142(1)
Examining the Shape of Students' Growth Trajectories
143(2)
Graphing the Linear Growth Trajectories With SPSS Menu Commands
145(6)
Examining Growth Trajectories Using Repeated Measures ANOVA
151(1)
Conducting Repeated Measures ANOVA With SPSS Menu Commands
151(3)
Interpreting the Output From the Repeated Measures ANOVA
154(1)
Adding Between-Subjects Predictors
155(1)
Adding Between-Subjects Predictors With SPSS Menu Commands
156(3)
Interpreting the Output From Adding Between-Subjects Predictors
159(1)
Using SPSS Mixed to Examine Individual Change
160(28)
Developing a Two-Level Model of Individual Change
162(1)
Level 1 Covariance Structure
162(3)
Level 2 Covariance Structure
165(1)
Does the Slope Vary Randomly Across Individuals?
165(1)
Defining Model 1 With SPSS Menu Commands
166(3)
Interpreting the Output From Model 1
169(2)
Investigating Other Level 1 Covariance Structures
171(2)
Investigating Other Level 1 Covariance Structures Using SPSS Menu Commands
173(5)
Adding the Between-Subjects Predictors
178(1)
Defining Model 2 With SPSS Menu Commands
178(6)
Interpreting the Output From Model 2
184(3)
Graphing the Growth Rate Trajectories With SPSS Menu Commands
187(1)
Summary
188(1)
Methods for Examining Organizational-Level Change
189(34)
Examining Changes in Institutions' Graduation Rates
189(33)
Research Questions
190(1)
Data
191(1)
Defining the Model
191(1)
Level 1 Model
191(1)
Level 2 Model
192(1)
Level 3 Model
192(2)
Null Model: No Predictors
194(1)
Level 1 Error Structures
194(2)
Defining the Null Model (No Predictors) With SPSS Menu Commands
196(5)
Interpreting the Output From the Null Modle
201(1)
Adding Growth Rates
202(1)
Level 1 Model
202(1)
Coding the Time Variable
202(2)
Defining Model 1 With SPSS Menu Commands
204(3)
Interpreting the Output From Model 1
207(1)
Adding Time-Varying Covariates
208(1)
Defining Model 2 With SPSS Menu Commands
209(2)
Interpreting the Output From Model 2
211(1)
Explaining Differences in Growth Trajectories Between Institutions
211(1)
Defining Model 3 With SPSS Menu Commands
212(4)
Interpreting the Output From Model 3
216(1)
Adding a Model to Examine Growth Rates at Level 3
217(1)
Defining Model 4 With SPSS Menu Commands
218(3)
Interpreting the Output From Model 4
221(1)
Other Types of Random-Coefficients Growth Models
222(1)
Summary
222(1)
Multivariate Multilevel Models
223(48)
Multilevel Latent-Outcome Model
223(24)
Research Questions
224(1)
The Data
224(2)
Defining a Latent Variable for a Multilevel Analysis
226(1)
Null Model: No Predictors
227(2)
Defining the Null Model (No Predictors) With SPSS Menu Commands
229(5)
Interpreting the Output of the Null Model
234(1)
Building a Three-Level Model
234(1)
Defining Model 1 With SPSS Menu Commands
235(2)
Interpreting the Output of Model 1 (Explaining Student Achievement)
237(1)
Investigating a Random Slope
238(1)
Defining Model 2 With SPSS Menu Commands
238(3)
Interpreting the Output of Model 2
241(1)
Explaining Variation in Slopes
241(1)
Defining Model 3 (Variation in Academic Achievement Slopes) With SPSS Menu Commands
242(4)
Interpreting the Output of Model 3
246(1)
Comparing Model Estimates
246(1)
Multivariate Multilevel Model for Correlated Outcomes
247(23)
The Data
247(1)
Research Questions
248(1)
Formulating the Basic Model
248(1)
Null Model (No Predictors)
249(13)
Building a Complete Model (Predictors and Cross-Level Interactions)
262(6)
Testing the Hypotheses
268(1)
Covariance Components
268(2)
Summary
270(1)
Cross-Classified Multilevel Models
271(48)
Students Cross-Classified in High Schools and Universities
271(20)
Research Questions
271(1)
The Data
271(2)
Descriptive Statistics
273(1)
Defining Models in SPSS
274(1)
Adding a Set of Level 1 and Level 2 Predictors
275(1)
Defining Model 1 With SPSS Menu Commands
276(5)
Interpreting the Output From Model 1
281(1)
Investigating a Random Slope
282(1)
Defining Model 2 With SPSS Menu Commands
282(4)
Interpreting the Output From Model 2
286(1)
Explaining Variation Between Variables
286(1)
Defining Model 3 With SPSS Menu Commands
287(3)
Interpreting the Output From Model 3
290(1)
Developing a Cross-Classified Teacher Effectiveness Model
291(27)
The Data Structure and Model
291(1)
Research Questions
292(1)
Intercept-Only Model
293(1)
Defining Model 1 With SPSS Menu Commands
294(6)
Defining the Cross-Classified Model With Previous Achievement
300(1)
Defining Model 2 With SPSS Menu Commands
301(2)
Interpreting the Output From Models 1 and 2
303(1)
Adding Teacher Effectiveness and a Student Background Control
304(1)
Defining Model 3 With SPSS Menu Commands
305(2)
Interpreting the Output From Model 3
307(1)
Adding a School-Level Predictor and a Random Slope
308(1)
Defining Model 4 With SPSS Menu Commands
308(3)
Interpreting the Output From Model 4
311(1)
Examining Level 3 Differences Between Institutions
311(1)
Defining Model 5 With SPSS Menu Commands
312(2)
Interpreting the Output From Model 5
314(1)
Adding a Level 3 Cross-Level Interaction
315(1)
Defining Model 6 With SPSS Menu Commands
315(3)
Interpreting the Output From Model 6
318(1)
Summary
318(1)
Concluding Thoughts
319(4)
References
323(16)
Appendices
Syntax Statements
325(10)
Model Comparisons Across Software Applications
335(4)
Author Index 339(2)
Subject Index 341
Ronald H. Heck is professor of education at the University of Hawaii Manoa. His areas of interest include organizational theory, leadership, policy, and quantitative research methods.



Scott L. Thomas is professor in the School of Educational Studies at Claremont Graduate University. His specialities include sociology of education, policy, and quantitative research methods.



Lynn N. Tabata is a Research Consultant and affiliate graduate faculty member at the University of Hawaii.