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E-raamat: Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Application, Second Edition 2nd edition [Taylor & Francis e-raamat]

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Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Application, Second Edition 2nd editionSuurem pilt
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Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
In this revision of the 1999 edition, scientists at the Oregon Research Institute, Eugene, introduce the statistical methodology of latent variable growth curve modeling (LGM) for studying change. This approach is compared with longitudinal panel models more commonly used in the social and behavioral sciences. Examples with graphical representations illustrate applications using latent growth structural equation modeling software programs (LISREL, EQS, Mplus, Amos). The companion CD-ROM contains sample program input, data, and output files for the examples presented, of particular value to researchers in specifying and testing their own models. Graduate level statistics is recommended. Annotation ©2006 Book News, Inc., Portland, OR (booknews.com)

This book provides a comprehensive introduction to latent variable growth curve modeling (LGM) for analyzing repeated measures. It presents the statistical basis for LGM and its various methodological extensions, including a number of practical examples of its use. It is designed to take advantage of the reader’s familiarity with analysis of variance and structural equation modeling (SEM) in introducing LGM techniques. Sample data, syntax, input and output, are provided for EQS, Amos, LISREL, and Mplus on the book’s CD. Throughout the book, the authors present a variety of LGM techniques that are useful for many different research designs, and numerous figures provide helpful diagrams of the examples.
 
Updated throughout, the second edition features three new chapters–growth modeling with ordered categorical variables, growth mixture modeling, and pooled interrupted time series LGM approaches. Following a new organization, the book now covers the development of the LGM, followed by chapters on multiple-group issues (analyzing growth in multiple populations, accelerated designs, and multi-level longitudinal approaches), and then special topics such as missing data models, LGM power and Monte Carlo estimation, and latent growth interaction models. The model specifications previously included in the appendices are now available on the CD so the reader can more easily adapt the models to their own research.
 
This practical guide is ideal for a wide range of social and behavioral researchers interested in the measurement of change over time, including social, developmental, organizational, educational, consumer, personality and clinical psychologists, sociologists, and quantitative methodologists, as well as for a text on latent variable growth curve modeling or as a supplement for a course on multivariate statistics. A prerequisite of graduate level statistics is recommended.
Preface ix
Acknowledgments xii
Introduction
1(16)
Typical Approaches to Studying Change
1(2)
Toward an Integrated Developmental Model
3(2)
Organization of the Book
5(4)
Related Literature on LGM
9(1)
Software Implementation
10(3)
Evaluation of Model Fit
13(4)
Specification of the LGM
17(24)
Two-Factor LGM for Two Time Points
17(2)
LGM Parameters
19(2)
LGM Assumptions
21(1)
Expressing Model Parameters as Functions of Measured Means, Variances, and Covariances
21(2)
Interpretation of the Growth Factors
23(3)
Representing the Shape of Growth Over Time
26(1)
Example 2.1: Three-Factor Polynomial LGM
26(5)
Example 2.2: Unspecified Two-Factor LGM
31(4)
Example 2.3: The Single-Factor LGM
35(3)
Summary
38(3)
LGM, Repeated Measures ANOVA, and the Mixed Linear Model
41(22)
Example 3.1: The Unconditional Growth Curve Model
42(8)
Including Predictors and Sequelae of Change in Growth Curve Models
50(2)
Example 3.2: Growth Curve Models Involving Predictors of Change
52(4)
Example 3.3: Growth Curve Models Involving Sequelae of Change
56(2)
Example 3.4: The Full Growth Curve Model Involving Predictors and Sequelae of Change
58(3)
Summary
61(2)
Multivariate Representations of Growth and Development
63(18)
Example 4.1: Associative LGM
64(3)
Higher Order LGMs
67(1)
Example 4.2: Factor-of-Curves LGM
68(1)
Example 4.3: Curve-of-Factors LGM
69(5)
Example 4.4: Including Structural Parameters
74(3)
Summary
77(4)
Analyzing Growth in Multiple Populations
81(12)
Equality of Sets of Parameters of an LGM
83(1)
Example 5.1: Multiple-Sample Analysis of Change
84(2)
Lagrange Multipliers
86(2)
Example 5.2: Alternative Multiple-Sample Analysis of ``Added Growth'' LGM
88(2)
Summary
90(3)
Accelerated Designs
93(10)
Cohort-Sequential LGM
94(3)
Example 6.1: Cohort-Sequential LGM
97(1)
Example 6.2: Unspecified Cohort-Sequential LGM
98(2)
Summary
100(3)
Multilevel Longitudinal Approaches
103(22)
Example 7.1: Full Information Maximum Likelihood Estimation (FIML)
105(4)
Example 7.2: Multilevel LGM (MLGM)
109(6)
Example 7.3: Extension of the Hierarchical LGM to Four Levels
115(7)
Summary
122(3)
Growth Mixture Modeling
125(26)
Latent Class Analysis of Dynamic Models
125(1)
Covariance Structure Analysis Mixture Modeling
126(1)
Growth Mixture Modeling
127(1)
Model Specifications
128(3)
Model Estimation
131(1)
Example 8.1: The Single-Class Growth Curve Model
132(3)
Example 8.2: Determining Sample Heterogeneity: Multiple-Class Growth Curve Models
135(3)
Alternative Methods for Estimating the Number of Classes and Parameter Starting Values
138(3)
Example 8.3: Including Covariates in the Mixture Modeling Framework
141(2)
Example 8.4: Including Mixture Indicators
143(4)
Summary
147(4)
Piecewise and Pooled Interrupted Time Series LGMs
151(14)
Example 9.1: Piecewise Models
153(4)
Example 9.2: Pooled Interrupted Time Series LGM
157(3)
Example 9.3: Simple Change LGM
160(2)
Summary
162(3)
Latent Growth Curve Modeling With Categorical Variables
165(14)
Measurement Characteristics of the Ordered Categorical Variable
167(1)
Growth Modeling With Categorical Outcome Variables
168(1)
Software Implementation
169(3)
Example 10.1: LGM of Ordered Categorical Outcomes
172(4)
Summary
176(3)
Missing Data Models
179(16)
A Taxonomy of Methods for Partial Missingness
179(1)
A Taxonomy of Missingness
180(1)
Model-Based Approaches to Analyses With Partial Missingness
181(3)
Example 11.1: Multiple-Group Analyses Incorporating Missing Data
184(1)
Example 11.2: Full Information Maximum Likelihood (FIML) Extensions of the Multiple-Group Approach
185(3)
Example 11.3: Multiple Imputation of Missing Data
188(4)
Summary
192(3)
Latent Variable Framework for LGM Power Estimation
195(18)
Power Estimation Within a Latent Variable Framework
196(2)
Example 12.1: Power Estimation in LGM
198(4)
Example 12.2: Power Estimation in a Multiple-Population Context
202(4)
Example 12.3: Monte Carlo Power Estimation
206(2)
Summary
208(5)
Testing Interaction Effects in LGMs
213(12)
Example 13.1: The Two-Factor Intercept-Slope Model
214(6)
Summary
220(5)
Summary
225(8)
Advantages of LGM
226(4)
Limitations of LGM
230(1)
Concluding Remarks
231(2)
References 233(16)
Author Index 249(6)
Subject Index 255(6)
About the Authors 261


Terry E. Duncan, Susan C. Duncan and Lisa A. Strycker
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