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E-raamat: Longitudinal Structural Equation Modeling with Mplus: A Latent State-Trait Perspective

(Utah State University, United States)
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Longitudinal Structural Equation Modeling with Mplus: A Latent State-Trait PerspectiveSuurem pilt
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An in-depth guide to executing longitudinal confirmatory factor analysis (CFA) and structural equation modeling (SEM) in Mplus, this book uses latent statetrait (LST) theory as a unifying conceptual framework, including the relevant coefficients of consistency, occasion specificity, and reliability. Following a standard format, chapters review the theoretical underpinnings, strengths, and limitations of the various models; present data examples; and demonstrate each model's application and interpretation in Mplus, with numerous screen shots and output excerpts. Coverage encompasses both traditional models (autoregressive, change score, and growth curve models) and LST models for analyzing single- and multiple-indicator data. The book discusses measurement equivalence testing, intensive longitudinal data modeling, and missing data handling, and provides strategies for model selection and reporting of results. User-friendly features include special-topic boxes, chapter summaries, and suggestions for further reading. The companion website features data sets, annotated syntax files, and output for all of the examples.

Arvustused

"Geiser introduces readers to longitudinal SEM by building from simple to more complex models. Assuming only basic prior knowledge about SEM and factor analysis, Geiser offers a careful, clear analysis of advantages and limitations of each method, and includes discussions of missing data, Bayesian analysis, dynamic SEM for intensive longitudinal data, and model selection strategies. This book is an ideal companion text for a second course in SEM that tackles the analysis of longitudinal data. It is a useful reference for more experienced researchers and methodologists who want to learn about LST models. The book is unique in using LST theory to scaffold the presentation of a variety of models for longitudinal data. Discussion of the various models is skillfully interwoven with path diagrams, equations, and Mplus code. Even a beginning SEM user will have no trouble understanding this book."--Kristopher J. Preacher, PhD, Department of Psychology and Human Development, Peabody College, Vanderbilt University

"This is the first English-language book to introduce longitudinal latent variable analysis based on the crucial distinctions between states, traits, measurement error, and method effects. The book provides an extended and readable introduction to LST theory and, in particular, its implications for a meaningful analysis of longitudinal data. I highly recommend this book for students who want to learn about fundamental concepts of differential and developmental psychology; latent variables, in general; and the analysis of latent variables in longitudinal designs with Mplus, the most comprehensive program for the analysis of manifest and latent variables. I have no doubt that students will profit a lot from this great book written by an outstanding, authoritative scientist in the field of latent variable modeling."--Rolf Steyer, PhD, Institute of Psychology, Friedrich-Schiller University of Jena, Germany

"A much-needed addition to the literature. Geiser writes in an extremely clear and engaging style, avoiding unnecessary jargon while communicating essential concepts in a rigorous manner. The boxes within the chapters provide additional technical information to provide readers with a deeper dive into certain topics without interrupting the flow of the book. The most appealing aspect of this book for an instructor using it as a didactic tool or a researcher using it as a reference is the well-documented Mplus examples. These are truly key to bringing the methods alive for the reader. I recommend this book as a primary text in a course on longitudinal data analysis or latent variable modeling. It provides students with both the technical background to understand LST models and the applied tools to fit them using Mplus."--Holmes Finch, PhD, George and Frances Ball Distinguished Professor of Educational Psychology, Ball State University

"The book offers a profound, flexible, and extremely useful framework for longitudinal SEM. It will be of interest to users and theoreticians alike--it has much to offer for graduate courses or for researchers working with longitudinal data in psychology and related disciplines. Even though the primary topic is modeling, the book also provides inspiration for formulating research questions. Coming from a different angle with a different modeling language, I found it highly accessible and appealing. The book builds on earlier developments in LST theory and extrapolates the basic principles to a large variety of possible model specifications."--Paul De Boeck, PhD, Department of Psychology, The Ohio State University

"Using a measurement theory perspective, Geiser explains the rationale and procedures for a variety of longitudinal models, including simplex models, latent change score models, latent growth curve models, latent state models, and LST models. He translates longitudinal modeling techniques into digestible concepts and frames the relations between these concepts using LST theory. Geiser guides readers through complex issues related to analyzing longitudinal data so that readers can apply these methods to their own research. Easy-to-follow examples and annotated Mplus syntax and output clarify the concepts and illustrate the techniques. While this book is broadly accessible to substantive researchers, its technical rigor also will satisfy quantitative specialists. It can serve as a text for graduate-level courses or self-study of longitudinal data analysis and SEM."--Sara Finney, PhD, Department of Graduate Psychology, James Madison University-An excellent text on longitudinal structural equation modeling. Social science researchers use longitudinal models to measure the change in attitudes, personality traits, intelligence, feelings, cognitive abilities, etc., over time (Geiser, 2021). With the popularity of these types of models and the abundant use of Mplus, this book does a great job marrying the two areas to provide readers with an understanding of how longitudinal models work and how to estimate them using the powerful software, Mplus.I enjoyed the detail Geiser provides with respect to the fundamental knowledge of the models presented and the Mplus software. The organization of the chapters builds upon each other so the reader can easily follow the model building process and the author does an excellent job explaining each model.The amount of detail provided about Mplus syntax and interpretation of the results is impressive. Another highlight of the book are the BOXES presented throughout the chapters that highlight fundamental topics. From a pedagogical standpoint these BOXES are an efficient way to draw the reader's attention to important topics in the field of SEM. I recommend this book for researchers who are interested in learning about longitudinal SEM or as a reference for teaching a graduate-level SEM course.--Structural Equation Modeling, 2/5/2021 "Geiser introduces readers to longitudinal SEM by building from simple to more complex models. Assuming only basic prior knowledge about SEM and factor analysis, Geiser offers a careful, clear analysis of advantages and limitations of each method, and includes discussions of missing data, Bayesian analysis, dynamic SEM for intensive longitudinal data, and model selection strategies. This book is an ideal companion text for a second course in SEM that tackles the analysis of longitudinal data. It is a useful reference for more experienced researchers and methodologists who want to learn about LST models. The book is unique in using LST theory to scaffold the presentation of a variety of models for longitudinal data. Discussion of the various models is skillfully interwoven with path diagrams, equations, and Mplus code. Even a beginning SEM user will have no trouble understanding this book."--Kristopher J. Preacher, PhD, Department of Psychology and Human Development, Peabody College, Vanderbilt University

"This is the first English-language book to introduce longitudinal latent variable analysis based on the crucial distinctions between states, traits, measurement error, and method effects. The book provides an extended and readable introduction to LST theory and, in particular, its implications for a meaningful analysis of longitudinal data. I highly recommend this book for students who want to learn about fundamental concepts of differential and developmental psychology; latent variables, in general; and the analysis of latent variables in longitudinal designs with Mplus, the most comprehensive program for the analysis of manifest and latent variables. I have no doubt that students will profit a lot from this great book written by an outstanding, authoritative scientist in the field of latent variable modeling."--Rolf Steyer, PhD, Institute of Psychology, Friedrich-Schiller University of Jena, Germany

"A much-needed addition to the literature. Geiser writes in an extremely clear and engaging style, avoiding unnecessary jargon while communicating essential concepts in a rigorous manner. The boxes within the chapters provide additional technical information to provide readers with a deeper dive into certain topics without interrupting the flow of the book. The most appealing aspect of this book for an instructor using it as a didactic tool or a researcher using it as a reference is the well-documented Mplus examples. These are truly key to bringing the methods alive for the reader. I recommend this book as a primary text in a course on longitudinal data analysis or latent variable modeling. It provides students with both the technical background to understand LST models and the applied tools to fit them using Mplus."--Holmes Finch, PhD, George and Frances Ball Distinguished Professor of Educational Psychology, Ball State University

"The book offers a profound, flexible, and extremely useful framework for longitudinal SEM. It will be of interest to users and theoreticians alike--it has much to offer for graduate courses or for researchers working with longitudinal data in psychology and related disciplines. Even though the primary topic is modeling, the book also provides inspiration for formulating research questions. Coming from a different angle with a different modeling language, I found it highly accessible and appealing. The book builds on earlier developments in LST theory and extrapolates the basic principles to a large variety of possible model specifications."--Paul De Boeck, PhD, Department of Psychology, The Ohio State University

"Using a measurement theory perspective, Geiser explains the rationale and procedures for a variety of longitudinal models, including simplex models, latent change score models, latent growth curve models, latent state models, and LST models. He translates longitudinal modeling techniques into digestible concepts and frames the relations between these concepts using LST theory. Geiser guides readers through complex issues related to analyzing longitudinal data so that readers can apply these methods to their own research. Easy-to-follow examples and annotated Mplus syntax and output clarify the concepts and illustrate the techniques. While this book is broadly accessible to substantive researchers, its technical rigor also will satisfy quantitative specialists. It can serve as a text for graduate-level courses or self-study of longitudinal data analysis and SEM."--Sara Finney, PhD, Department of Graduate Psychology, James Madison University-An excellent text on longitudinal structural equation modeling. Social science researchers use longitudinal models to measure the change in attitudes, personality traits, intelligence, feelings, cognitive abilities, etc., over time (Geiser, 2021). With the popularity of these types of models and the abundant use of Mplus, this book does a great job marrying the two areas to provide readers with an understanding of how longitudinal models work and how to estimate them using the powerful software, Mplusâ¦.I enjoyed the detail Geiser provides with respect to the fundamental knowledge of the models presented and the Mplus software. The organization of the chapters builds upon each other so the reader can easily follow the model building process and the author does an excellent job explaining each modelâ¦.The amount of detail provided about Mplus syntax and interpretation of the results is impressive. Another highlight of the book are the BOXES presented throughout the chapters that highlight fundamental topics. From a pedagogical standpoint these BOXES are an efficient way to draw the reader's attention to important topics in the field of SEM. I recommend this book for researchers who are interested in learning about longitudinal SEM or as a reference for teaching a graduate-level SEM course.--Structural Equation Modeling, 2/5/2021

List of Abbreviations
xix
Guide to Statistical Symbols xxi
1 A Measurement Theoretical Framework for Longitudinal Data: Introduction to Latent State--Trait Theory
1(15)
1.1 Introduction
1(2)
1.2 Latent State--Trait Theory
3(4)
1.2.1 Introduction
3(1)
1.2.2 Basic Idea
3(2)
1.2.3 Random Experiment
5(1)
1.2.4 Variables in LST-R Theory
5(2)
Box 1.1 Key Concepts and Definitions in CTT
7(3)
1.2.5 Properties
10(1)
1.2.6 Coefficients
11(1)
Box 1.2 Properties of the Latent Variables in LST-R Theory
12(2)
1.3
Chapter Summary
14(1)
1.4 Recommended Readings
15(1)
2 Single-Factor Longitudinal Models for Single-Indicator Data
16(29)
2.1 Introduction
16(1)
2.2 The Random Intercept Model
17(1)
2.2.1 Introduction
17(1)
2.2.2 Model Description
17(2)
Box 2.1 Available Information, Model Degrees of Freedom, and Model Identification in Single-Indicator Longitudinal Designs
19(1)
Box 2.2 Defining the Random Intercept Model Based on LST-R Theory
20(1)
2.2.3 Variance Decomposition and Reliability Coefficient
21(1)
2.2.4 Mplus Application
22(2)
Box 2.3 Model Fit Assessment and Model Comparisons
24(2)
2.2.5 Summary
26(2)
2.3 The Random and Fixed Intercepts Model
28(1)
2.3.1 Introduction
28(1)
2.3.2 Model Description
28(1)
Box 2.4 Means of Linear Combinations
28(3)
Box 2.5 Defining the Random and Fixed Intercepts Model Based on LST-R Theory
31(3)
2.3.3 Variance Decomposition and Reliability Coefficient
32(1)
2.3.4 Mplus Application
32(2)
2.3.5 Summary
34(1)
2.4 The xi;-Congeneric Model
34(1)
2.4.2 Introduction
34(1)
2.4.2 Model Description
35(2)
Box 2.6 Defining the xi;-Congeneric Model Based on LST Theory
37(1)
2.4.3 Variance Decomposition and Reliability Coefficient
38(1)
2.4.4 Mplus Application
38(2)
Box 2.7 The Model Constraint And Model Test Options In Mplus
40(3)
2.4.5 Summary
43(1)
2.5
Chapter Summary
43(1)
2.6 Recommended Reading
44(1)
Note
44(1)
3 Multifactor Longitudinal Models for Single-Indicator Data
45(1)
3.1 Introduction
45(1)
3.2 The Simplex Model
45(3)
3.2.1 Introduction
45(1)
3.2.2 Model Description
46(2)
Box 3.1 Defining the Simplex Model Based on LST-R Theory
48(3)
Box 3.2 Should a Researcher Constrain State Residual or Measurement Error Variances in the Simplex Model?
51(1)
3.2.3 Variance Decomposition and Coefficients
51(2)
3.2.4 Assessing Stability and Change in the Simplex Model
53(1)
Box 3.3 Endogenous versus Exogenous Variables in Structural Equation Models and Mplus
54(2)
3.2.5 Mplus Application
56(1)
Box 3.4 Specifying the Simplex Model with Equal State Residual Factor Variances
57(4)
Box 3.5 Direct versus Indirect (Mediated) Variable Effects in the Simplex Model
61(1)
3.2.6 Summary
62(1)
3.3 The Latent Change Score Model
62(1)
3.3.1 Introduction
62(1)
3.3.2 Model Description
63(1)
3.3.3 Variance Decomposition and Coefficients
64(1)
3.3.4 Mplus Application
65(3)
3.3.5 Summary
68(1)
3.4 The Trait--State--Error Model
69(1)
3.4.1 Introduction
69(1)
3.4.2 Model Description
69(3)
Box 3.6 Defining the TSE Model Based on LST-R Theory
72(3)
3.4.3 Variance Decomposition and Coefficients
75(1)
Box 3.7 The Mean Structure in the TSE Model
76(3)
3.4.4 Mplus Application
79(4)
Box 3.8 Estimation Problems and Bias in the TSE Model
83(1)
3.4.5 Computing the Con(τt), Tcon(τt), Scon(τt), and Osp(τt) Coefficients in Mplus
85(1)
3.4.6 Summary
86(2)
3.5 Latent Growth Curve Models
88(1)
3.5.1 Introduction
88(1)
3.5.2 The Linear LGC Model
88(2)
Box 3.9 Defining the Linear LGC Model Based on LST-R Theory
91(6)
3.5.3 The LGC Model with Unspecified Growth Pattern
97(2)
Box 3.10 Defining the LGC Model with Unspecified Growth Pattern Using the Concepts of LST-R Theory
99(6)
3.6
Chapter Summary
105(4)
Box 3.11 Using Ordered Categorical Observed Variables as Indicators
109(1)
3.7 Recommended Readings
110(1)
Notes
110(3)
4 Latent State Models and Measurement Equivalence Testing in Longitudinal Studies
113(1)
4.1 Introduction
113(1)
4.2 The Latent State Model
114(1)
4.2.1 Introduction
114(1)
4.2.2 Model Description
114(2)
4.2.3 Scale Setting
116(1)
Box 4.1 Relationships Between the LS and CTT Models
116(1)
Box 4.2 Available Information and Model Degrees of Freedom in Multiple-Indicator Longitudinal Designs
117(3)
Box 4.3 Alternative Methods of Defining the Scale of Latent State Variables
120(1)
4.2.4 Model Definition Based on LST-R Theory
120(1)
4.2.5 Variance Decomposition and Reliability Coefficient
120(1)
Box 4.4 Defining the LS Model Based on LST-R Theory
121(2)
4.2.6 Testing ME across Time
122(1)
Box 4.5 Levels of ME According to Widaman and Reise (1997)
123(1)
Box 4.6 Nested Models and Chi-Square Difference Testing
124(4)
4.2.7 Other Features of the LS Model
125(1)
4.2.8 Mplus Application
126(2)
Box 4.7 Different Ways to Specify and Analyze the Mean Structure in the LS Model
128(8)
4.2.9 Summary
134(2)
4.3 The LS Model with Indicator-Specific Residual Factors
136(1)
4.3.1 Introduction
136(1)
4.3.2 Model Description
136(1)
Box 4.8 Indicator-Specific Effects in Longitudinal Data
137(3)
Box 4.9 Defining the LS-IS Model Based on LST-R Theory
140(4)
4.3.3 Variance Decomposition and Coefficients
142(2)
Box 4.10 Correlated Errors: An Alternative Way to Model Indicator Specificity in Longitudinal Data
144(3)
4.3.4 Mplus Application
145(2)
Box 4.11 Providing User-Defined Starting Values in Mplus
147(4)
4.3.5 Summary
151(1)
4.4
Chapter Summary
151(2)
4.5 Recommended Readings
153(1)
Notes
153(2)
5 Multiple-Indicator Longitudinal Models
155(76)
5.1 Introduction
155(1)
5.2 Latent State Change Models
156(5)
5.2.1 Introduction
156(1)
5.2.2 Model Description
157(2)
5.2.3 Variance Decomposition and Coefficients
159(1)
5.2.4 Mplus Application
160(1)
5.2.5 Summary
160(1)
5.3 The Latent Autoregressive/Cross-Lagged States Model
161(3)
5.3.1 Introduction
161(1)
5.3.2 Model Description
162(2)
Box 5.1 Defining the LAS Model Based on LST-R Theory
164(7)
5.3.3 Variance Decomposition and Coefficients
164(1)
5.3.4 Other Features of the Model
165(1)
5.3.5 Multiconstruct Extension
165(2)
5.3.6 Mplus Application
167(3)
5.3.7 Summary
170(1)
5.4 Latent State-Trait Models
171(4)
5.4.1 Introduction
171(1)
5.4.2 The Singletrait--Multistate Model
172(3)
Box 5.2 Defining the STMS Model Based on LST-R Theory
175(2)
Box 5.3 Some Guidelines for the Interpretation of the Con, Osp, and Rel Coefficients
177(2)
Box 5.4 Specifying an STMS Model as a Bifactor Model
179(6)
Box 5.5 STMS Models without versus with Autoregressive Effects
185(3)
5.4.3 The STMS Model with Indicator-Specific Residual Factors
186(2)
Box 5.6 Defining the STMS-IS Model Based on LST-R Theory
188(6)
5.4.4 The Multitrait--Multistate Model
192(2)
Box 5.7 Defining the MTMS Model Based on LST-R Theory
194(6)
Box 5.8 The MTMS Model with Autoregression
200(2)
5.5 Latent Trait-Change Models
202(5)
5.5.1 Introduction
202(2)
5.5.2 The 1ST Trait-Change Model
204(3)
Box 5.9 Defining the LST-TC Model Based on LST-R Theory
207(2)
Box 5.10 Alternative Equivalent Ways to Specify the LST-TC Model
209(7)
5.5.3 Multiple-Indicator Latent Growth Curve Models
212(4)
Box 5.11 Defining the Linear ISG Model Based on LST-R Theory
216(7)
5.6
Chapter Summary
223(1)
5.6.1 Advantages of Multiple-Indicator Models
223(5)
5.6.2 Limitations of Multiple-Indicator Models
228(1)
5.7 Recommended Readings
229(1)
Notes
230(1)
6 Modeling Intensive Longitudinal Data
231(1)
6.1 Introduction
231(1)
6.2 Special Features of Intensive Longitudinal Data
232(1)
6.2.1 Introduction
232(1)
6.2.2 Wide-versus Long-Format Data
232(2)
6.2.3 Imbalanced Time Points
234(1)
6.2.4 Autoregressive Effects
235(1)
6.3 Specifying Longitudinal SEMs for Intensive Longitudinal Data
235(3)
6.3.1 Introduction
235(1)
6.3.2 The Random Intercept Model as a Multilevel Model
235(3)
Box 6.1 Wide-to-Long Data Transformation of Data in Mplus
238(38)
6.3.3 The Linear Growth Model as a Multilevel Model
243(4)
6.3.4 The Multitrait--Multistate Model as a Multilevel Model
247(5)
6.3.5 The Indicator-Specific Growth Model as a Multilevel Model
252(5)
6.3.6 Modeling Autoregressive Effects Using DSEM
257(19)
6.4
Chapter Summary
276(1)
6.5 Recommended Readings
277(2)
7 Missing Data Handling
279(28)
7.1 Introduction
279(1)
7.2 Missing Data Mechanisms
280(5)
7.2.1 Missing Completely at Random
281(1)
7.2.2 Missing at Random
282(1)
7.2.3 Missing Not at Random
283(2)
7.3 ML Missing Data Handling
285(5)
7.3.1 Introduction
285(1)
7.3.2 ML Missing Data Analysis in Mplus
286(4)
7.3.3 Summary
290(1)
7.4 Multiple Imputation
290(6)
7.4.1 Introduction
290(1)
7.4.2 Ml in Mplus
291(5)
7.4.3 Summary
296(1)
7.5 Planned Missing Data Designs
296(1)
7.5.1 Introduction
296(1)
7.5.2 Analysis of Planned Missing Data and Simulations in Mplus
297(6)
7.6
Chapter Summary
303(2)
7.7 Recommended Readings
305(1)
Note
306(1)
8 How to Choose between Models and Report the Results
307(1)
8.1 Model Selection
307(3)
8.2 Reporting Results
310(1)
8.2.1 General Recommendations
310(1)
8.2.2 Methods Section
311(4)
8.2.3 Results Section
315(5)
8.3
Chapter Summary
320(1)
8.4 Recommended Readings
320(1)
Notes
321(2)
References 323(6)
Author Index 329(3)
Subject Index 332(12)
About the Author 344
Christian Geiser, PhD, is a former professor of quantitative psychology. He currently works as an instructor and statistical consultant. His areas of expertise are in structural equation modeling, longitudinal data analysis, latent class modeling, multitraitmultimethod analysis, and measurement. His website is https://christiangeiser.com/.
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