상세정보

This valuable book is now in a fully updated second edition that presents the latest developments in longitudinal structural equation modeling (SEM) and new chapters on missing data, the random intercepts cross-lagged panel model (RI-CLPM), longitudinal mixture modeling, and Bayesian SEM. Emphasizing a decision-making approach, leading methodologist Todd D. Little describes the steps of modeling a longitudinal change process. He explains the big picture and technical how-tos of using longitudinal confirmatory factor analysis, longitudinal panel models, and hybrid models for analyzing within-person change. User-friendly features include equation boxes that translate all the elements in every equation, tips on what does and doesn't work, end-of-chapter glossaries, and annotated suggestions for further reading. The companion website provides data sets for the examples--including studies of bullying and victimization, adolescents' emotions, and healthy aging--along with syntax and output, chapter quizzes, and the book’s figures.

New to This Edition:
*Chapter on missing data, with a spotlight on planned missing data designs and the R-based package PcAux.
*Chapter on longitudinal mixture modeling, with Whitney Moore.
*Chapter on the random intercept cross-lagged panel model (RI-CLPM), with Danny Osborne.
*Chapter on Bayesian SEM, with Mauricio Garnier.
*Revised throughout with new developments and discussions, such as how to test models of experimental effects.


This valuable book is now in a fully updated second edition that presents the latest developments in longitudinal structural equation modeling (SEM) and new chapters on missing data, the random intercepts cross-lagged panel model (RI-CLPM), longitudinal mixture modeling, and Bayesian SEM.

정보제공 :

PROLOGUE
* A personal introduction and what to expect
How statistics came into my life
My approach to the book
Key features of the book
Overview of the book
* Datasets and measures used
My dataset with the Inventory Felt Energy and Emotion in Life (I FEEL) measure
     The I FEEL
Gallagher and Johnson''s MIDUS example
     Neuroticism
     Negative affect
Dorothy Espelage''s bullying and victimization examples
     Peer victimization
     Substance use
     Family conflict
     Family closeness
     Bullying
     Homophobic teasing
* Overdue gratitude
* Prophylactic apologies
1.  OVERVIEW AND SEM FOUNDATIONS
* An overview of the conceptual foundations of SEM
Concepts, constructs, and indicators
From concepts to constructs to indicators to good models
* Sources of variance in measurement
Classical test theorem
Expanding classical test theorem
* Characteristics of indicators and constructs
Types of indicators and constructs
Categorical versus metrical indicators and constructs
Types of correlation coefficients that can be modeled
* A simple taxonomy of indicators and their roles
* Rescaling variables
* Parceling
* What changes and how?
* Some advice for SEM programming
* Philosophical issues and how I approach research
* Summary
* Key terms and concepts introduced in this chapter
* Recommended readings
2.  DESIGN ISSUES IN LONGITUDINAL STUDIES
* Timing of measurements and conceptualizing time
Cross-sectional design
Single-cohort longitudinal design
Cross-sequential design
Cohort-sequential design
Time-sequential design
Other validity concerns
Temporal design
Lags within the interval of measurement
Episodic and Experiential Time
* Missing data imputation and planned missing designs
Missing data mechanisms
Recommendations and caveats
Planned missing data designs in longitudinal research
* Modeling developmental processes in context
* Summary
* Key terms and concepts introduced in this chapter
* Recommended readings
3.  THE MEASUREMENT MODEL
* Drawing and labeling conventions
* Defining the parameters of a construct
* Scale setting
* Identification
* Adding means to the model: Scale setting and identification with means
* Adding a longitudinal component to the CFA model
* Adding phantom constructs to the CFA model
* Summary
* Key terms and concepts introduced in this chapter
* Recommended Readings
4.  MODEL FIT, SAMPLE SIZE, AND POWER
* Model fit and types of fit indices
Statistical rationale
Modeling rationale
The longitudinal null model
Summary and cautions
* Sample Size
* Power
* Summary
* Key terms and concepts introduced in this chapter
* Recommended readings
5.  THE LONGITUDINAL CFA MODEL
* Factorial invariance
* A small (nearly perfect) data example
Configural factorial invariance
Weak factorial invariance
Strong factorial invariance
Evaluating invariance constraints
Model modification
Partial invariance
* A larger example followed by tests of the latent construct relations
Testing the latent construct parameters
* An application of a longitudinal SEM to a repeated-measures experiment
* Summary
* Key terms and concepts introduced in this chapter
* Recommended readings
6.  SPECIFYING AND INTERPRETING A LONGITUDINAL PANEL MODEL
* Basics of a panel model
* The basic simplex change process
* Building a panel model
Covariate/control variables
Building the panel model of positive and negative affect
* Illustrative examples of panel models
A simplex model of cognitive development
Two simplex models of non-longitudinal data
A panel model of bullying and homophobic teasing
* Summary
* Key terms and concepts introduced in this chapter
* Recommended readings
7.  MULTIPLE-GROUP MODELS
* Multiple-group longitudinal SEM
Step 1: Estimate missing data and evaluate the descriptive statistics
Step 2: Perform any supplemental analysis to rule out potential confounds
Step 3: Fit an appropriate multiple-group longitudinal null model
Step 4: Fit the configurally invariant model across time and groups
Step 5: Test for weak factorial (loadings) invariance
Step 6: Test for strong factorial invariance
Step 7: Test for mean-level differences in the latent constructs
Step 8: Test for the homogeneity of the variance–covariance matrix among the latent constructs
Step 9: Test the longitudinal SEM model in each group
* A dynamic p-technique multiple-group longitudinal model
* Summary
* Key terms and concepts introduced in this chapter
* Recommended readings
8.  MULTILEVEL GROWTH CURVES AND SEM
* Longitudinal growth curve model
* Multivariate growth curve models
* Multilevel longitudinal model
* Summary
* Key terms and concepts introduced in this chapter
* Recommended readings
9.  MEDIATION AND MODERATION
* Making the distinction between mediators and moderators
Cross-sectional mediation
Half-longitudinal mediation
Full longitudinal mediation
* Moderation
* Summary
* Key terms and concepts introduced in this chapter
* Recommended readings
10.  JAMBALAYA: COMPLEX CONSTRUCT REPRESENTATIONS AND DECOMPOSITIONS
* Multitrait-multimethod models
* Pseudo-MTMM models
* Bifactor and higher order factor models
* Contrasting different variance decompositions
* Digestif
* Key terms and concepts introduced in this chapter
* Recommended readings