Structural equation modeling with AMOS: basic concepts, applications, and programming / Barbara M. Byrne

Incluye bibliografía: páginas 367-383 e índices

Preface.. Acknowledgments.. Section I: Introduction.. Chapter 1 Structural equation models: The basics.. Basic concepts.. Latent versus observed variables.. Exogenous versus endogenous latent variables.. The factor analytic model.. The full latent variable model.. General purpose and process of statistical modeling.. The general structural equation model.. Symbol notation.. The path diagram.. Structural equations.. Nonvisible components of a model.. Basic composition.. The formulation of covariance and mean structures.. Endnotes.. Chapter 2 Using the AMOS program.. Working with AMOS Graphics: Example 1. Initiating AMOS Graphics.. AMOS modeling tools.. The hypothesized model.. Drawing the path diagram.. Understanding the basic components of model 1.. The concept of model identification.. Working with AMOS Graphics: Example 2.. The hypothesized model.. Drawing the path diagram.. Working with AMOS Graphics: Example 3.. The hypothesized model.. Drawing the path diagram.. Endnotes.. Section II: Applications in single-group analyses.. Chapter 3 Testing for the factorial validity of a theoretical construct (First-order CFA model.. The hypothesized model.. Hypothesis 1: Self-concept is a four-factor structure.. Modeling with AMOS Graphics.. Model specification.. Data specification.. Calculation of estimates.. AMOS text output: Hypothesized four-factor model.. Model summary.. Model variables and parameters.. Model evaluation.. Parameter estimates.. Feasibility of parameter estimates.. Appropriateness of standard errors.. Statistical significance of parameter estimates.. Model as a whole.. The model-fitting process.. The issue of statistical significance.. The estimation process.. Goodness-of-fit statistics.. Model misspecification.. Residuals.. Modification indices.. Post hoc analyses.. Hypothesis 2: Self-concept is a two-factor structure.. Selected AMOS text output: Hypothesized two-factor model

Hypothesis 3: Self-concept is a one-factor structure.. Endnotes.. Chapter 4 Testing for the factorial validity of scores from a measuring instrument (First-order CFA model.. The measuring instrument under study.. The hypothesized model.. Modeling with AMOS Graphics.. Selected AMOS output: The hypothesized model.. Model summary.. Assessment of normality.. Assessment of multivariate outliers.. Model evaluation.. Goodness-of-fit summary.. Modification indices.. Post hoc analyses.. Model 2.. Selected AMOS output: Model 2.. Model 3.. Selected AMOS output: Model 3.. Model 4.. Selected AMOS output: Model 4.. Comparison with robust analyses based on the Satorra-Bentler scaled statistic.. Endnotes.. Chapter 5 Testing for the factorial validity of scores from a measuring instrument (Second-order CFA model.. The hypothesized model.. Modeling with AMOS Graphics.. Selected AMOS output: Preliminary model.. Selected AMOS output: The hypothesized model.. Model evaluation.. Goodness-of-fit summary.. Model maximum likelihood (ML estimates.. Estimation of continuous versus categorical variables.. Categorical variables analyzed as continuous variables.. The issues.. Categorical variables analyzed as categorical variables.. The theory.. The assumptions.. General analytic strategies.. The AMOS approach to analysis of categorical variables.. What is Bayesian estimation?.. Application of Bayesian estimation.. Chapter 6 Testing for the validity of a causal structure.. The hypothesized model.. Modeling with AMOS Graphics.. Formulation of indicator variables.. Confirmatory factor analyses.. Selected AMOS output: Hypothesized model.. Model assessment.. Goodness-of-fit summary.. Modification indices.. Post Hoc analyses.. Selected AMOS output: Model 2.. Model assessment.. Goodness-of-fit summary.. Modification indices.. Selected AMOS output: Model 3.. Model assessment.. Modification indices.. Selected AMOS output: Model 4.. Model Assessment

Modification indices.. Selected AMOS output: Model 5 assessment.. Goodness-of-fit summary.. Modification indices.. Selected AMOS output: Model 6.. Model assessment.. The issue of model parsimony.. Selected AMOS output: Model 7 (final model.. Model assessment.. Parameter estimates.. Endnotes.. Section III: Applications in multiple-group analyses.. Chapter 7 Testing for the factorial equivalence of scores from a measuring instrument (First-order CFA model.. Testing for multigroup invariance: The general notion.. The testing strategy.. The hypothesized model.. Establishing baseline models: The general notion.. Establishing the baseline models: Elementary and secondary teachers.. Modeling with AMOS Graphics.. Testing for multigroup invariance: The configural model.. Selected AMOS output: The configural model (No equality constraints imposed.. Model assessment.. Testing for measurement and structural invariance: The specification process.. The manual multiple-group approach.. The automated multiple-group approach.. Testing for measurement and structural invariance: Model assessment.. Testing for multigroup invariance: The measurement model.. Model assessment.. Testing for multigroup invariance: The structural model.. Endnotes.. Chapter 8 Testing for the equivalence of latent mean structures (First-order CFA model.. Basic concepts underlying tests of latent mean structures.. Estimation of latent variable means.. Model identification.. Factor identification.. The hypothesized model.. The baseline models.. Modeling with AMOS Graphics.. The structured means model.. Testing for latent mean differences.. The hypothesized multigroup model.. Steps in the testing process.. Testing for configural invariance.. Testing for measurement invariance.. Testing for latent mean differences.. Selected AMOS output: Model summary.. Selected AMOS output: Goodness-of-fit statistics.. Selected AMOS output: Parameter estimates

High-track students.. Low-track students.. Endnotes.. Chapter 9 Testing for the equivalence of a causal structure.. Cross-validation in covariance structure modeling.. Testing for invariance across calibration and validation samples.. The hypothesized model.. Establishing a baseline model.. Modeling with AMOS Graphics.. Testing for the invariance of causal structure using the automated approach.. Selected AMOS output: Goodness-of-fit statistics for comparative tests of multigroup invariance.. The traditional 乂2 difference approach.. The practical CFI difference approach.. Section IV: Other important applications.. Chapter 10 Testing for construct validity: The multitrait-multimethod model.. The general CFA approach to MTMM analyses.. Model 1: Correlated traits/correlated methods.. Model 2: No traits/correlated methods.. Model 3: Perfectly correlated traits/freely correlated methods.. Model 4: Freely correlated traits/uncorrelated methods.. Testing for evidence of convergent and discriminant validity: MTMM matrix-level analyses.. Comparison of models.. Evidence of convergent validity.. Evidence of discriminant validity.. Testing for evidence of convergent and discriminant validity: MTMM parameter-level analyses.. Examination of parameters.. Evidence of convergent validity.. Evidence of discriminant validity.. The correlated uniqueness approach to MTMM analyses.. Model 5: Correlated uniqueness model.. Endnotes.. Chapter 11 Testing for change over time: The latent growth curve model.. Measuring change in individual growth over time: The general notion.. The hypothesized dual-domain LGC model.. Modeling intraindividual change.. Modeling interindividual differences in change.. Testing latent growth curve models: A dual-domain model.. The hypothesized model.. Selected AMOS output: Hypothesized model.. Testing latent growth curve models: Gender as a time-invariant predictor of change.. Endnotes

Section V: Other important topics.. Chapter 12 Bootstrapping as an aid to nonnormal data.. Basic principles underlying the bootstrap procedure.. Benefits and limitations of the bootstrap procedure.. Caveats regarding the use of bootstrapping in SEM.. Modeling with AMOS Graphics.. The hypothesized model.. Characteristics of the sample.. Applying the bootstrap procedure.. Selected AMOS output.. Parameter summary.. Assessment of normality.. Statistical evidence of nonnormality.. Statistical evidence of outliers.. Parameter estimates and standard errors.. Sample ML estimates and standard errors.. Bootstrap ML standard errors.. Bootstrap bias-corrected confidence intervals.. Endnote.. Chapter 13 Addressing the issue of missing data.. Basic patterns of incomplete data.. Common approaches to handling incomplete data.. Listwise deletion.. Pairwise deletion.. Single imputation.. The AMOS approach to handling missing data.. Modeling with AMOS Graphics.. The hypothesized model.. Selected AMOS output: Parameter and model summary Information.. Selected AMOS output: Parameter estimates.. Selected AMOS output: Goodness-of-fit statistics.. Endnote.. References.. Author Index.. Subject Index

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This bestselling text provides a practical guide to the basic concepts of structural equation modeling (SEM) and the AMOS program (Versions 17 & 18). The author reviews SEM applications based on actual data taken from her research. Noted for its non-mathematical language, this book is written for the novice SEM user. With each chapter, the author "walks" the reader through all steps involved in testing the SEM model including: • An explanation of the issues addressed. • An illustration of the hypothesized and posthoc models tested. • AMOS input and output with accompanying interpretation and explanation. • The function of the AMOS toolbar icons and their related pull-down menus. • The data and published reference upon which the model was based. With over 50% new material, highlights of the new edition include: • All new screen shots featuring Version 17 of the AMOS program. • All data files now available at www.psypress.com/sem-with-amos. • Application of a multitrait-mulitimethod model, latent growth curve model, and second-order model based on categorical data. • All applications based on the most commonly used graphical interface. • The automated multi-group approach to testing for equivalence. The book opens with an introduction to the fundamental concepts of SEM and the basics of the AMOS program. The next 3 sections present applications that focus on single-group, multiple-group, and multitrait-mutimethod and latent growth curve models. The book concludes with a discussion about non-normal and missing (incomplete) data and two applications capable of addressing these issues. eng

Intended for researchers, practitioners, and students who use SEM and AMOS in their work, this book is an ideal resource for graduate level courses on SEM taught in departments of psychology, education, business, and other social and health sciences and/or as a supplement in courses on applied statistics, multivariate statistics, statistics II, intermediate or advanced statistics, and/or research design. Appropriate for those with limited or no previous exposure to SEM, a prerequisite of basic statistics through regression analysis is recommended. eng

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