Scientific Software International (SSI) publishes statistical data analysis software: LISREL (structural equation model/SEM, survey generalized linear model/SGLIM), 
HLM (hierarchical linear modeling, multilevel model), SuperMix (mixed models, mixed-effects program, MIXREG, MIXOR, MIXNO and MIXPREG) and Item Response Theory/IRT (BILOG-MG, MULTILOG, PARSCALE)Scientific Software International (SSI) publishes statistical data analysis software: LISREL (structural equation model/SEM, survey generalized linear model/SGLIM), 
HLM (hierarchical linear modeling, multilevel model), SuperMix (mixed models, mixed-effects program, MIXREG, MIXOR, MIXNO and MIXPREG) and Item Response Theory/IRT (BILOG-MG, MULTILOG, PARSCALE)Scientific Software International (SSI) publishes statistical data analysis software: LISREL (structural equation model/SEM, survey generalized linear model/SGLIM), 
HLM (hierarchical linear modeling, multilevel model), SuperMix (mixed models, mixed-effects program, MIXREG, MIXOR, MIXNO and MIXPREG) and Item Response Theory/IRT (BILOG-MG, MULTILOG, PARSCALE)

S  Structural equation modeling: present and future

The text honors Dr. Karl Jöreskog's outstanding academic career through contributions of current researchers in Structural Equation Modeling.

 The book contains the following sections:

  • Part A: History and Perspectives: This section will be indispensable to educators and students alike who want to explore the roots of factor analysis, including some more personal accounts by two of Dr. Jöreskog's former students.
  • Part B: Robustness, Reliability, and Fit Assessment: Six chapters explore the evolution and current execution of the methodology in greater depth.
  • Part C: Repeated Measurements, Experimental Design: Investigations and discussion of longitudinal data analysis, including some new approaches to model design.
  • Part D: Ordinal Data and Interaction Models: Some modern extensions of structural modeling theory are explored and expanded in this section.

For depth and breadth, Structural Equation Modeling: Present and Future is definitely a worthy addition to the library of anyone who is involved in the field. Overall, it will provide wonderful insight into the progress that has come from the ongoing work of Dr. Jöreskog and countless others in Factor Analysis and Structural Equation Modeling.Copyright 2001 Scientific Software International, Inc.
ISBN: 0-89498-049-1
Price: $40. For ordering information, click here.

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P  Preface

This book is divided into four parts.

Students and teachers of SEM will be especially interested in Part A, "History and Perspectives.'' It starts with two of Karl's former doctoral students presenting a personal overview of his career and placing him in a long tradition of famous statisticians while tracing the development of factor analysis. In combination with Karl's academic resume that follows this preface, the reader is offered a good idea of the scope and depth of Karl's (still ongoing) career.

This first part also has a contribution on SEM as a teaching tool and a discussion of its philosophical merits. Two further chapters focus on the role of the measurement model within the SEM methodology and the issue of estimation within factor analysis.

Part B - Robustness, Reliability, and Fit Assessment - includes two chapters on robustness, two chapters with new reliability measures, a report on how the number of variables influences the assessment of fit in SEM, and a new treatment of the two-stage least squares estimation method that has recently come to the fore again. This part should be particularly interesting for the applied researcher who wants to learn more about the methodological aspects of SEM .

Part C deals with models for "Repeated Measures and Experimental Design.'' Use of SEM in longitudinal data analysis has become one of the most valuable domains of application of the methodology. Two of the five chapters in this section summarize new approaches, one dealing with repeated measures data, and a second that investigates time series analysis. The investigation of issues in experimental design from the SEM perspective opens up a number of valuable data analysis possibilities.

Finally, the contributions in Part D focus on "Ordinal Data and Interaction Models.'' These five chapters nicely illustrate some of the newest areas of research in SEM, and show how the classic SEM theoretical framework is being extended to both nonlinear models and ordinal data. This work demonstrates that methodological developments quickly translate into important tools for substantive researchers.

Many of the chapters have examples that were analyzed with the LISREL program. The interested reader may download data and syntax files for these examples from each author's abstract page or the examples page.

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T  Table of contents

A: HISTORY AND PERSPECTIVES

  • 1 Dag Sörbom
    Karl Jöreskog and LISREL: a personal story 3
  • 2 Gösta Hägglund
    Milestones in the history of Factor Analysis 11
  • 3 Robert C. MacCallum, Ledyard R Tucker, & Nancy E. Briggs
    An alternative perspective on parameter estimation in factor analysis and related methods 39
  • 4 Stanley A. Mulaik
    Objectivity and other metaphors of structural equation modeling 59
  • 5 Steffen Kühnel
    The didactical power of Structural Equation Modeling 79
  • 6 Willem E.Saris
    Measurement models in sociology and political science 97

B: ROBUSTNESS, RELIABILITY, FIT ASSESSMENT 117

  • 7 Kenneth A. Bollen
    Two-stage least squares and latent variable models: simultaneous estimation and robustness to misspecifications 119
  • 8 Anne Boomsma & Jeffrey J. Hoogland
    The robustness of LISREL modeling revisited 139
  • 9 Einar Breivik & Ulf H. Olsson
    Adding variables to improve fit: the effect of model size on fit assessment in LISREL 169
  • 10 Gregory R. Hancock & Ralph O. Mueller
    Rethinking construct reliability within latent variable systems 195
  • 11 Tenko Raykov
    Studying change in scale reliability for repeated multiple measurements via covariance structure modeling 217
  • 12 Albert Satorra
    Goodness of fit testing of structural equation models with multiple group data and nonnormality 231

C: REPEATED MEASUREMENTS, EXPERIMENTAL DESIGN 257

  • 13 Stephen H.C. du Toit & Robert Cudeck
    The analysis of nonlinear random coefficient regression models with LISREL using constraints 259
  • 14 Stephen H.C. du Toit & Michael W. Browne
    The covariance structure of a vector ARMA time series 279
  • 15 David Kaplan, Polina Harik, & Lawrence Hotchkiss
    Cross-sectional estimation of dynamic structural equation models in disequilibrium 315
  • 16 John J. McArdle
    A latent difference score approach to longitudinal dynamic structural analysis 341
  • 17 Yutaka Kano
    Structural equation modeling for experimental data 381

D: ORDINAL DATA, INTERACTION MODELS 403

  • 18 Fan Yang-Wallentin, Peter Schmidt, & Sebastian Bamberg
    Testing Interactions with three different methods in the theory of planned behavior: analysis of traffic behavior data 405
  • 19 Fan Yang-Wallentin
    Comparisons of the ML and TSLS estimators for the Kenny-Judd model 425
  • 20 Peder Blom & Anders Christoffersson
    Estimation of nonlinear structural equation models using empirical characteristic functions 443
  • 21 Irini Moustaki
    A review of exploratory factor analysis for ordinal categorical data 461
  • 22 Rolf Steyer & Ivailo Partchev
    Latent state-trait modeling with logistic item response models 481

CONTRIBUTORS 521

REFERENCES 527

AUTHOR INDEX 581

SUBJECT INDEX 589

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