Structural equation modeling

Publisher Summary Structural equation modeling (SEM) is a flexible analytic tool that can combine regression, correlation, and factor analyses simultaneously to address important issues in the social sciences, biological sciences, and humanities. This chapter provides an overview of some of the possible uses of this technique, and discusses some of the important assumptions and necessary conditions for using SEM and for interpreting the results. Its strengths include simultaneous assessment of various types of relations among variables and the ability to rigorously examine and compare similarities among and differences between two or more groups of study participants. However, one of its major limitations is the ease with which researchers can misinterpret their results when anxious to “prove” the validity of a model or to attempt to assess causality in the relation between two or among more variables when the research design does not allow for such conclusions. Furthermore, there is still much to be learned about SEM, including appropriate uses of various fit indices and the interpretation of odd or impossible parameter estimates.

[1]  K. Bollen A New Incremental Fit Index for General Structural Equation Models , 1989 .

[2]  K. Mardia Measures of multivariate skewness and kurtosis with applications , 1970 .

[3]  Richard G. Lomax,et al.  A Beginner's Guide to Structural Equation Modeling , 2022 .

[4]  R. P. McDonald,et al.  Goodness-of-fit indexes in confirmatory factor analysis : The effect of sample size , 1988 .

[5]  John J. McArdle,et al.  An Applied Comparison of Methods for Least- Squares Factor Analysis of Dichotomous Variables , 1991 .

[6]  Jacob Cohen,et al.  Applied multiple regression/correlation analysis for the behavioral sciences , 1979 .

[7]  P. Bentler,et al.  Comparative fit indexes in structural models. , 1990, Psychological bulletin.

[8]  P. Bentler,et al.  Evaluating model fit. , 1995 .

[9]  Robert C. MacCallum,et al.  Model specification: Procedures, strategies, and related issues. , 1995 .

[10]  R. P. McDonald,et al.  An index of goodness-of-fit based on noncentrality , 1989 .

[11]  Leslie A. Hayduk Structural equation modeling with LISREL: essentials and advances , 1987 .

[12]  P. Bentler,et al.  Significance Tests and Goodness of Fit in the Analysis of Covariance Structures , 1980 .

[13]  R. Goffin A Comparison of Two New Indices for the Assessment of Fit of Structural Equation Models , 1993 .

[14]  Robert J. Mislevy,et al.  Recent Developments in the Factor Analysis of Categorical Variables , 1986 .

[15]  Peter M. Bentler,et al.  Practical Issues in Structural Modeling , 1987 .

[16]  L. Tucker,et al.  A reliability coefficient for maximum likelihood factor analysis , 1973 .

[17]  M. Rovine,et al.  Latent variables models and missing data analysis. , 1994 .

[18]  James C. Anderson,et al.  Monte Carlo Evaluations of Goodness of Fit Indices for Structural Equation Models , 1992 .

[19]  P. Bentler MULTIVARIATE ANALYSIS WITH LATENT VARIABLES: CAUSAL MODELING , 1980 .

[20]  B. Byrne Structural equation modeling with EQS : basic concepts, applications, and programming , 2000 .

[21]  Karl G. Jöreskog,et al.  Lisrel 8: Structural Equation Modeling With the Simplis Command Language , 1993 .

[22]  J. Jaccard,et al.  LISREL Approaches to Interaction Effects in Multiple Regression , 1998 .

[23]  B. Muthén A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators , 1984 .

[24]  David Kaplan,et al.  Statistical power in structural equation modeling. , 1995 .

[25]  J. S. Tanaka,et al.  Confirmatory hierarchical factor analyses of psychological distress measures. , 1984 .

[26]  Stephen G. West,et al.  Structural equation models with non-normal variables: Problems and remedies , 1995 .

[27]  S. Wright The Method of Path Coefficients , 1934 .

[28]  Kenneth A. Bollen,et al.  Structural Equations with Latent Variables , 1989 .

[29]  B. Byrne Book Review: Structural Equation Modeling with EQS and EQS/Windows: Basic Concepts, Applications, and Programming , 1994 .

[30]  J. H. Steiger Statistically based tests for the number of common factors , 1980 .

[31]  P. Bentler Comparative Fit Indices in Structural Models , 1990 .

[32]  Sewall Wright,et al.  Path coefficients and path regressions: Alternative or complementary concepts? , 1960 .

[33]  W. Velicer,et al.  Relation of sample size to the stability of component patterns. , 1988, Psychological bulletin.

[34]  D. A. Kenny,et al.  The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. , 1986, Journal of personality and social psychology.

[35]  Peter M. Bentler,et al.  Estimates and tests in structural equation modeling. , 1995 .

[36]  R. Hoyle The structural equation modeling approach: Basic concepts and fundamental issues. , 1995 .

[37]  James C. Anderson,et al.  STRUCTURAL EQUATION MODELING IN PRACTICE: A REVIEW AND RECOMMENDED TWO-STEP APPROACH , 1988 .

[38]  C Loehlin John,et al.  Latent variable models: an introduction to factor, path, and structural analysis , 1986 .

[39]  Peter M. Bentler,et al.  EQS : structural equations program manual , 1989 .

[40]  E. J. van den Oord,et al.  Effects of Censored Variables on Family Studies , 1997, Behavior genetics.