Abstract
- Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Psychological Bulletin, 51, 1173–1182.
- Bentler, P. M. (1998). EQS structural equations model program (Version 5.7) [Computer software]. Encino, CA: Multivariate Software.
- Bollen, K. A. (1989). Structural equation modeling with latent variables. New York: Wiley.
- Bollen, K. A., & Long, J. S. (1993). Testing structural equation models. New York: Wiley.
- Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Thousand Oaks, CA: Sage.
- Campbell, D. T., & Kenny, D. A. (1999). A primer on regression artifacts. New York: Guilford.
- Curran, P. J., West, S. G., & Finch, J. F. (1996). The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychological Methods, 1, 16–29.
- Diamantopoulos, A., & Siguaw, J. A. (2000). Introducing LISREL. Thousand Oaks, CA: Sage.
- Duncan, T. E., Duncan, S. C., Strycker, L. A., Li, F., & Alpert, A. (1999). An introduction to latent variable growth curve modeling: Concepts, issues, and applications. Mahwah, NJ: Erlbaum.
- Gerbing, D. W., & Anderson, J. C. (1993). Monte Carlo evaluations of goodness of fit indices for structural equation models. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 40–65). Thousand Oaks, CA: Sage.
- Gribbons, B. C., & Hocevar, D. (1998). Levels of aggregation in higher level confirmatory factor analysis: Applications for academic self-concept. Structural Equation Modeling, 5, 377–390.
- Heck, R. H., & Thomas, S. L. (2000). An introduction to multilevel modeling techniques. Mahwah, NJ: Erlbaum.
- Hoyle, R. H. (1995). The structural equation modeling approach: Basic concepts and fundamental issues. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 158–176). Thousand Oaks, CA: Sage.
- Hoyle, R. H., & Panter, A. T. (1995). Writing about structural equation models. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 158–176). Thousand Oaks, CA: Sage.
- Hu, L., & Bentler, P. M. (1995). Evaluating model fit. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 76–99). Thousand Oaks, CA: Sage.
- Hu, L., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to unparameterized model misspecification. Psychological Methods, 3, 424–453.
- Hu, L., & Bentler, P. M. (1999). Cut-off criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55.
- Kaplan, D. (2000). Structural equation modeling: Foundations and extensions. Thousand Oaks, CA: Sage.
- Kelloway, E. K. (1998). Using LISREL for structural equation modeling: A researcher’s guide. Thousand Oaks, CA: Sage.
- Kenny, D. A. (1979). Correlation and causality. New York: Wiley.
- Kenny, D. A., Kashy, D. A., & Bolger, N. (1998). Data analysis in social psychology. In D. Gilbert, S. Fiske, & G. Lindzey (Eds.), Handbook of social psychology (4th ed., pp. 252–258). Boston: McGraw-Hill.
- Kline, R. B. (1998). Principles and practice of structural equation modeling. New York: Guilford.
- Loehlin, J. C. (1998). Latent variable models. Factor, path, and structural analysis (3rd ed.). Mahwah, NJ: Erlbaum.
- Marcoulides, G. A., & Schumacker, R. E. (1996). Advanced structural equation modeling: Issues and techniques. Mahwah, NJ: Erlbaum.
- Marcoulides, G. A., & Schumacker, R. E. (2001). New developments in structural equation modeling. Mahwah, NJ: Erlbaum.
- Maruyama, G. M. (1998). Basics of structural equation modeling. Thousand Oaks, CA: Sage.
- McCoach, D. B., & Siegle, D. (2003). The structure and function of academic self-concept in gifted and general education students. Roeper Review, 25, 61–65.
- Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York: McGraw-Hill.
- Raykov, T., & Marcoulides, G. A. (2000). A first course in structural equation modeling. Mahwah, NJ: Erlbaum.
- Schumacker, R. E., & Lomax, R. G. (1996). A beginner’s guide to structural equation modeling. Mahwah, NJ: Erlbaum.
- Schumacker, R. E., & Marcoulides, G. A. (1998). Interaction and nonlinear effects in structural equation modeling. Mahwah, NJ: Erlbaum.
- West, S. G., Finch, J. F., & Curran, P. J. (1995). Structural equation models with nonnormal variables: Problems and remedies. In R. H. Hoyle (Ed.), Structural equation modeling (pp. 56–75). Thousand Oaks, CA: Sage.
- Volume 27
- Issue 1
- Publication Date: Fall 2003
SEM Isn’t Just the Schoolwide Enrichment Model Anymore: Structural Equation Modeling (SEM) in Gifted Education
D. Betsy McCoach
Structural equation modeling (SEM) refers to a family of statistical techniques that explores the relationships among a set of variables. Structural equation modeling provides an extremely versatile method to model very specific hypotheses involving systems of variables, both measured and unmeasured. Researchers can use SEM to study patterns of interrelationships among variables, compare different groups to each other, study change over time, and do many other types of sophisticated analyses. This paper will present an overview of SEM, present an illustration of research using SEM, and provide suggestions for ways that this powerful technique can be used to answer a variety of research questions within the field of gifted education.
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