The aim of this Methodological Brief to structural equation modeling (SEM) is to help readers become more aware of its uses and how to understand research using it. SEM provides many benefits over traditional multiple regression, including the ability to model multiple outcomes simultaneously and to account for measurement error. This Brief provides a general overview of the purposes of SEM and reviews how SEM can estimate models with both observed variables as well as their underlying constructs and how the adequacy of models is assessed in SEM. Finally, this Brief introduces path diagrams and latent variable models with an illustration of how to understand diagrams and standardized estimates.
BentlerP. M.BonettD. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88, 588-606.
2.
BollenK. A. (1989). Structural equations with latent variables. New York, NY: Wiley Interscience.
3.
BrowneM. W.CudeckR. (1993). Alternative ways of assessing model fit. In BollenK. A.LongJ. S. (Eds.), Testing structural equation models (pp. 136-162). Newbury Park, CA: Sage.
4.
ByrneB. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). Mahwah, NJ: Lawrence Erlbaum.
5.
ByrneB. M. (2011). Structural equation modeling with Mplus: Basic concepts, applications, and programming. Mahwah, NJ: Lawrence Erlbaum.
6.
CramondB.Matthews-MorganJ.BandalosD.ZuoL. (2005). A report on the 40-year follow-up of the Torrance Tests of Creative Thinking. Gifted Child Quarterly, 49, 283-291.
7.
GridleyB. E. (2002). In search of an elegant solution: Reanalysis of Plucker, Callahan, and Tomchin, with respects to Pyryt and Plucker. Gifted Child Quarterly, 46, 224-234.
8.
HancockG. R.MuellerR. O. (Eds.). (2006). Structural equation modeling: A second course. Greenwich, CT: Information Age.
9.
HoekmanK.McCormickJ.BarnettK. (2005). The important role of optimism in a motivational investigation of the education of gifted adolescents. Gifted Child Quarterly, 49, 99-110.
10.
HuL.BentlerP. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1-55.
11.
JohnsenS. K. (2009). Identification. In KerrB. (Ed.), Encyclopedia of giftedness, creativity, and talent (Vol. 1, pp. 439-443). Thousand Oaks, CA: Sage.
12.
KlineR. B. (2010). Principles and practice of structural equation modeling (3rd ed.). New York, NY: Guilford Press.
13.
MacCallumR. C.BrowneM. W.SugawaraH. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1, 130-149.
14.
MarshH. W. (1992). Self-Description Questionnaire (SDQ) I: A theoretical and empirical basis for the measurement of multiple dimensions of preadolescent self-concept. An interim test manual and research monograph. Macarthur, New South Wales, Australia: University of Western Sydney, Faculty of Education.
15.
MarshH. W.HauK. T.WenZ. L. (2004). In search of golden rules: Comment on hypothesis testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler (1999) findings. Structural Equation Modeling, 11, 320-341.
16.
PetersS. J.GentryM. (2010). Multigroup construct validity evidence of the HOPE Scale: Instrumentation to identify low-income elementary students for gifted programs. Gifted Child Quarterly, 54, 298-313.
17.
PfeifferS. I. (2002). Identifying gifted and talented students. Journal of Applied School Psychology, 19, 31-50.
18.
RafteryA. E. (1995). Bayesian model selection in social research. Sociological Methodology, 25, 111-163.
19.
RobertS.PashlerH. (2000). How persuasive is a good fit? A comment on theory testing in psychology. Psychological Review, 107, 358-367.
