A common myth among organizational researchers who use structural equations is that adequate fit values from the composite (overall) model speak directly to the adequacy of the proposed theory. However, this is a fallacious assumption, as significant misspecification among the theoretical paths between constructs can occur and be missed when there is an overreliance on global fit indices. To debunk this myth and provide a more valid alternative, the authors begin with an overview of latent variable model estimation procedures, emphasizing how they affect values obtained with fit measures for composite models. The authors next demonstrate problems with these measures, using an algebraic analysis of two popular composite fit indices (CFI, RMSEA) and results from simulation analysis with these indices. They then highlight two alternative indices that focus more on path model relations (NSCI-P, RMSEA-P) and that outperform the composite fit indices in the detection of model misspecification among the latent variables. The authors conclude with recommendations how these latter indices can be incorporated into model evaluation and theory testing.

Anderson, J.C. , & Gerbing, D.W. ( 1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103, 411-423. doi:10.1037/0033-2909.103.3.411.
Google Scholar | Crossref | ISI
Anderson, J.C. , & Gerbing, D.W. ( 1992). Assumptions and comparative strengths of the two-step approach: Comment on Fornell and Yi. Sociological Methods and Research, 20, 321-333. doi:10.1177/ 0049124192020003002.
Google Scholar | SAGE Journals | ISI
Bentler, P.M. ( 1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238-246. doi:10.1037/0033-2909.107.2.238.
Google Scholar | Crossref | Medline | ISI
Bentler, P.M. , & Bonett, D.G. ( 1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88, 588-606. doi:10.1037/0033-2909.88.3.588.
Google Scholar | Crossref | ISI
Bollen, K.A. ( 2000). Modeling strategies In search of the Holy Grail. Structural Equations Modeling, 7, 74-81. doi:10.1207/S15328007SEM0701_03.
Google Scholar | Crossref | ISI
Brown, T.A. ( 2006). Confirmatory factor analysis for applied research. New York: Guilford.
Google Scholar
Chen, F. , Curran, P.J. , Bollen, K.A. , Kirby, J. , & Paxton, P. ( 2008). An empirical evaluation of the use of fixed cutoff points in RMSEA test statistic in structural equation models. Sociological Methods and Research, 36, 462-494. doi:10.1177/0049124108314720.
Google Scholar | SAGE Journals | ISI
Duncan, O. , Haller, A. , & Portes, A. ( 1971). Peer influence on aspirations: A reinterpretation. In H. M. Blalock (Ed.), Causal models in the social sciences (pp. 219-244). Chicago: Aldine-Atherton.
Google Scholar
Ecob, R. ( 1987). Applications of structural equation modeling to longitudinal educational data. In P. Cuttance & R. Ecob (Eds.), Structural modeling by example (pp. 138-159). New York: Cambridge University Press.
Google Scholar
Fornell, C. , & Yi, Y. ( 1992a). Assumptions of the two-step approach to latent variable modeling. Sociological Methods and Research, 20, 291-320. doi:10.1177/0049124192020003001.
Google Scholar | SAGE Journals | ISI
Fornell, C. , & Yi, Y. ( 1992b). Assumptions of the two-step approach: Reply to Anderson and Gerbing. Sociological Methods and Research, 20, 334-339. doi:10.1177/0049124192020003003.
Google Scholar | SAGE Journals | ISI
Hayduk, L.A. , & Glaser, D.N. ( 2000). Jiving the four-step, waltzing around factor analysis, and other serious fun (with discussion). Structural Equation Modeling, 7, 1-35. doi:10.1207/S15328007SEM0701_01.
Google Scholar | Crossref | ISI
James, L.R. , & James, L.A. ( 1989). Causal modeling in organizational research. In C. L. Cooper & I. Robertson (Eds.), International review of industrial and organizational psychology (pp. 371-404). New York: John Wiley.
Google Scholar
James, L.R. , Mulaik, S.A. , & Brett, J. ( 1982). Causal analysis: Models, assumptions, and data. Beverly Hills, CA: Sage.
Google Scholar
Jöreskog, K.G. , & Sörbom, D. (1996). LISREL 8 user’s reference guide. Chicago: Scientific Software .
Google Scholar
Kaiser, F.G. ( 2006). A moral extension of the theory of planned behaviour: Norms and anticipated feelings of regret in conservationism. Personality and Individual Differences, 41, 71-81.
Google Scholar | Crossref | ISI
Kaiser, F.G. , Hübner, G. , & Bogner, F.X. ( 2005). Contrasting the theory of planned behavior with the value-belief-norm model in explaining conservation behavior. Journal of Applied Social Psychology, 35, 2150-2170.
Google Scholar | Crossref | ISI
Kaiser, F.G. , Schultz, P.W. , & Scheuthle, H. ( 2007). The theory of planned behavior without compatibility? Beyond method bias and past trivial associations. Journal of Applied Social Psychology, 37, 1522-1544.
Google Scholar | Crossref | ISI
Kline, R.B. ( 2005). Principles and practices of structural equation modeling (2nd ed.) New York: Guilford.
Google Scholar
Lance, C. , Butts, M. , & Michels, L. ( 2006). The sources of four commonly reported cutoff criteria . Organizational Research Methods, 9, 202-220. doi:10.1177/1094428105284919.
Google Scholar | SAGE Journals | ISI
MacCallum, R. ( 1986). Specification searches in covariance structure modeling . Psychological Bulletin, 100, 107-120. doi:10.1037/0033-2909.100.1.107.
Google Scholar | Crossref | ISI
Marsh, H.W. , Balla, J.R. , & McDonald, R.P. (1988). Goodness-of-fit indices in confirmatory factor analysis. Psychological Bulletin, 103, 391-410. Retrieved from www.eric.ed.gov/ERICWebPortal/recordDetail? accno=ED267091
Google Scholar | Crossref | ISI
McDonald, R.P. , & Ho, M.H. R. (2002). Principles and practice in reporting structural equation analyses. Psychological Methods, 7, 64-82. doi:10.1037/1082-989X.7.1.64.
Google Scholar | Crossref | Medline | ISI
Medsker, G.J. , Williams, L.J. , & Holahan, P.J. ( 1994). A review of current practices for evaluating causal models in organizational behavior and human resources management research. Journal of Management, 20, 439. doi:10.1016/0149-2063(94)90022-1.
Google Scholar | SAGE Journals | ISI
Millsap, R.E. ( 2002). Structural equation modeling: A user’s guide. In F. Drasgow & N. Schmitt (Eds.), Measuring and analyzing behavior in organizations: Advances in measurement and data analysis (pp. 257-301). San Francisco : Jossey-Bass.
Google Scholar
Mulaik, S.A. ( 2009). Linear causal modeling with structural equations. Boca Raton, FL: Chapman & Hall/ CRC.
Google Scholar | Crossref
Mulaik, S.A. , James, L.R. , Van Alstine, J. , Bennett, N. , Lind, S. , & Stilwell, C.D. (1989). Evaluation of goodness-of-fit indices for structural equation models. Psychological Bulletin , 105, 430-445. doi:10.1037/0033-2909.105.3.430.
Google Scholar | Crossref | ISI
Mulaik, S.A. , & Millsap, R.E. ( 2000). Doing the four-step right. Structural Equation Modeling, 7, 36-73. doi:10.1207/S15328007SEM0701_02.
Google Scholar | Crossref | ISI
O’Boyle, E.H., Jr. , & Williams, L.J. (2010). Decomposing model fit: Measurement versus theory in organizational research using latent variables . Journal of Applied Psychology, Advance online publication. doi:10.1037/a0020539.
Google Scholar | ISI
Schwab, D. ( 2005). Research methods for organizational studies (2nd ed.). Mahwah, NJ: Lawrence Erlbaum.
Google Scholar
Shook, C.L. , Ketchen, D. , Hult, G.T.M. , & Kacmar, K.M. ( 2004). An assessment of the use of structural equation modeling in strategic management research. Strategic Management Journal , 25, 397-404. doi:10.1002/smj.385.
Google Scholar | Crossref | ISI
Sobel, M.E. , & Bohrnstedt, G.W. (1985). Use of null models in evaluating the fit of covariance structure models. Sociological Methodology , 15, 152-178. doi:10.2307/270849.
Google Scholar | Crossref
Steiger, J.H. , & Lind, J.C. ( 1980, May). Statistically based tests for the number of common factors. Paper presented at the annual spring meeting of the Psychometric Society, Iowa City, IA.
Google Scholar
Tomarken, A.J. , & Waller, N.G. ( 2003). Potential problems with ‘‘well fitting’’ models. Journal of Abnormal Psychology, 112, 578-598. doi:10.1037/0021-843X.112.4.578.
Google Scholar | Crossref | Medline | ISI
Vandenberg, R.J. , & Grelle, D.M. 2009. Alternative model specifications in structural equation modeling: Facts, fictions, and truth. In C. E. Lance and R. J. Vandenberg (Eds.), Statistical and methodological myths and urban legends: Received doctrine, verity, and fable in the organizational and social sciences (pp. 165-191). New York: Routledge.
Google Scholar
Williams, L.J. , & Holahan, P.J. ( 1994). Parsimony-based fit indices for multiple-indicator models: Do they work? Structural Equation Modeling, 1, 161-189. doi:10.1080/10705519409539970.
Google Scholar | Crossref | ISI
Access Options

My Account

Welcome
You do not have access to this content.



Chinese Institutions / 中国用户

Click the button below for the full-text content

请点击以下获取该全文

Institutional Access

does not have access to this content.

Purchase Content

24 hours online access to download content

Research off-campus without worrying about access issues. Find out about Lean Library here

Your Access Options


Purchase

ORM-article-ppv for $37.50
Single Issue 24 hour E-access for $434.33

Cookies Notification

This site uses cookies. By continuing to browse the site you are agreeing to our use of cookies. Find out more.
Top