Investigating the Sensitivity of Goodness-of-Fit Indices to Detect Measurement Invariance in a Bifactor Model

Structural Equation Modeling

Volumn 22, Issue 4, 2015, Pages 531-541

  Khojasteh, Jam ^a   Lo, Wen Juo ^b  

^a OKLAHOMA STATE UNIVERSITY   (United States)

^b University of Arkansas   (United States)

Author keywords

bifactor model; fit indices; measurement invariance

Indexed keywords

EXPERIMENTAL CONDITIONS; FIT INDICES; GOODNESS OF FIT; GOODNESS-OF-FIT INDICES; LIKELIHOOD RATIO TESTS; SAMPLE SIZES; TYPE-I ERROR; VARIANCE COMPONENT ANALYSIS;

MONTE CARLO METHODS;

EID: 84942193996     PISSN: 10705511     EISSN: 15328007     Source Type: Journal    
DOI: 10.1080/10705511.2014.937791     Document Type: Article

References (29)

1. P.M.Bentler, (1990). Comparative fit indices in structural models. Psychological Bulletin, 107, 238–246.
2. K.A.Bollen, (1989). Structural equations with latent variables. New York, NY: Wiley.
3. B.M.Byrne,, R.J.Shavelson,, & B.Muthén, (1989). Testing for equivalence of factor covariance and mean structures: The issue of partial measurement invariance. Psychological Bulletin, 105, 456–466.
4. F.F.Chen, (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling, 14, 464–504.
5. F.F.Chen,, S.G.West,, & K.H.Sousa, (2006). A comparison of bifactor and second-order models of quality of life. Multivariate Behavioral Research, 41, 189–225.
6. G.W.Cheung,, & R.S.Lau, (2012). A direct comparison approach for testing measurement invariance. Organizational Research Methods, 15, 167–198.
7. G.W.Cheung,, & R.B.Rensvold, (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9, 233–255.
8. C.DiStefano,, & R.W.Motl, (2006). Further investigating method effects associated with negatively worded items on self-report surveys. Structural Equation Modeling, 13, 440–464.
9. X.Fan,, & S.A.Sivo, (2009). Using ΔGoodness-of-fit indexes in assessing mean structure invariance. Structural Equation Modeling, 16, 54–69.
10. B.French,, & W.Finch, (2006). Confirmatory factor analytic procedures for the determination of measurement invariance. Structural Equation Modeling, 13, 378–402.
11. J.Gustafsson,, & G.Balke, (1993). General and specific abilities as predictors of school achievement. Multivariate Behavioral Research, 28, 407–434.
12. M.R.Harwell, (1997). Analyzing the results of Monte Carlo studies in item response theory. Educational and Psychological Measurement, 57, 266–279.
13. M.R.Harwell,, E.N.Rubinstein,, W.S.Hayes,, & C.C.Olds, (1992). Summarizing Monte Carlo results in methodological research: The one- and two-factor fixed effects ANOVA cases. Journal of Educational Statistics, 17, 315–339.
14. K.J.Holzinger,, & R.Swineford, (1937). The bifactor method. Psychometrika, 2, 41–54.
15. J.L.Horn,, & J.J.McArdle, (1992). A practical and theoretical guide to measurement invariance in aging research. Experimental Aging Research, 18, 117–144.
16. K.G.Jöreskog,, & D.Sörbom, (1981). LISREL V: Analysis of linear structural relationships by the method of maximum likelihood. Chicago, IL: National Educational Resources.
17. R.B.Kline, (2011). Principles and practice of structural equation modeling (3rd ed.). New York, NY: Guilford.
18. A.W.Meade,, E.C.Johnson,, & P.W.Braddy, (2008). Power and sensitivity of alternative fit indices in tests of measurement invariance. Journal of Applied Psychology, 93, 568–592.
19. W.Meredith, (1993). Measurement invariance, factor analysis, and factorial invariance. Psychometrika, 58, 525–543.
20. W.Meredith,, & R.E.Millsap, (1992). On the misuse of manifest variables in the detection of measurement bias. Psychometrika, 57, 289–311.
21. R.E.Millsap, (2005). Four unresolved problems in studies of factorial invariance. In A.Maydeu-Olivares & J.J.McArdle (Eds.). Contemporary psychometrics: A Festschrift for Roderick P. McDonald (pp. 153−171). Mahwah NJ: Erlbaum.
22. R.E.Millsap, (2007). Structural equation modeling made difficult. Personality and Individual Differences, 42, 875–881.
23. R.E.Millsap,, & H.T.Everson, (1993). Methodology review: Statistical approaches for assessing measurement bias. Applied Psychological Measurement, 17, 297–334.
24. R.E.Millsap,, & O.Kwok, (2004). Evaluating the impact of partial factorial invariance on selection in two populations. Psychological Methods, 9, 93–115.
25. S.P.Reise,, K.F.Widaman,, & R.H.Pugh, (1993). Confirmatory factor analysis and item response theory: Two approaches for exploring measurement invariance. Psychological Bulletin, 114, 552–566.
26. J.H.Steiger, (1989). EzPATH causal modeling: A supplementary module for SYSTAT and SYSGRAPH. Evanston, IL: SYSTAT.
27. R.J.Vandenburg, (2002). Toward a fuller understanding of and improvement in measurement invariance methods and procedures. Organizational Research Methods, 5, 139–158.
28. R.J.Vandenburg,, & C.E.Lance, (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3, 4–69.
29. M.Yoon,, & R.E.Millsap, (2007). Detecting violations of factorial invariance using data-based specification searches: A Monte Carlo study. Structural Equation Modeling, 14, 435–463.