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Quality of Life Research
Gepubliceerd in: Quality of Life Research 9/2013

01-11-2013 | Commentary

Strengthening the assessment of factorial invariance across population subgroups: a commentary on Varni et al. (2013)

Auteur: Cameron N. McIntosh

Gepubliceerd in: Quality of Life Research | Uitgave 9/2013

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Abstract

Objectives

This article provides a commentary in response to “Varni et al. (Qual Life Res. doi:10.​1007/​s11136-013-0370-4, 2013)."

Methods and results

The commentary argues that the approximate model fit indexes commonly used in maximum-likelihood confirmatory factor analysis and factorial invariance testing are seriously flawed, as they overlook potentially serious model misspecifications that could bias parameter estimates and compromise inference.

Conclusions

Flexible and convenient Bayesian estimation approaches are presented that can substantially aid in: (1) resolving commonly encountered specification errors in confirmatory factor models and (2) locating specific measurement parameters that are non-invariant across population subgroups. It is recommended that these methods should be more widely adopted for evaluating the factorial invariance of patient-reported outcome measures and other types of instruments.
vorige artikel Factorial invariance of pediatric patient self-reported fatigue across age and gender: a multigroup confirmatory factor analysis approach utilizing the PedsQL™ Multidimensional Fatigue Scale
volgende artikel Commentary on strengthening the assessment of factorial invariance across population subgroups: a commentary on Varni et al. (2013)
Literatuur
1.
Doward, L. C., Gnanasakthy, A., & Baker, M.G. (2010). Patient reported outcomes: looking beyond the label claim. Health and Quality of Life Outcomes, 8(89), 1–9.
2.
Dinan, M. A., Compton, K. L., Dhillon, J. K., Hammill, B. G., Dewitt, E. M., Weinfurt, K. P., et al. (2011). Use of patient-reported outcomes in randomized, double-blind, placebo-controlled clinical trials. Medical Care, 49(4), 415–419.PubMed
3.
Sprangers, M. A. (2010). Disregarding clinical trial-based patient-reported outcomes is unwarranted: Five advances to substantiate the scientific stringency of quality-of-life measurement. Acta Oncologica, 49(2), 155–163.PubMedCrossRef
4.
Snyder, C. F., Aaronson, N. K., Choucair, A. K., Elliott, T. E., Greenhalgh, J., Halyard, M. Y., et al. (2012). Implementing patient-reported outcomes assessment in clinical practice: A review of the options and considerations. Quality of Life Research, 21(8), 1305–1314.PubMedCrossRef
5.
McKenna, S. P. (2011). Measuring patient-reported outcomes: moving beyond misplaced common sense to hard science. BMC Medicine, 9(86), 1–12.
6.
Jones, P., Miravitlles, M., van der Molen, T., & Kulich, K. (2012). Beyond FEV1 in COPD: A review of patient-reported outcomes and their measurement. International Journal of Chronic Obstructive Pulmonary Disease, 7, 697–709.PubMedCrossRef
7.
Swartz, R. J., Schwartz, C., Basch, E., Cai, L., Fairclough, D. L., McLeod, L., et al. (2011). The king’s foot of patient-reported outcomes: Current practices and new developments for the measurement of change. Quality of Life Research, 20(8), 1159–1167.PubMedCrossRef
8.
Cook, K. F., Bamer, A. M., Amtmann, D., Molton, I. R., & Jensen, M. P. (2012). Six patient-reported outcome measurement information system short form measures have negligible age- or diagnosis-related differential item functioning in individuals with disabilities. Archives of Physical Medicine and Rehabilitation, 93(7), 1289–1291.PubMedCrossRef
9.
Coons, S. J., Gwaltney, C. J., Hays, R. D., Lundy, J., Sloan, J. A., Revicki, D. A., et al. (2009). Recommendations on evidence needed to support measurement equivalence between electronic and paper-based patient-reported outcome (PRO) measures: ISPOR ePRO good research practices task force report. Value in Health, 12(4), 419–429.PubMedCrossRef
10.
Varni, J. W., Beaujean, A. A., & Limbers, C. A. (2013). Factorial invariance of pediatric patient self-reported fatigue across age and gender: A multigroup confirmatory factor analysis approach utilizing the PedsQL™ Multidimensional Fatigue Scale. Quality of Life Research, Online First. doi:10.​1007/​s11136-013-0370-4.
11.
Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34, 183–202.CrossRef
12.
Yuan, K.-H., & Bentler, P. M. (2004). On Chi square difference and z tests in mean and covariance structure analysis when the base model is misspecified. Educational and Psychological Measurement, 64(5), 737–757.CrossRef
13.
Steiger, J. H., Shapiro, A., & Browne, M. W. (1985). On the multivariate distribution of sequential Chi square statistics. Psychometrika, 50(3), 253–264.CrossRef
14.
Marsh, H. W., Hau, K.-T., & Grayson, D. (2005). Goodness of fit evaluation in structural equation modeling. In A. Maydeu-Olivares & J. McCardle (Eds.), Contemporary psychometrics: A festschrift to Roderick P. McDonald (pp. 275–340). Mahwah, NJ: Erlbaum.
15.
Bagozzi, R. P., & Yi, Y. (2012). ‘Specification, evaluation, and interpretation of structural equation models. Journal of the Academy of Marketing Science, 40, 8–34.CrossRef
16.
Preacher, K. J., & Merkle, E. C. (2012). The problem of model selection uncertainty in structural equation modelling. Psychological Methods, 17(1), 1–14.PubMedCrossRef
17.
Byrne, B. M. (2008). Testing for multigroup equivalence of a measuring instrument: A walk through the process. Psicothema, 20(4), 872–882.PubMed
18.
Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling, 14(3), 464–504.CrossRef
19.
Meade, A. W., Johnson, E. C., & Braddy, P. W. (2008). Power and sensitivity of alternative fit indices in tests of measurement invariance. Journal of Applied Psychology, 93(3), 568–592.PubMedCrossRef
20.
McIntosh, C. N. (2007). Rethinking fit assessment in structural equation modelling: A commentary and elaboration on Barrett (2007). Personality and Individual Differences, 42(5), 859–867.CrossRef
21.
McIntosh, C. N. (2007). Improving the evaluation of model fit in confirmatory factor analysis: A commentary on Gundy, C. M., Fayers, P. M., Groenvold, M., Petersen, M. Aa., Scott, N. W., Sprangers, M. A. J., Velikov, G., Aaronson, N. K. (2011). Comparing higher-order models for the EORTC QLQ-C30. Quality of Life Research. Quality of Life Research, 21(9), 1619–1621. doi:10.​1007/​s11136-011-0082-6.
22.
Hayduk, L. A., Cummings, G., Boadu, K., Pazderka-Robinson, H., & Boulianne, S. (2007). Testing! testing! one, two, three—testing the theory in structural equation models! Personality and Individual Differences, 42(5), 841–850.CrossRef
23.
Hayduk, L. A., & Glaser, D. N. (2000). Jiving the four-step, waltzing around factor analysis, and other serious fun. Structural Equation Modeling, 7(1), 1–35.CrossRef
24.
Shipley, B. (2002). Cause and correlation in biology: A user’s guide to path analysis, structural equations and causal inference (2nd ed.). Cambridge, UK: Cambridge University Press.
25.
Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). New York: Guilford Press.
26.
Saris, W. E., Satorra, A., & van der Veld, W. (2009). Testing structural equation models or detection of misspecifications? Structural Equation Modeling, 16(4), 561–582.CrossRef
27.
Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2010). On making causal claims: A review and recommendations. The Leadership Quarterly, 21(6), 1086–1120.CrossRef
28.
Yuan, K.-H., Kouros, C. D., & Kelley, K. (2008). Diagnosis for covariance structure models by analyzing the path. Structural Equation Modeling, 15, 564–602.CrossRef
29.
Kolenikov, S. (2011). Biases of parameter estimates in misspecified structural equation models. Sociological Methodology, 41(1), 119–157.CrossRef
30.
McDonald, R. P. (1999). Test theory: A unified treatment. Mahwah, New Jersey: Erlbaum.
31.
Reise, S. P. (2012). The rediscovery of bifactor measurement models. Multivariate Behavioral Research, 47(5), 667–696.PubMedCrossRef
32.
Asparouhov, T., & Muthén, B. (2009). Exploratory structural equation modeling. Structural Equation Modeling, 16, 397–438.CrossRef
33.
Morin, A. J. S., Marsh, H. W., & Nagengast, B. (in press). Exploratory structural equation modeling. To appear in G.R. Hancock & R.O. Mueller (Eds.), Structural equation modeling: A second course (2nd ed.) Charlotte, NC: Information Age Publishing.
34.
Cole, D. A., Ciesla, J. A., & Steiger, J. H. (2007). The insidious effects of failing to include design-driven correlated residuals in latent variable covariance structure analysis. Psychological Methods, 12(4), 381–398.PubMedCrossRef
35.
Saris, W. E., & Aalberts, C. (2003). Different explanations for correlated disturbance terms in MTMM studies. Structural Equation Modeling, 10(2), 193–213.CrossRef
36.
Reddy, S. K. (1992). Effects of ignoring correlated measurement error in structural equation models. Educational and Psychological Measurement, 52(3), 549–570.CrossRef
37.
Meehl, P. E. (1990). Why summaries of research on psychological theories are often uninterpretable. Psychological Reports, 66, 195–244.CrossRef
38.
MacCallum, R. C., Roznowski, M., & Necowitz, L. B. (1992). Model modification in covariance structure analysis: The problem of capitalization on chance. Psychological Bulletin, 111, 490–504.PubMedCrossRef
39.
Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley.
40.
Muthén, B., & Asparouhov, T. (2012). Bayesian structural equation modeling: a more flexible representation of substantive theory. Psychological Methods, 17(3), 313–335.PubMedCrossRef
41.
42.
Golay, P., Reverte, I., Rossier, J., Favez, N., & Lecerf, T. (2012). Further insights on the French WISC-IV factor structure through Bayesian structural equation modeling. Psychological Assessment, Online First. doi:10.​1037/​a0030676.
43.
Andrews, M., & Baguley, T. (2013). Prior approval: The growth of Bayesian methods in psychology. British Journal of Mathematical and Statistical Psychology, 66(1), 1–7.PubMedCrossRef
44.
Wetzels, R., & Wagenmakers, E.-J. (2010). Exemplary introduction to Bayesian statistical inference. (book review of “Bayesian modeling using WinBUGS”). Journal of Mathematical Psychology, 54, 466–469.CrossRef
45.
Poirier, D. J. (2006). The growth of Bayesian methods in statistics and economics since 1970. Bayesian Analysis, 1(4), 969–980.CrossRef
46.
Alston, C. L., Mengersen, K. L., & Pettitt, A. N. (Eds.). (2013). Case studies in Bayesian statistical modelling and analysis. Chichester, UK: Wiley.
47.
Jackman, S. (2009). Bayesian analysis for the social sciences. West Sussex, UK: Wiley.CrossRef
48.
Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003). Bayesian data analysis (2nd ed.). Boca Raton, FL: Chapman and Hall/CRC Press.
49.
Gelman, A., Jakulin, A., Pittau, M. G., & Su, Y.-S. (2008). A weakly informative default prior distribution for logistic and other models. The Annals of Applied Statistics, 2(4), 1360–1383.CrossRef
50.
Chow, S. M., Tang, N., Yuan, Y., Song, X., & Zhu, H. (2011). Bayesian estimation of semiparametric nonlinear dynamic factor analysis models using the Dirichlet process prior. British Journal of Mathematical and Statistical Psychology, 64(1), 69–106.PubMedCrossRef
51.
Kyung, M., Gill, J., & Casella, G. (2011). New findings from terrorism data: Dirichlet process random-effects models for latent groups. Journal of the Royal Statistical Society: Series C (Applied Statistics), 60(5), 701–721.CrossRef
52.
Brooks, S., Gelman, A., Jones, G., & Meng, X.-L. (Eds.). (2011). Handbook of Markov chain Monte Carlo. Boca Raton, FL: Chapman and Hall/CRC.
53.
Eberly, L. E., & Casella, G. (2003). Estimating Bayesian credible intervals. Journal of Statistical Planning and Inference, 112, 115–132.CrossRef
54.
Curran, J. M. (2005). An introduction to Bayesian credible intervals for sampling error in DNA profiles. Law, Probability and Risk, 4, 115–126.CrossRef
55.
Kruschke, J. K., Aguinis, H., & Joo, H. (2012). The time has come: Bayesian methods for data analysis in the organizational sciences. Organizational Research Methods, 15, 722–752.CrossRef
56.
Yuan, Y., & MacKinnon, D. P. (2009). Bayesian mediation analysis. Psychological Methods, 14(4), 301–322.PubMedCrossRef
57.
Lee, S.-Y., & Song, X.-Y. (2012). Basic and advanced Bayesian structural equation modeling: with applications in the medical and behavioral sciences. Chichester, UK: Wiley.
58.
Comrey, A. L., & Lee, H. B. (1992). A first course in factor analysis (2nd ed.). San Diego, CA: Academic Press.
59.
Levy, R. (2011). Bayesian data-model fit assessment for structural equation modeling. Structural Equation Modeling, 18(4), 663–685.CrossRef
Metagegevens
Titel
Strengthening the assessment of factorial invariance across population subgroups: a commentary on Varni et al. (2013)
Auteur
Cameron N. McIntosh
Publicatiedatum
01-11-2013
Uitgeverij
Springer Netherlands
Gepubliceerd in
Quality of Life Research / Uitgave 9/2013
Print ISSN: 0962-9343
Elektronisch ISSN: 1573-2649
DOI
https://doi.org/10.1007/s11136-013-0465-y

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