The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Maximum Likelihood Estimation of Structural Equation Models for Continuous Data: Standard Errors and Goodness of Fit
Abstract
Classical accounts of maximum likelihood (ML) estimation of structural equation models for continuous outcomes involve normality assumptions: standard errors (SEs) are obtained using the expected information matrix and the goodness of fit of the model is tested using the likelihood ratio (LR) statistic. Satorra and Bentler (1994) introduced SEs and mean adjustments or mean and variance adjustments to the LR statistic (involving also the expected information matrix) that are robust to nonnormality. However, in recent years, SEs obtained using the observed information matrix and alternative test statistics have become available. We investigate what choice of SE and test statistic yields better results using an extensive simulation study. We found that robust SEs computed using the expected information matrix coupled with a mean- and variance-adjusted LR test statistic (i.e., MLMV) is the optimal choice, even with normally distributed data, as it yielded the best combination of accurate SEs and Type I errors.
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Cites
lavaan : An R Package for Structural Equation Modeling
Rosseel, Y. (2012) · Journal of Statistical Software · doi:10.18637/jss.v048.i02 · Cited by 27,955
Alternative Ways of Assessing Model Fit
Browne, M. W., & Cudeck, R. (1992) · Sociological Methods & Research · doi:10.1177/0049124192021002005 · Cited by 27,200
Comparative fit indexes in structural models.
Bentler, P. M. (1990) · Psychological Bulletin · doi:10.1037/0033-2909.107.2.238 · Cited by 25,266
Significance tests and goodness of fit in the analysis of covariance structures.
Bentler, P. M., & Bonett, D. G. (1980) · Psychological Bulletin · doi:10.1037//0033-2909.88.3.588 · Cited by 19,023
Curran, P. J., West, S. G., & Finch, J. F. (1996) · Psychological Methods · doi:10.1037/1082-989x.1.1.16 · Cited by 5,512
Authentic leadership development: Getting to the root of positive forms of leadership
Avolio, B. J., & Gardner, W. L. (2005) · The Leadership Quarterly · doi:10.1016/j.leaqua.2005.03.001 · Cited by 5,478
Rhemtulla, M., Brosseau-Liard, P. É., & Savalei, V. (2012) · Psychological Methods · doi:10.1037/a0029315 · Cited by 2,816
A comparison of some methodologies for the factor analysis of non‐normal Likert variables
Muthén, B., & Kaplan, D. (1985) · British Journal of Mathematical and Statistical Psychology · doi:10.1111/j.2044-8317.1985.tb00832.x · Cited by 1,778 · cited 3×
“To conduct analyses, data with five ordered categories were generated, given that this is the minimum number of categories found to be acceptable for considering item-level ordinal data as continuous (Muthén & Kaplan, 1985; Rhemtulla, BrosseauLiard, & Savalei, 2012).”
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Hellfeldt, K., López-Romero, L., & Andershed, H. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17010045 · Cited by 278
SLBS-6: Validation of a Short Form of the Servant Leadership Behavior Scale
Sendjaya, S., Eva, N., Butar Butar, I., Robin, M., & Castles, S. (2019) · Journal of Business Ethics · doi:10.1007/s10551-017-3594-3 · Cited by 172
The use of social media by Australian preadolescents and its links with mental health.
Fardouly, J., Magson, N. R., Rapee, R. M., Johnco, C. J., & Oar, E. L. (2020) · Journal of Clinical Psychology · doi:10.1002/jclp.22936 · Cited by 102
Servidio, R., Sinatra, M., Griffiths, M. D., & Monacis, L. (2021) · Addictive Behaviors · doi:10.1016/j.addbeh.2021.107014 · Cited by 94
Müller, S. M., Wegmann, E., Stolze, D., & Brand, M. (2020) · Computers in Human Behavior · doi:10.1016/j.chb.2020.106296 · Cited by 76
Metadata
- Authors
- Maydeu-Olivares, A.
- Venue
- Structural Equation Modeling: A Multidisciplinary Journal
- Publisher
- Informa UK Limited
- Published
- 2017-02-06
- Volume
- 24
- Issue
- 3
- Pages
- 383-394
- ISSN
- 1070-5511 (ISSN-L)
- 1532-8007
- OpenAlex
- W2586441698
- Cited by
- 377
- References
- 8
- FWCI
- 17.89
- Type
- journal article
- Language
- en
- Open access
- closed
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- Maydeu-Olivares, A. (2017). Maximum Likelihood Estimation of Structural Equation Models for Continuous Data: Standard Errors and Goodness of Fit. Structural Equation Modeling: A Multidisciplinary Journal. https://doi.org/10.1080/10705511.2016.1269606
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