Multivariate non-normal distributions and their effects on statistical power and measures of fit in structural equation modeling
Erstveröffentlichung
2023-03-21Gutachter
Moshagen, MortenKlein, Andreas
Stadnitski, Tatjana
Dissertation
Fakultäten
Fakultät für Ingenieurwissenschaften, Informatik und PsychologieInstitutionen
Institut für Psychologie und PädagogikZusammenfassung
The dissertation investigates the effects of multivariate non-normal data on measures of fit and statistical power in structural equation modeling. Different non-normality conditions were generated by means of Monte Carlo simulations considering various estimation methods and corrections to the likelihood-ratio model test statistic. Furthermore, the dissertation provides important insights regarding the simultaneous effect of multivariate non-normality and missing data. The results show that the multivariate distribution has an effect on measures of fit as well as on statistical power in finite samples.
Erstellung / Fertigstellung
2022
Kumulative Dissertation mit folgenden Artikeln
• Jobst, L. J., Bader, M., & Moshagen, M. (2021). A tutorial on assessing statistical power and determining sample size for structural equation models. Psychological Methods. Advance online publication. https://doi.org/10.1037/met0000423
• Jobst, L. J., Auerswald, M., & Moshagen, M. (2021). The effect of latent and error non-normality on measures of fit in structural equation modeling. Educational and Psychological Measurement. Advance online publication. https://doi.org/10.1177/00131644211046201
• Jobst, L. J., Auerswald, M., & Moshagen, M. (2022). The effect of latent and error non-normality on corrections to the test statistic in structural equation modeling. Behavior Research Methods. Advance online publication. https://doi.org/10.3758/s13428-021-01729-9
• Jobst, L. J., Heine, C., Auerswald, M., & Moshagen, M. (2021). Effects of multivariate non-normality and missing data on the root mean square error of approximation. Structural Equation Modeling: A Multidisciplinary Journal, 28(6), 851–858. https://doi.org/10.1080/10705511.2021.1933987
• Jobst, L. J., Auerswald, M., & Moshagen, M. (2021). The effect of latent and error non-normality on measures of fit in structural equation modeling. Educational and Psychological Measurement. Advance online publication. https://doi.org/10.1177/00131644211046201
• Jobst, L. J., Auerswald, M., & Moshagen, M. (2022). The effect of latent and error non-normality on corrections to the test statistic in structural equation modeling. Behavior Research Methods. Advance online publication. https://doi.org/10.3758/s13428-021-01729-9
• Jobst, L. J., Heine, C., Auerswald, M., & Moshagen, M. (2021). Effects of multivariate non-normality and missing data on the root mean square error of approximation. Structural Equation Modeling: A Multidisciplinary Journal, 28(6), 851–858. https://doi.org/10.1080/10705511.2021.1933987
Schlagwörter
[GND]: Strukturgleichungsmodell | Multivariate Analyse[LCSH]: Structural equation modeling | Multivariate analysis | Statistical hypothesis testing
[Freie Schlagwörter]: Non-normal multivariate data | Source of non-normality | Statistical power
[DDC Sachgruppe]: DDC 150 / Psychology | DDC 310 / General statistics
Metadata
Zur LanganzeigeDOI & Zitiervorlage
Nutzen Sie bitte diesen Identifier für Zitate & Links: http://dx.doi.org/10.18725/OPARU-47813
Jobst, Lisa Jasmin Désirée (2023): Multivariate non-normal distributions and their effects on statistical power and measures of fit in structural equation modeling. Open Access Repositorium der Universität Ulm und Technischen Hochschule Ulm. Dissertation. http://dx.doi.org/10.18725/OPARU-47813
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