Dein Suchergebnis zum Thema: Hecht

Bayesian hierarchical moderated factor analysis for testing measurement invariance in multilevel data: Model development, simulation studies, and experience sampling application – Leibniz-Institut für die Pädagogik der Naturwissenschaften und Mathematik

https://www.leibniz-ipn.de/de/forschen/publikationen/bayesian-hierarchical-moderated-factor-analysis-for-testing-measurement-invariance-in-multilevel-data

Moderated Nonlinear Latent Factor Analysis (MNLFA) has been introduced as a flexible approach for testing measurement invariance among categorical and continuous covariates. Equipped with Bayesian shrinkage priors, MNLFA can handle large numbers of covariates and potentially invariant item parameters. The present study extends the capabilities of the Bayesian MNLFA to multilevel and longitudinal confirmatory factor analysis. We show how a Bayesian hierarchical MNLFA (BH-MNLFA) can be implemented and provide two simulation studies to demonstrate its functionality. Focusing on invariance explorations in experience sampling data as a potential use case in the context of longitudinal data analysis, we showcase the utility of BH-MNLFA with data from educational psychology, and test invariance of state self-concepts measures across time and school subjects.
begutachtet Publikationsdaten Von Julian Franz Lohmann, Steffen Zitzmann, Martin Hecht

Bayesian hierarchical moderated factor analysis for testing measurement invariance in multilevel data: Model development, simulation studies, and experience sampling application – Leibniz-Institut für die Pädagogik der Naturwissenschaften und Mathematik

https://www.leibniz-ipn.de/de/forschen/publikationen/bayesian-hierarchical-moderated-factor-analysis-for-testing-measurement-invariance-in-multilevel-data?show_navhelper=1

Moderated Nonlinear Latent Factor Analysis (MNLFA) has been introduced as a flexible approach for testing measurement invariance among categorical and continuous covariates. Equipped with Bayesian shrinkage priors, MNLFA can handle large numbers of covariates and potentially invariant item parameters. The present study extends the capabilities of the Bayesian MNLFA to multilevel and longitudinal confirmatory factor analysis. We show how a Bayesian hierarchical MNLFA (BH-MNLFA) can be implemented and provide two simulation studies to demonstrate its functionality. Focusing on invariance explorations in experience sampling data as a potential use case in the context of longitudinal data analysis, we showcase the utility of BH-MNLFA with data from educational psychology, and test invariance of state self-concepts measures across time and school subjects.
begutachtet Publikationsdaten Von Julian Franz Lohmann, Steffen Zitzmann, Martin Hecht

Bericht zur 16. SH-Sommeruniversität in Sankelmark: Gemeinsam neue Wege gehen – Leibniz-Institut für die Pädagogik der Naturwissenschaften und Mathematik

https://www.leibniz-ipn.de/de/das-ipn/aktuelles/aktuelle-meldungen/bericht-zur-16-sh-sommeruniversitaet-in-sankelmark-gemeinsam-neue-wege-gehen

Vom 1. bis 3. August 2024 fand die 16. SH-Sommeruniversität im Akademiezentrum Sankelmark statt. Veranstaltet wurde die Tagung vom IPN, der CAU zu Kiel, dem MBWFK und dem IQSH. Das Thema lautete „Gemeinsam neue Wege gehen – Aktuelle Konzepte der Schul- und Unterrichtsentwicklung“.
Malte Hecht, Unternehmer im Ed-Tech Bereich, präsentierte die Anwendung Fiete.AI,