Showing results (1-10 of 17) with videos related to
Sort By:
Pageof 2
Multivariate Behavioral Research|November 3, 2025
Detecting Model Misfit in Structural Equation Modeling with Machine Learning-A Proof of ConceptMelanie Viola Partsch, David GoretzkoPsychological Methods|May 1, 2025
Embrace the heterogeneity in exploratory factor analysis but be transparent about what you do-A commentary on Manapat et al. (2023)David Goretzko, Melanie Viola Partsch, Philipp SternerEducational and Psychological Measurement|April 21, 2022
Factor Retention in Exploratory Factor Analysis With Missing DataDavid GoretzkoPsychological Methods|February 13, 2025
How many factors to retain in exploratory factor analysis? A critical overview of factor retention methodsDavid GoretzkoPsychological Methods|March 6, 2020
One model to rule them all? Using machine learning algorithms to determine the number of factors in exploratory factor analysisDavid Goretzko, Markus BühnerEducational and Psychological Measurement|April 20, 2026
Controlling the False Discovery Rate in DIF Detection With e-Values: Evidence From Multidimensional and Testlet SimulationsShan Huang, David GoretzkoPsychological Methods|September 15, 2025
Exploratory graph analysis trees-A network-based approach to investigate measurement invariance with numerous covariatesDavid Goretzko, Philipp SternerBehavior Research Methods|June 29, 2023
The comparison data forest: A new comparison data approach to determine the number of factors in exploratory factor analysisDavid Goretzko, John RuscioApplied Psychological Measurement|July 11, 2022
Factor Retention Using Machine Learning With Ordinal DataDavid Goretzko, Markus BühnerEducational and Psychological Measurement|July 4, 2020
Investigating Parallel Analysis in the Context of Missing Data: A Simulation Study Comparing Six Missing Data MethodsDavid Goretzko, Christian Heumann, Markus BühnerPageof 2