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David Goretzko

Showing results (1-10 of 18) with videos related to

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Educational and Psychological Measurement|April 21, 2022
Factor Retention in Exploratory Factor Analysis With Missing DataDavid Goretzko
Psychological Methods|February 13, 2025
How many factors to retain in exploratory factor analysis? A critical overview of factor retention methodsDavid Goretzko
Psychological 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 Bühner
Educational 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 Goretzko
Psychological Methods|September 15, 2025
Exploratory graph analysis trees-A network-based approach to investigate measurement invariance with numerous covariatesDavid Goretzko, Philipp Sterner
Multivariate Behavioral Research|November 3, 2025
Detecting Model Misfit in Structural Equation Modeling with Machine Learning-A Proof of ConceptMelanie Viola Partsch, David Goretzko
Behavior 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 Ruscio
Applied Psychological Measurement|July 11, 2022
Factor Retention Using Machine Learning With Ordinal DataDavid Goretzko, Markus Bühner
Educational 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 Bühner
Educational and Psychological Measurement|January 22, 2024
Evaluating Model Fit of Measurement Models in Confirmatory Factor AnalysisDavid Goretzko, Karik Siemund, Philipp Sterner
Pageof 2

Showing results (1-10 of 18) with videos related to

Sort By:
Pageof 2
Educational and Psychological Measurement|April 21, 2022
Factor Retention in Exploratory Factor Analysis With Missing DataDavid Goretzko
Psychological Methods|February 13, 2025
How many factors to retain in exploratory factor analysis? A critical overview of factor retention methodsDavid Goretzko
Psychological 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 Bühner
Educational 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 Goretzko
Psychological Methods|September 15, 2025
Exploratory graph analysis trees-A network-based approach to investigate measurement invariance with numerous covariatesDavid Goretzko, Philipp Sterner
Multivariate Behavioral Research|November 3, 2025
Detecting Model Misfit in Structural Equation Modeling with Machine Learning-A Proof of ConceptMelanie Viola Partsch, David Goretzko
Behavior 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 Ruscio
Applied Psychological Measurement|July 11, 2022
Factor Retention Using Machine Learning With Ordinal DataDavid Goretzko, Markus Bühner
Educational 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 Bühner
Educational and Psychological Measurement|January 22, 2024
Evaluating Model Fit of Measurement Models in Confirmatory Factor AnalysisDavid Goretzko, Karik Siemund, Philipp Sterner
Pageof 2