Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Filters

Carolin Strobl

Showing results (11-20 of 29) with videos related to

Pageof 3
Sort By:
Psychological Methods|December 9, 2009
An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forestsCarolin Strobl, James Malley, Gerhard Tutz
Educational and Psychological Measurement|November 16, 2023
What Affects the Quality of Score Transformations? Potential Issues in True-Score Equating Using the Partial Credit ModelCarolina Fellinghauer, Rudolf Debelak, Carolin Strobl
Educational and Psychological Measurement|May 26, 2018
Rasch Mixture Models for DIF Detection: A Comparison of Old and New Score SpecificationsHannah Frick, Carolin Strobl, Achim Zeileis
Journal of School Psychology|April 3, 2025
One model may not fit all: Subgroup detection using model-based recursive partitioningMarjolein Fokkema, Mirka Henninger, Carolin Strobl
BMC Bioinformatics|April 9, 2013
An AUC-based permutation variable importance measure for random forestsSilke Janitza, Carolin Strobl, Anne-Laure Boulesteix
Behavior Research Methods|December 17, 2021
An R toolbox for score-based measurement invariance tests in IRT modelsLennart Schneider, Carolin Strobl, Achim Zeileis, et al.
Psychological Methods|May 25, 2023
Interpretable machine learning for psychological research: Opportunities and pitfallsMirka Henninger, Rudolf Debelak, Yannick Rothacher, et al.
BMC Bioinformatics|January 27, 2007
Bias in random forest variable importance measures: illustrations, sources and a solutionCarolin Strobl, Anne-Laure Boulesteix, Achim Zeileis, et al.
The British Journal of Mathematical and Statistical Psychology|June 7, 2022
Score-based measurement invariance checks for Bayesian maximum-a-posteriori estimates in item response theoryRudolf Debelak, Samuel Pawel, Carolin Strobl, et al.
Psychometrika|November 19, 2017
Score-Based Tests of Differential Item Functioning via Pairwise Maximum Likelihood EstimationTing Wang, Carolin Strobl, Achim Zeileis, et al.
Pageof 3

Showing results (11-20 of 29) with videos related to

Sort By:
Pageof 3
Psychological Methods|December 9, 2009
An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forestsCarolin Strobl, James Malley, Gerhard Tutz
Educational and Psychological Measurement|November 16, 2023
What Affects the Quality of Score Transformations? Potential Issues in True-Score Equating Using the Partial Credit ModelCarolina Fellinghauer, Rudolf Debelak, Carolin Strobl
Educational and Psychological Measurement|May 26, 2018
Rasch Mixture Models for DIF Detection: A Comparison of Old and New Score SpecificationsHannah Frick, Carolin Strobl, Achim Zeileis
Journal of School Psychology|April 3, 2025
One model may not fit all: Subgroup detection using model-based recursive partitioningMarjolein Fokkema, Mirka Henninger, Carolin Strobl
BMC Bioinformatics|April 9, 2013
An AUC-based permutation variable importance measure for random forestsSilke Janitza, Carolin Strobl, Anne-Laure Boulesteix
Behavior Research Methods|December 17, 2021
An R toolbox for score-based measurement invariance tests in IRT modelsLennart Schneider, Carolin Strobl, Achim Zeileis, et al.
Psychological Methods|May 25, 2023
Interpretable machine learning for psychological research: Opportunities and pitfallsMirka Henninger, Rudolf Debelak, Yannick Rothacher, et al.
BMC Bioinformatics|January 27, 2007
Bias in random forest variable importance measures: illustrations, sources and a solutionCarolin Strobl, Anne-Laure Boulesteix, Achim Zeileis, et al.
The British Journal of Mathematical and Statistical Psychology|June 7, 2022
Score-based measurement invariance checks for Bayesian maximum-a-posteriori estimates in item response theoryRudolf Debelak, Samuel Pawel, Carolin Strobl, et al.
Psychometrika|November 19, 2017
Score-Based Tests of Differential Item Functioning via Pairwise Maximum Likelihood EstimationTing Wang, Carolin Strobl, Achim Zeileis, et al.
Pageof 3