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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Robust Haebara Linking for Many Groups: Performance in the Case of Uniform DIF
Alexander Robitzsch
Psych (2020) Vol. 2, Iss. 3, pp. 155-173
Open Access | Times Cited: 13

Showing 13 citing articles:

Estimation of Standard Error, Linking Error, and Total Error for Robust and Nonrobust Linking Methods in the Two-Parameter Logistic Model
Alexander Robitzsch
Stats (2024) Vol. 7, Iss. 3, pp. 592-612
Open Access | Times Cited: 7
Comparing Robust Linking and Regularized Estimation for Linking Two Groups in the 1PL and 2PL Models in the Presence of Sparse Uniform Differential Item Functioning
Alexander Robitzsch
Stats (2023) Vol. 6, Iss. 1, pp. 192-208
Open Access | Times Cited: 11
Mean Comparisons of Many Groups in the Presence of DIF: An Evaluation of Linking and Concurrent Scaling Approaches
Alexander Robitzsch, Oliver Lüdtke
Journal of Educational and Behavioral Statistics (2021) Vol. 47, Iss. 1, pp. 36-68
Open Access | Times Cited: 26
A Comparison of Linking Methods for Two Groups for the Two-Parameter Logistic Item Response Model in the Presence and Absence of Random Differential Item Functioning
Alexander Robitzsch
Foundations (2021) Vol. 1, Iss. 1, pp. 116-144
Open Access | Times Cited: 22
Robust and Nonrobust Linking of Two Groups for the Rasch Model with Balanced and Unbalanced Random DIF: A Comparative Simulation Study and the Simultaneous Assessment of Standard Errors and Linking Errors with Resampling Techniques
Alexander Robitzsch
(2021)
Open Access | Times Cited: 22
SIMEX-Based and Analytical Bias Corrections in Stocking–Lord Linking
Alexander Robitzsch
Analytics (2024) Vol. 3, Iss. 3, pp. 368-388
Open Access | Times Cited: 3
Extensions to Mean–Geometric Mean Linking
Alexander Robitzsch
Mathematics (2024) Vol. 13, Iss. 1, pp. 35-35
Open Access | Times Cited: 2
Deep Neural Networks for Detecting Statistical Model Misspecifications. The Case of Measurement Invariance
Artur Pokropek, Ernest Pokropek
Structural Equation Modeling A Multidisciplinary Journal (2022) Vol. 29, Iss. 3, pp. 394-411
Open Access | Times Cited: 10
Deep Item Response Theory as a Novel Test Theory Based on Deep Learning
Emiko Tsutsumi, Ryo Kinoshita, Maomi Ueno
Electronics (2021) Vol. 10, Iss. 9, pp. 1020-1020
Open Access | Times Cited: 14
SIMEX-based and analytical bias corrections in Stocking-Lord linking
Alexander Robitzsch
(2024)
Open Access | Times Cited: 1
About the Equivalence of the Latent D-Scoring Model and the Two-Parameter Logistic Item Response Model
Alexander Robitzsch
(2021)
Open Access | Times Cited: 6
Robust Haebara Linking for Many Groups in the Case of Partial Invariance
Alexander Robitzsch
(2020)
Open Access | Times Cited: 5
About the Equivalence of the Latent D-Scoring Model and the Two-Parameter Logistic Item Response Model
Alexander Robitzsch
Mathematics (2021) Vol. 9, Iss. 13, pp. 1465-1465
Open Access | Times Cited: 1
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