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Structural Equation Modeling: A Multidisciplinary Journal Volume 29, 2022 - Issue 3
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Research Article

Deep Neural Networks for Detecting Statistical Model Misspecifications. The Case of Measurement Invariance

Artur Pokropeka Institute of Philosophy and Sociology, Polish Academy of SciencesCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-5899-2917View further author information
ORCID Icon &
Ernest Pokropekb Division of Robotics, Perception and Learning, KTH Royal Institute of TechnologyORCID Iconhttps://orcid.org/0000-0002-5585-9862View further author information
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Pages 394-411 | Published online: 26 Jan 2022

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Alexander Robitzsch & Oliver Lüdtke. (2023) Why Full, Partial, or Approximate Measurement Invariance Are Not a Prerequisite for Meaningful and Valid Group Comparisons. Structural Equation Modeling: A Multidisciplinary Journal 0:0, pages 1-12.
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Articles from other publishers (3)

Heinz Leitgöb, Daniel Seddig, Tihomir Asparouhov, Dorothée Behr, Eldad Davidov, Kim De Roover, Suzanne Jak, Katharina Meitinger, Natalja Menold, Bengt Muthén, Maksim Rudnev, Peter Schmidt & Rens van de Schoot. (2023) Measurement invariance in the social sciences: Historical development, methodological challenges, state of the art, and future perspectives. Social Science Research 110, pages 102805.
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Jose A. Aleman, Peter Schmidt, Katharina Meitinger & Bart Meuleman. (2022) Editorial: Comparative political science and measurement invariance: Basic issues and current applications. Frontiers in Political Science 4.
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Alexander Robitzsch. (2022) Estimation Methods of the Multiple-Group One-Dimensional Factor Model: Implied Identification Constraints in the Violation of Measurement Invariance. Axioms 11:3, pages 119.
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