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  1. 2020

  2. A review of different scaling approaches under full invariance, partial invariance, and noninvariance for cross-sectional country comparisons in large-scale assessments

    Robitzsch, A. & Lüdtke, O., 06.2020, In: Psychological Test and Assessment Modeling. 62, 2, p. 233-279 47 p.

    Research output: Contribution to journal › Journal article › Research › peer-review

  3. Robust Haebara linking for many groups: Performance in the case of uniform DIF

    Robitzsch, A., 28.07.2020, In: Psych. 2, 3, p. 155-173 19 p.

    Research output: Contribution to journal › Journal article › Research › peer-review

  4. Überblick über Modelle der Item-Response-Theorie (IRT)

    Kelava, A., Noventa, S. & Robitzsch, A., 08.2020, Testtheorie und Fragebogenkonstruktion. Moosbrugger, H. & Kelava, A. (eds.). 3. ed. Springer, p. 425-446

    Research output: Chapter in anthology/conference proceeding › Contribution to collected edition/anthology › Research › peer-review

  5. Lp loss functions in invariance alignment and Haberman linking with few or many groups

    Robitzsch, A., 05.08.2020, In: Stats. 3, 3, p. 246-283

    Research output: Contribution to journal › Journal article › Research › peer-review

  6. Regularized latent class analysis for polytomous item responses: An application to SPM-LS data

    Robitzsch, A., 14.08.2020, In: Journal of Intelligence. 8, 3, 24 p., 30.

    Research output: Contribution to journal › Journal article › Research › peer-review

  7. Why ordinal variables can (almost) always be treated as continuous variables: Clarifying assumptions of robust continuous and ordinal factor analysis estimation methods

    Robitzsch, A., 10.2020, In: Frontiers in Education. 5, 7 p., 589965.

    Research output: Contribution to journal › Journal article › Research › peer-review

  8. Disentangling different sources of stability and change in students’ academic self-concepts: An integrative data analysis using the STARTS model

    Jansen, M., Lüdtke, O. & Robitzsch, A., 11.2020, In: Journal of Educational Psychology. 112, 8, p. 1614-1631 18 p.

    Research output: Contribution to journal › Journal article › Research › peer-review

  9. Leistungsveränderungen in TIMSS zwischen 2015 und 2019: Die Rolle des Testmediums und des methodischen Vorgehens bei der Trendschätzung

    Robitzsch, A., Lüdtke, O., Schwippert, K., Goldhammer, F., Kröhne, U. & Köller, O., 12.2020, TIMSS 2019: Mathematische und naturwissenschaftliche Kompetenzen von Grundschulkindern in Deutschland im internationalen Vergleich. Schwippert, K., Kasper, D., Köller, O., McElvany, N., Selter, C., Steffensky, M. & Wendt, H. (eds.). Münster: Waxmann, p. 169-186

    Research output: Chapter in anthology/conference proceeding › Contribution to collected edition/anthology › Research

  10. 2021

  11. On the performance of Bayesian approaches in small samples: A comment on Smid, McNeish, Miocevic, and van de Schoot (2020)

    Zitzmann, S., Lüdtke, O., Robitzsch, A. & Hecht, M., 02.01.2021, In: Structural Equation Modeling: A Multidisciplinary Journal. 28, 1, p. 40-50 11 p.

    Research output: Contribution to journal › Journal article › Research › peer-review

  12. A Bayesian item response model for examining item position effects in complex survey data

    Trendtel, M. & Robitzsch, A., 02.2021, In: Journal of Educational and Behavioral Statistics. 46, 1, p. 34-57

    Research output: Contribution to journal › Journal article › Research › peer-review

  13. A comparison of penalized maximum likelihood estimation and Markov Chain Monte Carlo techniques for estimating confirmatory factor analysis models with small sample sizes

    Lüdtke, O., Ulitzsch, E. & Robitzsch, A., 29.04.2021, In: Frontiers in Psychology. 12, 25 p., 615162.

    Research output: Contribution to journal › Journal article › Research › peer-review

  14. A note on a computationally efficient implementation of the EM algorithm in item response models

    Robitzsch, A., 11.05.2021, In: Quantitative and Computational Methods in Behavioral Sciences. 1, 1, 16 p., e3783.

    Research output: Contribution to journal › Journal article › Research › peer-review

  15. A comparison of estimation methods for the Rasch model

    Robitzsch, A., 06.2021, Book of short papers: SIS 2021. Perna, C., Salvati, N. & Schirripa Spagnolo, F. (eds.). Pearson, p. 157-162

    Research output: Chapter in anthology/conference proceeding › Contribution to collected edition/anthology › Research › peer-review

  16. Validating theoretical assumptions about reading with cognitive diagnosis models

    George, A. C. & Robitzsch, A., 06.2021, In: International Journal of Testing. 21, 2, p. 105-129 25 p.

    Research output: Contribution to journal › Journal article › Research › peer-review

  17. About the equivalence of the latent D-scoring model and the two-parameter logistic item response model

    Robitzsch, A., 22.06.2021, In: Mathematics. 9, 13, 17 p., 1465.

    Research output: Contribution to journal › Journal article › Research › peer-review

  18. A semiparametric approach for item response function estimation to detect item misfit

    Köhler, C., Robitzsch, A., Fährmann, K., von Davier, M. & Hartig, J., 07.2021, In: British Journal of Mathematical and Statistical Psychology. 74, S1, p. 157-175 19 p.

    Research output: Contribution to journal › Journal article › Research › peer-review

  19. Item parameter estimation in multistage designs: A comparison of different estimation approaches for the Rasch model

    Steinfeld, J. & Robitzsch, A., 08.07.2021, In: Psych. 3, 3, p. 279-307 29 p.

    Research output: Contribution to journal › Journal article › Research › peer-review

  20. On the treatment of missing data in background questionnaires in educational large-scale assessments: An evaluation of different procedures

    Grund, S., Lüdtke, O. & Robitzsch, A., 08.2021, In: Journal of Educational and Behavioral Statistics. 46, 4, p. 430-465 36 p.

    Research output: Contribution to journal › Journal article › Research › peer-review

  21. 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

    Robitzsch, A., 15.09.2021, In: Foundations. 1, 1, p. 116-144

    Research output: Contribution to journal › Journal article › Research › peer-review

  22. A continuous HYBRID IRT model for modeling changes in guessing behavior in proficiency tests

    Nagy, G. & Robitzsch, A., 30.09.2021, In: Psychological Test and Assessment Modeling. 63, 3, p. 361-395 35 p.

    Research output: Contribution to journal › Journal article › Research › peer-review

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