Publications
Preprints, Publications and Talks
Preprints
Grund, S., Lüdtke, O., & Robitzsch, A. (2023). Multiple imputation of missing data in large studies with many variables: A fully conditional specification approach using partial least squares. PsyArXiv. 15 December 2023. https://doi.org/10.31234/osf.io/473tw Supplement [Google Scholar]
Publications
Accepted/Online First
Grund, S., Lüdtke, O., & Robitzsch, A. (accepted). Missing data in the analysis of multilevel and dependent data. In M. Stemmler, W. Wiedermann, & F. Huang (Eds.), Dependent data in social sciences research - Forms, issues and methods of analysis (second edition) (p. xxx-xxx). xxx Supplement [Google Scholar] (published xxx)
Preprint: Grund, S., Lüdtke, O., & Robitzsch, A. (2024). Missing data in the analysis of multilevel and dependent data.. PsyArXiv. 12 January 2024. https://doi.org/10.31234/osf.io/6j9a8
Grund, S., Lüdtke, O., & Robitzsch, A. (online first). Using synthetic data to improve the reproducibility of statistical results in psychological research. Psychological Methods, xx(xx), xxx-xxx. https://doi.org/10.1037/met0000526 Supplement [Google Scholar] (published 4 August 2022)
Preprint: Grund, S., Lüdtke, O., & Robitzsch, A. (2021). Using synthetic data to improve the reproducibility of statistical results in psychological research. PsyArXiv. 23 July 2021. https://doi.org/10.31234/osf.io/d7zwj
Lüdtke, O., & Robitzsch, A. (online first). ANCOVA versus change score for the analysis of two-wave data. The Journal of Experimental Education, xx(x), xxx-xxx. https://doi.org/10.1080/00220973.2023.2246187 [Google Scholar] (published 23 August 2023)
Preprint (V2): Lüdtke, O., & Robitzsch, A. (2022). ANCOVA versus change score for the analysis of nonexperimental two-wave data: An overview of different modeling decisions. PsyArXiv. 29 July 2022. https://doi.org/10.31234/osf.io/ajf9d
Preprint (V1): Lüdtke, O., & Robitzsch, A. (2020). ANCOVA versus change score for the analysis of nonexperimental two-wave data: A structural modeling perspective. PsyArXiv. 12 September 2020. https://doi.org/10.31234/osf.io/5zdme
Steinfeld, J., & Robitzsch, A. (online first). Conditional maximum likelihood estimation in probability-based multistage designs. Behaviormetrika, xx(x), xxx-xxx. https://doi.org/10.1007/s41237-024-00228-3 [Google Scholar] (published 30 March 2024)
Preprint: Steinfeld, J., & Robitzsch, A. (2021). Conditional maximum likelihood estimation in probability-branched multistage designs. PsyArXiv. 21 March 2021. https://doi.org/10.31234/osf.io/ew27f
2024
Robitzsch, A. (2024). Examining differences of invariance alignment in the Mplus software and the R package sirt. Mathematics, 12(5), 770. https://doi.org/10.3390/math12050770 Supplement [Google Scholar] (published 5 March 2024)
Preprints: Robitzsch, A. (2024). Examining differences of invariance alignment in the Mplus software and the R package sirt. PsyArXiv. 19 February 2024. https://doi.org/10.31234/osf.io/8v52b
Robitzsch, A. (2024). Pairwise likelihood estimation of the 2PL model with locally dependent item responses. Applied Sciences, 14(6), 2652. https://doi.org/10.3390/app14062652 Supplement [Google Scholar] (published 21 March 2024)
Robitzsch, A. (2024). Smooth information criterion for regularized estimation of item response models. Algorithms, 17(4), 153. https://doi.org/10.3390/a17040153 Supplement [Google Scholar] (published 6 April 2024)
Robitzsch, A., & Lüdtke, O. (2024). An examination of the linking error currently used in PISA. Measurement: Interdisciplinary Research and Perspectives, 22(1), 61-77. https://doi.org/10.1080/15366367.2023.2198915 Supplement [Google Scholar] (published 20 February 2024)
Preprint: Robitzsch, A., & Lüdtke, O. (2022). An examination of the linking error currently used in PISA. OSF Preprints. 16 December 2022. https://doi.org/10.31219/osf.io/pnmkf
2023
Grund, S., Lüdtke, O., & Robitzsch, A. (2023). Handling missing data in cross-classified multilevel analyses: An evaluation of different multiple imputation approaches. Journal of Educational and Behavioral Statistics, 48(4), 454-489. https://doi.org/10.3102/10769986231151224 Supplement [Google Scholar] (published 16 February 2023)
Preprint: Grund, S., Lüdtke, O., & Robitzsch, A. (2022). Handling missing data in cross-classified multilevel analyses: An evaluation of different multiple imputation approaches . PsyArXiv. 18 February 2022. https://doi.org/10.31234/osf.io/sny45
Grund, S., Lüdtke, O., & Robitzsch, A. (2023). Pooling methods for likelihood ratio tests in multiply imputed data sets. Psychological Methods, 28(5), 1207-1221. https://doi.org/10.1037/met0000556 Supplement [Google Scholar] (published 27 April 2023)
Preprint: Grund, S., Lüdtke, O., & Robitzsch, A. (2021). Pooling methods for likelihood ratio tests in multiply imputed data sets. PsyArXiv. 29 January 2021. https://doi.org/10.31234/osf.io/d459g
Harrison, S., Kroehne, U., Goldhammer, F., Lüdtke, O., & Robitzsch, A. (2023). Comparing the score interpretation across modes in PISA: An investigation of how item facets affect difficulty. Large-scale Assessments in Education, 11, 8. https://doi.org/10.1186/s40536-023-00157-9 [Google Scholar] (published 11 March 2023)
Lüdtke, O., Robitzsch, A., & Ulitzsch, E. (2023). A Bayesian approach to estimating reciprocal effects with the bivariate STARTS model. Multivariate Behavioral Research, 58(3), 560-579. https://doi.org/10.1080/00273171.2022.2039585 Supplement [Google Scholar] (published 16 March 2022)
Preprint: Lüdtke, O., Robitzsch, A., & Ulitzsch, E. (2021). A Bayesian approach to estimating reciprocal effects with the bivariate STARTS model using Markov chain Monte Carlo. PsyArXiv. 26 January 2021. https://doi.org/10.31234/osf.io/u5sfa
Olaru, G., Robitzsch, A., Hildebrandt, A., & Schroeders, U. (2023). An illustration of local structural equation modeling for longitudinal data: Examining differences in competence development in secondary schools. In S. Weinert, G. J. Blossfeld, & H.-P. Blossfeld (Eds.), Education, Competence Development and Career Trajectories (pp. 153-176). Cham: Springer. https://doi.org/10.1007/978-3-031-27007-9_7 [Google Scholar] (published 14 April 2023)
Preprint: Olaru, G., Robitzsch, A., Hildebrandt, A., & Schroeders, U. (2020). Local structural equation modeling for longitudinal data. PsyArXiv. 24 April 2020. https://doi.org/10.31234/osf.io/q79c5
Robitzsch, A. (2023). Analytical approximation of the jackknife linking error in item response models utilizing a Taylor expansion of the log-likelihood function. AppliedMath, 3(1), 49-59. https://doi.org/10.3390/appliedmath3010004 [Google Scholar] (published 5 January 2023)
Robitzsch, A. (2023). 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. Stats, 6(1), 192-208. https://doi.org/10.3390/stats6010012 Supplement [Google Scholar] (published 25 January 2023)
Robitzsch, A. (2023). Editorial to the special issue "Computational aspects and software in psychometrics II". Psych, 5(3), 996-1000. https://doi.org/10.3390/psych5030065 [Google Scholar] (published 12 September 2023)
Robitzsch, A. (2023). Editorial to the special issue "Feature papers in psychometrics and educational measurement". Psych, 5(3), 1001-1003. https://doi.org/10.3390/psych5030066 [Google Scholar] (published 12 September 2023)
Robitzsch, A. (2023). Estimating local structural equation models. Journal of Intelligence, 11(9), 175. https://doi.org/10.3390/jintelligence11090175 Supplement [Google Scholar] (published 1 September 2023)
Robitzsch, A. (2023). Implementation aspects in invariance alignment. Stats, 6(4), 1160-1178. https://doi.org/10.3390/stats6040073 Supplement [Google Scholar] (published 25 October 2023)
Robitzsch, A. (2023). Implementation aspects in regularized structural equation models. Algorithms, 16(9), 446. https://doi.org/10.3390/a16090446 Supplement [Google Scholar] (published 18 September 2023)
Robitzsch, A. (2023). L0 and Lp loss functions in model-robust estimation of structural equation models. Psych, 5(4), 1122-1139. https://doi.org/10.3390/psych5040075 Supplement [Google Scholar] (published 20 October 2023)
Robitzsch, A. (2023). Linking error in the 2PL model. J, 6(1), 58-84. https://doi.org/10.3390/j6010005 [Google Scholar] (published 11 January 2023)
Robitzsch, A. (2023). Model-robust estimation of multiple-group structural equation models. Algorithms, 16(4), 210. https://doi.org/10.3390/a16040210 Supplement [Google Scholar] (published 17 April 2023)
Preprint: Robitzsch, A. (2023). Model-robust estimation of multiple-group structural equation models. PsyArXiv. 17 March 2023. https://doi.org/10.31234/osf.io/25md9 Supplement [Google Scholar]
Robitzsch, A. (2023). Modeling model misspecification in structural equation models. Stats, 6(2), 689-705. https://doi.org/10.3390/stats6020044 Supplement [Google Scholar] (published 14 June 2023)
Preprint: Robitzsch, A. (2023). Quantifying model misspecification in structural equation models in standard errors. PsyArXiv. 10 May 2023. https://doi.org/10.31234/osf.io/se9bj
Robitzsch, A. (2023). Nonignorable consequences of (partially) ignoring missing item responses: Students omit (constructed response) items due to a lack of knowledge. Knowledge, 3(2), 215-231. https://doi.org/10.3390/knowledge3020015 [Google Scholar] (published 30 April 2023)
Preprint: Robitzsch, A. (2020). About still nonignorable consequences of (partially) ignoring missing item responses in large-scale assessment. OSF Preprints. 20 October 2020. https://doi.org/10.31219/osf.io/hmy45 [Google Scholar]
Robitzsch, A. (2023). Regularized generalized logistic item response model. Information, 14(6), 306. https://doi.org/10.3390/info14060306 [Google Scholar] (published 26 May 2023)
Robitzsch, A. (2023). Regularized Mislevy-Wu model for handling nonignorable missing item responses. Information, 14(7), 368. https://doi.org/10.3390/info14070368 Supplement [Google Scholar] (published 28 June 2023)
Robitzsch, A. (2023). Relating the one-parameter logistic diagnostic classification model to the Rasch model and one-parameter logistic mixed, partial, and probabilistic membership diagnostic classification models. Foundations, 3(3), 621-633. https://doi.org/10.3390/foundations3030037 Supplement [Google Scholar] (published 21 September 2023)
Robitzsch, A. (2023). Relating the Ramsay quotient model to the classical D-scoring rule. Analytics, 2(4), 824-835. https://doi.org/10.3390/analytics2040043 Supplement [Google Scholar] (published 17 October 2023)
Robitzsch, A. (2023). To check or not to check? A comment on the contemporary psychometrics (ConPsy) checklist for the analysis of questionnaire items. European Journal of Investigation in Health, Psychology and Education, 13(10), 2150-2159. https://doi.org/10.3390/ejihpe13100151 [Google Scholar] (published 6 October 2023)
Preprint: Robitzsch, A. (2023). To check or not to check? A comment on the contemporary psychometrics (ConPsy) checklist of psychometric scales. PsyArXiv. 30 August 2023. https://doi.org/10.31234/osf.io/9tr28
Robitzsch, A., & Lüdtke, O. (2023). Comparing different trend estimation approaches in country means and standard deviations in international large-scale assessment studies. Large-scale Assessments in Education, 11, 26. https://doi.org/10.1186/s40536-023-00176-6 Supplement [Google Scholar] (published 19 July 2023)
Preprint: Robitzsch, A., & Lüdtke, O. (2022). Comparing different trend estimation approaches in international large-scale assessment studies. OSF Preprints. 12 November 2022. https://doi.org/10.31219/osf.io/u8kf5 [Google Scholar]
Robitzsch, A., & Lüdtke, O. (2023). Vertiefende Trendanalysen für PISA 2018 bis 2022 [In-depth trend analyses for PISA 2018 to 2022] (S. 399-403). In D. Lewalter, J. Diedrich, F. Goldhammer, O. Köller, & K. Reiss (Hrsg.), PISA 2022. Analyse der Bildungsergebnisse in Deutschland. Münster: Waxmann. Link
Robitzsch, A., & Lüdtke, O. (2023). Why full, partial, or approximate measurement invariance are not a prerequisite for meaningful and valid group comparisons. Structural Equation Modeling: A Multidisciplinary Journal, 30(6), 859-870. https://doi.org/10.1080/10705511.2023.2191292 [Google Scholar] (published 3 May 2023)
Preprint: Robitzsch, A., & Lüdtke, O. (2022). Why measurement invariance is not necessary for valid group comparisons. PsyArXiv. 31 October 2022. https://doi.org/10.31234/osf.io/cjyqp
Rolfes, T., Robitzsch, A., & Heinze, A. (2023). Früher war alles besser? Mathematikleistungen von Abiturientinnen und Abiturienten von 1964 und 1996 im Vergleich [Everything was better in the past? Comparing mathematics performance of upper secondary school graduates in 1964 and 1996]. Zeitschrift für Erziehungswissenschaft, 26, 1321-1347. https://doi.org/10.1007/s11618-023-01176-6 [Google Scholar] (published 8 August 2023)
Steinfeld, J., Robitzsch, A. (2023). Estimating item parameters in multistage designs with the tmt package in R. Quantitative and Computational Methods in Behavioral Science, 3, e10087. https://doi.org/10.5964/qcmb.10087 [Google Scholar] (published 6 November 2023)
Ulitzsch, E., Lüdtke, O., & Robitzsch, A. (2023). Alleviating estimation problems in small sample structural equation modeling - A comparison of constrained maximum likelihood, Bayesian estimation, and fixed reliability approaches. Psychological Methods, 28(3), 527-557. https://doi.org/10.1037/met0000435 [Google Scholar] (published 20 December 2021)
Ulitzsch, E., Lüdtke, O., & Robitzsch, A. (2023). The role of response style adjustments in cross-country comparisons - A case study using data from the PISA 2015 questionnaire. Educational Measurement: Issues and Practice, 42(3), 65-79. https://doi.org/10.1111/emip.12552 [Google Scholar] (published 1 May 2023)
2022
Fuentes, A., Lüdtke, O., & Robitzsch, A. (2022). Causal inference with multilevel data: A comparison of different propensity score weighting approaches. Multivariate Behavioral Research, 57(6), 916-939. https://doi.org/10.1080/00273171.2021.1925521 Supplement [Google Scholar] (published 15 June 2021)
Heine, J.-H., & Robitzsch, A. (2022). Evaluating the effects of analytical decisions in large-scale assessments: analyzing PISA mathematics 2003-2012. Large-scale Assessments in Education, 10, 10. https://doi.org/10.1186/s40536-022-00129-5 Supplement [Google Scholar] (published 27 August 2022)
Lüdtke, O., & Robitzsch, A. (2022). A comparison of different approaches for estimating cross-lagged effects from a causal inference perspective. Structural Equation Modeling: A Multidisciplinary Journal, 29(6), 888-907. https://doi.org/10.1080/10705511.2022.2065278 Supplement [Google Scholar] (published 10 June 2022)
Preprint (V2): Lüdtke, O., & Robitzsch, A. (2022). A comparison of different approaches for estimating cross-lagged effects from a causal inference perspective. PsyArXiv. 30 March 2022. https://doi.org/10.31234/osf.io/gcvb4
Preprint (V1): Lüdtke, O., & Robitzsch, A. (2021). A critique of the random intercept cross-lagged panel model. PsyArXiv. 29 July 2021. https://doi.org/10.31234/osf.io/6f85c Supplement [Google Scholar]
Nestler, S., Lüdtke, O., & Robitzsch, A. (2022). Analyzing longitudinal social relations model data using the social relations structural equation model. Journal of Educational and Behavioral Statistics, 47(2), 231-260. https://doi.org/10.3102/10769986211056541 Supplement [Google Scholar] (published 14 December 2021)
Olaru, G., Robitzsch, A., Hildebrandt, A., & Schroeders, U. (2022). Examining moderators of vocabulary acquisition from kindergarten throughout elementary school using local structural equation modeling. Learning and Individual Differences, 95, 102136. https://doi.org/10.1016/j.lindif.2022.102136 [Google Scholar] (published 1 April 2022)
Preprint: Olaru, G., Robitzsch, A., Hildebrandt, A., & Schroeders, U. (2020). Examining moderators of vocabulary acquisition from kindergarten throughout elementary school using local structural equation modeling. PsyArXiv. 27 April 2020. https://doi.org/10.31234/osf.io/bcmd8
Robitzsch, A. (2022). Comparing the robustness of the structural after measurement (SAM) approach to structural equation modeling (SEM) against local model misspecifications with alternative estimation approaches. Stats, 5(3), 631-672. https://doi.org/10.3390/stats5030039 Supplement [Google Scholar] (published 22 July 2022)
Preprint: Robitzsch, A. (2022). Is it really more robust? Comparing the robustness of the structural after measurement (SAM) approach to structural equation modeling (SEM) against local model misspecifications with alternative estimation approaches. PsyArXiv. 22 June 2022. https://doi.org/10.31234/osf.io/ry8za
Robitzsch, A. (2022). Editorial of the Psych special issue “Computational aspects, statistical algorithms and software in psychometrics”. Psych, 4(1), 114-118. https://doi.org/10.3390/psych4010011 [Google Scholar] (published 2 March 2022)
Robitzsch, A. (2022). Estimation methods of the multiple-group one-dimensional factor model: Implied identification constraints in the violation of measurement invariance. Axioms, 11(3), 119. https://doi.org/10.3390/axioms11030119 [Google Scholar] (published 9 March 2022)
Robitzsch, A. (2022). Exploring the multiverse of analytical decisions in scaling educational large-scale assessment data: A specification curve analysis for PISA 2018 mathematics data. European Journal of Investigation in Health, Psychology and Education, 12(7), 731-753. https://doi.org/10.3390/ejihpe12070054 [Google Scholar] (published 7 July 2022)
Preprint: Robitzsch, A. (2022). Exploring the multiverse of analytical decisions in scaling educational large-scale assessment data: A specification curve analysis for PISA data. OSF Preprints. 3 June 2022. https://doi.org/10.31219/osf.io/5bjzn
Robitzsch, A. (2022). Four-parameter guessing model and related item response models. Mathematical and Computational Applications, 27(6), 95. https://doi.org/10.3390/mca27060095 [Google Scholar] (published 17 November 2022)
Preprint: Robitzsch, A. (2022). Four-parameter guessing model and related item response models. Preprints, 2022100430. https://doi.org/10.20944/preprints202210.0430.v1
Robitzsch, A. (2022). On the bias in confirmatory factor analysis when treating discrete variables as ordinal instead of continuous. Axioms, 11(4), 162. https://doi.org/10.3390/axioms11040162 [Google Scholar] (published 1 April 2022)
Preprint: Robitzsch, A. (2022). On the bias in structural equation modeling when treating discrete variables as ordinal instead of continuous. PsyArXiv. 7 March 2022. https://doi.org/10.31234/osf.io/xfrca
Robitzsch, A. (2022). On the choice of the item response model for scaling PISA data: Model selection based on information criteria and quantifying model uncertainty. Entropy, 24(6), 760. https://doi.org/10.3390/e24060760 [Google Scholar] (published 27 May 2022)
Robitzsch, A. (2022). Regularized mixture Rasch model. Information, 13(11), 534. https://doi.org/10.3390/info13110534 Supplement [Google Scholar] (published 10 November 2022)
Robitzsch, A. (2022). Statistical properties of estimators of the RMSD item fit statistic. Foundations, 2(2), 488-503. https://doi.org/10.3390/foundations2020032 [Google Scholar] (published 6 June 2022)
Robitzsch, A., & Lüdtke, O. (2022). Mean comparisons of many groups in the presence of DIF: An evaluation of linking and concurrent scaling approaches. Journal of Educational and Behavioral Statistics, 47(1), 36-68. https://doi.org/10.3102/10769986211017479 Supplement [Google Scholar] (published 8 June 2021)
Preprint: Robitzsch, A., & Lüdtke, O. (2020). Mean comparisons of many groups in the presence of DIF: An evaluation of linking and concurrent scaling approaches. OSF Preprints. 22 May 2020. https://doi.org/10.31219/osf.io/ce5sq
Robitzsch, A., & Lüdtke, O. (2022). Some thoughts on analytical choices in the scaling model for test scores in international large-scale assessment studies. Measurement Instruments for the Social Sciences, 4, 9. https://doi.org/10.1186/s42409-022-00039-w [Google Scholar] (published 3 September 2022)
Preprint: Robitzsch, A., & Lüdtke, O. (2021). Reflections on analytical choices in the scaling model for test scores in international large-scale assessment studies. PsyArXiv. 31 August 2021. https://doi.org/10.31234/osf.io/pkjth
2021
George, A. C., & Robitzsch, A. (2021). Validating theoretical assumptions about reading with cognitive diagnosis models. International Journal of Testing, 21(2), 105-129. https://doi.org/10.1080/15305058.2021.1931238 [Google Scholar] (published 9 June 2021)
Grund, S., Lüdtke, O., & Robitzsch, A. (2021). Multiple imputation of missing data in multilevel models with the R package mdmb: A flexible sequential modeling approach. Behavior Research Methods, 53(6), 2631-2649. https://doi.org/10.3758/s13428-020-01530-0 Supplement [Google Scholar] (published 23 May 2021)
Grund, S., Lüdtke, O., & Robitzsch, A. (2021). On the treatment of missing data in background questionnaires in educational large-scale assessments: An evaluation of different procedures. Journal of Educational and Behavioral Statistics, 46(4), 430-465. https://doi.org/10.3102/1076998620959058 [Google Scholar] (published 27 October 2020)
Köhler, C., Robitzsch, A., Fährmann, K., von Davier, M., & Hartig, J. (2021). A semiparametric approach for item response function estimation to detect item misfit. British Journal of Mathematical and Statistical Psychology, 74(S1), 157-175. https://doi.org/10.1111/bmsp.12224 [Google Scholar] (published 17 December 2020)
Lüdtke, O., Ulitzsch, E., & Robitzsch, A. (2021). A comparison of penalized maximum likelihood estimation and Markov chain Monte Carlo techniques for estimating confirmatory factor analysis models with small sample sizes. Frontiers in Psychology, 12, 615162. https://doi.org/10.3389/fpsyg.2021.615162 Supplement [Google Scholar] (published 29 April 2021)
Preprint: Lüdtke, O., Ulitzsch, E., & Robitzsch, A. (2020). A comparison of penalized maximum likelihood estimation and Markov chain Monte Carlo techniques for estimating confirmatory factor analysis models with small sample sizes. PsyArXiv. 6 October 2020. https://doi.org/10.31234/osf.io/u3qag
Nagy, G., & Robitzsch, A. (2021). A continuous HYBRID IRT model for modeling changes in guessing behavior in proficiency tests. Psychological Test and Assessment Modeling, 63(3), 361-395. https://bit.ly/3FHtA6l [Google Scholar] (published 30 September 2021)
Robitzsch, A. (2021). A comparison of estimation methods for the Rasch model. In C. Perna, N. Salvati & F. S. Spagnolo (Eds.), Book of short papers - SIS 2021 (pp. 157-162). Pearson. https://bit.ly/3l2QVro [Google Scholar]
Preprint: Robitzsch, A. (2021). A comparison of estimation methods for the Rasch model. Preprints, 2021030011. https://doi.org/10.20944/preprints202103.0011.v1
Robitzsch, A. (2021). 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. Foundations, 1(1), 116-144. https://doi.org/10.3390/foundations1010009 [Google Scholar] (published 15 September 2021)
Robitzsch, A. (2021). A comprehensive simulation study of estimation methods for the Rasch model. Stats, 4(4), 814-836. https://doi.org/10.3390/stats4040048 [Google Scholar] (published 1 October 2021)
Robitzsch, A. (2021). A note on a computationally efficient implementation of the EM algorithm in item response models. Quantitative and Computational Methods in Behavioral Sciences, 1(1), e3783. https://doi.org/10.5964/qcmb.3783 [Google Scholar] (published 11 May 2021)
Preprint: Robitzsch, A. (2020). A note on a computationally efficient implementation of the EM algorithm in item response models. PsyArXiv. 28 May 2020. https://doi.org/10.31234/osf.io/xsm2f
Robitzsch, A. (2021). About the equivalence of the latent D-scoring model and the two-parameter logistic item response model. Mathematics, 9(13), 1465. https://doi.org/10.3390/math9131465 [Google Scholar] (published 22 June 2021)
Preprint: Robitzsch, A. (2021). About the equivalence of the latent D-scoring model and the two-parameter logistic item response model. Preprints, 2021050699. https://doi.org/10.20944/preprints202105.0699.v1
Robitzsch, A. (2021). On the treatment of missing item responses in educational large-scale assessment data: An illustrative simulation study and a case study using PISA 2018 mathematics data. European Journal of Investigation in Health, Psychology and Education, 11(4), 1653-1687. https://doi.org/10.3390/ejihpe11040117 [Google Scholar] (published 14 December 2021)
Preprint: Robitzsch, A. (2021). On the treatment of missing item responses in educational large-scale assessment data: The case of PISA 2018 mathematics. Preprints, 2021100107. https://doi.org/10.20944/preprints202110.0107.v1
Robitzsch, A. (2021). 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. Symmetry, 13(11), 2198. https://doi.org/10.3390/sym13112198 [Google Scholar] (published 18 November 2021)
Preprint: Robitzsch, A. (2021). 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. PsyArXiv. 16 October 2021. https://doi.org/10.31234/osf.io/qv76r
Shi, Q., Ma, W., Robitzsch, A., Sorrel, M. A., & Man, K. (2021). Cognitively diagnostic analysis using the G-DINA model in R. Psych, 3(4), 812-835. https://doi.org/10.3390/psych3030052 [Google Scholar] (published 8 December 2021)
Steinfeld, J., & Robitzsch, A. (2021). Item parameter estimation in multistage designs: A comparison of different estimation approaches for the Rasch model. Psych, 3(3), 279-307. https://doi.org/10.3390/psych3030022 [Google Scholar] (published 8 July 2021)
Trendtel, M., & Robitzsch, A. (2021). A Bayesian item response model for examining item position effects in complex survey data. Journal of Educational and Behavioral Statistics, 46(1), 34-57. https://doi.org/10.3102/1076998620931016 [Google Scholar] (published 23 June 2020)
Zitzmann, S., Lüdtke, O., Robitzsch, A., & Hecht, M. (2021). On the performance of Bayesian approaches in small samples: A comment on Smid, McNeish, Miocevic, and van de Schoot (2020). Structural Equation Modeling: A Multidisciplinary Journal, 28(1), 40-50. https://doi.org/10.1080/10705511.2020.1752216 [Google Scholar] (published 29 May 2020)
2020
Jansen, M., Lüdtke, O., & Robitzsch, A. (2020). Disentangling different sources of stability and change in students’ academic self-concepts: An integrative data analysis using the STARTS model. Journal of Educational Psychology, 112(8), 1614-1631. https://dx.doi.org/10.1037/edu0000448 [Google Scholar]
Keleva, A., Noventa, S., & Robitzsch, A. (2020). Überblick über Modelle der Item-Response-Theorie (IRT) [An overview of item response models]. In H. Moosbrugger & A. Keleva (Eds.). Testtheorie und Fragebogenkonstruktion (S. 425-446). Berlin: Springer. https://doi.org/10.1007/978-3-662-61532-4_18 [Google Scholar]
Köhler, C., Robitzsch, A., & Hartig, J. (2020). A bias corrected RMSD item fit statistic: An evaluation and comparison to alternatives. Journal of Educational and Behavioral Statistics, 45(3), 251-273. https://doi.org/10.3102/1076998619890566 [Google Scholar] (published 19 December 2019)
Lüdtke, O., & Robitzsch, A. (2020). Commentary regarding the section 'Modeling the effectiveness of teaching quality': Methodological challenges in assessing the causal effects of teaching. Zeitschrift für Pädagogik, 66(1), 210-222. [Google Scholar]
Postprint: Lüdtke, O., & Robitzsch, A. (2021). Methodological challenges in assessing the causal effects of teaching. PsyArXiv. 2 July 2021. https://doi.org/10.31234/osf.io/bpk4a
Lüdtke, O., Robitzsch, A., & West, S. (2020). Analysis of interactions and nonlinear effects with missing data: A factored regression modeling approach using maximum likelihood estimation. Multivariate Behavioral Research, 55(3), 361-381. https://doi.org/10.1080/00273171.2019.1640104 [Google Scholar] (published 31 July 2019)
Lüdtke, O., Robitzsch, A., & West, S. (2020). Regression models involving nonlinear effects with missing data: A sequential modeling approach using Bayesian estimation. Psychological Methods, 25(2), 157-181. https://doi.org/10.1037/met0000233 [Google Scholar] (published 2 September 2019)
Nestler, S., Lüdtke, O., & Robitzsch, A. (2020). Maximum likelihood estimation of a social relations structural equation model. Psychometrika, 85(4), 870-889. https://doi.org/10.1007/s11336-020-09728-z Supplement [Google Scholar] (published 22 October 2020)
Pokropek, A., Lüdtke, O., & Robitzsch, A. (2020). An extension of the invariance alignment method for scale linking. Psychological Test and Assessment Modeling, 62(2), 305-334. https://bit.ly/2UEp9GH [Google Scholar]
Robitzsch, A. (2020). Book review: Modern psychometrics with R. Frontiers in Psychology, 11, 606. https://doi.org/10.3389/fpsyg.2020.00606 [Google Scholar]
Robitzsch, A. (2020). Lp loss functions in invariance alignment and Haberman linking with few or many groups. Stats, 3(3), 246-283. https://doi.org/10.3390/stats3030019 [Google Scholar] (published 5 August 2020)
Preprint: Robitzsch, A. (2020). Lp loss functions in invariance alignment and Haberman linking. Preprints, 2020060034. https://doi.org/10.20944/preprints202006.0034.v1
Robitzsch, A. (2020). Regularized latent class analysis for polytomous item responses: An application to SPM-LS data. Journal of Intelligence, 8(3), 30. https://doi.org/10.3390/jintelligence8030030 [Google Scholar] (published 14 August 2020)
Preprint: Robitzsch, A. (2020). Regularized latent class analysis for polytomous item responses: An application to SPM-LS data. Preprints, 2020070269. https://doi.org/10.20944/preprints202007.0269.v1
Robitzsch, A. (2020). Robust Haebara linking for many groups: Performance in the case of uniform DIF. Psych, 2(3), 155-173. https://doi.org/10.3390/psych2030014 [Google Scholar] (published 28 July 2020)
Preprint: Robitzsch, A. (2020). Robust Haebara linking for many groups in the case of partial invariance. Preprints, 2020060035. https://doi.org/10.20944/preprints202006.0035.v1
Robitzsch, A. (2020). Why ordinal variables can (almost) always be treated as continuous variables: Clarifying assumptions of robust continuous and ordinal factor analysis estimation methods. Frontiers in Education, 5, 589965. https://doi.org/10.3389/feduc.2020.589965 [Google Scholar] (published 8 October 2020)
Preprint: Robitzsch, A. (2020). Why ordinal variables can (almost) always be treated as continuous variables: Clarifying assumptions of robust continuous and ordinal factor analysis estimation methods. PsyArXiv. 31 July 2020. https://doi.org/10.31234/osf.io/hgz9m
Robitzsch, A., & Lüdtke, O. (2020). A review of different scaling approaches under full invariance, partial invariance, and noninvariance for cross-sectional country comparisons in large-scale assessments. Psychological Test and Assessment Modeling, 62(2), 233-279. https://bit.ly/3ezBB05 Supplement [Google Scholar]
Robitzsch, A., Lüdtke, O., Goldhammer, F., Kroehne, U., & Köller, O. (2020). Reanalysis of the German PISA data: A comparison of different approaches for trend estimation with a particular emphasis on mode effects. Frontiers in Psychology, 11, 884. https://doi.org/10.3389/fpsyg.2020.00884 [Google Scholar]
Robitzsch, A., Lüdtke, O., Schwippert, K., Goldhammer, F., Kroehne, U., & Köller, O. (2020). Leistungsveränderungen in TIMSS zwischen 2015 und 2019: Die Rolle des Testmediums und des methodischen Vorgehens bei der Trendschätzung [Achievement trends in TIMSS between 2015 and 2019: The role of the test medium and methodological approaches in trend estimation] (S. 169-186). In Schwippert, K., Kasper, D., Köller, O., McElvany, N., Selter, C., Steffensky, M., & Wendt, H. (Hrsg.), TIMSS 2019. Mathematische und naturwissenschaftliche Kompetenzen von Grundschulkindern in Deutschland im internationalen Vergleich. Münster: Waxmann. https://doi.org/10.31244/9783830993193
2019
George, A. C., Robitzsch, A., Krelle, M., & Breit, S. (2019). Ein empirischer Vergleich von Konzepten der Lesekompetenz in PIRLS [An empirical comparison of reading literacy concepts in PIRLS]. In C. Wallner-Paschon & U. Itzlinger-Bruneforth (Hrsg.). Lesekompetenz der 10-jährigen im Trend. Vertiefende Analysen zu PIRLS (S. 53-68). Graz: Laykam. pdf
George, A. C., Robitzsch, A., & Schreiner, C. (2019). Eine Diskussionsgrundlage zur Weiterentwicklung von Rückmeldungen aus standardisierten Kompetenzmessungen am Beispiel Mathematik [A discussion for improving the feedback from standardized competence measurements for Mathematics]. In A. C. George, C. Schreiner, C. Wiesner, M. Pointinger, & K. Pacher (Hrsg.). Fünf Jahre flächendeckende Bildungsstandardüberprüfungen in Österreich. Vertiefende Analysen zum Zyklus 2012 bis 2016 (S. 225-238). Münster: Waxmann.
Goldhammer, F., Harrison, S., Bürger, S., Kröhne, U., Lüdtke, O., Robitzsch, A., Köller, O., Heine, J.-H., & Mang, J. (2019). Vertiefende Analysen zur Umstellung des Modus von Papier auf Computer [In-depth analyses for the switch to the from paper to computer]. In K. Reiss, M. Weis, E. Klieme, & O. Köller (Hrsg.), PISA 2018. Grundbildung im internationalen Vergleich (S. 163-186). Münster: Waxmann. doi: 10.31244/9783830991007
Grund, S., Lüdtke, O., & Robitzsch, A. (2019). Missing data in multilevel research. In S. E. Humphrey, & J. M. LeBreton (Eds.). Handbook for multilevel theory, measurement, and analysis (pp. 365-386). American Psychological Association. doi: 10.1037/0000115-017
Robitzsch, A., & George, A. C. (2019). The R package CDM for diagnostic modeling. In M. von Davier & Y.-S. Lee (Eds.). Handbook of diagnostic classification models (pp. 549-572). Springer. doi: 10.1007/978-3-030-05584-4_26 [Google Scholar]
Robitzsch, A., & Lüdtke, O. (2019). Linking errors in international large-scale assessments: Calculation of standard errors for trend estimation. Assessment in Education: Principles, Policy & Practice, 26(4), 444-465. doi: 10.1080/0969594X.2018.1433633 [Google Scholar] (published 23 March 2018)
Wagner, J., Lüdtke, O., & Robitzsch, A. (2019). Does personality become more stable with age? Disentangling state and trait effects for the big five across the life span using local structural equation modeling. Journal of Personality and Social Psychology, 116(4), 666-680. doi: 10.1037/pspp0000203 [Google Scholar]
2018
George, A. C., & Robitzsch, A. (2018). Focusing on interactions between content and cognition: A new perspective on gender differences in mathematical sub-competencies. Applied Measurement in Education, 31(1), 79-97. doi: 10.1080/08957347.2017.1391260
Grund, S., Lüdtke, O., & Robitzsch, A. (2018). Multiple imputation of missing data at level 2: A comparison of fully conditional and joint modeling in multilevel designs. Journal of Educational and Behavioral Statistics, 43(3), 316-353. doi: 10.3102/1076998617738087
Grund, S., Lüdtke, O., & Robitzsch, A. (2018). Multiple imputation of multilevel data in organizational research. Organizational Research Methods, 21(1), 111-149. doi: 10.1177/1094428117703686
Hülür, G., Gasimova, F., Robitzsch, A., & Wilhelm, O. (2018). Change in fluid and crystallized intelligence and student achievement: The role of intellectual engagement. Child Development, 89(4), 1074-1087. doi: 10.1111/cdev.12791
Lüdtke, O., & Robitzsch, A., & Trautwein, U. (2018). Integrating covariates into social relations models: A plausible value approach for handling measurement error in perceiver and target effects. Multivariate Behavioral Research, 53(1), 102-124. doi: 10.1080/00273171.2017.1406793
Lüdtke, O., Robitzsch, A., & Wagner, J. (2018). More stable estimation of the STARTS model: A Bayesian approach using Markov Chain Monte Carlo techniques. Psychological Methods, 23(3), 570-593. doi: 10.1037/met0000155
Ravand, H., & Robitzsch, A. (2018). Cognitive diagnostic model of best choice: A study of reading comprehension. Educational Psychology, 38(10), 1255-1277. doi: 10.1080/01443410.2018.1489524
Robitzsch, A., & Steinfeld, J. (2018). Item response models for human ratings: Overview, estimation methods, and implementation in R. Psychological Test and Assessment Modeling, 60(1), 101-138. https://bit.ly/3mFnn3U
Trendtel, M., & Robitzsch, A. (2018). Modeling item position effects with a Bayesian item response model applied to PISA 2009–2015 data. Psychological Test and Asessment Modeling, 60(2), 241-263. https://bit.ly/35o0TeK
Wagner, J., Lüdtke, O., Robitzsch, A., Göllner, R., & Trautwein, U. (2018). Self-esteem development in the school context: The roles of intrapersonal and interpersonal social predictors. Journal of Personality, 86(3), 471-497. doi: 10.1111/jopy.12330
2017
George, A. C., & Robitzsch, A. (2017). An alternative approach for measuring gender differences in mathematical sub-competencies. Open Online Journal for Research and Education. Special Issue #6, 1–7.
Hülür, G., Gasimova, F., Robitzsch, A., & Wilhelm, O. (2017). A longitudinal study on the development of student achievement over two years (LUISE). In D. Leutner, J. Fleischer, J. Grünkorn, & E. Klieme (Eds.). Competence Assessment in Education: Research, Models and Instruments (pp. 333-354). Cham: Springer. doi: 10.1007/978-3-319-50030-0_20
List, M. K., Robitzsch, A., Lüdtke, O., Köller, O. & Nagy, G. (2017). Performance decline in low-stakes educational assessments: Different mixture modeling approaches. Large-scale Assessments in Education, 5(15), 1-25. doi: 10.1186/s40536-017-0049-3
Lösch, T., Lüdtke, O., Robitzsch, A., Keleva, A., Nagengast, B., & Trautwein, U. (2017). A well-rounded view: Using an interpersonal approach to predict achievement by academic self-concept and peer ratings of competence. Contemporary Educational Psychology, 51, 198-208. doi: 10.1016/j.cedpsych.2017.07.003
Lüdtke, O., & Robitzsch, A. (2017). Eine Einführung in die Plausible-Value-Technik für die psychologische Forschung. Diagnostica, 63, 193-205. doi: 10.1026/0012-1924/a000175
Lüdtke, O., Robitzsch, A., & Grund, S. (2017). Multiple imputation of missing data in multilevel designs: A comparison of different strategies. Psychological Methods, 22, 141-164. doi: 10.1037/met0000096
Robitzsch, A., Lüdtke, O., Köller, O., Kröhne, U., Goldhammer, F., & Heine, J.-H. (2017). Herausforderungen bei der Schätzung von Trends in Schulleistungsstudien. Eine Skalierung der deutschen PISA-Daten. Diagnostica, 63(2), 148-165. doi: 10.1026/0012-1924/a000177
2016
Bakker, M., van den Heuvel-Panhuizen, M., & Robitzsch, A. (2016). Effects of mathematics computer games on special education students’ multiplicative reasoning ability. British Journal of Educational Technology, 47, 633-648.
Bruneforth, M., Oberwimmer, K., & Robitzsch, A. (2016). Reporting und Analysen. In S. Breit & C. Schreiner (Hrsg.). Large-Scale Assessment mit R: Methodische Grundlagen der österreichischen Bildungsstandardüberprüfung (S. 333-362). Wien: facultas. Link
Fellinger, R., Kiefer, T., Robitzsch, A., & Trendtel, M. (2016). Aspekte der Validierung. In S. Breit & C. Schreiner (Hrsg.). Large-Scale Assessment mit R: Methodische Grundlagen der österreichischen Bildungsstandardüberprüfung (S. 363-398). Wien: facultas. Link
Freunberger, R., Robitzsch, A., & Luger-Bazinger, C. (2016). Statistische Analysen produktiver Kompetenzen. In S. Breit & C. Schreiner (Hrsg.). Large-Scale Assessment mit R: Methodische Grundlagen der österreichischen Bildungsstandardüberprüfung (S. 225-258). Wien: facultas. Link
George, A. C., Robitzsch, A., Kiefer, T., Ünlü, A., & Groß, J. (2016). The R package CDM for cognitive diagnosis models. Journal of Statistical Software, 74(2), 1-24.
Groß, J., Robitzsch, A., & George, A. C. (2016). Cognitive diagnosis models for baseline testing of educational standards in math. Journal of Applied Statistics, 43, 229-243.
Grund, S., Lüdtke, O., & Robitzsch, A. (2016). Multiple imputation of missing covariate values in multilevel models with random slopes: A cautionary note. Behavior Research Methods, 48, 640-649.
Grund, S., Lüdtke, O., & Robitzsch, A. (2016). Multiple imputation of multilevel missing data: An introduction to the R package pan. SAGE Open, 6(4), 1-17. doi: 10.1177/2158244016668220
Grund, S., Lüdtke, O., & Robitzsch, A. (2016). Pooling ANOVA results from multiply imputed datasets: A simulation study. Methodology, 12, 75-88.
Hildebrandt, A., Lüdtke, O., Robitzsch, A., Sommer, C., & Wilhelm, O. (2016). Exploring factor model parameters across continuous variables with local structural equation models. Multivariate Behavioral Research, 51, 257-278.
Itzlinger-Bruneforth, U., Bruneforth, M., Robitzsch, A., & Freunberger, R. (2016). Fairer Vergleich und Sozialindex in österreichischen Bildungsstandardüberprüfungen. In B. Groot-Wilken, K. Isaac, & J.-P. Schräpler (Hrsg.). Sozialindices für Schulen (S. 97-117). Münster: Waxmann.
Pham, G., Freunberger, R., Robitzsch, A., Itzlinger-Bruneforth, U., & Bruneforth, M. (2016). Reliabilität und Stabilität des Index der sozialen Benachteiligung und Kompositionseffekt der Schulen. Zeitschrift für Bildungsforschung, 6, 345-364.
Pham, G., Robitzsch, A., George, A. C., & Freunberger, R. (2016). Fairer Vergleich in der Rückmeldung. In S. Breit & C. Schreiner (Hrsg.). Large-Scale Assessment mit R: Methodische Grundlagen der österreichischen Bildungsstandardüberprüfung (S. 295-332). Wien: facultas. Link
Robitzsch, A. (2016). Essays zu methodischen Herausforderungen im Large-Scale Assessment. Dissertationsschrift. Humboldt-Universität zu Berlin. https://doi.org/10.18452/17424pdf
Robitzsch, A. (2016). Zu nichtignorierbaren Konsequenzen des (partiellen) Ignorierens fehlender Item Responses im Large-Scale Assessment. In B. Suchan, C. Wallner-Paschon & C. Schreiner (Hrsg.). PIRLS & TIMSS 2011. Die Kompetenzen in Lesen, Mathematik und Naturwissenschaften am Ende der Volksschule. Österreichischer Expertenbericht (S. 55-64). Graz, Leykam. pdf Preprint
Robitzsch, A., Pham, G., & Yanagida, T. (2016). Fehlende Daten und Plausible Values. In S. Breit & C. Schreiner (Hrsg.). Large-Scale Assessment mit R: Methodische Grundlagen der österreichischen Bildungsstandardüberprüfung (S. 259-293). Wien, facultas. Link
van den Heuvel-Panhuizen, M., Elia, I., & Robitzsch, A. (2016). Effects of reading picture books on kindergartners' performance. Educational Psychology, 36, 323-346.
Zitzmann, S., Lüdtke, O., & Robitzsch, A. (2016). A Bayesian approach for estimating multilevel latent contextual models. Structural Equation Modeling: A Multidisciplinary Journal, 23, 661-679.
2015
Bakker, M., van den Heuvel-Panhuizen, M., & Robitzsch, A. (2015). Effects of playing mathematics computer games on primary school students' multiplicative reasoning ability. Contemporary Educational Psychology, 40, 55-71.
Bakker, M., van den Heuvel-Panhuizen, M., & Robitzsch, A. (2015). Longitudinal data on the effectiveness of mathematics mini-games in primary education. British Journal of Educational Technology, 46, 999-1004.
George, A. C., & Robitzsch, A. (2015). Cognitive diagnosis models in R: A didactic. The Quantitative Methods for Psychology, 11(3), 189-205.
Ravand, H., & Robitzsch, A. (2015). Cognitive diagnostic modeling using R. Practical Assessment, Research & Evaluation, 20(11).
Robitzsch, A., Freunberger, R., Itzlinger-Bruneforth, U., Breit, S., & Schreiner, C. (2015). Ein Kommentar zu Vohns: "Bildungsstandards M8 - Wie kommen die offiziellen Zahlen zustande und was sagen sie (nicht) aus?" pdf
Robitzsch, A., & Lüdtke, O. (2015). Kommentar zum Beitrag "Lokale Abhängigkeit von Items im TestDaF-Leseverstehen" von Thomas Eckes. Diagnostica, 61, 107-109. Preprint
Schwabe, F., Trendtel, M., Robitzsch, A. & McElvany, N. (2015). Die Bedeutung des Antwortformats bei Lesetestaufgaben für unterschiedliche Schülersubgruppen unter besonderer Berücksichtigung von Fähigkeitsunterschieden. In H. Wendt, T. C. Stubbe, K. Schwippert & W. Bos (Hrsg.). 10 Jahre international vergleichende Schulleistungsforschung in der Grundschule. Vertiefende Analysen zu IGLU und TIMSS 2001 bis 2011 (S. 97–116). Münster: Waxmann.
van den Heuvel-Panhuizen, M., Elia, I., & Robitzsch, A. (2015). Kindergartners' performance in two types of imaginary perspective-taking. ZDM: The International Journal on Mathematics Education, 47, 345-362.
Zitzmann, S., Lüdtke, O., & Robitzsch, A. (2015). A Bayesian approach to more stable estimates of group-level effects in contextual studies. Multivariate Behavioral Research, 50, 688-705.
2014
Bakker, M., van den Heuvel-Panhuizen, M., & Robitzsch, A. (2014). First-graders' knowledge of multiplicative reasoning before formal instruction in this domain. Contemporary Educational Psychology, 39, 59-73.
Gasimova, F., Robitzsch, A., Hülür, G., & Wilhelm, O. (2014). A hierarchical Bayesian model with correlated residuals for investigating stability and change in intensive longitudinal data settings. Methodology, 10, 126-137.
Gasimova, F., Robitzsch, A., Wilhelm, O., Boker, S., Hu, Y., & Hülür, G. (2014). Dynamical systems analysis applied to working memory data. Frontiers in Psychology, 5:687. doi: 10.3389/fpsyg.2014.00687
George, A. C., & Robitzsch, A. (2014). Multiple group cognitive diagnosis models, with an emphasis on differential item functioning. Psychological Test and Assessment Modeling, 56(4), 405-432.
Robitzsch, A., & Lüdtke, O. (2014). Zur (Nicht-)Modellierung lokaler Abhängigkeiten in Messmodellen: Weshalb der Modellfit kein geeignetes Kriterium für die Modellwahl ist.
Postprint: Robitzsch, A., & Lüdtke, O. (2023). Zur (Nicht-)Modellierung lokaler Abhängigkeiten in Messmodellen: Weshalb der Modellfit kein geeignetes Kriterium für die Modellwahl ist. OSF Preprints. 17 March 2023. https://doi.org/10.31219/osf.io/9vdja
Schroeders, U., Robitzsch, A., & Schipolowski, S. (2014). A comparison of different psychometric approaches to modeling testlet structures: An example with c-tests. Journal of Educational Measurement, 51, 400-418.
Wijaya, A., van den Heuvel-Panhuizen, M., Doorman, M., & Robitzsch, A. (2014). Difficulties in solving context-based PISA mathematics tasks: An analysis of students’ errors. The Mathematics Enthusiast, 11(3), 555-584.
2013
Bakker, M., van den Heuvel-Panhuizen, M., van Borkulo, S., & Robitzsch, A. (2013). Effecten van online mini-games op multiplicatieve vaardigheden van leerlingen in groep 4. Pedagogische Studien, 90, 21-36.
Lüdtke, O., Robitzsch, A., Kenny, D., & Trautwein, U. (2013). A general and flexible approach to estimating the social relations model using Bayesian methods. Psychological Methods, 18, 101-119.
Robitzsch, A. (2013). Wie robust sind Struktur-und Niveaumodelle? Wie zeitlich stabil und über Situationen hinweg konstant sind Kompetenzen? Zeitschrift für Erziehungswissenschaft, 16(1), 41-45.
van den Heuvel-Panhuizen, M., Kolovou, A., & Robitzsch, A. (2013). Primary school students' strategies in early algebra problem solving supported by an online game. Educational Studies in Mathematics, 84, 281-307.
2012
Bakker, M., van den Heuvel-Panhuizen, M., van Borkulo, S., & Robitzsch, A. (2012). Effects of mini-games for enhancing multiplicative abilities: A first exploration. In S. De Wannemacker, S. Vandercruysse & G. Clarebout (Eds.). Serious games. The challenge (pp. 53-57). Heidelberg: Springer.
Kröner, S., Vock, M., Robitzsch, A., & Köller, O. (2012). Highbrow cultural activities, social background and openness in lower-secondary level students. Journal for Educational Research Online, 4, 3-28.
Peltenburg, M., van den Heuvel-Panhuizen, M., & Robitzsch, A. (2012). Special education students' use of indirect addition in solving subtraction problems up to 100 - A proof of the didactical potential of an ignored procedure. Educational Studies in Mathematics, 79, 351-369. pdf
Winkelmann, H., Robitzsch, A., Stanat, P., & Köller, O. (2012). Mathematische Kompetenzen in der Grundschule. Struktur, Validierung und Zusammenspiel mit allgemeinen kognitiven Fähigkeiten. Diagnostica, 58, 15-30.
2011
Groeneveld, I., Knigge, M., & Robitzsch, A. (2011). Soziale Disparitäten und Schutzfaktoren bei Schülerinnen und Schülern in der Primarstufe. Psychologie in Erziehung und Unterricht, 58, 268-279.
Hannighofer, J., van den Heuvel-Panhuizen, M., Weirich, S., & Robitzsch, A. (2011).Revealing German primary school students’ achievement in measurement. ZDM: The International Journal on Mathematics Education, 43, 651-665.
Hülür, G., Wilhelm, O., & Robitzsch, A. (2011). Intelligence dedifferentation in early childhood. Journal of Individual Differences, 32, 170-179.
Hülür, G., Wilhelm, O., & Robitzsch, A. (2011). Multivariate Veränderungsmodelle für Schulnoten und Schülerleistungen in Deutsch und Mathematik. Zeitschrift für Entwicklungspsychologie und pädagogische Psychologie, 43, 173-185.
Lüdtke, O., Marsh, H., Robitzsch, A., & Trautwein, U. (2011). A 2x2 taxonomy of latent contextual models: Accuracy-bias trade-offs in full and partial error-correction models. Psychological Methods, 16, 444-467.
Robitzsch, A., Dörfler, T., Pfost, M., & Artelt, C. (2011). Die Bedeutung der Itemauswahl und der Modellwahl für die längsschnittliche Erfassung von Kompetenzen: Lesekompetenzentwicklung in der Primarstufe. Zeitschrift für Entwicklungspsychologie und pädagogische Psychologie, 43, 213-227. Preprint
2010
Böhme, K., Robitzsch, A., & Buse, A.-K. (2010). Zur Abgrenzung des Hörverstehens gegenüber dem Leseverstehen mit Hilfe schwierigkeitsbestimmender Merkmale bei der Entwicklung von Textaufgaben. In V. Bernius & M. Imhof (Hrsg.). Zuhörkompetenz in Unterricht und Schule (S. 81-104). Münster: Waxmann.
Bremerich-Vos, A., Behrens, U., Böhme, K., Krelle, M., Neumann, D., Robitzsch, A., Schipolowski, A., & Köller, O. (2010): Kompetenzstufenmodelle für das Fach Deutsch. In: O. Köller, M. Knigge & B. Tesch (Hrsg.), Sprachliche Kompetenzen im Ländervergleich. Überprüfung der Bildungsstandards in den Fächern Deutsch und erste Fremdsprache in der neunten Jahrgangsstufe (S. 37-50). Münster: Waxmann.
Lüdtke, O., & Robitzsch, A. (2010). Umgang mit fehlenden Daten in der empirischen Bildungsforschung. In S. Maschke & L. Stecher (Hrsg.). Enzyklopädie Erziehungswissenschaft Online. Fachgebiet Methoden der empirischen erziehungswissenschaftlichen Forschung, Quantitative Forschungsmethoden. Weinheim: Juventa.
Lüdtke, O., & Robitzsch, A. (2010). Missing Data. In H. Holling & B. Schmitz (Hrsg.), Handbuch der Psychogischen Methoden und Evaluation. Göttingen: Hogrefe.
Lüdtke, O., Robitzsch, A., Köller, O., & Winkelmann, H. (2010). Kausale Effekte in der Empirischen Bildungsforschung. Ein Vergleich verschiedener Ansätze zur Schätzung des Effekts des Einschulungsalters. In W. Bos, E. Klieme & O. Köller (Hrsg.). Schulische Lerngelegenheiten und Kompetenzentwicklung. Festschrift für Jürgen Baumert (S. 257-284). Münster: Waxmann. pdf, Link zum Buch
Peltenburg, M., van den Heuvel-Panhuizen, M., & Robitzsch, A. (2010). ICT-based dynamic assessment to reveal special education students' potential in mathematics. Research Papers in Education, 25, 319-334.
Porsch, R., & Robitzsch, A. (2010). Umgang mit nicht bearbeiteten Textproduktionsaufgaben – Konsequenzen bei der Anwendung von Multi-Facetten-Raschmodellen. In: B. Schwarz, P. Nenniger & R. S. Jäger (Hrsg.), Erziehungswissenschaftliche Forschung – nachhaltige Bildung. Beiträge zur 5. DGfE-Sektionstagung „Empirische Bildungsforschung“. Erziehungswissenschaft, Band 28 (S. 305-312). Landau: Verlag Empirische Pädagogik. pdf
Robitzsch, A. (2010). TIMSS 1995 und 2007: Trend der mathematischen Kompetenzen in Österreich. In B. Suchan, C. Wallner-Paschon & C. Schreiner (Hrsg.). TIMSS 2007. Österreichischer Expertenbericht (S. 56-63). Graz: Leykam. pdf (https://www.bifie.at/buch/1191/1/3)
2009
Böhme, K., Bremerich-Vos, A. & Robitzsch, A. (2009). Aspekte der Kodierung von Schreibaufgaben: Vergleich holistischer und analytischer Kodierungen unter besonderer Berücksichtigung der Interraterreliabilität. In A. Bremerich-Vos, D. Granzer & O. Köller (Hrsg.). Bildungsstandards Deutsch und Mathematik (S. 290-329). Weinheim: Beltz Pädagogik.
Böhme, K. & Robitzsch, A. (2009). Lesekompetenzdiagnostik. In A. Bremerich-Vos, D. Granzer & O. Köller (Hrsg.). Bildungsstandards Deutsch und Mathematik (S. 219-249). Weinheim: Beltz Pädagogik.
Pietsch, M., Böhme, K., Robitzsch, A. & Stubbe, T. (2009) Das Stufenmodell zur Lesekompetenz der länderübergreifenden Bildungsstandards im Vergleich zu IGLU 2006. In Bremerich-Vos, A., Granzer, D. & Köller, O. (Hrsg.). Bildungsstandards Deutsch und Mathematik (S. 393-416). Weinheim: Beltz Pädagogik.
Hildebrandt, A., Wilhelm, O., & Robitzsch, A. (2009). Complementary and competing factor analytic approaches for the investigation of measurement invariance. Review of Psychology, 16, 87-102. pdf
Lüdtke, O. & Robitzsch, A. (2009). Assessing within-group agreement: A critical examination of a random-group resampling approach. Organizational Research Methods, 12, 461-487.
Lüdtke, O., Robitzsch, A., Trautwein, U. & Kunter, M. (2009). Assessing the impact of learning environments: How to use student ratings in multilevel modelling. Contemporary Educational Psychology, 34, 120-131.
Marsh, H.W., Muthén, B., Asparouhov, T., Lüdtke, O., Robitzsch, A., Morin, A. J. S. & Trautwein, U. (2009). Exploratory structural equation modeling, integrating CFA and EFA: Applications to students’ evaluations of university teaching. Structural Equation Modeling, 16, 439-476.
Marsh, H.W., Lüdtke, O., Robitzsch, A., Trautwein, U., Asparouhov, T., Muthén, B.O., & Nagengast, B. (2009). Doubly-latent models of school contextual effects: Integrating multilevel and structural equation approaches to control measurement and sampling error. Multivariate Behavioral Research, 44, 764–802.
Robitzsch, A. (2009). Methodische Herausforderungen bei der Kalibrierung von Leistungstests. In A. Bremerich-Vos, D. Granzer & O. Köller (Hrsg.). Bildungsstandards Deutsch und Mathematik (S. 42-106). Weinheim: Beltz Pädagogik. pdf (modifizierte Version)
Robitzsch, A. & Karius, I. (2009). Predicting Item Difficulties and Local Dependencies for C-Tests. pdf
Robitzsch, A. & Rupp, A. A. (2009). The impact of missing data on the detection of differential item functioning. Educational Psychological Measurement, 69, 18-34.
van den Heuvel-Panhuizen, M., Robitzsch, A. & Treffers, A. (2009) Large-scale assessment of change in student achievement: Dutch primary school students’ results on written division in 1997 and 2004 as an example. Psychometrika, 74, 351-365.
Wilhelm, O., & Robitzsch, A. (2009). Have cognitive diagnostic models delivered their goods? Some substantial and methodological concerns. Measurement, 7, 53-57.
Winkelmann, H. & Robitzsch, A. (2009). Kompetent ... kompetenter. Eine Erhebung der Klassenstufenunterschiede mathematischer Kompetenzen. Grundschule, 41(6), 24-28.
Winkelmann, H. & Robitzsch, A. (2009). Modelle mathematischer Kompetenzen: Empirische Befunde zur Dimensionalität. In A. Bremerich-Vos, D. Granzer & O. Köller (Hrsg.). Bildungsstandards Deutsch und Mathematik (S. 169-196). Weinheim: Beltz Pädagogik.
2008
Lüdtke, O., Marsh, H.W., Robitzsch, A., Trautwein, U., Asparouhov, T. & Muthén, B. (2008). The multilevel latent covariate model: A new, more reliable approach to group-level effects in contextual studies. Psychological Methods, 13, 203-229.
Lüdtke, O., Robitzsch, A., Trautwein, U. & Köller, O. (2008). Steht Transparenz einer adäquaten Datenauswertung im Wege? Eine Antwort auf Wuttke (2008). Psychologische Rundschau, 58, 180-181.
Winkelmann, H., van den Heuvel-Panhuizen, M. & Robitzsch, A. (2008). Gender differences in the mathematics achievements of German primary school students: results from a German large-scale study. ZDM - The International Journal on Mathematics Education, 40, 601-616.
2007 and earlier
Granzer, D., Winkelmann, H., Robitzsch, A., Böhme, K. (2007). Bildungsstandards und Evaluation. Ein Weg zur Professionalisierung? Perspektiven zur pädagogischen Professionalisierung (Pädagogische Hochschule Heidelberg), 72, 9-15.
Lüdtke, O., Robitzsch, A., Trautwein, U. & Köller, O. (2007). Umgang mit fehlenden Werten in der psychologischen Forschung: Probleme und Lösungen. Psychologische Rundschau, 58, 103-117.
Lüdtke, O., Robitzsch, A., Trautwein, U., Kreuter, F. & Ihme, J. M. (2007). Are there test administrator effects in large-scale educational assessments? Using cross-classified multilevel analysis to probe for effects on math achievement and sample attrition. Methodology, 3, 149-159.
Robitzsch, A. & Wilhelm, O. (2007). Review zu Borsboom, D. (2005) Measuring the mind. Zeitschrift für Pädagogische Psychologie, 21, 183-184.
Alisch, L.-M. & Robitzsch, A. (2004). Zur Methodologie dynamischer Modellierung in der Sozialpsychologie. In E. H. Witte (Hrsg.) Methodologische, methodische und historische Entwicklungen in der Sozialpsychologie. Pabst, Münster.
Robitzsch, A. (2003). Kontraste und M-Schätzer in der Independent Component Analysis. Unveröffentlichte Diplomarbeit, Technische Universität Dresden.
Lüdtke, O., Robitzsch, A. & Köller, O. (2002). Statistische Artefakte bei der Untersuchung von Kontexteffekten in der pädagogisch psychologischen Forschung. Zeitschrift für Pädagogische Psychologie, 16, 217-231.