• Ausrichter (Tagungspräsident) 13. Tagung der Fachgruppe Methoden & Evaluation [FGME 2017] der Deutschen Gesellschaft für Psychologie; 18.-20. September 2017
• Ausrichter des Structural Equation Modeling Working Group Meetings [SEM 2019]; 28.02.-01.03.2019

• Alexander von Humboldt Stiftung / Foundation
• Biological Psychology
• British Journal of Mathematical and Statistical Psychology
• Deutsche Forschungsgemeinschaft (DFG; German Research Foundation)
• Diagnostica
• Einstein Stiftung Berlin (Einstein Foundation Berlin)
• Empirische Pädagogik
• European Journal of Psychological Assessment (EJPA)
• European Journal of Work and Organizational Psychology
• Frontiers in Psychology
• IEEE Transactions on Neural Networks and Learning Systems
• IEEE Transactions on Signal Processing
• Journal of Educational and Behavioral Statistics (JEBS)
• Journal of Educational Measurement (JEM)
• Journal of Educational Psychology
• Journal of Individual Differences (JID)
• Journal of Numerical Cognition
• Journal of the American Statistical Association (JASA)
• Methodology – European Journal of Research Methods for the Behavioral and Social Sciences
• Multivariate Behavioral Research (MBR)
• Personality and Individual Differences (PAID)
• Psychoanalytic Psychology
• Psychologische Rundschau
• Psychological Test and Assessment Modeling
• Psychology Science Quaterly (PSQ)
• Psychometrika
• Review of Psychology
• Sociological Methods and Research
• Statistics in Medicine
• Structural Equation Modeling
• Swiss National Science Foundation (SNSF)
• Zeitschrift für Erziehungswissenschaft
• Zeitschrift für Pädagogische Psychologie

• Deutsche Gesellschaft für Psychologie [DGPs; German Psychological Society]
• European Association of Methodology (EAM)
• Gesellschaft für Empirische Bildungsforschung [GEBF; Association for Empirical Educational Research]
• Psychometric Society

• 2020: Mitglied der Kommission zur Evaluierung des Zentrums für internationale Bildungsvergleichsstudien (ZIB) des Bundesministerium für Bildung und Forschung (BMBF) und der Ständigen Konferenz der Kultusminister der Länder in der Bundesrepublik Deutschland (KMK)
• seit 10/2019: Mitglied im Bereit "Auswahl und Eignung" des Ministeriums für Wissenschaft und Kunst des Landes Baden-Württemberg
• seit 2016: Berater am Institut für Qualitätssicherung und Transparenz im Gesundheitswesen (IQTIG), Berlin
• 2011/12: Mitglied der Kommission "Jungwissenschaftler" der Deutschen Gesellschaft für Psychologie (DGPs)
• 2010-2012: Stellvertretender Sprecher aller Jungmitglieder der Deutschen Gesellschaft für Psychologie (DGPs)
• 2009-2012: Stellvertretender Sprecher der Jungmitglieder der Fachgruppe Methoden und Evaluation der Deutschen Gesellschaft für Psychologie (DGPs)

Moosbrugger, H. & Kelava, A. (Hrsg.) (2020). Testtheorie und Fragebogenkonstruktion. (3., vollst. neu bearb., erw. u. akt. Aufl.). Heidelberg: Springer. doi: 10.1007/978-3-662-61532-4

Auch als Download verfügbar: https://www.springer.com/de/book/9783662615317

Für eine ausführlichere Liste siehe Google Scholar.

• Dorner, F. E., Sühr, T., Samadi, S., & Kelava, A. (2023). Do personality tests generalize to Large Language Models? Socially Responsible Language Modelling Research (SoLaR) 2023 Workshop at NeurIPS 2023. Link
• Nagel, M., Fischer, L., Pawlowski, T., & Kelava, A. (in press). An alternative prior for estimation in high-dimensional settings. Structural Equation Modeling.
• Kilian, P., Leyhr, D., Urban, C. J., Höner, O., & Kelava, A. (2023). A deep learning factor analysis model based on importance-weighted variational inference and normalizing flow priors: Evaluation within a set of multidimensional performance assessments in youth elite soccer players. Statistical Analysis and Data Mining, 16(5), 474-487. Link 
• Andriamiarana, V., Kilian, P., Kelava, A., & Brandt, H. (2023). On the requirements of non-linear dynamic latent class SEM: A simulation study with varying numbers of subjects and time points. Structural Equation Modeling, 30(5), 789-806. Link
• Kilian, P., Ye, S., & Kelava, A. (2023). Mixed effects in machine learning - A flexible mixedML framework to add random effects to supervised machine learning regression. Transactions on Machine Learning Research (TMLR). Link
• Merk, S., Groß Ophoff, J., & Kelava, A. (2023). Rich data, poor information? Teachers’ perceptions of mean differences in graphical feedback from statewide tests. Learning and Instruction, 84, 101717. Link
• Kelava, A., Kilian, P., Glaesser, J., Merk, S., & Brandt, H. (2022). Forecasting intraindividual changes of affective states taking into account interindividual differences using intensive longitudinal data from a university student drop out study in math. Psychometrika, 87(2), 533-558. Link
• Schneider, J., Rosman, T., Kelava, A., & Merk, S. (2022). Do Open Science Badges increase trust in scientists among undergraduates, scientists, and the public? Psychological Science, 33(9), 1588-1604. Link
• Kilian, P., Loose, F., & Kelava, A. (2020). Predicting math student success in the initial phase of college with sparse information using approaches from statistical learning. Frontiers in Education, 5. Link
• Brandt, H., Umbach, N., Kelava, A., & Bollen, K. (2020). Comparing estimators for latent interaction models under structural and distributional misspecifications. Psychological Methods. Link
• Kelava, A. & Brandt, H. (2019). A Nonlinear Dynamic Latent Class Structural Equation Model. Structural Equation Modeling, 26,509-528. Link
• Noventa, S., Spoto, A., Heller, J., & Kelava, A. (2019). On a generalization of local independence in item response theory based on knowledge space theory. Psychometrika, 84, 395-421. Link
• Graser, J., Heimlich, C., Kelava, A., Hofmann, S.G., Stangier, U., & Schreiber, F. (2019). Erfassung der Emotionsregulation bei Jugendlichen anhand des „Affective Style Questionnaire (ASQ-Y)“ . [Assessment of emotion regulation strategies of youth using the Affective Style Questionnaire (ASQ-Y).] Diagnostica, 65, 49-59. Link
• Leyhr, D., Raabe, J., Schultz, F., Kelava, A., & Höner, O. (2019). The adolescent motor performance development of elite female soccer players: A study of prognostic relevance for future success in adulthood using multilevel modelling. Journal of Sports Sciences, 1-10. Link
• Brandt, H., Cambria, J., & Kelava, A. (2018). An adaptive Bayesian lasso approach with spike-and-slab priors to identify linear and interaction effects in structural equation models. Structural Equation Modeling, 25, 956-960. Link
• Gemein, C., Roos, M., Wolf, A., Hermann, N., Kelava, A., Chasan, R., Weipert, K., Helmig, I., Bogossian, H., Hamm, C.W., Neumann, T., Schmitt, J., & Erkapic, D. (2018). Tilt testing and what you should know about it – Experience with 835 consecutive patients with syncope of unknown origin. International Journal of Cardiology, 258, 90–96. Link
• Barth, B., Mayer-Carius, K., Strehl, U., Kelava, A., Häußinger, F.B., Fallgatter, A.J., & Ehlis, A.-C. (2018). Identification of neurophysiological biotypes in attention deficit hyperactivity disorder. Psychiatry and Clinical Neurosciences, 72, 836–848. Link
• Merk, S., Rosman, T., Muis, K., Kelava, A., & Bohl, T. (2018). Topic specific epistemic beliefs: Extending the theory of integrated domains in personal epistemology. Learning and Instruction, 56, 84-97. Link
• Leyhr, D., & Kelava, A., Raabe, J., & Höner, O. (2018). Longitudinal motor performance development in early adolescence and its relationship to adult success: An 8-year prospective study of highly talented soccer players. PLoS ONE 13(5), e0196324. doi: 10.1371/journal.pone.0196324 Link
• Hennings, A., Heil, J., Wagner, A., Rath, M., Moosbrugger, H., Kelava, A., Golotta, M., Hug, S., Riedel, F., Rauch, G., & Feisst, M. (2018). Development and psychometric validation of a shorter version of the Breast Cancer Treatment Outcome Scale (BCTOS-12). The Breast, 38, 58-65. Link
• Koch, T., Kelava, A., & Eid, M. (2018). Analyzing Different Types of Moderated Method Effects in Confirmatory Factor Models for Structurally Different Methods. Structural Equation Modeling, 25, 179-200. Link
• Lösch, T., Kelava, A., Nagengast, B., Trautwein, U., & Lüdtke, O. (2017). Perspective matters: The internal/external frame of reference model for self- and peer ratings of achievement. Learning and Instruction, 52, 80-89. Link
• Lösch, T., Lüdtke, O., Robitzsch, A., Kelava, 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. Link
• Höner, O., Leyhr, D., & Kelava, A. (2017). The influence of speed abilities and technical skills in early adolescence on adult success in soccer: A long-term prospective analysis using SEM approaches. PLOS ONE 12(8): e0182211. Link
• Merk, S., Schneider, J., Bohl, T., Kelava, A., & Syring, M. (2017). Epistemologische Überzeugungen von Lehramtsstudierenden bezüglich pädagogischen Wissens: Gegenstands-, Quellen- und Kontextspezifität. Journal for Educational Research Online, 9(1), 169-189. Link
• Grissmann, S., Spüler, M., Faller, J., Krumpe, T., Zander, T., Kelava, A., Scharinger, C., & Gerjets, P. (2017). Context sensitivity of EEG-based  workload classification under different affective valence. IEEE Transactions on Affective Computing. Link
• Grissmann, S., Zander, T. O., Faller, J., Brönstrup, J., Kelava, A., Gramann, K., & Gerjets, P. (2017). Affective aspects of perceived loss of control and potential implications for brain-computer interfaces. Frontiers in Human Neuroscience, 11:370. Link
• Mayer, A., Umbach, N., Flunger, B., & Kelava, A. (2017). Detailed effect analysis using non-linear structural equation mixture modeling. Structural Equation Modeling, 24, 556-570. Link
• Kelava, A., Kohler, M., Krzyzak, A., & Schaffland, T. (2017). Nonparametric estimation of a latent variable model. Journal of Multivariate Analysis, 154, 112-134. Link
• Umbach, N., Naumann, K., Brandt, H., Kelava, A. (2017). Fitting Nonlinear Structural Equation Models in R with Package nlsem. Journal of Statistical Software, 77 (7). Link
• Kelava, A. (2016). A Review of Confirmatory Factor Analysis for Applied Research (Second Edition). Journal of Educational and Behavioral Statistics, 41, 443-447. Link
• Nitsch, R., Bruder, R., & Kelava, A. (2016). Schülerhandlungen als Elemente fachdidaktisch motivierter Kompetenzmodellierungen. Journal for Didactics of Mathematics, 37, 289-317.
• Schnabel, D. B. L., Kelava, A., van de Vijver, F. J. R., & Seifert, L. (2016). The effects of using collaborative assessment with students going abroad: Intercultural competence development, self-understanding, self-confidence, and stages of change. Journal of College Student Development, 57, 79-94.
• Guo, J., Nagengast, B., Marsh, H.W., Kelava, A., Gaspard, H., Brandt H., Cambria, J., Flunger, B., Dicke, A.-L., Häfner, I., Brisson, B., Trautwein, U. (2016). Probing the Unique Contributions of Self-Concept, Task Values, and Their Interactions Using Multiple Value Facets and Multiple Academic Outcomes. AERA Open, 2(1), 1–20. Link
• Brandt, H., Umbach, N., & Kelava, A. (2015). The Standardization of Linear and Nonlinear Effects in Direct and Indirect Applications of Structural Equation Mixture Models for Normal and Nonnormal Data. Front. Psychol. 6:1813. doi: 10.3389/fpsyg.2015.01813 Link
• Schnabel, D., Kelava, A., van de Vijver, F. J. R., & Seifert, L. (2015). Examining Psychometric Properties, Measurement Invariance, and Construct Validity of a Short Version of the Test to Measure Intercultural Competence (TMIC-S) in Germany and Brazil. International Journal of Intercultural Relations, 49, 137-155. [doi:10.1016/j.ijintrel.2015.08.002] Link
• Kelava, A., Muma, M., Deja, M., & Zoubir, A. M. (2015). A new approach for the quantification of coherence of multivariate non-stationary data with an application to psychophysiological measures during emotion regulation. Frontiers in Psychology (Quantitative Psychology and Measurement), 5:1507. [doi: 10.3389/fpsyg.2014.01507]. Link
• Schnabel, D. B. L., Kelava, A., Seifert, L., & Kuhlbrodt, B. (2015). Konstruktion und Validierung eines multimethodalen berufsbezogenen Tests zur Messung interkultureller Kompetenz. Diagnostica, 61, 3-21. [doi: 10.1026/0012-1924/a000110]. Link
• Nitsch, R., Fredebohm, A., Bruder, R., Kelava, A., Naccarella, D., Leuders, & T. Wirtz, M. (2014). Students’ competencies in working with functions in secondary mathematics education – empirical examination of a competence structure model. International Journal of Science and Mathematics Education, 1-26. [doi: 10.1007/s10763-013-9496-7]. Link
• Kelava, A. & Brandt, H. (2014). A general nonlinear multilevel structural equation mixture model. Frontiers in Psychology (Quantitative Psychology and Measurement), 5:748. [doi: 10.3389/fpsyg.2014.00748]. Link
• Kelava, A., Nagengast, B., & Brandt, H. (2014). A nonlinear structural equation mixture modeling approach for non-normally distributed latent predictor variables. Structural Equation Modeling, 21, 468-481. Link
• Brandt, H., Kelava, A., & Klein, A. G. (2014). A Simulation study comparing recent approaches for the estimation of nonlinear effects in SEM under the condition of non-normality. Structural Equation Modeling, 21, 181-195. Link
• Nagengast, B., Trautwein, U., Kelava, A., & Lüdtke, O. (2013). Synergistic effects of expectancy and value on homework engagement: The case for a within-person perspective. Multivariate Behavioral Research, 48, 428-460. Link
• Klug, J., Bruder, S., Kelava, A., Spiel, C., & Schmitz, B. (2013). Diagnostic competence of teachers: A process model that accounts for diagnosing learning behavior tested by means of a case scenario. Teaching and Teacher Education, 30, 38-46. Link
• Kelava, A. & Nagengast, B. (2012). A Bayesian approach for the estimation of latent interaction and quadratic effects when latent variables are non-normally distributed. Multivariate Behavioral Research, 47, 717-742. Link
• Kelava, A., Werner, C., Schermelleh-Engel, K., Moosbrugger, H., Zapf, D., Ma, Y., Cham, H., Aiken, L.S., & West, S.G. (2011). Advanced nonlinear structural equation modeling: Theoretical properties and empirical application of the distribution-analytic LMS and QML estimators. Structural Equation Modeling, 18, 465-491. Link

Kilian, P., Kelava, A. (2024). Enhancing Multilevel Models Through Supervised Machine Learning. In: Hwang, H., Wu, H., Sweet, T. (eds) Quantitative Psychology. IMPS 2023. Springer Proceedings in Mathematics & Statistics, vol 452. Springer, Cham. https://doi.org/10.1007/978-3-031-55548-0_14