Steffen Zitzmann
Steffen Zitzmann
Assistant professor of educational research methods, University of Tübingen
Verified email at
Cited by
Cited by
A meta-analysis of relations between achievement and self-concept
J Möller, S Zitzmann, F Helm, N Machts, F Wolff
Review of Educational Research 90 (3), 376-419, 2020
Examining the presence and determinants of operational momentum in childhood
A Knops, S Zitzmann, K McCrink
Frontiers in Psychology 4, 325, 2013
Going beyond convergence in Bayesian estimation: Why precision matters too and how to assess it
S Zitzmann, M Hecht
Structural Equation Modeling: A Multidisciplinary Journal 26 (4), 646-661, 2019
A Bayesian approach for estimating multilevel latent contextual models
S Zitzmann, O Lüdtke, A Robitzsch, HW Marsh
Structural Equation Modeling: A Multidisciplinary Journal 23 (5), 661-679, 2016
A Bayesian approach to more stable estimates of group-level effects in contextual studies
S Zitzmann, O Lüdtke, A Robitzsch
Multivariate Behavioral Research 50 (6), 688-705, 2015
On the performance of Bayesian approaches in small samples: A comment on Smid, McNeish, Miocevic, and van de Schoot (2020)
S Zitzmann, O Lüdtke, A Robitzsch, M Hecht
Structural Equation Modeling: A Multidisciplinary Journal 28, 40-50, 2021
A computationally more efficient and more accurate stepwise approach for correcting for sampling error and measurement error
S Zitzmann
Multivariate Behavioral Research 53 (5), 612-632, 2018
Disapproved, but tolerated: The role of respect in outgroup tolerance
B Simon, S Eschert, CD Schaefer, KM Reininger, S Zitzmann, HJ Smith
Personality and Social Psychology Bulletin 45 (3), 406-415, 2019
Sample size recommendations for continuous-time models: Compensating shorter time series with larger numbers of persons and vice versa
M Hecht, S Zitzmann
Structural Equation Modeling: A Multidisciplinary Journal 28, 229-236, 2021
Integrating out nuisance parameters for computationally more efficient Bayesian estimation–An illustration and tutorial
M Hecht, C Gische, D Vogel, S Zitzmann
Structural Equation Modeling: A Multidisciplinary Journal 27 (3), 483-493, 2020
A computationally more efficient Bayesian approach for estimating continuous-time models
M Hecht, S Zitzmann
Structural Equation Modeling: A Multidisciplinary Journal 27 (6), 829-840, 2020
Multilevel analysis of mediation, moderation, and nonlinear effects in small samples, using expected a posteriori estimates of factor scores
S Zitzmann, C Helm
Structural Equation Modeling: A Multidisciplinary Journal, 2021
The "situative nature" of competence and value beliefs and the predictive power of autonomy support: A multilevel investigation of repeated observations
C Parrisius, H Gaspard, S Zitzmann, U Trautwein, B Nagengast
Journal of Educational Psychology, 1-24, 2021
Prior specification for more stable Bayesian estimation of multilevel latent variable models in small samples: A comparative investigation of two different approaches
S Zitzmann, C Helm, M Hecht
Frontiers in Psychology 11, 611267, 2021
Moderators of dimensional comparison effects: A comprehensive replication study putting prior findings on five moderators to the test and going beyond
F Wolff, S Zitzmann, J Möller
Journal of Educational Psychology 113, 621-640, 2021
Politicization as an antecedent of polarization: Evidence from two different political and national contexts
B Simon, KM Reininger, CD Schaefer, S Zitzmann, S Krys
British Journal of Social Psychology 58 (4), 769-785, 2019
Dynamics of respect: Evidence from two different national and political contexts
KM Reininger, CD Schaefer, S Zitzmann, B Simon
Journal of Social and Political Psychology 8 (2), 542-559, 2020
Comparing group means with the total mean in random samples, surveys, and large-scale assessments: A tutorial and software illustration
S Weirich, M Hecht, B Becker, S Zitzmann
Behavior Research Methods, 2021
Early school adjustment: Do social integration and persistence mediate the effects of school-entry skills on later achievement?
D Schmerse, S Zitzmann
Learning and Instruction 71, 101374, 2021
Using the effective sample size as the stopping criterion in Markov chain Monte Carlo with the Bayes module in Mplus
S Zitzmann, S Weirich, M Hecht
Psych, 2021
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