Our research activities span various fields ranging from genuinely methodological research in the context of mathematical modeling and psychological assessment, over individual differences research, to basic and applied cognition.
Find a brief description on our research in various fields along with some representative publications below.
Find our complete list of publications here.
We also develop methodological software.
Structural equation models are a family of methods used to model relationships between observed and latent variables, as well as among latent variables. Our research centers on model fit and statistical power, proper representations of hierarchically structured constructs, and general issues surrounding these types of models.
Representative publications
Bader, M., & Moshagen, M. (2024). Assessing the Fitting Propensity of Factor Models. Psychological Methods, 30(2), 254–270. https://doi.org/10.1037/met0000529
Moshagen, M., & Bader, M. (2024). semPower: General Power Analysis for Structural Equation Models. Behavior Research Methods, 56, 2901–2922. https://doi.org/10.3758/s13428-023-02254-7
Auerswald, M. , & Moshagen, M. (2019). How to determine the number of factors to retain in exploratory factor analysis: A comparison of extraction methods under realistic conditions. Psychological Methods, 24, 468-491. https://doi.org/10.1037/met0000200
Moshagen, M., & Auerswald, M. (2018). On Congruence and Incongruence of Measures of Fit in Structural Equation Modeling. Psychological Methods, 23, 318-336. https://doi.org/10.1037/met0000122
Multinomial processing tree models attempt to explain categorical data in stochastic terms by a sequence of latent states that are typically interpreted as psychological processes. Among other things, our research considers model comparisons and regression approaches for MPT parameters.
Representative publications
Jobst, L. J., Heck, D. W., & Moshagen, M. (2020). A Comparison of Correlation and Regression Approaches for Multinomial Processing Tree Models. Journal of Mathematical Psychology, 98, 102400. https://doi.org/10.1016/j.jmp.2020.102400
Heck, D. W., Moshagen, M., & Erdfelder, E. (2014). Model selection by minimum description length: Lower bound sample sizes for the Fisher information approximation. Journal of Mathematical Psychology, 60, 29-34. https://doi.org/10.1016/j.jmp.2014.06.002
Moshagen, M. (2010). multiTree: A computer program for the analysis of multinomial processing tree models. Behavior Research Methods, 42, 42-54. https://doi.org/10.3758/BRM.42.1.42
Erdfelder, E., Auer, T.-S., Hilbig, B. E., Aßfalg, A., Moshagen, M., & Nadarevic, L. (2009). Multinomial processing tree models: A review of the literature. Zeitschrift für Psychologie, 217, 108-124. https://doi.org/10.1027/0044-3409.217.3.108
Time series methods analyze longitudinal data with respect to time and by taking account of an internal structure of data points. Our research focuses on psychometric aspects of time series analyses and the proper implementation of the methods in psychology and psychosomatic.
Representative publications
Stadnitski, T. (2020). Time series analyses with psychometric data. PLoS ONE 15(4): e0231785. https://doi.org/10.1371/journal.pone.0231785
Stadnitski, T., & Wild, B. (2019). How to deal with temporal relationships between bio-psycho-social variables – a practical guide to time series analysis. Psychosomatic Medicine: Journal of Biobehavioral Medicine, 81(3), 289 - 304. https://doi.org/10.1097/PSY.0000000000000680
Wild, B., Stadnitski, T., Wesche, D., Stroe-Kunold, E., Schultz, J-H., Rudofsky, G., Maser-Gluth, Ch., Herzog, W., & Friederich, H-Ch. (2016). Temporal relationships between awakening cortisol and psychosocial variables in inpatients with anorexia nervosa – a time series approach. International Journal of Psychophysiology, 102, 25-32. https://doi.org/10.1016/j.ijpsycho.2016.03.002
Stadnitski, T. (2012). Measuring Fractality. Frontiers in Physiology, 127 (3), 1-13. https://doi.org/10.3389/fphys.2012.00127
A common issue in research involving self-reports is that participants exhibit systematic response biases thus leading to distorted data. Our research scrutinizes the utility of various approaches to deal with a particular type of response bias, namely socially desirable responding, with a focus on impression management scales as well as the overclaiming technique.
Representative Publications
Müller, S., & Moshagen, M. (2019). True Virtue, Self-Presentation, or Both? A Behavioral Test of Impression Management and Overclaiming. Psychological Assessment, 31, 181-191. https://doi.org/10.1037/pas0000657
Müller, S., & Moshagen, M. (2019). Controlling for Response Bias in Self-Ratings of Personality – A Comparison of Impression Management Scales and the Overclaiming Technique. Journal of Personality Assessment, 101, 229-236. https://doi.org/10.1080/00223891.2018.1451870
Müller, S., & Moshagen, M. (2018). Overclaiming Shares Processes With the Hindsight Bias. Personality and Individual Differences, 134, 298-300. https://doi.org/10.1016/j.paid.2018.06.035
Zettler, I., Hilbig, B. E., Moshagen, M., & de Vries, R. E. (2015). Dishonest responding or true virtue? A behavioural test of Impression Management. Personality and Individual Differences, 81, 107-111. https://doi.org/10.1016/j.paid.2014.10.007
One way to improve the validity of surveys involving sensitive issues is to maximize the anonymity of respondents by employing indirect (as opposed to traditional direct) questioning. Our reseach centers on novel approaches in the context of the randomize response technique, which is family of methods that maximize anonymity by adding random noise to the responses.
Representative Publications
Heck, D. W., & Moshagen, M. (2018). RRreg: An R Package for Correlation and Regression Analyses of Randomized Response Data. Journal of Statistical Software, 85, 1-29. https://doi.org/10.18637/jss.v085.i02
Heck, D. W., Hoffmann, A. & Moshagen, M. (2018). Detecting nonadherence without loss in efficiency: A simple extension of the Crosswise Model. Behavior Research Methods, 50, 1895–1905. https://doi.org/10.3758/s13428-017-0957-8
Moshagen, M., Hilbig, B. E., Erdfelder, E., & Moritz, A. (2014). An experimental validation method for questioning techniques that assess sensitive issues. Experimental Psychology, 61, 48-54. https://doi.org/10.1027/1618-3169/a000226
Moshagen, M., Musch, J., & Erdfelder, E. (2012). A stochastic lie detector. Behavior Research Methods, 44, 222-231. https://doi.org/10.3758/s13428-011-0144-2
Aversive ("dark") personality traits are stable dispositions that are linked with ethically and socially aversive behavior. Our research centers on the Dark Factor of Personality, which is assumed to represent the basic disposition underlying any more specific dark trait and thereby represents their commonalities.
Representative Publications
Zettler, I., Lilleholt, L., Bader, M., Hilbig, B. E., & Moshagen, M. (2025). Aversive societal conditions explain differences in 'dark' personality across countries and US states. Proceedings of the National Academy of Sciences, 122 (20): e2500830122. https://doi.org/10.1073/pnas.2500830122
Moshagen, M., Hilbig, B. E., & Zettler, I. (2024). How and why aversive personality is expressed in political preferences. Journal of Personality and Social Psychology, 127, 664–683. https://doi.org/10.1037/pspp0000498
Hilbig, B. E., Moshagen, M., Thielmann, I., & Zettler, I. (2022). Making rights from wrongs: The crucial role of beliefs and justifications for the expression of aversive personality. Journal of Experimental Psychology: General, 151, 2730–2755. http://dx.doi.org/10.1037/xge0001232
Moshagen, M., Hilbig, B. E., & Zettler, I. (2018). The dark core of personality. Psychological Review, 125, 656–688. https://doi.org/0.1037/rev0000111
Our research in the field of behavioral ethics investigates situational determinants of unethical behavior (such as cheating) and develops appropriate statistical methods for commonly used paradigms.
Representative Publications
Heck, D. W., Thielmann, I., Moshagen, M., & Hilbig, B. E. (2018). Who lies? A large-scale reanalysis linking basic personality traits to unethical decision making. Judgment and Decision Making, 13, 356-371.
Moshagen, M., & Hilbig, B. E. (2017). The statistical analysis of cheating paradigms. Behavior Research Methods, 49, 724 - 732. https://doi.org/10.3758/s13428-016-0729-x
Schild, C., Moshagen, M., Scigala, K. A., & Zettler, I. (2020). May the odds—or your personality—be in your favor: Probability of observing a favorable outcome, Honesty-Humility, and dishonest behavior. Judgment and Decision Making, 15, 600-610.
Moshagen, M., Hilbig, B. E., & Musch, J. (2011). Defection in the dark? A randomized-response investigation of cooperativeness in social dilemma games. European Journal of Social Psychology, 41, 638-644. https://doi.org/10.1002/ejsp.793
Structural models of personality attempt to provide an organizing framework concerning stable differences between individuals. Our research investigates the lexically derived HEXACO model, also in comparison to the Five–Factor Model.
Representative Publications
Hilbig, B. E. & Moshagen, M. (2025). Four big problems of Big Five Agreeableness. Current Opinion in Psychology, 65:102086. https://doi.org/10.1016/j.copsyc.2025.102086
Thielmann, I., Moshagen, M., Hilbig, B. E., & Zettler, I. (2022). On the comparability of basic personality models: Meta-analytic correspondence, scope, and orthogonality of the Big Five and HEXACO dimensions. European Journal of Personality, 36, 870 - 900. https://doi.org/10.1177/0890207021102679
Zettler, I., Thielmann, I., Hilbig, B. E., & Moshagen, M. (2020). The Nomological Net of the HEXACO Model of Personality: A Large-scale Meta-analytic Investigation. Perspectives on Psychological Science, 15, 723-760. https://doi.org/10.1177/1745691619895036
Moshagen, M., Thielmann, I., Hilbig, B. E., & Zettler, I. (2019). Meta-analytic investigations of the HEXACO Personality Inventory(-Revised): Reliability generalization, self-observer agreement, intercorrelations, and relations to demographic variables. Zeitschrift für Psychologie, 227, 186-194. https://doi.org/10.1027/2151-2604/a000377
Decision makers often face situations in which judgments must be made based on uncertain information. Our research focusses on the formalization of decision strategies as well as on methods to recover strategies individuals most likely used to arrive at a decision in such probabilistic inferences.
Representative publications
Heck, D. W., Hilbig, B. E., & Moshagen, M. (2017). From Information Processing to Decisions: Formalizing and Comparing Psychologically Plausible Choice Models. Cognitive Psychology, 96, 26-40. https:/doi.org/10.1016/j.cogpsych.2017.05.003
Hilbig, B. E., & Moshagen, M. (2014). Generalized outcome-based strategy classification: Comparing deterministic and probabilistic choice models. Psychonomic Bulletin &Review, 21, 1431-1443. https:/doi.org/10.3758/s13423-014-0643-0
Moshagen, M., & Hilbig, B. E. (2011). Methodological notes on model comparisons and strategy classification: A falsificationist proposition. Judgment and Decision Making, 6, 814-820.
We investigate the role of specific cognitions, namely outcome-related expectancies and processes regarding inference control, in the etiology and maintainance of various types of addictive behaviors, in particular alcohol consumption and pathological buying.
Representative publications
Lindheimer, N. C., Nicolai, J., & Moshagen, M. (2020). General rather than specific: Cognitive deficits in suppressing task irrelevant stimuli are associated with buying-shopping disorder. PLOS One, 15, e0237093. https:/doi.org/10.1371/journal.pone.0237093
Nicolai, J., & Moshagen, M. (2018). Pathological buying symptoms are associated with distortions in judging elapsed time. Journal of Behavioral Addictions, 7, 752–759. https:/doi.org/10.1556/2006.7.2018.80
Nicolai, J., Darancó, S., & Moshagen, M. (2016). Effects of mood state on impulsivity in pathological buying. Psychiatry Research, 244, 351-356. https:/doi.org/10.1016/j.psychres.2016.08.009
Nicolai, J., Moshagen, M., & Demmel, R. (2012). Patterns of alcohol expectancies and alcohol use across age and gender. Drug and Alcohol Dependence, 126, 347-353. https:/doi.org/10.1016/j.drugalcdep.2012.05.040
