Methods Center

Research at the Institute


Student Selection in Medicine

Due to a ruling of the Federal Constitutional Court, the previous regulation on the allocation of study places, by which a place was allocated after deduction of an advance quota according to the best Abitur grades (20%), after waiting time (20%) and a selection by the universities (60%), will have to be changed. In addition to the Abitur grade, further aptitude-based criteria are to be incorporated into the selection procedure of the universities. At present, corresponding selection criteria, including professional aptitude and voluntary services as well as competitions, are being agreed upon. The question of professional suitability thus represents a further aspect for student selection and the endeavour to select the prospective students with respect to their professional suitability has also existed for years due to the high number of applicants. So far, however, it is still unclear to what extent a selection based on previous practical professional experience actually represents a relevant additional qualification for later students. So far, a practical/professional pre-qualification bonus has been postulated as a success factor for vocational aptitude. The background of this is often the idea that applicants could have better access to the contents of their studies and also to later concrete professional practice on the basis of their previous experience, since they have already come into contact with the field in advance. It is therefore expected that applicants with vocational qualifications will be able to perform better in their studies, profit from their professional experience positively during their studies and later careers. Additionally it is to be expected that they will be better informed than applicants without a practical/professional pre-qualification about what to expect in their everyday medical careers.

This is the starting point for the proposed study with the following two core questions and subquestions: 

1. Are there any indications that students with previous practical experience in a profession perform better in their studies due to their previous experience than students without this previous experience?

a. Do students with previous work experience perform better in general, cognitive, psychosocial or practical activities than students without previous work experience? Is the bonus for the cognitive part in student selection justified? Or are they merely effects of self-enhancement from the individual's point of view

b. Do the advantages of the previous work experience remain constant, do they only occur at the beginning (more cognitive challenges) or only towards the end (more practical challenges)?

2. Are there any indications that a certain previous work experience shows lasting effects and that these significantly influence the medical specialisation after their studies (e.g. surgeon, general practitioner) (e.g. Azizzadeh, McCollum, Miller, Holliday, Shilstone, & Lucci Jr, 2003)?

This project is done in cooperation with the University of Heidelberg and the University of Freiburg under the lead of Prof. Dr. Zipfel of the medical faculty at the University of Tübingen and Prof. Dr. Kelava of the Methods Center at the University of Tübingen.


This project is funded by the  Ministry for Science, Research and Art of the county Baden-Württemberg.


The project started on Jan 1, 2019

NON-NORM Project

In the past years, there was a growing insight that using linear latent variable models did not suffice neither to answer detailed research questions nor to account for the challenges of complex empirical data. Typical challenges are multilevel data structures, non-normal data and nonlinear relationships between variables. The modeling of such data specifities has two advantages: First, if these data specificites were neglected, the results and conclusion drawn by the researchers might be spurious. And, second, a more detailed modeling allows for a more in-depth analysis of research questions, for example, by identifying unobserved subgroups or the differentiation of nonlinear relationships on individual and cluster level.

The NON-NORM research project addresses the following extensions of (nonlinear) latent variable models: A general nonlinear multilevel structural equation mixture model (GNM-SEMM) will be extended to allow for an analysis of longitudinal heteroskedastic data. An R-package will be provided for substantial researchers. Semiparametric structural equation models will be generalized to non-Bayesian, nonparametric, distribution-free structural equation models. The finite sample properties of the developed semi- and nonparametric models will be examined in simulation studies. Finally, multidimensional item response models will be extended to allow for nonlinear semiparametric effects.

Project team: Holger Brandt, Augustin Kelava, Stefano Noventa, Tim Schaffland, & Nora Umbach


Funded by The Deutsche Forschungsgemeinschaft (DFG): BR 5175-1-2, KE 1664/1-1, KE 1664/1-2

Status: closed (funding: April 1, 2013 to July 31, 2018)


nlsem: Fitting Structural Equation Mixture Models – Estimation of structural equation models with nonlinear effects and underlying nonnormal distributions.


Matlab: An implementation of the non-parametric structural equation modeling approach (Kelava, Kohler, Krzyzak, & Schaffland, 2017) can be found here:

SAM – College drop-out in Mathematics (German: “Studienabbruch in der Mathematik”)

The high drop-out rate in mathematics and natural sciences during the university B.Sc. phase is a well-known phenomenon (Heublein, 2014). The research project “University drop-out in Mathematics” addresses the following questions: a) what is the relative importance of individual predictors of drop-out in the interaction of multiple causes? b) How can drop-out be modelled as a process (taking into account the interdependencies of the predictors)? c) How can the probability of dropping out of university be reduced in term time (given very limited resources)?

The modelling of university drop-out will provide detailed insights into the temporal antecedents of drop-out. In the first step, a preliminary forecast model is developed (based on the reanalysis of extensive data sets). In Tübingen, this result is to be used initially to identify the multiple risk factors in existing cohorts and to make predictions on the probability of dropping out. The risk assessment makes it possible to advise students at risk. In the course of the project, further longitudinal data will be collected, and analytical techniques and the identification of risk constellations will be improved, so that a more targeted approach to those at risk can be implemented.

The sub-project “Determinants and Intervention” at the University of Stuttgart can in turn be divided into two sub-projects. Competence measurement instruments will be developed or existing instruments will be adapted in order to construct time-sensitive prediction models of drop-out (sub-project 1). This will enable us to track the subject-specific achievements of students in the first year of their studies, which is a particularly sensitive period for drop-out. In addition to this, an intervention involving an experimental and control group design is being developed and carried out in order to reduce the performance and motivation-related drop-outs or change of course of study in mathematics (sub-project 2). This intervention follows the Cognitive Apprenticeship Approach (e.g. Collins, Brown & Newman, 1989) Several studies in the vocational school sector have already shown this approach to be effective in improving competence and motivation.


This research is funded by a grant of the Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung; BMBF).

Status: running (since April 1, 2017)

Parsimonious Estimation of Latent Variable Models

Topic: Regularization in latent variable structural equation models

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, 946-960.

Connection to "Student selection in medicine" project
Interdisciplinary teaching: block seminaries, workshops, lecture series
Responsible: Prof. Dr. Holger BrandtProf. Dr. Augustin Kelava

Development of Estimators in the Context of (Dynamic) Latent Variable Models

Kelava, A. & Brandt, H. (2019). A Nonlinear Dynamic Latent Class Structural Equation Model. Structural Equation Modeling, 26, 509-528. DOI: 10.1080/10705511.2018.1555692

Connection to NON-Norm project
Responsible: Prof. Augustin Kelava

Non- and Semi-Parametric Estimation (incl. Factor Score Estimation)

Development of non- and semi-parametric estimation techniques (incl. factor score estimation).

Kelava, A., Kohler, M., Krzyzak, A., & Schaffland, T. (2017). Nonparametric estimation of a latent variable model. Journal of Multivariate Analysis, 154, 112-134Link

Responsible: Prof. Dr. Augustin Kelava, Dr. Stefano Noventa, Tim Schaffland

Reproducibility, Statistical Misconceptions, Open Science

Reproducible Reporting, Preregistration, Open Educational Resources

Involvement: Education Sciences, TüSE
Interdisciplinary Teaching: Workshops
Responsible: Jun.-Prof. Samuel Merk

Methodological reflection/Theory

Grounded Theory, Situation Analysis and Pragmatic Social Research

Grounded Theory; Situation Analysis; Science, Technology and Medicine Studies; Organization Studies; Gender Studies

Bridge function/participation: AG Pragmatism and Social Research; QualiNet; Schools Qualitative Research; Research Group Situation Analysis; Research Group Documentary Methods
Interdisciplinary Teaching: Spring and Summer Schools Qualitative Research; ESIT Bridge Courses; Method Consulting for Master Students and Doctoral Students
Responsible: Jun.-Prof. mit Schwerpunkt Lehre Ursula Offenberger