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.
Funded by The Deutsche Forschungsgemeinschaft (DFG): KE 1664/1-1, KE 1664/1-2, BR 5175-1-2
Status: April 1, 2013 - July 31, 2018 (completed)
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: https://github.com/tifasch/nonparametric
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)
Hochdimensionale Probleme; Regularisierung; Bayessche Modellierung
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. https://doi.org/10.1080/10705511.2018.1474114
Schätzer, Separation intra-individueller Veränderungen und inter-individuellen Differenzen; unbeobachtete Heterogenität
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
Verantwortlich: Augustin Kelava
Entwicklung von nicht- und semiparametrischen frequentistischen Schätzverfahren (incl. factor score Schätzung).
Kelava, A., Kohler, M., Krzyzak, A., & Schaffland, T. (2017). Nonparametric estimation of a latent variable model. Journal of Multivariate Analysis, 154, 112-134. Link
Matlab: Eine Implementation des nicht-parametrischen Ansatzes (Kelava, Kohler, Krzyzak, & Schaffland, 2017) ist unter nachfolgender Quelle verfügbar: https://github.com/tifasch/nonparametric
Reproducible Reporting, Preregistration, Open Educational Resources
Brückenfunktion/Beteiligung: Institut für Erziehungswissenschaft, TüSE
Interdisziplinäre Lehre: Workshops
Verantwortlich: Jun.-Prof. Samuel Merk
Grounded Theory; Situationsanalyse; Science, Technology and Medicine Studies; Organization Studies; Gender Studies
Brückenfunktion/Beteiligung: AG Pragmatismus und Sozialforschung; QualiNet; Schools Qualitativ Forschen; Forschungsgruppe Situationsanalyse; Forschungsgruppe Dokumentarische Methode
Interdisziplinäre Lehre: Spring und Summer Schools Qualitativ Forschen; ESIT-Brückenkurse; Methodenberatung für Masterstudierende und Promovierende
Verantwortlich: Jun.-Prof. mit Schwerpunkt Lehre Ursula Offenberger