Anna Scharl

// DISSERTATION PROJECT

Conditioning Variable Selection, Model Diagnostics and Comparison of Missing Data Handling Schemes in the Context of Plausible Values Estimation

Plausible values are widely used to report competence scores in educational research (e.g., PISA, TIMSS, PIRLS). However, most large scale assessments are cross-sectional in nature. Thus, the intersection of plausible values estimation and longitudinal research designs has not been a focus of plausible values research so far. In particular, the dissertation addresses model diagnostics (e.g., using posterior predictive model checking), missing data handling strategies and their impact on the estimated plausible values, and variable selection for the background model in plausible values estimation in the context of longitudinal designs.

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// ACADEMIC BACKGROUND

2017 - ongoing
Bachelor's Degree (B.Sc.) Applied Computer Science, University of Bamberg

2014 - 2017
Master's Degree (M.Sc.) in Psychology, University of Bamberg

2011 - 2014
Bachelor's Degree (B.Sc.) in Psychology, University of Bamberg

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// EXPERIENCE

Current Position
Research Fellow, Leibniz Institute for Educational Trajectories (LIfBi) in Bamberg