This dataset contains all data and R code necessary to follow the tutorial "Sensitivity analysis in psychological and medical research: A tutorial using multiple imputation in a longitudinal case study".

People interested in learning sensitivity analysis with multiple imputation are free to download the data and R code and follow along.

MIcrosoft Excel, 2019

R Statistical Software, 4.4.1

Data was collected within a project of medical education. Medical students could attend a six-week, once a week, trainings seminar aimed at reducing test anxiety. Training started five weeks prior to the first exam. Training was based on Kaluza (2018). Training included learning strategies, time management strategies, stress prevention and relaxation. In addition, students were encouraged to talk about their stressors, with the aim to improve group cohesion. The projects hypothesis was that an increase in group cohesion leads to a decrease in test anxiety.

Notes for Version 2: During the revision process reviewer's criticised our first imputation model - although congenial - to not correctly account for the hierarchically clustered data. Thus, we changed the imputation model to better fit the data. We also added some calculation according to the wishes of the reviewer's.

Minor changes: 1. We removed the figures as requested by the reviewer. 2. We added Rscript files, in addition to RMarkdown files, for better working with the code and "click-along" during the tutorial. 3. In version 1, we made a minor mistake during the calculation of proportion of missing data. We noticed this and corrected it during the revision. This did not affect any kind of results. 4. Variable names were changed to English.