A Dynamic Multilevel Modeling Approach To University Students' Anxiety And Hope Regulation

Preparing for an exam entails various exam-related emotions. To ensure psychologically healthy behavior and an effective learning process, emotion regulation is highly important. In the present study, we used a dynamic multilevel modeling approach to analyse the interplay of exam-related anxiety and hope, as well as four regulation strategies over time. This study provides valuable insights into students' emotion regulation in academic settings. Specifically, we measured students' emotions and regulation strategies (cognitive approach, cognitive avoidance, behavioral approach, behavioral avoidance) across two weeks of measurement. In total, 217 university students answered several questionnaires via an electronic device during two different measurement periods lasting 7 days each. These measurement periods were 5 weeks (day 35-29) and one week (day 7 to 1) prior to an important exam. During their everyday life, they answered these questionnaires six times a day. We applied dynamic multilevel modeling to analyse the data. Results revealed cross-lagged relationships of exam related emotions on regulation strategies, with the strongest cross-lagged relationship being found for hope and the cognitive approach regulation strategy. Moreover, the temporal distance to the exam affected the strength and direction of these cross-lagged relationships. This study suggests the importance of considering the mechanisms of emotion regulation on the situation level, and that strategies should be tailored to the individual situation rather than employing universal strategies. Further, the extent to which we can interpret the lagged relationships as suggestive of causal mechanisms are discussed.