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Project

Statistical methods for capturing emotional connectedness

Interpersonal relationships impact our health and well-being, whereby interpersonal similarity in for example experienced emotions is an important facilitator. Until now, this interpersonal similarity has mostly been examined using variable-centered (i.e., one variable at a time) and cross-sectional (i.e., one measurement at a time) approaches. However, interpersonal similarity often concerns multiple variables simultaneously (e.g., multiple discrete emotions) and can be expected to change over time and contexts. In this PhD-project, we therefore propose a new statistical framework that combines existing methods to optimally capture dyadic similarity (i.e., between two persons) in a multivariate, timepoint-specific way. We propose to compute similarity in person-specific profiles for each timepoint of each dyad separately. This will allow to examine the similarity of profiles in moment-to-moment experiences, how this similarity changes over time and how it can be associated to time-varying and person-level covariates.

However, building a new framework, albeit based on existing techniques, brings some challenges. This PhD project will handle these challenges in  two parts: the first part will tackle the statistical challenges that come with the new framework and the second part will go deeper into challenges related to the application of the framework. 

Date:1 Oct 2021 →  Today
Keywords:dyadic data analysis
Disciplines:Statistics and data analysis
Project type:PhD project