Methodology for Identification, Visualization, and Clustering of Similar Behaviors in Dyadic Sequences Analyzed Through the Longitudinal Actor-Partner Interdependence Model With Markov Chains

Abstract

The longitudinal actor-partner interdependence model (L-APIM) is frequently used to study dyadic relationships over time. When one deals with categorical longitudinal data, Markov chains emerge as a valuable analytical tool. This approach allows for the identification of interaction patterns in the L-APIM framework through the examination of the transition matrix. In the context of dyadic sample, investigating the similarity of behaviors between individuals becomes important. To address this question, visualization and grouping analysis are employed, providing valuable tools for discerning relationships with behavioral data. We introduce a novel methodological approach to ascertain such behavioral similarity using the probabilities into the transition matrix. In this article, we describe the utilization of multidimensional scaling and hierarchical clustering for identifying analogous behaviors within a dyadic sample. We illustrate the complete methodology using a simulated dataset. Codes in R language are included for implementation.

Keywords

• dyadic sequence; apim model; markov chains
• r