Methodology for Identification, Visualization, and Clustering of Similar Behaviors in Dyadic Sequences Analyzed Through the Longitudinal Actor-Partner Interdependence Model With Markov Chains
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...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Université d'Ottawa
2024-03-01
|
| Series: | Tutorials in Quantitative Methods for Psychology |
| Subjects: | |
| Online Access: | https://www.tqmp.org/RegularArticles/vol20-1/p017/p017.pdf |
| _version_ | 1797236635434221568 |
|---|---|
| author | Bollenrücher, Mégane Darwiche, Joëlle Antonietti, Jean-Philippe |
| author_facet | Bollenrücher, Mégane Darwiche, Joëlle Antonietti, Jean-Philippe |
| author_sort | Bollenrücher, Mégane |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T17:06:59Z |
| format | Article |
| id | doaj.art-3236e7c5bb994083b0c2a04914ca066f |
| institution | Directory Open Access Journal |
| issn | 1913-4126 |
| language | English |
| last_indexed | 2024-04-24T17:06:59Z |
| publishDate | 2024-03-01 |
| publisher | Université d'Ottawa |
| record_format | Article |
| series | Tutorials in Quantitative Methods for Psychology |
| spelling | doaj.art-3236e7c5bb994083b0c2a04914ca066f2024-03-28T20:58:06ZengUniversité d'OttawaTutorials in Quantitative Methods for Psychology1913-41262024-03-01201173210.20982/tqmp.20.1.p017Methodology for Identification, Visualization, and Clustering of Similar Behaviors in Dyadic Sequences Analyzed Through the Longitudinal Actor-Partner Interdependence Model With Markov ChainsBollenrücher, MéganeDarwiche, JoëlleAntonietti, Jean-PhilippeThe 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.https://www.tqmp.org/RegularArticles/vol20-1/p017/p017.pdfdyadic sequence; apim model; markov chainsr |
| spellingShingle | Bollenrücher, Mégane Darwiche, Joëlle Antonietti, Jean-Philippe Methodology for Identification, Visualization, and Clustering of Similar Behaviors in Dyadic Sequences Analyzed Through the Longitudinal Actor-Partner Interdependence Model With Markov Chains Tutorials in Quantitative Methods for Psychology dyadic sequence; apim model; markov chains r |
| title | Methodology for Identification, Visualization, and Clustering of Similar Behaviors in Dyadic Sequences Analyzed Through the Longitudinal Actor-Partner Interdependence Model With Markov Chains |
| title_full | Methodology for Identification, Visualization, and Clustering of Similar Behaviors in Dyadic Sequences Analyzed Through the Longitudinal Actor-Partner Interdependence Model With Markov Chains |
| title_fullStr | Methodology for Identification, Visualization, and Clustering of Similar Behaviors in Dyadic Sequences Analyzed Through the Longitudinal Actor-Partner Interdependence Model With Markov Chains |
| title_full_unstemmed | Methodology for Identification, Visualization, and Clustering of Similar Behaviors in Dyadic Sequences Analyzed Through the Longitudinal Actor-Partner Interdependence Model With Markov Chains |
| title_short | Methodology for Identification, Visualization, and Clustering of Similar Behaviors in Dyadic Sequences Analyzed Through the Longitudinal Actor-Partner Interdependence Model With Markov Chains |
| title_sort | methodology for identification visualization and clustering of similar behaviors in dyadic sequences analyzed through the longitudinal actor partner interdependence model with markov chains |
| topic | dyadic sequence; apim model; markov chains r |
| url | https://www.tqmp.org/RegularArticles/vol20-1/p017/p017.pdf |
| work_keys_str_mv | AT bollenruchermegane methodologyforidentificationvisualizationandclusteringofsimilarbehaviorsindyadicsequencesanalyzedthroughthelongitudinalactorpartnerinterdependencemodelwithmarkovchains AT darwichejoelle methodologyforidentificationvisualizationandclusteringofsimilarbehaviorsindyadicsequencesanalyzedthroughthelongitudinalactorpartnerinterdependencemodelwithmarkovchains AT antoniettijeanphilippe methodologyforidentificationvisualizationandclusteringofsimilarbehaviorsindyadicsequencesanalyzedthroughthelongitudinalactorpartnerinterdependencemodelwithmarkovchains |