Exploratory Approaches for Studying Social Interactions, Dynamics, and Multivariate Processes in Psychological Science

In this article, I argue for the need of more use of exploratory techniques to identify dynamics in social interactions. I describe several approaches as they are applied to multivariate time series data. The first approach is an algorithm that searches for periods of variability and stability at th...
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Autor*in:	Ferrer, Emilio [verfasserIn]

Format:	Artikel
Sprache:	Englisch

Erschienen:	2016

Rechteinformationen:	Nutzungsrecht: © Taylor & Francis Group, LLC 2016

Schlagwörter:	networks exploratory data analysis dyadic interactions Dynamical systems longitudinal data analysis

Übergeordnetes Werk:	Enthalten in: Multivariate behavioral research - Philadelphia, Pa. : Taylor & Francis, 1966, 51(2016), 2-3, Seite 240-256
Übergeordnetes Werk:	volume:51 ; year:2016 ; number:2-3 ; pages:240-256

Links:	Volltext Link aufrufen Link aufrufen Link aufrufen

DOI / URN:	10.1080/00273171.2016.1140629

Katalog-ID:	OLC1977653901

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topic_title	150 DNB Exploratory Approaches for Studying Social Interactions, Dynamics, and Multivariate Processes in Psychological Science networks exploratory data analysis dyadic interactions Dynamical systems longitudinal data analysis
topic	ddc 150 misc networks misc exploratory data analysis misc dyadic interactions misc Dynamical systems misc longitudinal data analysis
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title	Exploratory Approaches for Studying Social Interactions, Dynamics, and Multivariate Processes in Psychological Science
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title_full	Exploratory Approaches for Studying Social Interactions, Dynamics, and Multivariate Processes in Psychological Science
author_sort	Ferrer, Emilio
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title_sort	exploratory approaches for studying social interactions, dynamics, and multivariate processes in psychological science
title_auth	Exploratory Approaches for Studying Social Interactions, Dynamics, and Multivariate Processes in Psychological Science
abstract	In this article, I argue for the need of more use of exploratory techniques to identify dynamics in social interactions. I describe several approaches as they are applied to multivariate time series data. The first approach is an algorithm that searches for periods of variability and stability at the individual level as well as for patterns of overlap in such periods between the two individuals in a couple. These patterns describe the daily ups and downs in the couples' affect and are predictive of the state of the couples 1 to 2 years later. The second approach, hierarchical segmentation, is based on the idea of partitioning the time series in segments with distinct data patterns. In the case of data from dyads, as in the illustration, the patterns can be compared in terms of coherence between the 2 individuals in the dyad. The third approach is based on network analysis, and its use is shown as a method to examine data transitions at the individual and dyadic level as well as system-wide coherence in multivariate systems. For each approach, I provide examples of its use with empirical data. The article ends with general guidelines and recommendations for researchers interested in using exploratory methods as a way to examine psychological processes.
abstractGer	In this article, I argue for the need of more use of exploratory techniques to identify dynamics in social interactions. I describe several approaches as they are applied to multivariate time series data. The first approach is an algorithm that searches for periods of variability and stability at the individual level as well as for patterns of overlap in such periods between the two individuals in a couple. These patterns describe the daily ups and downs in the couples' affect and are predictive of the state of the couples 1 to 2 years later. The second approach, hierarchical segmentation, is based on the idea of partitioning the time series in segments with distinct data patterns. In the case of data from dyads, as in the illustration, the patterns can be compared in terms of coherence between the 2 individuals in the dyad. The third approach is based on network analysis, and its use is shown as a method to examine data transitions at the individual and dyadic level as well as system-wide coherence in multivariate systems. For each approach, I provide examples of its use with empirical data. The article ends with general guidelines and recommendations for researchers interested in using exploratory methods as a way to examine psychological processes.
abstract_unstemmed	In this article, I argue for the need of more use of exploratory techniques to identify dynamics in social interactions. I describe several approaches as they are applied to multivariate time series data. The first approach is an algorithm that searches for periods of variability and stability at the individual level as well as for patterns of overlap in such periods between the two individuals in a couple. These patterns describe the daily ups and downs in the couples' affect and are predictive of the state of the couples 1 to 2 years later. The second approach, hierarchical segmentation, is based on the idea of partitioning the time series in segments with distinct data patterns. In the case of data from dyads, as in the illustration, the patterns can be compared in terms of coherence between the 2 individuals in the dyad. The third approach is based on network analysis, and its use is shown as a method to examine data transitions at the individual and dyadic level as well as system-wide coherence in multivariate systems. For each approach, I provide examples of its use with empirical data. The article ends with general guidelines and recommendations for researchers interested in using exploratory methods as a way to examine psychological processes.
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title_short	Exploratory Approaches for Studying Social Interactions, Dynamics, and Multivariate Processes in Psychological Science
url	http://dx.doi.org/10.1080/00273171.2016.1140629 http://www.tandfonline.com/doi/abs/10.1080/00273171.2016.1140629 http://www.ncbi.nlm.nih.gov/pubmed/27049811 http://search.proquest.com/docview/1802838335
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