Longitudinal social network methods for the educational and psychological sciences
Longitudinal social network methods for the educational and psychological sciences
Social network analysis is useful for obtaining a better understanding of antecedents and mechanisms of relationship formation and interactions between individuals in educational and psychological contexts. Research utilising descriptive and cross-sectional applications of network analysis is regularly reported, but longitudinal analyses of networks have received less scrutiny. In this methodological article, we compare three commonly applied approaches for analysing longitudinal social network data: Multiple Regression Quadratic Assignment Procedure (MRQAP), Separable Temporal Exponential Random Graph Models (STERGM), and Stochastic Actor Oriented Modelling (SAOM) with research questions about correlations, social structures and mechanisms respectively. We highlight advantages and disadvantages of the methods and illustrate differences between these methods by analysing longitudinal peer-communication network data of pre-service teachers. The key considerations by the researcher is summarised as “FACTS” (Focus, Assumptions, Conceptualisation, Time points, and Size) and aid researchers to select the most appropriate method for the analysis of longitudinal social network data.
Bokhove, Christian
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Brouwer, Jasperina
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Downey, Chris
bb95b259-2e31-401b-8edf-78e8d76bfb8c
Bokhove, Christian
7fc17e5b-9a94-48f3-a387-2ccf60d2d5d8
Brouwer, Jasperina
bf1e4876-09a9-4a65-9e14-97102b8f7c7e
Downey, Chris
bb95b259-2e31-401b-8edf-78e8d76bfb8c
Bokhove, Christian, Brouwer, Jasperina and Downey, Chris
(2025)
Longitudinal social network methods for the educational and psychological sciences.
International Journal of Social Research Methodology.
(doi:10.1080/13645579.2025.2478927).
Abstract
Social network analysis is useful for obtaining a better understanding of antecedents and mechanisms of relationship formation and interactions between individuals in educational and psychological contexts. Research utilising descriptive and cross-sectional applications of network analysis is regularly reported, but longitudinal analyses of networks have received less scrutiny. In this methodological article, we compare three commonly applied approaches for analysing longitudinal social network data: Multiple Regression Quadratic Assignment Procedure (MRQAP), Separable Temporal Exponential Random Graph Models (STERGM), and Stochastic Actor Oriented Modelling (SAOM) with research questions about correlations, social structures and mechanisms respectively. We highlight advantages and disadvantages of the methods and illustrate differences between these methods by analysing longitudinal peer-communication network data of pre-service teachers. The key considerations by the researcher is summarised as “FACTS” (Focus, Assumptions, Conceptualisation, Time points, and Size) and aid researchers to select the most appropriate method for the analysis of longitudinal social network data.
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Longitudinal social network methods for the educational and psychological sciences
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Accepted/In Press date: 5 March 2025
e-pub ahead of print date: 31 March 2025
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Local EPrints ID: 498419
URI: http://eprints.soton.ac.uk/id/eprint/498419
ISSN: 1364-5579
PURE UUID: 72c449c3-e884-4074-97a8-d344c7b87e4a
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Date deposited: 18 Feb 2025 17:36
Last modified: 02 Sep 2025 01:46
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Author:
Jasperina Brouwer
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