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Network Analysis on Attitudes: A Brief Tutorial

In this article, we provide a brief tutorial on the estimation, analysis, and simulation on attitude networks using the programming language R. We first discuss what a network is and subsequently show how one can estimate a regularized network on typical attitude data. For this, we use open-access d...

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Autores principales: Dalege, Jonas, Borsboom, Denny, van Harreveld, Frenk, van der Maas, Han L. J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5582642/
https://www.ncbi.nlm.nih.gov/pubmed/28919944
http://dx.doi.org/10.1177/1948550617709827
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author Dalege, Jonas
Borsboom, Denny
van Harreveld, Frenk
van der Maas, Han L. J.
author_facet Dalege, Jonas
Borsboom, Denny
van Harreveld, Frenk
van der Maas, Han L. J.
author_sort Dalege, Jonas
collection PubMed
description In this article, we provide a brief tutorial on the estimation, analysis, and simulation on attitude networks using the programming language R. We first discuss what a network is and subsequently show how one can estimate a regularized network on typical attitude data. For this, we use open-access data on the attitudes toward Barack Obama during the 2012 American presidential election. Second, we show how one can calculate standard network measures such as community structure, centrality, and connectivity on this estimated attitude network. Third, we show how one can simulate from an estimated attitude network to derive predictions from attitude networks. By this, we highlight that network theory provides a framework for both testing and developing formalized hypotheses on attitudes and related core social psychological constructs.
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spelling pubmed-55826422017-09-15 Network Analysis on Attitudes: A Brief Tutorial Dalege, Jonas Borsboom, Denny van Harreveld, Frenk van der Maas, Han L. J. Soc Psychol Personal Sci Special Issue Articles In this article, we provide a brief tutorial on the estimation, analysis, and simulation on attitude networks using the programming language R. We first discuss what a network is and subsequently show how one can estimate a regularized network on typical attitude data. For this, we use open-access data on the attitudes toward Barack Obama during the 2012 American presidential election. Second, we show how one can calculate standard network measures such as community structure, centrality, and connectivity on this estimated attitude network. Third, we show how one can simulate from an estimated attitude network to derive predictions from attitude networks. By this, we highlight that network theory provides a framework for both testing and developing formalized hypotheses on attitudes and related core social psychological constructs. SAGE Publications 2017-07-10 2017-07 /pmc/articles/PMC5582642/ /pubmed/28919944 http://dx.doi.org/10.1177/1948550617709827 Text en © The Author(s) 2017 http://creativecommons.org/licenses/by-nc/4.0/ This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Special Issue Articles
Dalege, Jonas
Borsboom, Denny
van Harreveld, Frenk
van der Maas, Han L. J.
Network Analysis on Attitudes: A Brief Tutorial
title Network Analysis on Attitudes: A Brief Tutorial
title_full Network Analysis on Attitudes: A Brief Tutorial
title_fullStr Network Analysis on Attitudes: A Brief Tutorial
title_full_unstemmed Network Analysis on Attitudes: A Brief Tutorial
title_short Network Analysis on Attitudes: A Brief Tutorial
title_sort network analysis on attitudes: a brief tutorial
topic Special Issue Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5582642/
https://www.ncbi.nlm.nih.gov/pubmed/28919944
http://dx.doi.org/10.1177/1948550617709827
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