SRM_R:一个基于网络的闪亮的社会关系分析应用程序

IF 8.9 2区 管理学 Q1 MANAGEMENT Organizational Research Methods Pub Date : 2022-11-20 DOI:10.1177/10944281221134104
Man-Nok Wong, D. Kenny, A. Knight
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引用次数: 1

摘要

组织研究中的许多主题都涉及到考察团队成员的人际感知和行为。可以使用社会关系模型(SRM)来分析所得到的数据。该模型使研究人员能够解决有关关系现象的几个重要问题。在模型中,方差可以划分为群体、参与者、伙伴和关系;互惠可以从个人和二人组的角度进行评估;并且可以分析这些级别中的每一个级别的预测因子。然而,使用当前可用的SRM软件分析数据可能具有挑战性,并可能阻止组织研究人员使用该模型。在本文中,我们提供了SRM分析的“入门”介绍,并提出了SRM_R(https://davidakenny.shinyapps.io/SRM_R/),一个可访问且用户友好的基于web的SRM分析应用程序。应用程序中进行SRM分析的基本步骤由47个团队、228名成员和884个二元观察的样本数据集说明,使用参与者对同事寻求建议行为的评分。
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SRM_R: A Web-Based Shiny App for Social Relations Analyses
Many topics in organizational research involve examining the interpersonal perceptions and behaviors of group members. The resulting data can be analyzed using the social relations model (SRM). This model enables researchers to address several important questions regarding relational phenomena. In the model, variance can be partitioned into group, actor, partner, and relationship; reciprocity can be assessed in terms of individuals and dyads; and predictors at each of these levels can be analyzed. However, analyzing data using the currently available SRM software can be challenging and can deter organizational researchers from using the model. In this article, we provide a “go-to” introduction to SRM analyses and propose SRM_R ( https://davidakenny.shinyapps.io/SRM_R/ ), an accessible and user-friendly, web-based application for SRM analyses. The basic steps of conducting SRM analyses in the app are illustrated with a sample dataset of 47 teams, 228 members, and 884 dyadic observations, using the participants’ ratings of the advice-seeking behavior of their fellow employees.
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来源期刊
CiteScore
23.20
自引率
3.20%
发文量
17
期刊介绍: Organizational Research Methods (ORM) was founded with the aim of introducing pertinent methodological advancements to researchers in organizational sciences. The objective of ORM is to promote the application of current and emerging methodologies to advance both theory and research practices. Articles are expected to be comprehensible to readers with a background consistent with the methodological and statistical training provided in contemporary organizational sciences doctoral programs. The text should be presented in a manner that facilitates accessibility. For instance, highly technical content should be placed in appendices, and authors are encouraged to include example data and computer code when relevant. Additionally, authors should explicitly outline how their contribution has the potential to advance organizational theory and research practice.
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