{"title":"Hello World! Building Computational Models to Represent Social and Organizational Theory","authors":"James A. Grand, Michael T. Braun, Goran Kuljanin","doi":"10.1177/10944281241261913","DOIUrl":null,"url":null,"abstract":"Computational modeling holds significant promise as a tool for improving how theory is developed, expressed, and used to inform empirical research and evaluation efforts. However, the knowledge and skillsets needed to build computational models are rarely developed in the training received by social and organizational scientists. The purpose of this manuscript is to provide an accessible introduction to and reference for building computational models to represent theory. We first discuss important principles and recommendations for “thinking about” theory and developing explanatory accounts in ways that facilitate translating their core assumptions, specifications, and ideas into a computational model. Next, we address some frequently asked questions related to building computational models that introduce several fundamental tasks/concepts involved in building models to represent theory and demonstrate how they can be implemented in the R programming language to produce executable model code. The accompanying supplemental materials describes additional considerations relevant to building and using computational models, provides multiple examples of complete computational model code written in R, and an interactive application offering guided practice on key model-building tasks/concepts in R.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"1 1","pages":""},"PeriodicalIF":8.9000,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Organizational Research Methods","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1177/10944281241261913","RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MANAGEMENT","Score":null,"Total":0}
引用次数: 0
Abstract
Computational modeling holds significant promise as a tool for improving how theory is developed, expressed, and used to inform empirical research and evaluation efforts. However, the knowledge and skillsets needed to build computational models are rarely developed in the training received by social and organizational scientists. The purpose of this manuscript is to provide an accessible introduction to and reference for building computational models to represent theory. We first discuss important principles and recommendations for “thinking about” theory and developing explanatory accounts in ways that facilitate translating their core assumptions, specifications, and ideas into a computational model. Next, we address some frequently asked questions related to building computational models that introduce several fundamental tasks/concepts involved in building models to represent theory and demonstrate how they can be implemented in the R programming language to produce executable model code. The accompanying supplemental materials describes additional considerations relevant to building and using computational models, provides multiple examples of complete computational model code written in R, and an interactive application offering guided practice on key model-building tasks/concepts in R.
计算模型作为一种工具,在改进理论的开发、表达和使用方式,为实证研究和评估工作提供信息方面大有可为。然而,建立计算模型所需的知识和技能很少在社会和组织科学家接受的培训中得到发展。本手稿的目的是为建立代表理论的计算模型提供通俗易懂的介绍和参考。我们首先讨论了 "思考 "理论和开发解释性描述的重要原则和建议,这些原则和建议有助于将理论的核心假设、规范和观点转化为计算模型。接下来,我们讨论了一些与建立计算模型有关的常见问题,介绍了建立模型以表示理论所涉及的几项基本任务/概念,并演示了如何用 R 编程语言实现这些任务/概念,以生成可执行的模型代码。随书附赠的补充材料介绍了与构建和使用计算模型相关的其他注意事项,提供了多个用 R 语言编写的完整计算模型代码示例,并提供了一个交互式应用程序,指导读者练习用 R 语言构建模型的关键任务/概念。
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.