Quantitative research in entrepreneurship using R software for data analysis

Daniel do Prado Pagotto, Cândido Borges
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引用次数: 2

Abstract

Objective of the study: this editorial aims to present an overview of Brazilian quantitative research in entrepreneurship, as well as describing possibilities for advancing this methodological approach. Methodology and approach: the article consists of an editorial publication, built from bibliographic research of entrepreneurship literature and theoretical reflections. Main Results: Most national entrepreneurship research follows a qualitative approach. Despite its relevance, quantitative research also has multiple potentialities, especially associated with the use of data originating from secondary sources. Main theoretical and methodological contributions: We present public databases that can be used by entrepreneurship researchers to advance theory. Some strategies for using these bases are exemplified through a brief tutorial in R language. We further debate about strategies to strengthen quantitative research in the area. Finally, we bring a research agenda. Relevance/Originality: contents that are still little explored in the national literature are presented, such as the use of secondary data and machine learning. Social and managerial contributions: some of the databases presented in the study come from government sources and can be used to support the construction of public policies for entrepreneurship. In addition, the precepts on quantitative research presented in this editorial can support managers who work with data analysis to perform more robust studies, regardless of the area, whether practical or academic.
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用R软件进行数据分析的创业定量研究
研究目的:这篇社论旨在概述巴西在创业方面的定量研究,并描述推进这种方法方法的可能性。方法和方法:本文由一篇编辑出版物组成,建立在对创业文献的书目研究和理论反思的基础上。主要结论:大多数国家创业研究采用定性方法。定量研究尽管具有相关性,但也具有多种潜力,特别是与使用二手来源的数据有关。主要的理论和方法贡献:我们提供了创业研究人员可以使用的公共数据库来推进理论。在R语言的一个简短教程中举例说明了使用这些基的一些策略。我们进一步讨论加强该领域定量研究的策略。最后,我们带来一个研究议程。相关性/原创性:在国家文献中仍然很少探索的内容,例如二手数据和机器学习的使用。社会和管理方面的贡献:研究报告中提供的一些数据基来自政府,可用于支持制定促进企业家精神的公共政策。此外,这篇社论中提出的定量研究的原则可以支持从事数据分析工作的管理人员进行更有力的研究,无论该领域是实用的还是学术的。
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