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首页> 外文期刊>International journal of human-computer interaction >A Review of Exploratory Factor Analysis Decisions and Overview of Current Practices: What We Are Doing and How Can We Improve?
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A Review of Exploratory Factor Analysis Decisions and Overview of Current Practices: What We Are Doing and How Can We Improve?

机译：探索性因素分析决策的回顾和当前实践的概述：我们在做什么以及如何改进？

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摘要

Authors within the fields of cyberpsychology and human-computer interaction have demonstrated a particular interest in measurement and scale creation, and exploratory factor analysis (EFA) is an extremely important statistical method for these areas of research. Unfortunately, EFA requires several statistical and methodological decisions to which the best choices are often unclear. The current article reviews five primary decisions and provides direct suggestions for best practices. These decisions are (a) the data inspection techniques, (b) the factor analytic method, (c) the factor retention method, (d) the factor rotation method, and (e) the factor loading cutoff. Then the article reviews authors' choices for these five EFA decisions in every relevant article within seven cyberpsychology and/or human-computer interaction journals. The results demonstrate that authors do not employ the recommended best practices for most decisions. Particularly, most authors do not inspect their data for violations of assumptions, apply inappropriate factor analytic methods, utilize outdated factor retention methods, and omit the justification for their factor rotation methods. Further, many authors omit altogether their EFA decisions. To rectify these concerns, the current article provides a step-by-step guide and checklist that authors can reference to ensure the use of recommended best practices. Together, the current article identifies concerns with current research and provides direct solutions to these concerns.
机译：网络心理学和人机交互领域的作者对度量和规模创建表现出了特别的兴趣，而探索性因素分析（EFA）对于这些研究领域来说是极其重要的统计方法。不幸的是，全民教育需要做出一些统计和方法上的决定，而对于这些决定的最佳选择通常是不清楚的。当前文章回顾了五个主要决策，并为最佳实践提供了直接建议。这些决定是（a）数据检查技术，（b）因子分析方法，（c）因子保留方法，（d）因子旋转方法和（e）因子加载截止。然后，本文在七种网络心理学和/或人机交互期刊的每篇相关文章中，回顾了作者对这五个EFA决定的选择。结果表明，作者在大多数决策中没有采用推荐的最佳实践。特别是，大多数作者不会检查其数据是否违反假设，不会应用不合适的因子分析方法，不会使用过时的因子保留方法，而忽略其因子轮换方法的合理性。此外，许多作者完全省略了他们的EFA决定。为了纠正这些问题，本文提供了分步指南和清单，作者可以参考这些清单和清单，以确保使用推荐的最佳实践。总之，当前的文章确定了当前研究的关注点，并为这些关注点提供了直接的解决方案。

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