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一般线性模型中实质性分析的统计假设:一篇小型综述。

Statistical assumptions of substantive analyses across the general linear model: a mini-review.

作者信息

Nimon Kim F

机构信息

Learning Technologies, University North Texas Denton, TX, USA.

出版信息

Front Psychol. 2012 Aug 28;3:322. doi: 10.3389/fpsyg.2012.00322. eCollection 2012.

Abstract

The validity of inferences drawn from statistical test results depends on how well data meet associated assumptions. Yet, research (e.g., Hoekstra et al., 2012) indicates that such assumptions are rarely reported in literature and that some researchers might be unfamiliar with the techniques and remedies that are pertinent to the statistical tests they conduct. This article seeks to support researchers by concisely reviewing key statistical assumptions associated with substantive statistical tests across the general linear model. Additionally, the article reviews techniques to check for statistical assumptions and identifies remedies and problems if data do not meet the necessary assumptions.

摘要

从统计检验结果得出的推论的有效性取决于数据满足相关假设的程度。然而,研究(例如,Hoekstra等人,2012年)表明,此类假设在文献中很少被报告,而且一些研究人员可能不熟悉与其所进行的统计检验相关的技术和补救措施。本文旨在通过简要回顾与一般线性模型中的实质性统计检验相关的关键统计假设来支持研究人员。此外,本文还回顾了检验统计假设的技术,并确定了如果数据不满足必要假设时的补救措施和问题。

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