Significance Testing in Accounting Research: A Critical Evaluation Based on Evidence.
From a survey of the papers published in leading accounting journals in 2014, we find that accounting researchers conduct significance testing almost exclusively at a conventional level of significance, without considering key factors such as the sample size or power of a test. We present evidence t...
| Publicado en: | Abacus Vol. 54; no. 4; pp. 524 - 547 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Wiley-Blackwell
Dec2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=133724164&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 133724164 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00013072 AUB jtl: Abacus issn: 00013072 maglogo: Y pubinfo: dt: Dec2018 vid: 54 iid: 4 pid: 480 pub: Wiley-Blackwell artinfo: ui: 133724164 10.1111/abac.12141 ppf: 524 ppct: 23 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P size: 535KB tig: atl: Significance Testing in Accounting Research: A Critical Evaluation Based on Evidence. aug: au: Kim, Jae H. Ahmed, Kamran Ji, Philip Inyeob affil: La Trobe University Dongguk University Seoul su: Quantitative research Scientific community Data analysis Empirical research Accounting methods sug: subj: Quantitative research Scientific community Data analysis Empirical research Accounting methods keyword: Bayesian inference Research credibility Sample size Statistical power Statistical significance ab: From a survey of the papers published in leading accounting journals in 2014, we find that accounting researchers conduct significance testing almost exclusively at a conventional level of significance, without considering key factors such as the sample size or power of a test. We present evidence that a vast majority of the accounting studies favour large or massive sample sizes and conduct significance tests with the power extremely close to or equal to one. As a result, statistical inference is severely biased towards Type I error, frequently rejecting the true null hypotheses. Under the 'p‐value less than 0.05' criterion for statistical significance, more than 90% of the surveyed papers report statistical significance. However, under alternative criteria, only 40% of the results are statistically significant. We propose that substantial changes be made to the current practice of significance testing for more credible empirical research in accounting. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Copyright of Abacus is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. item: Abacus holder: Wiley-Blackwell dt: @attributes: year: 2018 holdings: @attributes: islocal: N |
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