Testing What Matters (If You Must Test at All): A Context-Driven Approach to Substantive and Statistical Significance.
For over a half century, various fields in the behavioral and social sciences have debated the appropriateness of null hypothesis significance testing (NHST) in the presentation and assessment of research results. A long list of criticisms has fueled the so-called significance testing controversy. T...
| Publicado en: | American Journal of Political Science (John Wiley & Sons, Inc.) Vol. 59; no. 3; pp. 775 - 789 |
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| Formato: | Artículo |
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John Wiley & Sons, Inc.
Jul2015
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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=103640946&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 103640946 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00925853 LVDV jtl: American Journal of Political Science (John Wiley & Sons, Inc.) issn: 00925853 maglogo: N pubinfo: dt: Jul2015 vid: 59 iid: 3 pid: 52269 pub: John Wiley & Sons, Inc. artinfo: ui: 103640946 10.1111/ajps.12149 ppf: 775 ppct: 14 formats: tig: atl: Testing What Matters (If You Must Test at All): A Context-Driven Approach to Substantive and Statistical Significance. aug: au: Gross, Justin H. affil: University of North Carolina at Chapel Hill su: Null hypothesis Testing Political science Statistical significance Statistical hypothesis testing Confidence intervals sug: subj: Null hypothesis Testing Political science Statistical significance Statistical hypothesis testing Confidence intervals ab: For over a half century, various fields in the behavioral and social sciences have debated the appropriateness of null hypothesis significance testing (NHST) in the presentation and assessment of research results. A long list of criticisms has fueled the so-called significance testing controversy. The conventional NHST framework encourages researchers to devote excessive attention to statistical significance while underemphasizing practical (e.g., scientific, substantive, social, political) significance. I introduce a simple, intuitive approach that grounds testing in subject-area expertise, balancing the dual concerns of detectability and importance. The proposed practical and statistical significance test allows the social scientist to test for real-world significance, taking into account both sampling error and an assessment of what parameter values should be deemed interesting, given theory. The matter of what constitutes practical significance is left in the hands of the researchers themselves, to be debated as a natural component of inference and interpretation. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2015 holdings: @attributes: islocal: N |
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