Can we disregard the whole model? Omnibus non-inferiority testing for R2 in multi-variable linear regression and η^2 in ANOVA

Harlan Campbell (Corresponding author), Daniël Lakens

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

1 Citaat (Scopus)

Samenvatting

Determining a lack of association between an outcome variable and a number of different explanatory variables is frequently necessary in order to disregard a proposed model (i.e., to confirm the lack of a meaningful association between an outcome and predictors). Despite this, the literature rarely offers information about, or technical recommendations concerning, the appropriate statistical methodology to be used to accomplish this task. This paper introduces non-inferiority tests for ANOVA and linear regression analyses, which correspond to the standard widely used F test for η ^ 2 and R 2 , respectively. A simulation study is conducted to examine the Type I error rates and statistical power of the tests, and a comparison is made with an alternative Bayesian testing approach. The results indicate that the proposed non-inferiority test is a potentially useful tool for 'testing the null'.

Originele taal-2Engels
Pagina's (van-tot)64-89
Aantal pagina's26
TijdschriftBritish Journal of Mathematical and Statistical Psychology
Volume74
Nummer van het tijdschrift1
Vroegere onlinedatum13 feb 2020
DOI's
StatusGepubliceerd - feb 2021

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