2021
DOI: 10.31234/osf.io/ugc9e
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Determining Negligible Associations in Regression (Alter & Counsell, 2021)

Abstract: Psychological research is rife with inappropriately concluding lack of association or no effect between a predictor and the outcome in regression models following statistically nonsignificant results. This approach is methodologically flawed, however, because failing to reject the null hypothesis using traditional, difference-based tests does not mean the null is true (i.e., no relationship). This flawed methodology leads to high rates of incorrect conclusions that flood the literature. This thesis introduces … Show more

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Cited by 5 publications
(7 citation statements)
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“…Finally, group differences not detected in our multiple group statistical models may exist. However, our use of equivalence or “negligible effect” inference tests bolsters the argument that any group differences observed are likely to be practically and statistically negligible (Alter & Counsell, 2021).…”
Section: Discussionmentioning
confidence: 64%
See 1 more Smart Citation
“…Finally, group differences not detected in our multiple group statistical models may exist. However, our use of equivalence or “negligible effect” inference tests bolsters the argument that any group differences observed are likely to be practically and statistically negligible (Alter & Counsell, 2021).…”
Section: Discussionmentioning
confidence: 64%
“…A sample size calculator (Soper, 2022) indicated that our sample size of N = 1032 exceeded the minimum sample size of N = 200 needed to detect a small effect ( z = 0.1; Cohen, 1992) in our structural equation models (i.e., comprised of one latent variable and six manifest variables with a statistical power level of 0.8 and a probability level of 0.05). There are well‐documented limitations of drawing a conclusion of “no group differences” based on nonsignificant results from null hypothesis significant tests (see Alter & Counsell, 2021; Altman & Bland, 1995; Counsell & Cribbie, 2015; Jabbari & Cribbie, 2022). Thus, we also conducted equivalence, or “negligible effect” tests, using the R‐based negligible application (Cribbie et al, 2022) for associations that were significant for at least one group.…”
Section: Methodsmentioning
confidence: 99%
“…Analyses were conducted in R statistical software using the psych package (Revelle, 2014), the performance package (Lüdecke et al, 2021), and the stats package. We used the reg.equiv.fd function for equivalence tests (Alter & Counsell, 2021). For moderation analyses, key variables were centered (focal predictor and moderator) during data processing in SPSS, and their product was computed.…”
Section: Methodsmentioning
confidence: 99%
“…and the stats package. We used the reg.equiv.fd function for equivalence tests (Alter & Counsell, 2021). For moderation analyses, key variables were centered (focal predictor and moderator) during data processing in SPSS, and their product was computed.…”
Section: Analytic Planmentioning
confidence: 99%
“…Equivalence analysis allows for direct inferences to be made about the absence of an effect or the presence of a negligibly small effect. We used the Anderson-Hauck procedure (Anderson & Hauck, 1983) in the reg.equiv function in R (Alter & Counsell, 2021). The Anderson-Hauck procedure has been shown to have greater statistical power than the Two One Sided Test (TOST; Schuirmann, 1987) when comparing regression coefficients at smaller sample sizes (e.g., Alter & Counsell, 2021;Counsell & Cribbie, 2015).…”
Section: Equivalence Analysismentioning
confidence: 99%