An Equivalence Testing Approach for Evaluating Substantial Mediation

Beribisky, Nataly; Mara, Constance A.; Cribbie, Robert A.

doi:10.20982/tqmp.16.4.p424

Cited by 5 publications

(3 citation statements)

References 48 publications

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“…and Counsell et al (2020) proposed equivalence testing to replace the chi-square difference test commonly used in measurement invariance. Beribisky et al (2020) introduced an equivalencebased approach to establishing substantial mediation in path analysis. Lastly, Campbell and Lakens (2021) proposed an equivalence-based approach (non-inferiority tests) for an ANOVA or linear regression model, as whole, which applies to the F test for the ANOVA η 2 parameter or the coefficient of determination (R 2 ), respectively.…”

Section: Other Applications Of Equivalence Testing In Psychologymentioning

confidence: 99%

Equivalence Testing for Multiple Regression

Alter

2024

Preprint

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<p>Psychological research is rife with inappropriately concluding “no effect” between predictors and 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. Using this approach leads to high rates of incorrect conclusions which floods psychological literature. This thesis introduces a novel, methodologically sound alternative; I demonstrate how to apply equivalence testing to evaluate whether predictors have negligible effects on the outcome in multiple regression. I constructed a simulation study to evaluate the performance of two equivalence-based methods and compared it to the traditional test. I further developed two R functions which accompany this thesis to supply researchers with open-access and easy-to-use tools. The use of the proposed equivalence-based methods and R functions is illustrated through examples from the literature, and recommendations for results reporting and interpretations are discussed.</p>

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Section: Other Applications Of Equivalence Testing In Psychologymentioning

confidence: 99%

Equivalence Testing for Multiple Regression

Alter

2024

Preprint

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“…For example, Goertzen and Cribbie (2010) demonstrated how tests of equivalence can be used to determine negligible effects in simple correlations. Beribisky et al (2020) showed how to test whether an indirect effect is negligible for a substantial mediation. Yuan and Chan (2016) and Counsell et al (2020) proposed to use equivalence testing to assess negligible effects for measurement invariance.…”

Section: Testing For Negligible Association Using Equivalence Testsmentioning

confidence: 99%

Determining Negligible Associations in Regression

Alter¹,

Counsell²

2023

TQMP

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Psychological research is rife with inappropriately concluding "no effect" between predictors and outcome in regression models following statistically nonsignificant results. However, this approach is methodologically flawed because failing to reject the null hypothesis using traditional, difference-based tests does not mean the null is true. Using this approach leads to high rates of incorrect conclusions that flood psychological literature. This paper introduces a novel, methodologically sound alternative. In this paper, we demonstrate how an equivalence testing approach can be applied to multiple regression (which we refer to here as "negligible effect testing") to evaluate whether a predictor (measured in standardized or unstandardized units) has a negligible association with the outcome. In the first part of the paper, we evaluate the performance of two equivalence-based techniques and compare them to the traditional, difference-based test via a Monte Carlo simulation study. In the second part of the paper, we use examples from the literature to illustrate how researchers can implement the recommended negligible effect testing methods in their own work using open-access and user-friendly tools (negligible R package and Shiny app). Finally, we discuss how to report and interpret results from negligible effect testing and provide practical recommendations for best research practices based on the simulation results. All materials, including R code, results, and additional resources, are publicly available on the Open Science Framework (OSF): osf.io/w96xe/.

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“…In this paper, we address the variance sum law, an advanced topic for students in social and behavioral, and health sciences (and even researchers! ), that has many implications in data modelling (for recent example, see Achim, 2020;Beribisky, Constance, & Cribbie, 2020;Caron & Valois, 2018;Caron, Valois, & Gellen-Kamel, 2020;Lorah, 2020, and there is many more). Basic understanding of linear regression is helpful.…”

Section: Introductionmentioning

confidence: 99%

The variance sum law and its implications for modelling

Caron¹,

Lemardelet²

2021

TQMP

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Basic understanding of linear model is primordial in quantitative psychology and modelling. As an advanced topic for students in psychology, modelling data according to given sets of parameters can be challenging. In this paper, we address the variance sum law which states how to combine variance of variables into a new one. The paper uses a progressive pedagogical approach, presenting step by step the statistical prerequisites. An example with code in R is offered to support the presentation. Some matrix algebra is gently introduced to stimulate readers to more advanced topics. We hope this paper can stimulate research in data modelling and invite newcomers to the fields.

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An Equivalence Testing Approach for Evaluating Substantial Mediation

Cited by 5 publications

References 48 publications

Equivalence Testing for Multiple Regression

Equivalence Testing for Multiple Regression

Determining Negligible Associations in Regression

The variance sum law and its implications for modelling

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