2021
DOI: 10.1037/xge0000920
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Logistic or linear? Estimating causal effects of experimental treatments on binary outcomes using regression analysis.

Abstract: When the outcome is binary, psychologists often use nonlinear modeling strategies such as logit or probit. These strategies are often neither optimal nor justified when the objective is to estimate causal effects of experimental treatments. Researchers need to take extra steps to convert logit and probit coefficients into interpretable quantities, and when they do, these quantities often remain difficult to understand. Odds ratios, for instance, are described as obscure in many textbooks (e.g., Gelman & Hill, … Show more

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Cited by 325 publications
(200 citation statements)
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References 39 publications
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“…The copyright holder for this preprint this version posted April 13, 2021. ; https://doi.org/10.1101/2021.04.09.21255159 doi: medRxiv preprint (sample positivity) was coded as a binary variable (1=detected, 0=not-detected). A linear model was used to predict the outcome variable as it provided improved interpretability of coefficients and also model convergence stability 18 .…”
Section: Discussionmentioning
confidence: 99%
“…The copyright holder for this preprint this version posted April 13, 2021. ; https://doi.org/10.1101/2021.04.09.21255159 doi: medRxiv preprint (sample positivity) was coded as a binary variable (1=detected, 0=not-detected). A linear model was used to predict the outcome variable as it provided improved interpretability of coefficients and also model convergence stability 18 .…”
Section: Discussionmentioning
confidence: 99%
“…To take into account the heteroscedasticity and non-normality the method implies, we estimate robust standard errors [e.g. 61,84]. All data curation and statistical analyses were performed with SAS version 9.4 [85].…”
Section: Plos Onementioning
confidence: 99%
“…One outcome variable was ordinal (Fear of Protesters) and the other was continuous (Support Police Control). However, recent methodological research demonstrated that in experiments with categorical outcomes, linear regression is appropriate (Gomila, 2020;Huang, 2019). Additionally, linear regression is advantageous for testing moderation (Mood, 2010).…”
Section: Analytic Strategymentioning
confidence: 99%