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• Hi Johnny, perfect, that helps a lot. Thank you very much! Best Alexa

  in Linear regression analyses: 1. Robustness check / 2. Interpretation (models include covariates) Comment by alexa May 2023
• Hey E. J., this would be perfect. I still did not fix theses issues. Thanks a lot! Alexa

  in Linear regression analyses: 1. Robustness check / 2. Interpretation (models include covariates) Comment by alexa May 2023
• Hello, I already solved the issue of question 1. I commited a fallacy, as I simply do not have a linear relationship between some IV / DV. There is simply no logistic / quadratic relationship so I do not need to transform my variables. Concerning qu…

  in Linear regression analyses: Assumptions of linearity and normally distributed residuals Comment by alexa May 2023
• Hey E.J., thank your for your answer that helps me already! Best Alexa!

  in Classification scheme for the interpretation of BF inclusion / posterior inclusion probability Comment by alexa May 2020
• Thank you E.J.,! The paper is very informative and helps me choosing a uniform prior over the number of included predictors.

  in Prior model probabilities Comment by alexa May 2020
• Hi E.J., I have the same problem as I re-ran my analyses with the new JASP version and now the prior model probabilites are not the same anymore. Did you publish the paper meanwhile? I am a little bit unsecure which method is the best for my data. O…

  in Prior model probabilities Comment by alexa May 2020
• Thank you very much EJ for the helpful feedback!!

  in Regression analysis: dichotomous covariate and reporting results Comment by alexa May 2020
• E.J., thank you very much for this helpful and fast answer. 1. / 2. Ah, I understand. Actually, I wanted to present the correlation table just as “an overview” (as I assessed several constructs), but I didn’t want to test any hypothesis. Although, o…

  in Prior distribution regression analyses // different results using frequentist and Bayesian inference Comment by alexa January 2020
• Okay, thank you very much! Just to understand it correctly: for DV2, the data are about 20 times (1/0,048) more likely to occur under the null model than under the full model (all subscales included, highest BF01) and about 3 times (1/0,328) more l…

  in Interpreting Bayesian linear regression in JASP Comment by alexa April 2018
• Thank you very much for the quick answer! OK, yes, but BF01 is just the same as 1/BF10, isn't it? When reporting this result, can I write that the results indicate evidence for H0? Does it make sense that I also report that the highest evidence for …

  in Interpreting Bayesian linear regression in JASP Comment by alexa April 2018