https://rpsychologist.com Interactive visualizations for understanding statistics, and writing on quantitative methods and causal inference, by Kristoffer Magnusson. en https://rpsychologist.com/odds-ratio https://rpsychologist.com/odds-ratio Mon, 02 Nov 2026 11:00:00 GMT An interactive article about odds ratios, risk ratios (relative risk), and risk differences, and why the interpretation of odds ratios is perhaps more odd than expected. https://rpsychologist.com/descriptive-adjustment https://rpsychologist.com/descriptive-adjustment Thu, 25 Jun 2026 10:00:00 GMT An interactive article about how regression adjustment works and the pitfalls of conditional associations in descriptive studies. https://rpsychologist.com/introducing-powerlmmjs https://rpsychologist.com/introducing-powerlmmjs Wed, 11 Dec 2024 17:00:00 GMT A new tool for power analysis of longitudinal linear mixed-effects models (LMMs), with support for missing data, random slopes, and non-inferiority and equivalence tests. https://rpsychologist.com/cohens-d-translation-project https://rpsychologist.com/cohens-d-translation-project Sat, 29 Oct 2022 07:37:00 GMT My Cohen's d app supports multiple languages, and any help translating the content into more languages is very welcome. https://rpsychologist.com/lmm-slope-missingness https://rpsychologist.com/lmm-slope-missingness Thu, 09 Jul 2020 15:00:00 GMT Linear mixed-effects models are often used for their ability to handle missing data using maximum likelihood estimation. In this post I will present a simple example of when the LMM fails, and illustrate two MNAR sensitivity analyses: the pattern-mixture method and the joint model. https://rpsychologist.com/HTE-variance-ratio https://rpsychologist.com/HTE-variance-ratio Fri, 10 Jan 2020 19:00:00 GMT Recently there has been a couple of meta-analyses investigating heterogeneous treatment effects by analyzing the ratio of the outcome variances in the treatment and control group. The argument made in these articles is that if individuals differ in their response, then observed variances in the treatment and control group in RCTs should differ. In this post I explore this argument and provide a counterargument. https://rpsychologist.com/mediation-confounding-ME https://rpsychologist.com/mediation-confounding-ME Wed, 09 Oct 2019 17:00:00 GMT In this post, I give a brief simulation-based example of how confounding and measurement error impacts the estimation of direct and indirect effects in a mediation analysis. https://rpsychologist.com/treatment-response-subgroup https://rpsychologist.com/treatment-response-subgroup Mon, 19 Nov 2018 18:20:00 GMT The term "treatment response" is both easy to understand and simultaneously often used when causal language is clearly unwarranted. In this post, I present a non-technical example of when a naïve subgroup analysis leads to the wrong conclusion that a subgroup of patients is treatment non-responders. https://rpsychologist.com/therapists-effects-longitudinal https://rpsychologist.com/therapists-effects-longitudinal Fri, 24 Aug 2018 13:15:00 GMT In this post, I summarize our recent paper on the consequences of ignoring therapist effects in longitudinal data analysis, which presents the results of a large simulation study. https://rpsychologist.com/GLMM-part1-lognormal https://rpsychologist.com/GLMM-part1-lognormal Sun, 05 Aug 2018 12:55:00 GMT In this post I show how to make marginal inferences on the untransformed scaled when using multilevel models with a non-linear transformation applied to the dependent variable (a log-transformation is used as an example). Cluster-specific versus population-average (conditional versus marginal) effects are compared using both average effects on the untransformed scale and using relative (multiplicative) effects.