The Cohen's d app translation project
R Psychologist
by
2y ago
My Cohen's d page support multiple languages, any help with translation the contents into more languages is very welcome. The contents ..read more
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Why linear mixed-effects models are probably not the solution to your missing data problems
R Psychologist
by
4y ago
Linear mixed-effects models are often used for their ability to handle missing data using maximum likelihood estimation. In this post I will ..read more
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Heterogeneous treatment effects and homogeneous outcome variances
R Psychologist
by Kristoffer Magnusson
5y ago
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. For instance, Winkelbeiner et al. (2019) write: The SDs of the pretreatment and posttreatment outcome difference scores in the treatment and control groups consist of the same variance components, including the within-patient variation. The treatment g ..read more
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Mediation, confounding, and measurement error
R Psychologist
by Kristoffer Magnusson
5y ago
Mediation might be the ultimate example of how a method continues to be used despite a vast number of papers and textbooks describing the extremely strong assumptions required to estimate unbiased effects. My aim with this post is not to show some fancy method that could help reduce bias; rather I just want to present a small simulation-based example of the underappreciated consequences of measurement error and confounding. There's been many other people making the same point, for instance, Dunn & Bentall (2007) expressed some strong concerns about investigating mediators in psychological ..read more
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Change over time is not "treatment response"
R Psychologist
by Kristoffer Magnusson
5y ago
This will be a non-technical post illustrating the problems with identifying treatment responders or non-responders using inappropriate within-group analyses. Specifically, I will show why it is pointless to try to identify a subgroup of non-responders using a naïve analysis of data from one treatment group only, even though we have weekly measures over time. This type of unwarranted and misleading causal language is surprisingly common. You see this type of language even when the data comes from an RCT, often in secondary analyses of mediators, predictors, or in even trendier studies where t ..read more
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