Publications by Kristoffer Magnusson

Are the Current Criteria for Empirically Supported Treatments Too Lenient?

30.05.2013

The practice of classifying treatments as empirically supported (ESTs) has been widely debated for a long time. Recently Jessica Nasser published an article in the Journal of Contemporary Psychotherapy named “Empirically Supported Treatments and Efficacy Trials: What Steps Do We Still Need to Take?”. In the article the author raises several c...

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Visualizing a One-Way ANOVA using D3.js

31.05.2013

A while ago I was playing around with the JavaScript package D3.js, and I began with this visualization—that I never really finished—of how a one-way ANOVA is calculated. I wanted to make the visualization interactive, and I did integrate some interactive elements. For instance, if you hover over a data point it will show the residual, and it...

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Calculating the Overlap of Two Normal Distributions Using Monte Carlo Integration

08.01.2014

I read this post over at the blog Cartesian Faith about Probability and Monte Carlo methods. The post describe how to numerically intregate using Monte Carlo methods. I thought the results looked cool so I used the method to calculate the overlap of two normal distributions that are separated by a Cohen’s d of 0.8. You should head over to the ...

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New d3.js visualization: Interpreting Cohen’s d effect size

13.01.2014

I have created a new visualization in D3. The purpose is to aid in the interpretation of Cohen’s d. The visualization presents Cohen’s d in the following ways: Visually, Cohen’s U3, Probability of superiority, Percentage of overlap and Number needed to treat. The visualization should work on mobile phones and tablets, but it requires a mode...

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Expected overestimation of Cohen’s d under publication bias

27.01.2014

Earlier this week I read this article about “Why Publishing Everything Is More Effective than Selective Publishing of Statistically Significant Results” by Mercal et al (2014). The authors simulated different meta-analytic scenarios and came to the conclusion that publishing everything is more effective for the scientific collective. This got...

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New d3.js visualization: Understanding Significance Testing and Statistical Power

02.02.2014

Here is a new visualization created in the same manner as my Cohen’s d vizualisation. This new visualization is an interactive display of classical null hypothesis significance testing and statistical power. The visualization should work on mobile phones and tablets, but it requires a modern browser that supports SVG. Check it out here. Relate...

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Some exploratory evidence that wait-list conditions may act as a nocebo in psychotherapy trials

14.04.2014

The double-blinded placebo-controlled randomized trial have long been held as the gold standard in pharmacological research. Unfortunately, this design is impossible to mimic in clinical psychology. Even if we — at best — could try to keep the participants blinded to their treatment allocation, it would be rather hard to blind therapists to w...

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New d3.js visualization: Interpreting Correlations

01.08.2014

Here is a new visualization done in d3js. In this visualization I show a scatter plot of two variables with a given correlation. The variables are samples from the standard normal distribution, which are then transformed to have a given correlation by using Cholesky decomposition. By moving the slider you will see how the shape of the data change...

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Interpreting Confidence Intervals – new d3.js visualization

28.11.2014

I just published a new interactive visualization in my series of basic statistical concepts and techniques. This time I have tried to explain confidence intervals for means. This visualization shows a simulation of repeated sampling from a normal distribution with mean zero and a standard deviation of two. I also show the sampling distribution of...

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New visualization of the distribution of p-values using d3.js

21.03.2015

I just published a new interactive visualization in my series of basic statistical concepts and techniques. This time I am trying to show how p-values are distributed. Check it out here: rpsychologist.com/d3/pdist/ Related To leave a comment for the author, please follow the link and comment on their blog: R Psychologist. R-bloggers.com offer...

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