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Science

Predictions for Presidential Elections Weren’t That Bad

Nathan Silver’s FiveThirtyEight has had an excellent coverage of the US Presidential Elections with some great analytical pieces and very interesting insights in their models. Each and every poll predicted Hillary Clinton to win the election and FiveThirtyEight was no exception to that. Consequently, there was a lot of discussion on pollsters, their methods and how they – again after “Brexit” – failed to predict the outcome of the election. There are many parallels between the elections in the US and the Brexit-vote in the UK. At least for the US, however, the predictions weren’t that far off. And FiveThirtyEight in particular, gave Trump better chances than anyone else:

For most of the presidential campaign, FiveThirtyEight’s forecast gave Trump much better odds than other polling-based models. Our final forecast, issued early Tuesday evening, had Trump with a 29 percent chance of winning the Electoral College. By comparison, other models tracked by The New York Times put Trump’s odds at: 15 percent, 8 percent, 2 percent and less than 1 percent. And betting markets put Trump’s chances at just 18 percent at midnight on Tuesday, when Dixville Notch, New Hampshire, cast its votes.

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Science

New Paper: Reliability Estimates for Three Factor Score Estimators

Just a short post on a new paper that is available from our department. If you happen to have calculated factor score estimators after factor analysis, e.g. using Thurstone’s Regression Estimators, you might be interested in the reliability of the resulting scores. Our paper explains how to do this, compares the reliability of three different factor score estimators and provides R- and SPSS-scripts for easy estimation of the reliability. While some reviewers have argued, that this reliability cannot exist, I think, we have some good arguments how our perspective is in line with existing literature on psychometrics.

The paper is available as Open Access in the International Journal of Statistics and Probability and you can find the article here. I have uploaded the scripts to GitHub, so you can easily download them, add issues or create forks. The repository is at https://github.com/neurotroph/reliability-factor-score-estimators.

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Science

Confidence Intervals for Noncentrality Parameters

In recent years, it has become a notion to not only report point estimates of effect sizes, but also confidence intervals for said effect sizes. I have created a small R script to calculate the bounds of such a confidence interval in the case of t- and F-distributions. 

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Science

Differences in Pollster Predictions

The New York Times published an interesting piece on the differences between pollsters’ predictions. All five predictions used the same data set, so sampling differences are not of concern. Still, there was a difference of up to 5% between the predictions. 

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Science

Michael Inzlicht on loosing faith in science

Michael Inzlicht has posted an article on his blog about how he lost faith in psychological science after reading the now infamous paper on “false-positive psychology” 1.

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Science

Fraud in Medical Research

Already in September last year, Der Spiegel published an interview with Peter Wilmshurst, a British medical doctor and whistleblower who made fraudulent practices in medical research public:

In the course of the 66-year-old’s career, he conducted studies for pharmaceutical and medical devices companies, and unlike many of his colleagues, never hesitated to publish negative results. He’s been the subject of multiple cases of legal action and risked bankruptcy and his reputation to expose misconduct in the pharmaceutical industry.

A very interesting article that’s worth reading. Fact is, that companies who have a strong economic interest in the scientific process will have an impact on the quality of the research. It is, again and again, horrible to learn how far companies try to go – and often successfully do. While medical companies has always been an obvious target (and perpetrator), the problem runs deeper than the narrative of “Big Pharma”. 

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Science

Euro Cup predictions through Stan

After I calculated the probabilities of Germany dropping out of the world cup two years ago, I always wanted to do some Bayesian modeling for the Bundesliga or the Euro Cup that started yesterday. Unfortunately, I never came to it. But Andrew Gelman posted some model by Leonardo Egidi today on his blog:

Leonardo Egidi writes:

Inspired by your world cup model I fitted in Stan a model for the Euro Cup which start today, with two Poisson distributions for the goals scored at every match by the two teams (perfect prediction for the first match!).

The available PDF contains the results and the description for the model. Really interesting and already a perfectly predicted first match! But the model will not fit very well at the semi-finals… Germany losing to Italy? Again? Can’t be!

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Science

Choosing Cut-Offs in Tests

My last blog post was on the difference between Sensitivity, Specificity and the Positive Predictive Value. While showing that a positive test result can represent a low probability of actually having a trait or a disease, this example used the values of Sensitivity and Specificity as pre-known input. For established tests and measures they indeed are often available in literature together with recommended cut-off values.1

In this post, I would like to show how the choice of a cut-off value influences quality criteria such as Sensitivity, Specificity and the like. If you just want a tool to play with, see my Shiny web application here.

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Science

Visualizing Sensitivity and Specificity of a Test

In my university course on Psychological Assessment, I recently explained the different quality criteria of a test used for dichotomous decisions (yes/no, positive/negative, healthy/sick, …). A quite popular example in textbooks is the case of cancer screenings, where an untrained reader might be surprised by the low predictive value of a test. I created a small Shiny app to visualize different scenarios of this example. Read on for an explanation or go directly to the app here.

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Science

“Statistics is in a mess.”