Running Power Simulations with the Power of R!

Psychology, Science · · 3 comments

As one of the resident “stats people” (as opposed to “regular people”) in my department, one of the most common questions I get asked is about power analyses. This is partly because more psychology journals are requiring explicit discussions about statistical power. The problem is that if you do studies that require analyses anything more complicated than a t-test or correlations, things can get a little bit hairy. You can do calculations for analyses like regression, but the effect sizes used for these calculations (like Cohen’s f2) are often uncommon, to the point where you might not even be clear if you’re using the correct test or calculating the effect size correctly. And then, as you move up in complexity to analyses like multilevel models, SEM, etc., there isn’t even an analytic solution. That’s where power simulations come in.Continue Reading

What Psychology Can Learn from Machine Learning

Psychology, Science · · 4 comments

Over the past year or two I have been trying to delve into the world of machine learning, to angle myself for a job in data science. (Hire me!) Data science is a pretty broad discipline, and covers everything from basic descriptives and visualizations to complex deep learning algorithms and AI. But a key part of data science is machine learning. As I have gone through this process of understanding machine learning, however, I’ve realized that there are a number of tools and procedures that would be useful in psychology as well. So let me share with you some of the wonders of machine learning!Continue Reading

Normal Distribution

You (Probably) Don’t Need to Test Your Data for Normality

Anyone who analyzes data knows (or should know!) the importance of not violating the assumptions of the tests one runs. And for common tests like t-tests, correlation, ANOVA, and regression, one of the assumptions is that the variables are normally distributed. One method that some people use, then, is a test for normality of the data, such as the Kolmogorov-Smirnov (K-S) test or the Shapiro-Wilk (S-W) test. If the test indicates a deviation from normality, they might try a transformation, or use a more robust statistical test to analyze their data. I’m here to say that this is going to make life hard for yourself. Here’s the summary of this article right up front: If you want to see if normality assumptions are violated, don’t use a normality test.Continue Reading

Hipster Ariel - So Meta

Minding the Meta-Analysis

Psychology, Science · · 2 comments

A little over a week ago, I had the opportunity to go to yet another meeting of the Society for Personality and Social Psychology (SPSP). It’s always a great time, with plenty of very interesting talks and posters! It’s also always a pleasure to travel from the harsh Canadian winter to someplace warm to talk about psychology. Walking around in a t-shirt in February is not a common experience for me.

Perhaps this is just my perception, but over the past few years there seems to be a growing trend toward people doing meta-analyses of the studies they present. I’m sure you know what I’m talking about: they present three studies, and maybe the last one has only a marginal effect, but then they say, “But when you meta-analyze over all three studies, the overall effect is highly significant.” This year I saw at least a couple people do this in their talk, and I’ve seen it before at previous conferences and in other contexts. So I want to talk just a little bit about these informal mini-meta-analyses—to distinguish them from more formal meta-analyses, I’m going to call them “meso-analyses”—and talk about some of the caveats of this technique.Continue Reading

R-index, unbiased, varying num. studies

Evaluating the R-Index and the P-Curve

Psychology, Science · · 4 comments

Several years ago, Uri Simonsohn (along with Leif Nelson and Joe Simmons) introduced the psychology community to the idea of p-hacking, and his related concept for detecting p-hacking, the p-curve. He later demonstrated that this p-curve could be used as an estimate of true effect size in a way that was better at correcting for bias than the common trim-and-fill method.

Now, more recently, Ulrich Schimmack has been making a few waves himself, using his own metric called the R-index, which he has stated is useful as a test of how likely a result is to be replicable. He has also gained some attention for using it as what he refers to as a “doping test”, to identify areas of research—and researchers themselves—that are likely to have used questionable research practices (QRPs) that may have inflated the results. In his paper, he shows that his R-index indicates an increase in QRPs from research in 1960 to research in 2011. He also shows that this metric is able to predict the replicability of studies, by analyzing data from the Reproducibility Project and the Many Labs Project.Continue Reading

Sample size for given CI precision and effect size

The Price of Precision

Back in May, Uri Simonsohn posted an article to his blog about studying effect sizes in the lab, with the general conclusion that the sample sizes needed for a precise enough estimate of effect size make it essentially not feasible for the majority of lab studies. Although I was not at the recent SESP conference, I have been told he discussed this (and more!) there. Felix Sch√∂nbrodt further discussed Simonsohn’s point, noting that reporting the effect size estimates and confidence intervals is still important even if they are wildly imprecise, because they can still be used in a meta-analysis to achieve more precision. I think both of these posts are insightful, and recommend that you read them both. However, both of them use particular examples with a given level of precision or sample size to illustrate their points. I wanted to go a bit more in-depth on how the precision level and effect size changes the sample size needed, using a tool in R that Sch√∂nbrodt pointed out.Continue Reading

Psychology replication

All Effects are Real: Thoughts on Replication

I’ve been watching the recent debate about replication with interest, concern, and not just a little amusement. It seems everyone has their opinion on the matter (leave it to a field of scientists to have twice as many opinions as there are scientists in the field!), and at times the discussion has been quite heated. But as a grad student, it’s been difficult to know whether I should throw my own hat in the ring. With psychology heavyweights like Kahneman and Gilbert voicing their opinions, what room is there for a third-year grad student? But fortunately (or unfortunately), I’ve never been one to know when to keep my opinions to myself, so I want to present my own thoughts on the matter. My perspective is that, even if the issue gets heated at times, this discussion can be fruitful as we learn to navigate a changing discipline.Continue Reading