I blog about statistics and research design with an audience consisting of researchers in bilingualism, multilingualism, and applied linguistics in mind.

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What data patterns can lie behind a correlation coefficient?

21 November 2016

In this post, I want to, first, help you to improve your intuition of what data patterns correlation coefficients can represent and, second, hammer home the point that to sensibly interpret a correlation coefficient, you need the corresponding scatterplot.

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Common-language effect sizes

16 November 2016

The goal of this blog post is to share with you a simple R function that may help you to better communicate the extent to which two groups differ and overlap by computing common-language effect sizes.

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The Centre for Open Science's Preregistration Challenge: Why it's relevant and some recommended background reading

31 October 2016

This blog post is an edited version of a mail I sent round to my colleagues at the various language and linguistics departments in Fribourg. Nothing in this post is new per se, but I haven’t seen much discussion of these issues among linguists, applied linguists and bilingualism researchers.

I’d like to point you to an initiative of the Center for Open Science: the $1,000,000 Preregistration Challenge. The basic idea is to foster research transparency by offering a monetary reward to researchers who’ve outlined their study design and planned analyses in advance and report the results of these analyses in the report.

I’m not affiliated with this organisation, but I do think both it and its initiative are important developments. For those interested in knowing why I think so, I’ve written a brief text below that includes links to more detailed articles or examples; if you prefer reference lists, there’s one of those down below. Most of articles were written by and for psychologists, but I reckon pretty much all of it applies equally to research in linguistics and language learning.

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Tutorial: Drawing a dot plot

30 August 2016

In the fourth tutorial on drawing useful plots with ggplot2, we’re taking a closer look at dot plots – a useful and more flexible alternative to bar and pie charts.

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R tip: Ordering factor levels more easily

18 August 2016

By default, R sorts the levels of a factor alphabetically. When drawing graphs, this results in ‘Alabama First’ graphs, and it’s usually better to sort the elements of a graph by more meaningful principles than alphabetical order. This post illustrates three convenience functions you can use to sort factor levels in R according to another covariate, their frequency of occurrence, or manually.

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Classifying second-language learners as native- or non-nativelike: Don't neglect classification error rates

5 July 2016

I’d promised to write another installment on drawing graphs, but instead I’m going to write about something that I had to exclude, for reasons of space, from a recently published book chapter on age effects in second language (L2) acquisition: classifying observations (e.g., L2 learners) and estimating error rates.

I’m going to illustrate the usefulness of classification algorithms for addressing some problems in L2 acquisition research, but my broader aim is to show that there’s more to statistics than running significance tests and to encourage you to explore—even if superficially—what else is out there.

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Tutorial: Drawing a boxplot

21 June 2016

In the two previous blog posts, you learnt to draw simple but informative scatterplots and line charts. This time, you’ll learn how to draw boxplots.

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Tutorial: Drawing a line chart

13 June 2016

Graphs are incredibly useful both for understanding your own data and for communicating your insights to your audience. This is why the next few blog posts will consist of tutorials on how to draw four kinds of graphs that I find most useful: scatterplots, line charts, boxplots and some variations, and Cleveland dotplots. These tutorials are aimed primarily at the students in our MA programme. Today’s graph: the line chart.

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Tutorial: Drawing a scatterplot

2 June 2016

Graphs are incredibly useful both for understanding your own data and for communicating your insights to your audience. This is why the next few blog posts will consist of tutorials on how to draw four kinds of graphs that I find most useful: scatterplots, linecharts, boxplots and some variations, and Cleveland dotplots. These tutorials are aimed primarily at the students in our MA programme. Today’s graph: the scatterplot.

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Surviving the ANOVA onslaught

18 May 2016

At a workshop last week, we were asked to bring along examples of good and bad academic writing. Several of the bad examples were papers where the number of significance tests was so large that the workshop participants felt that they couldn’t make sense of the Results section. It’s not that they didn’t understand each test separately but rather that they couldn’t see the forest for the trees. I, too, wish researchers would stop inundating their readers with t, F and p-values (especially in the running text), but until such time readers need to learn how to survive the ANOVA onslaught. Below I present a list of guidelines to help them with that.

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