Basics: Highway to the Danger Zone: Why median-splitting your continuous data can ruin your results.

Not too long ago, I wrote an article here about advanced procedures for examining interactions in multiple regression. As I described some of the challenges researchers commonly face in trying to examine differences between people in a data set, I argued that when it comes to data analysis, splitting a continuous variable into a dichotomy (i.e. two categories) is kind of a dumb idea (MacCallum et al, 2002). Continue reading

Advanced topics: Plotting Better Interactions using the Johnson-Neyman Technique in Mplus

Today’s tutorial involves picking up a useful new weapon for your data analytic arsenal; one that I’ve used quite a bit over the past year of my graduate training. We’re going to look at a novel way of estimating & graphing interactions in the context of multiple regression (one that even extends to structural equation models), using my increasingly go-to program – Mplus. Note that the tips below have been tested in Mplus versions 6 and 7 effectively. Using these procedures in any earlier version is a total crap shoot — meaning I haven’t verified whether or not they work in version 5 or older — so bear that in mind.

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