# 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.

# Mplus Coefficient Cruncher (v 1.4)

Back again with a new Excel tool. This one, which I’ve titled the “Coefficient Cruncher” is a recent development that I’ve been using to write up various sets of results, and it has greatly accelerated my output rate.  One of the most tedious things about writing up results is… well, writing up results. This helps.

The Coefficient Cruncher takes a set of model results and regurgitates the information in two formats. The first format is your basic in-text statistical report, in the style:
(B=[###], SE = [###], p < [.###]).
The second format is a row-mapped table of your results in standard APA style, which you can then edit as necesary. It has made putting together tables of coefficients and talking about findings from Mplus much much much much much much easier and faster.
Notes: The Coefficient Cruncher will report any and all model results in the “B, SE, t, p” fashion (in accordance with Mplus output styling), so be sure to change anything in your output that isn’t actually a B estimate (e.g., the correlations generated in standardized  `WITH` statement outputs).

# Model Fit Aggregator v2.1

(Click here for information on the older version 1.2)

The Model Fit Aggregator is a tool I designed for use with Mplus model output. It compiles the results of goodness of fit tests and returns them to the user in easy-to-use APA-style for reporting in manuscripts, talks, posters, etc. It will also compare changes in goodness-of-fit across two nested models.  Continue reading

# Disentangling degrees of freedom for SEM

As you sally forth into the land of structural equation modeling (SEM), you’ll come across terms like identification, and ideas like degrees of freedom (df) for a chi-square goodness of fit test. For many students, df is one of the more puzzling aspects of SEM. Sometimes it isn’t entirely clear where those degrees of freedom come from or why they have the values that they do. Continue reading