Welcome to another stupidly-long, but hopefully informative instructional on introductory statistical concepts. Today we tackle regression analysis. Use the menu links below to jump around if you need/want to get a quick bit of info on any topic:
I recently set to the task of training undergraduate students in some of the basic data management & analysis procedures available in SPSS. This post consists of the primer that I’ve used to give my research assistants a footing in working with data sets in SPSS, and specifically, how to begin working with the SPSS programming language to create scales, instead of using point-and-click commands to do your work. Up and coming students might find this material useful, as may those of you who are training up new students too. Feel free to pass it along.
“Hello syntax, my old friend.”
Starting new syntax: To open a new syntax editor window, click FILE –> NEW –> SYNTAX. This will open up a blank syntax window that you can begin to write into. The left hand side shows a short list of the major commands in your syntax program. It will auto-update as you write new statements and commands in the main window on the right hand side. You can save syntax files to your hard drive as .sps files. Also note that you can also view the contents of any .sps file using a plain text program like Notepad (Windows) or TextEdit (Mac). This is handy when you just want to see what’s in a syntax file, but don’t have access to SPSS at the moment.
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 →
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.
Every field has its landmark papers. You know – the real game changers. In our field, these are often synonymous with those papers that make their way inexorably into almost every single social psychology lecture across the globe (e.g., Milgram, 1963). The papers I’ve listed here are perhaps not quite in that league [yet!]. However, they are papers that I’ve come across in my travels through grad school, to which I’ve found myself returning again and again. Continue reading →
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).
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 →