# SPSS resources at the Cross Validated tag-wiki

In both my work and personal projects I frequently use the statistical program SPSS to conduct data management, statistical analysis, and make statistical graphics. Over the years I have collected various resources for the program, and have subsequently compiled a list of them at the SPSS tag-wiki over at the Cross Validated Q/A site.

Instead of having a seperate page of these resources here at my blog, I figured the one at Cross Validated is sufficient. The Cross Validated resource is nice as well in that other people can edit/update it.

If you have some suggestions as to resources I missed feel free to add them in to the tag-wiki, or give me a comment here.

# Some example corrgrams in SPSS base graphics

I was first introduced to corrgrams in this post by Tal Gallil on the Cross Validated site. Corrgrams are visualization examples developed by Michael Friendly used to visualize large correlation matrices. I have developed a few examples using SPSS base graphics to mimic some of the corrgrams Friendly presents, in particular a heat-map and proportional sized dot plot. I’ve posted the syntax to produce these graphics at the SPSS developer forum in this thread.

Some other extensions could be made in base graphics fairly easily, such as the diagonal hashings in the heat-map, but some others would take more thought (such as plotting different graphics in the lower and upper diagonal, or sorting the elements in the matrix by some other criterion). I think this is a good start though, and I particularly like the ability to super-impose the actual correlations as labels on the chart, like how it is done in this example on Cross Validated. It should satisfy both the graph people and the table people! See this other brief article by Michael Friendly and Ernest Kwan (2011) (which is initially in response to Gelman, 2011) and this post by Stephen Few to see what I am talking about.

One of the limitations of these visualizations is that it simply plots the bi-variate correlation. Friendly has one obvious extension in in the corrgram paper when he plots the bi-variate ellipses and loess smoother line. Other potential readings of interest that go beyond correlations may be examining scagnostic characteristics of distributions (Wilkinson & Wills, 2008) or utilizing other metrics that capture non-linear associations, such as the recent MIC statistic proposed in Reshef et al. (2011). All of these are only applicable to bi-variate associations.

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