Various statistical analysis performed over the years with tools such as SPSS, SYSTAT, SAS, Excel, Python,R, etc. While the more interesting /complicated multivariate techniques are incredibly insightful, often simple cross-tabs, and a good design, can be used to find an answer to the problem at hand.
While multiple regression is a great tool for analyzing data, most clients (sadly) have a hard time “getting” it. Over the years of working with corporate executives I have found that regression trees are much easier for non-stat people to understand.
Below is a tree from SYSTAT that was created from data collected by employees in annual employee satisfaction study. Putting the tree together and making it “pretty” helped C-level executives understand what we needed to work on. Please be sure you transform your data before running your analtyics. Employee satisfaction data nearly always has negative skewness.
Employee satisfaction is a fascinating topic. A decent introduction and review can be found in this book:
Here’s a great book to get ideas on conveying data visually
I frequently play around with multivariate techniques like K-means cluster analysis in SPSS. There were some big holes in the SPSS procedure that performs cluster analysis so wrote anSPSS Macro to automate what I wanted to be done. Below is a quick demo of the macro in use. It can really save a bunch of time! A few things mine does differently is:
assigns labels to the segments (these will be changed later but butter than just a 1,2,3)
Computes frequencies on the size of the segments (Why SPSS doesn’t do this automatically is beyond me)
Older online vendor tools and databases would frequently put multi-select questions into one column having a pipe,tab,semicolon or comma delimiter (what was real fun is when they would use a comma for a delimiter in a CSV file).
This can be very problematic in nearly any tool. In this video I demonstrate how easy it can be to move data from one column to many with an SPSS macro.