From its beginning, Q-Assessor has utilized the centroid factor extraction method used in PQMethod’s FORTRAN public domain source code. Recent Q-Assessor users have reported the need to use some of PQMethod’s other tools — notably the PCA method. Although Q-Assessor was intended to provide an end-to-end solution where this wouldn’t be necessary, we have realized the value in providing this option.

Now you will find in the “General Configuration” section of your study two links to export your statements and sort data in PQMethod-compatible files. (Note: these links only appear when you actually have something to export.) These links also are on the Statements and Responses pages, respectively.

Clicking these links will bring up a dialog in your browser allowing you to save these files to your local computer. The files will be named thus: SIDxxx.sta and SIDxxx.dat, where xxx is your study’s ID number used by Q-Assessor, and .sta is the PQMethod suffix for the statements file and .dat is the PQMethod suffix for the sorts file. (Note: if you’re using Safari on a Mac, the browser will forceably add a .txt suffix — producing SIDxxx.sta.txt and SIDxxx.dat.txt — in a Microsoftian display of unwanted “helpfulness.” You will have to manually remove the .txt suffix from the file names before PQMethod will be able to use these files.)

Once these files are on your hard drive, drag them to your projects folder for PQMethod. Launch PQMethod and provide the path to the files along with the name SIDxxx — without any suffixes. This will tell PQMethod that it is working on your new project. You should then ignore the first two PQMethod menu options — because you don’t want to overwrite the files that already contain your statements and sort data. Instead, you will proceed directly to option 3 (centroid) or option 4 (PCA) factor extraction. You then perform subsequent steps just as per PQMethod procedures.

We think that for most Q-Assessor users, the basic centroid method and other analytic steps/reports will be quite satisfactory. For those who want to explore/manipulate their data further, though, this new Q-Assessor feature will make life much simpler and allow you to use Q-Assessor for all the study management features that it uniquely delivers while offloading alternative analytic steps to PQMethod.

One other related feature that we’ve added to Q-Assessor is display of the correlation matrix — on the analysis page as well as in the downloadable report. This is the same matrix that the PQMethod report includes, but those Q-Assessor users who want to continue to use Q-Assessor’s analytic features but also need the correlation matrix now can have both.

We hope that these developments make Q-Assessor an even more attractive solution to anyone wanting to do Q efficiently and effectively.