Survey responses for the assessment workshop next week are shown below. Nothing is hyperlinked, since these are just images. I had to do it this way to protect the data, so the individual responses can't be used to identify individuals.
Sunday, October 23, 2011
Tuesday, October 18, 2011
Mapping covariates, Part III
In my spare time (ha-ha) I refined the software I blogged about in the last two posts in order to automate almost everything about sorting out what's connected to what in a data set. Now I can create a folder with a data file, an index of variables, and an options list, drop that folder on a script on the desktop, and a few seconds later have a graph like the one below. This makes it easy to tweak parameters to find a nice picture. One that tells $2^{10}$ words, give or take.
For the graph below, I dug out the results of a semester of course evaluation using the new form I got implemented a year ago. I wrote previously about the odd fact that the summative evaluation of the course in Q12 and Q13 didn't seem to relate much to the learning outcomes. The closest other item in this topology is how enjoyable the students reported the course being.
This graph shows covariances instead of correlations. The latter have the nice property of being normalized to [-1,1], but suffer from the fact that nearly constant items all correlate highly with each other. The program can run either way. The graph below shows the top 30 correlates. The means are shown too.
For the graph below, I dug out the results of a semester of course evaluation using the new form I got implemented a year ago. I wrote previously about the odd fact that the summative evaluation of the course in Q12 and Q13 didn't seem to relate much to the learning outcomes. The closest other item in this topology is how enjoyable the students reported the course being.
This graph shows covariances instead of correlations. The latter have the nice property of being normalized to [-1,1], but suffer from the fact that nearly constant items all correlate highly with each other. The program can run either way. The graph below shows the top 30 correlates. The means are shown too.
Saturday, October 01, 2011
Creating Graphs with Perl and GraphViz
Yesterday I solved one of my data problems, but that just led to another one. I can now filter large correlation tables for (absolute) values above a threshold, but it's still laborious to connect those in a diagram that shows the relationships. So the next step was to look for a program to display graphs. Here, I don't mean bar graphs and pie charts and whatnot, but the mathematical object that was unfortunately also called a graph, which consists of vertices and edges. Or dots and lines connecting them, if you prefer. A map of a subway system is a kind of graph. Here's a very simple one:
With the correlations, I want to see what belongs with what on a graph that is generated automatically from a threshold I assign. In the example above, survey results show a perception that creativity is associated with artistic ability.
Fortunately, other people have already solved this problem, and it's just a matter of putting the machinery in place. I used two pieces of software, GraphViz (thanks to AT&T and the development team) and the perl interface for it (thanks to developer Leon Brocard). Both of these are open source and free to use.
The only other thing I had to do was extract the prompts for each item to correspond to the item codes. Otherwise, you get a nice graph showing that RATE1 connects to RATE2, which doesn't help understand what's going on.
I used the CIRP data with a .5 threshold to get these interesting networks of association. It takes just a few seconds to select the threshold value, run the scripts, and look at the resulting file. The code for generating the graphs from the output of the correlation summaries is here. There are many, many options for displaying the graphs. The gallery of GraphViz images shows off the versatility of the software.
This software does a great job of placing nodes logically so that the edges (the lines) are organized and neat.
Update: Here is a complete output with some cool new modifications.
With the correlations, I want to see what belongs with what on a graph that is generated automatically from a threshold I assign. In the example above, survey results show a perception that creativity is associated with artistic ability.
Fortunately, other people have already solved this problem, and it's just a matter of putting the machinery in place. I used two pieces of software, GraphViz (thanks to AT&T and the development team) and the perl interface for it (thanks to developer Leon Brocard). Both of these are open source and free to use.
The only other thing I had to do was extract the prompts for each item to correspond to the item codes. Otherwise, you get a nice graph showing that RATE1 connects to RATE2, which doesn't help understand what's going on.
I used the CIRP data with a .5 threshold to get these interesting networks of association. It takes just a few seconds to select the threshold value, run the scripts, and look at the resulting file. The code for generating the graphs from the output of the correlation summaries is here. There are many, many options for displaying the graphs. The gallery of GraphViz images shows off the versatility of the software.
This software does a great job of placing nodes logically so that the edges (the lines) are organized and neat.
Update: Here is a complete output with some cool new modifications.
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