Using SSD for R: Comparing the baseline to the intervention

With the summer semester beginning to wind down, we have reached what our students consider to the the most interesting part of the course - learning how to figure out whether the interventions they were doing with their clients are making a difference.

While we continued with some visual analysis, the students quickly learned why autocorrelated or trending data could not be analyzed as simply as one might think!  Therefore, we taught the students to use the ABbinomial() or ABttest() functions if neither phase has a trend or an issue with autocorrelation.  We also taught the students to use one of the chi-square functions if there was a trend in any phase and to use the critical dual criteria (CDC) if either has an issue of autocorrelation.

One thing that the students really liked was interpreting the statistical output with support from visual output.  

For example, in this example, we are comparing a client's level of enjoyment in the baseline to the level of enjoyment in the intervention.  The regabove() function informed us that there was a statistically significant improvement between phases with 40% of the baseline data being successful and 100% of the intervention data being successful.  This is really easy to visualize when you actually SEE that 2 out of the 5 baseline points are above the regression line in the baseline, while all the points are above it in the intervention phase.

Check out THIS cool output!

This really helps the student understand the notion of continually comparing the intervention to the baseline as they can visualize the baseline regression line being extended into the intervention.  

We only have three more classes to go, so we will be sure to keep you posted on how these final projects are shaping up!

For more information on SSD for R, please visit our website. As always, feel free to email us!