Using SSD for R in the classroom: FINAL projects

Well, there's nothing like summer in Washington Heights, but all good things must come to an end, and our summer semester is no exception.  We will not be teaching this class in the fall, but will be resuming in the Spring.  Before we move on to other topics, however, we thought we would share with you some highlights of presentations from student who shared their final projects with the class.

This week, we'd like to give a nod to an outstanding student who told me after his final presentation that he could not have imagined, at the beginning of the summer, understanding terms such as SSD for R, autocorrelation, and conservative dual criteria.  You will see below, however, that Rabbi Shmuel Maybruch did, in fact, not only learn those terms, but was able to apply these concepts to evaluating his own practice with great clarity....

The client that this student wanted to evaluate was Quentin, a young man who was looking to date in order to get married, but Quentin never got very far and he sought help.  The indicator that our student used to assess Quentin's progress over time was a self-anchoring scale measuring his despair with dating.  A score of 1 indicated the least amount of despair while a 10 indicated the most.

Five weeks of baseline data were collected before the intervention was initiated.  The intervention consisted of helping Quentin to be more appreciative of the women he dated by identifying and complimenting them on less-than-superficial traits and helping Quentin to identify the role that physical attraction played in his quest for a life partner.

The graph below shows a basic line graph comparing the baseline to the intervention.

Notice how Shmuel made a line graph labeling not only the phases, but added a mean line for each phase, clearly depicting a drop in means between phases.

When deciding how to evaluate his data further, Shmuel noted a problem with both autocorrelation AND trending in the intervention phase, so he decided to use the CDC (conservative dual criteria) to test statistically for a difference between the phases.

From the output in the Console of RStudio, Shmuel learned that he needed nine data points below both the mean and regression lines to achieve statistical significance, and he had twelve!  Looking good so far....

The students learned, however, that statistical significance is difficult to achieve with small samples, although Shmuel was able to detect this level of change in his project.  Effect sizes, however, are very important in intervention research because they can be indicative of clinical, or practical significance.  Shmuel noted a d-index of 1.822, which indicated nearly a 47% change between phases - a moderate change!

While it looks like the intervention worked well for Quentin, Shmuel shared with us what happened post-intervention....

Quentin and his bride are living happily ever after!

For more information about SSD for R, check out our website.  If you have any questions or comments about using SSD for R in your own practice or teaching using this software, feel free to contact us.