We just finished Week 3 of our 7 week summer semester here in sunny (finally) NYC, and we are spending more and more time in Practice Research and Evaluation course using SSD for R.
Now that our students know how to navigate SSD for R, build input files, and create basic graphs, we have moved on to teaching the students how to analyze their baseline data. We have taught them how to create one- and two-standard deviation band graphs to help them identify "typical" behavior and to look for outliers. We have shown them how to retrieve a host of descriptive statistics (sample size, mean, trimmed mean, standard deviation, coefficient of variation, quantiles) and produce a box plot depicting the variation in their data.
Today, we began talking about analyzing baseline data visually for trends and then to calculate a regression line to determine if any trend they may see could be statistically significant. One of the classes also began to talk about that niggling little problem of autocorrelation, which needs to be evaluated statistically since there is no way to visually determine if autocorrelation is problematic.
All this, of course, is a precursor to teaching the students how compare their baseline and intervention phases for their final projects.
As we are working through these lessons, we are referring the students to drafted chapters of the manuscript for our upcoming book. We are hoping that student feedback will help us make this book as useful and user-friendly as possible.
For more information on SSD for R, visit our website or email us!
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