Research Study: Missing Data and VAM-Based Bias
A new Assistant Professor here at ASU, from outside the College of Education but in the College of Mathematical and Natural Sciences also specializes in value-added modeling (and statistics). Her name is Jennifer Broatch, she is a rising star in this area of research, and she just sent me an article I missed, just read, and certainly found worth sharing with you all.
The peer-reviewed article, published in Statistics and Public Policy this past November, is fully cited and linked below so that you all can read it in full. But in terms of its CliffsNotes version, researchers evidenced the following two key findings:
First, researchers found that, “VAMs that include shorter test score histories perform fairly well compared to those with longer score histories.” The current thinking is that we need at least two if not three years of data to yield reliable estimates, or estimates that are consistent over time (which they should be). These authors argue that with three years of data the amount of data that go missing are not worth shooting for that target. Rather, again they argue, this is an issue of trade-offs. This is certainly something to consider, as long as we continue to understand that all of this is about “tinkering towards a utopia” (Tyack & Cuban, 1997) that I’m not at all certain exists in terms of VAMs and VAM-based