July 6, 2004

More on student net flows and dropping out

Sometimes it's hard to sit on the sidelines during a public-policy debate when an ongoing research project is relevant. I've seen that twice this year, first as arguments developed in Massachusetts over whether student dropout rates had increased after the creation of a graduation test and more recently when the Florida Department of Education gave the St. Petersburg Times erroneous figures on the ages of students taking the GED tests. From the first figures produced by Florida's government, the Times wrote an article implying that the new graduation test had pushed a large number of teenagers to drop out of school and take the GED instead.

I've said nothing other than to tell a few people about my project and say, "I'm pretty confident, but it's still in development." And sometimes I'm quite happy not to have overpromised things. Over the weekend, I discovered a notational error in the working paper I previously posted and a substantive error in something I sent a colleague, the latter a modification of that paper to adjust for mid-year promotions (or, rather, promotions between the end points of enrollment-count intervals).

This adjustment is important because students move into a new grade at the beginning of a school year, which can range from early August to September, depending on the state and district. But the fall enrollment data sent to the U.S. Department of Education is from October. I'm assuming that the absolute net-migrant count is evenly distributed over a year, and then all that's necessary is to provide a scaling factor and an additional term in one equation. I'll put something up sometime in the next week or so to fix the notational error and add the adjustment factor.

I also have been thinking about one bit of advice I received in May, about the stability of these estimates. Can I come up with maximum likelihood estimations of the net flows and then look at key figures (the diagonals in the information matrix, if anyone's interested)? The colleague I needed to apologize to about the substantive error volunteered to see if he could help me with that. But can I also use some Monte Carlo or bootstrap procedures? The key thing is to think about where an estimate might be wrong. There might be some errors in the assigned grade level or some missing students (or those who have dropped out but are still on the rolls). I suspect the biggest source of potential error is in the retention rate, so that should get the closest attention.

References


Ron Matus. 2004. State: FCAT may fuel big GED numbers. St. Petersburg Times, June 14, 2004. Retrieved July 6, 2004, from http://www.sptimes.com/2004/06/14/news_pf/State/FCAT_may_fuel_big_GED.shtml.

From FCAT to GED [editorial]. St. Petersburg Times, June 18, 2004. Retrieved July 6, 2004, from http://www.sptimes.com/2004/06/18/news_pf/Opinion/From_FCAT_to_GED.shtml

GED article based on inaccurate state statistics. St. Petersburg Times, June 28, 2004. Retrieved July 6, 2004, from http://www.sptimes.com/2004/06/28/State/Article_on_GED_based_.shtml

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Posted in Research on July 6, 2004 6:35 PM |