Data and Statistics

Actual Growth Measures Make A Big Difference When Measuring Growth

As a frequent critic of how states and districts present and interpret their annual testing results, I am also obliged (and indeed quite happy) to note when there is progress.

Recently, I happened to be browsing through New York City’s presentation of their 2014 testing results, and to my great surprise, on slide number four, I found proficiency rate changes between 2013 and 2014 among students who were in the sample in both years (which they call “matched changes”). As it turns out, last year, for the first time, New York State as a whole began publishing these "matched" year-to-year proficiency rate changes for all schools and districts. This is an excellent policy. As we’ve discussed here many times, NCLB-style proficiency rate changes, which compare overall rates of all students, many of whom are only in the tested sample in one of the years, are usually portrayed as “growth” or “progress.” They are not. They compare different groups of students, and, as we’ll see, this can have a substantial impact on the conclusions one reaches from the data. Limiting the sample to students who were tested in both years, though not perfect, at least permits one to measure actual growth per se, and provides a much better idea of whether students are progressing over time.

This is an encouraging sign that New York State is taking steps to improve the quality and interpretation of their testing data. And, just to prove that no good deed goes unpunished, let’s see what we can learn using the new “matched” data – specifically, by seeing how often the matched (longitudinal) and unmatched (cross-sectional) changes lead to different conclusions about student “growth” in schools.

Sample Size And Volatility In School Accountability Systems

It is generally well-known that sample size has an important effect on measurement and, therefore, incentives in test-based school accountability systems.

Within a given class or school, for example, there may be students who are sick on testing day, or get distracted by a noisy peer, or just have a bad day. Larger samples attenuate the degree to which unusual results among individual students (or classes) can influence results overall. In addition, schools draw their students from a population (e.g., a neighborhood). Even if the characteristics of the neighborhood from which the students come stay relatively stable, the pool of students entering the school (or tested sample) can vary substantially from one year to the next, particularly when that pool is small.

Classes and schools tend to be quite small, and test scores vary far more between- than within-student (i.e., over time). As a result, testing results often exhibit a great deal of nonpersistent variation (Kane and Staiger 2002). In other words, much of the differences in test scores between schools, and over time, is fleeting, and this problem is particularly pronounced in smaller schools. One very simple, though not original, way to illustrate this relationship is to compare the results for smaller and larger schools.

What You Need To Know About Misleading Education Graphs, In Two Graphs

There’s no reason why insisting on proper causal inference can’t be fun.

A weeks ago, ASCD published a policy brief (thanks to Chad Aldeman for flagging it), the purpose of which is to argue that it is “grossly misleading” to make a “direct connection” between nations’ test scores and their economic strength.

On the one hand, it’s implausible to assert that better educated nations aren’t stronger economically. On the other hand, I can certainly respect the argument that test scores are an imperfect, incomplete measure, and the doomsday rhetoric can sometimes get out of control.

In any case, though, the primary piece of evidence put forth in the brief was the eye-catching graph below, which presented trends in NAEP versus those in U.S. GDP and productivity.

Teachers And Education Reform, On A Need To Know Basis

A couple of weeks ago, the website Vox.com published an article entitled, “11 facts about U.S. teachers and schools that put the education reform debate in context." The article, in the wake of the Vergara decision, is supposed to provide readers with the “basic facts” about the current education reform environment, with a particular emphasis on teachers. Most of the 11 facts are based on descriptive statistics.

Vox advertises itself as a source of accessible, essential, summary information -- what you "need to know" -- for people interested in a topic but not necessarily well-versed in it. Right off the bat, let me say that this is an extraordinarily difficult task, and in constructing lists such as this one, there’s no way to please everyone (I’ve read a couple of Vox’s education articles and they were okay).

That said, someone sent me this particular list, and it’s pretty good overall, especially since it does not reflect overt advocacy for given policy positions, as so many of these types of lists do. But I was compelled to comment on it. I want to say that I did this to make some lofty point about the strengths and weaknesses of data and statistics packaged for consumption by the general public. It would, however, be more accurate to say that I started doing it and just couldn't stop. In any case, here’s a little supplemental discussion of each of the 11 items: