This is the first in what will be a mainstay of this blog–a discussion of a recent publication (peer-reviewed or not) that I think more folks should be reading and citing. Today’s article is both technically impressive and substantively important. It has the extremely un-thrilling name “Adding Design Elements to Improve Time Series Designs: No Child Left Behind as an Example of Causal Pattern-Matching,” and it appears in the most recent issue of the Journal for Research on Educational Effectiveness (the journal of the excellent SREE organization) [1].

The methodological purpose of this article is to add “design elements” to the Comparative Interrupted Time Series design (a common quasi-experimental design used to evaluate the causal impact of all manner of district- or state-level policies). The substantive purpose of this article is to identify the causal impact of NCLB on student achievement using NAEP data. While the latter has already been done (see for instance Dee and Jacob), this article strengthens Dee and Jacob’s findings through their design elements analysis.

In essence, what design elements bring to the CITS design for evaluating NCLB is a greater degree of confidence in the causal conclusions. Wong and colleagues, in particular, demonstrate NCLB’s impacts in multiple ways:

• By comparing public and Catholic schools.
• By comparing public and non-Catholic private schools.
• By comparing states with high proficiency bars and low ones.
• By using tests in 4th and 8th grade math and 4th grade reading.
• By using Main NAEP and long-term trend NAEP.
• By comparing changes in mean scores and time-trends.

The substantive findings are as follows:

1. We now have national estimates of the effects of NCLB by 2011.

2. We now know that NCLB affected eighth-grade math, something not statistically confirmed in either Wong, Cook, Barnett, and Jung (2008) or Dee and Jacob (2011) where positive findings were limited to fourth-grade math.

3. We now have consistent but statistically weak evidence of a possible, but distinctly smaller, fourth-grade reading effect.

4. Although it is not clear why NCLB affected achievement, some possibilities are now indicated.

These possibilities include a) consequential accountability, b) higher standards, and c) the combination of the two.

So why do I like this article so much? Well, of course, one reason is because it supports what I believe to be the truth about consequential standards-based accountability–that it has real, meaningfully large impacts on student outcomes [2][3]. But I also think this article is terrific because of its incredibly thoughtful design and execution and its clever use of freely available data. Regardless of one’s views on NCLB, this should be an article for policy researchers to emulate. And that’s why you should read it.

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[1] This article, like many articles I’ll review on this blog, is paywalled. If you want a PDF and don’t have access through your library, send me an email.

[2] See this post for a concise summary of my views on this issue.

[3] Edited to add: I figured it would be controversial to say that I liked an article because it agreed with my priors. Two points. First, I think virtually everyone prefers research that agrees with their priors, so I’m merely being honest; deal with it. Second, as Sherman Dorn points out via Twitter, this is conjunctional–I like it because it’s a very strong analysis AND it agrees with my priors. If it was a shitty analysis that agreed with my priors, I wouldn’t have blogged about it.