The week after the publication of Yun’s review, the report’s authors fired back on
the Mackinac Center’s blog. In the response below, Yun addresses that
blog item, focusing on the authors’ contention that their model has the
ability to accurately and reliably rank schools.
**
A Response to “Critique of CAP Report Card Fires Blanks”
By John T. Yun
Ben
DeGrow and Michael Van Beek have published a response to my NEPC review
of the Mackinac Center’s latest “Context and Performance [CAP] Report
Card.” In their response, they take issue with a several points made in
the review. I will not address their points one by one, since many of
them are minor and rest in the realm of reader interpretation. However,
one main point that I would like to address lies at the very heart of
the critique: the ability of their model to accurately and reliably rank schools using their CAP Score metric.
Putting
aside all the measurement issues with the CAP Index that are
highlighted in my review, consider the authors’ claim that they made a
choice for “simplicity” by using percent of free-lunch eligible 11thgrade
students as the sole predictor of a school’s predicted CAP Index.
Consider also the authors’ claim that in previous versions of the report
card, “When testing the impact of adding these other variables, we
found that ‘the improvements in predictive power [of the model] are
marginal, and including the additional variables would have only
increased the model’s complexity for little measurable gain.’”
In this statement, the authors are clearly conflating model fit with the reliability of a model’s predictions.
Given the key variables that were available and not included
(urbanicity, school size, racial composition, per/pupil expenditures,
percent special education students, percent English language learners,
availability of advanced courses, etc.) it is likely that model fit
would have been significantly improved, but more importantly the
inclusion of different variables would likely have yielded different
predicted scores for many of the schools.
For
example, under the Mackinac Center’s model, a small rural school with
low minority and high special education enrollments that had the same
percentage of free-lunch eligible students as a large, urban school with
high minority enrollments would receive the same predicted score on the
CAP Index. This would not happen
if those additional variables were included in the model. The result of
that new model would likely be a very different CAP Score for these
specific schools—even if the overall model was only marginally more
predictive. In addition, depending on the specific variables used (or
the specification of the model) the predicted scores are likely to
change from model to model. Thus, the school rankings are likely to
shift from model to model as well leading to very unreliable rankings at
the school level. My review’s critique, therefore, covered the specific
Mackinac model in addition to the usefulness of using any available
model to generate these sorts of school-level rankings.
In
their response, the authors of the Mackinac Center report seem to
suggest that simply acknowledging the limitations of their approach and
appealing to simplicity justifies the publication of their ranked
results. My position (and the position of most academic researchers) is
that the limitations of data limit the use to which you can put those
data. Given that the authors of the report—and previous reports—do not
demonstrate that their rankings are at all robust to different model
specifications, and given that they themselves recognize the serious
limitations in the data that they use, it should be very clear that
their approach for ranking schools in this very precise manner (e.g.,
School X scores 98.0 and is therefore ranked higher than School Y at
97.9) is simply outside the ability of the methods and the data that
they are using. This is the bottom line: the data and analytic approach
used by the Center do not warrant the claim that the schools can be
ranked reliably and precisely enough to publish them in this way.
If
the Mackinac authors wanted to appeal to simplicity, a conclusion that
would in fact be supported by this simple approach is that the share of
free-lunch eligible students powerfully predicts their CAP Index of
Michigan test scores, and the higher the percentage of students on free
lunch, the lower their predicted CAP Index. This conclusion is
consistent with a large body of prior research that argues student
poverty predicts performance on standardized test scores. But any
attempt to then extend these findings to tell us more about the relative
performance of specific schools is unwarranted and misleading.
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