Monthly Archives: April 2018

An Important but False Claim about the EVAAS in Ohio

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Just this week in Ohio – a state that continues to contract with SAS Institute Inc. for test-based accountability output from its Education Value-Added Assessment System – SAS’s EVAAS Director, John White, “defended” the use of his model statewide, during which he also claimed before Ohio’s Joint Education Oversight Committee (JEOC) that “poorer schools do no better or worse on student growth than richer schools” when using the EVAAS model.

For the record, this is false. First, about five years ago in Ohio, while the state of Ohio was using the same EVAAS model, Ohio’s The Plain Dealer in conjunction with StateImpact Ohio found that Ohio’s “value-added results show that districts, schools and teachers with large numbers of poor students tend to have lower value-added results than those that serve more-affluent ones.” They also found that:

  • Value-added scores were 2½ times higher on average for districts where the median family income is above $35,000 than for districts with income below that amount.
  • For low-poverty school districts, two-thirds had positive value-added scores — scores indicating students made more than a year’s worth of progress.
  • For high-poverty school districts, two-thirds had negative value-added scores — scores indicating that students made less than a year’s progress.
  • Almost 40 percent of low-poverty schools scored “Above” the state’s value-added target, compared with 20 percent of high-poverty schools.
  • At the same time, 25 percent of high-poverty schools scored “Below” state value-added targets while low-poverty schools were half as likely to score “Below.” See the study here.

Second, about three years ago, similar results were evidenced in Pennsylvania – another state that uses the same EVAAS statewide, although in Pennsylvania the model is known as the Pennsylvania Education Value-Added Assessment System (PVAAS). Research for Action (click here for more about the organization and its mission), more specifically, evidenced that bias also appears to exist particularly at the school-level. See more here.

Third, and related, in Arizona – my state that is also using growth to measure school-level value-added, albeit not with the EVAAS – the same issues with bias are being evidenced when measuring school-level growth for similar purposes. Just two days ago, for example, The Arizona Republic evidenced that the “schools with ‘D’ and ‘F’ letter grades” recently released by the state board of education “were more likely to have high percentages of students eligible for free and reduced-price lunch, an indicator of poverty” (see more here). In actuality, the correlation is as high or “strong” as r = -0.60 (e.g., correlation coefficient values that land between r = ± 0.50 and ± 1.00 are often said to indicate “strong” correlations). What this means in more pragmatic terms is that the better the school letter grade received the lower the level of poverty at the school (i.e., a negative correlation which indicates in this case that as the letter grade goes up the level of poverty goes down).

While the state of Arizona combines with growth a proficiency measure (always strongly correlated with poverty), and this explains at least some of the strength of this correlation (although combining proficiency with growth is also a practice endorsed and encouraged by John White), this strong correlation is certainly at issue.

More specifically at issue, though, should be how to get any such correlation down to zero or near-zero (if possible), which is the only correlation that would, in fact, warrant any such claim, again as noted to the JEOC this week in Ohio, that “poorer schools do no better or worse on student growth than richer schools”.

Identifying Effective Teacher Preparation Programs Using VAMs Does Not Work

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“A New Study [does not] Show Why It’s So Hard to Improve Teacher Preparation” Programs (TPPs). More specifically, it shows why using value-added models (VAMs) to evaluate TPPs, and then ideally improving them using the value-added data derived, is nearly if not entirely impossible.

This is precisely why yet another, perhaps, commonsensical but highly improbable federal policy move to imitate great teacher education programs and shut down ineffective ones, as based on their graduates’ students test-based performance over time (i.e., value-added) continues to fail.

Accordingly, in another, although not-yet peer-reviewed or published study referenced in the article above, titled “How Much Does Teacher Quality Vary Across Teacher Preparation Programs? Reanalyzing Estimates from [Six] States,” authors Paul T. von Hippel, from the University of Texas at Austin, and Laura Bellows, a PhD Student from Duke University, investigated “whether the teacher quality differences between TPPs are large enough to make [such] an accountability system worthwhile” (p. 2). More specifically, using a meta-analysis technique, they reanalyzed the results of such evaluations in six of the approximately 16 states doing this (i.e., in New York, Louisiana, Missouri, Washington, Texas, and Florida), each of which ultimately yielded a peer-reviewed publication, and they found “that teacher quality differences between most TPPs [were] negligible [at approximately] 0-0.04 standard deviations in student test scores” (p. 2).

They also highlight some of the statistical practices that exaggerated the “true” differences noted between TPPs in each of these but also these types of studies in general, and consequently conclude that the “results of TPP evaluations in different states may vary not for substantive reasons, but because of the[se] methodological choices” (p. 5). Likewise, as is the case with value-added research in general, when “[f]aced with the same set of results, some authors may [also] believe they see intriguing differences between TPPs, while others may believe there is not much going on” (p. 6). With that being said, I will not cover these statistical/technical issue more here. Do read the full study for these details, though, as also important.

Related, they found that in every state, the variation that they statistically observed was greater among relatively small TPPs versus large ones. They suggest that this occurs, accordingly, due to estimation or statistical methods that may be inadequate for the task at hand. However, if this is true this also means that because there is relatively less variation observed among large TPPs, it may be much more difficult “to single out a large TPP that is significantly better or worse than average” (p. 30). Accordingly, there are
several ways to mistakenly single out a TPP as exceptional or less than, merely given TPP size. This is obviously problematic.

Nonetheless, the authors also note that before they began this study, in Missouri, Texas, and Washington, that “the differences between TPPs appeared small or negligible” (p. 29), but in Louisiana and New York “they appeared more substantial” (p. 29). After their (re)analyses, however, their found that the results from and across these six different states were “more congruent” (p. 29), as also noted prior (i.e., differences between TPPs around 0 and 0.04 SDs in student test scores).

“In short,” they conclude, that “TPP evaluations may have some policy value, but the value is more modest than was originally envisioned. [Likewise, it] is probably not meaningful to rank all the TPPs in a state; the true differences between most TPPs are too small to matter, and the estimated differences consist mostly of noise” (p. 29). As per the article cited prior, they added that “It appears that differences between [programs] are rarely detectable, and that if they could be detected they would usually be too small to support effective policy decisions.”

To see a study similar to this, that colleagues and I conducted in Arizona, and that was recently published in Teaching Education, see “An Elusive Policy Imperative: Data and Methodological Challenges When Using Growth in Student Achievement to Evaluate Teacher Education Programs’ ‘Value-Added” summarized and referenced here.