Tag Archives: High Stakes

New Mexico’s Motion for Summary Judgment, Following Houston’s Precedent-Setting Ruling

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Recall that in New Mexico, just over two years ago, all consequences attached to teacher-level value-added model (VAM) scores (e.g., flagging the files of teachers with low VAM scores) were suspended throughout the state until the state (and/or others external to the state) could prove to the state court that the system was reliable, valid, fair, uniform, and the like. The trial during which this evidence was to be presented by the state was repeatedly postponed since, yet with teacher-level consequences prohibited all the while. See more information about this ruling here.

Recall as well that in Houston, just this past May, that a district judge ruled that Houston Independent School District (HISD) teachers’ who had VAM scores (as based on the Education Value-Added Assessment System (EVAAS)) had legitimate claims regarding how EVAAS use in HISD was a violation of their Fourteenth Amendment due process protections (i.e., no state or in this case organization shall deprive any person of life, liberty, or property, without due process). More specifically, in what turned out to be a huge and unprecedented victory, the judge ruled that because HISD teachers “ha[d] no meaningful way to ensure correct calculation of their EVAAS scores,” they were, as a result, “unfairly subject to mistaken deprivation of constitutionally protected property interests in their jobs.” This ruling ultimately led the district to end the use of the EVAAS for teacher termination throughout Houston. See more information about this ruling here.

Just this past week, New Mexico charged that the Houston ruling regarding Houston teachers’ Fourteenth Amendment due process protections also applies to teachers throughout the state of New Mexico.

As per an article titled “Motion For Summary Judgment Filed In New Mexico Teacher Evaluation Lawsuit,” the American Federation of Teachers and Albuquerque Teachers Federation filed a “motion for summary judgment in the litigation in our continuing effort to make teacher evaluations beneficial and accurate in New Mexico.” They, too, are “seeking a determination that the [state’s] failure to provide teachers with adequate information about the calculation of their VAM scores violated their procedural due process rights.”

“The evidence demonstrates that neither school administrators nor educators have been provided with sufficient information to replicate the [New Mexico] VAM score calculations used as a basis for teacher evaluations. The VAM algorithm is complex, and the general overview provided in the NMTeach Technical Guide is not enough to pass constitutional muster. During previous hearings, educators testified they do not receive an explanation at the time they receive their annual evaluation, and teachers have been subjected to performance growth plans based on low VAM scores, without being given any guidance or explanation as to how to raise that score on future evaluations. Thus, not only do educators not understand the algorithm used to derive the VAM score that is now part of the basis for their overall evaluation rating, but school administrators within the districts do not have sufficient information on how the score is derived in order to replicate it or to provide professional development, whether as part of a disciplinary scenario or otherwise, to assist teachers in raising their VAM score.”

For more information about this update, please click here.

Bias in VAMs, According to Validity Expert Michael T. Kane

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During the still ongoing, value-added lawsuit in New Mexico (see my most recent update about this case here), I was honored to testify as the expert witness on behalf of the plaintiffs (see, for example, here). I was also fortunate to witness the testimony of the expert witness who testified on behalf of the defendants – Thomas Kane, Economics Professor at Harvard and former Director of the Bill & Melinda Gates Foundation’s Measures of Effective Teaching (MET) studies. During Kane’s testimony, one of the highlights (i.e., for the plaintiffs), or rather the low-lights (i.e., for him and the defendants), in my opinion, was when one of the plaintiff’s attorney’s questioned Kane, on the stand, about his expertise in the area of validity. In sum, Kane responded that he defined himself as an “expert” in the area, having also been trained by some of the best. Consequently, the plaintiff’s attorney’s questioned Kane about different types of validity evidences (e.g., construct, content, criterion), and Kane could not answer those questions. The only form of validity evidence with which he was familiar, and which he could clearly define, was evidence related to predictive validity. This hardly made him the expert he proclaimed himself to be minutes prior.

Let’s not mince words, though, or in this case names.

A real expert in validity (and validity theory) is another Kane, who goes by the full name of Michael T. Kane. This Kane is The Samuel J. Messick Chair in Test Validity at the Educational Testing Service (ETS); this Kane wrote one of the best, most contemporary, and currently most foundational papers on validity (see here); and this Kane just released an ETS-sponsored paper on Measurement Error and Bias in Value-Added Models certainly of interest here. I summarize this piece below (see the PDF of this report here).

In this paper Kane examines “the origins of [value-added model (VAM)-based] bias and its potential impact” and indicates that bias that is observed “is an increasing linear function of the student’s prior achievement and can be quite large (e.g., half a true-score standard deviation) for very low-scoring and high-scoring students [i.e., students in the extremes of any normal distribution]” (p. 1). Hence, Kane argues, “[t]o the extent that students with relatively low or high prior scores are clustered in particular classes and schools, the student-level bias will tend to generate bias in VAM estimates of teacher and school effects” (p. 1; see also prior posts about this type of bias here, here, and here; see also Haertel (2013) cited below). Kane concludes that “[a]djusting for this bias is possible, but it requires estimates of generalizability (or reliability) coefficients that are more accurate and precise than those that are generally available for standardized achievement tests” (p. 1; see also prior posts about issues with reliability across VAMs here, here, and here).

Kane’s more specific points of note:

  • To accurately calculate teachers’/schools’ value-added, “current and prior scores have to be on the same scale (or on vertically aligned scales) for the differences to make sense. Furthermore, the scale has to be an interval scale in the sense that a difference of a certain number of points has, at least approximately, the same meaning along the scale, so that it makes sense to compare gain scores from different parts of the scale…some uncertainty about scale characteristics is not a problem for many applications of vertical scaling, but it is a serious problem if the proposed use of the scores (e.g., educational accountability based on growth scores) demands that the vertical scale be demonstrably equal interval” (p. 1).
  • Likewise, while some approaches can be used to minimize the need for such scales (e.g., residual gain scores, covariate-adjustment models, and ordinary least squares (OLS) regression approaches which are of specific interest in this piece), “it is still necessary to assume [emphasis added] that a difference of a certain number of points has more or less the same meaning along the score scale for the current test scores” (p. 2).
  • Related, “such adjustments can [still] be biased to the extent that the predicted score does not include all factors that may have an impact on student performance. Bias can also result from errors of measurement in the prior scores included in the prediction equation…[and this can be]…substantial” (p. 2).
  • Accordingly, “gains for students with high true scores on the prior year’s test will be overestimated, and the gains for students with low true scores in the prior year will be underestimated. To the extent that students with relatively low and high true scores tend to be clustered in particular classes and schools, the student-level bias will generate bias in estimates of teacher and school effects” (p. 2).
  • Hence, if not corrected, this source of bias could have a substantial negative impact on estimated VAM scores for teachers and schools that serve students with low prior true scores and could have a substantial positive impact for teachers and schools that serve mainly high-performing students” (p. 2).
  • Put differently, random errors in students’ prior scores may “tend to add a positive bias to the residual gain scores for students with prior scores above the population mean, and they [may] tend to add a negative bias to the residual gain scores for students with prior scores below the mean. Th[is] bias is associated with the well-known phenomenon of regression to the mean” (p. 10).
  • Although, at least this latter claim — that students with relatively high true scores in the prior year could substantially and positively impact their teachers’/schools value-added estimates — does run somewhat contradictory to other claims as evidenced in the literature in terms of the extent to which ceiling effects substantially and negatively impact their teachers’/schools value-added estimates (see, for example, Point #7 as per the ongoing lawsuit in Houston here, and see also Florida teacher Luke Flint’s “Story” here).
  • In sum, and as should be a familiar conclusion to followers of this blog, “[g]iven that the results of VAMs may be used for high-stakes decisions about teachers and schools in the context of accountability programs,…any substantial source of bias would be a matter of great concern” (p. 2).

Citation: Kane, M. T. (2017). Measurement error and bias in value-added models. Princeton, NJ: Educational Testing Service (ETS) Research Report Series. doi:10.1002/ets2.12153 Retrieved from http://onlinelibrary.wiley.com/doi/10.1002/ets2.12153/full

See also Haertel, E. H. (2013). Reliability and validity of inferences about teachers based on student test scores (14th William H. Angoff Memorial Lecture). Princeton, NJ: Educational Testing Service (ETS).

A North Carolina Teacher’s Guest Post on His/Her EVAAS Scores

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A teacher from the state of North Carolina recently emailed me for my advice regarding how to help him/her read and understand his/her recently received Education Value-Added Assessment System (EVAAS) value added scores. You likely recall that the EVAAS is the model I cover most on this blog, also in that this is the system I have researched the most, as well as the proprietary system adopted by multiple states (e.g., Ohio, North Carolina, and South Carolina) and districts across the country for which taxpayers continue to pay big $. Of late, this is also the value-added model (VAM) of sole interest in the recent lawsuit that teachers won in Houston (see here).

You might also recall that the EVAAS is the system developed by the now late William Sanders (see here), who ultimately sold it to SAS Institute Inc. that now holds all rights to the VAM (see also prior posts about the EVAAS here, here, here, here, here, and here). It is also important to note, because this teacher teaches in North Carolina where SAS Institute Inc. is located and where its CEO James Goodnight is considered the richest man in the state, that as a major Grand Old Party (GOP) donor “he” helps to set all of of the state’s education policy as the state is also dominated by Republicans. All of this also means that it is unlikely EVAAS will go anywhere unless there is honest and open dialogue about the shortcomings of the data.

Hence, the attempt here is to begin at least some honest and open dialogue herein. Accordingly, here is what this teacher wrote in response to my request that (s)he write a guest post:

***

SAS Institute Inc. claims that the EVAAS enables teachers to “modify curriculum, student support and instructional strategies to address the needs of all students.”  My goal this year is to see whether these claims are actually possible or true. I’d like to dig deep into the data made available to me — for which my state pays over $3.6 million per year — in an effort to see what these data say about my instruction, accordingly.

For starters, here is what my EVAAS-based growth looks like over the past three years:

As you can see, three years ago I met my expected growth, but my growth measure was slightly below zero. The year after that I knocked it out of the park. This past year I was right in the middle of my prior two years of results. Notice the volatility [aka an issue with VAM-based reliability, or consistency, or a lack thereof; see, for example, here].

Notwithstanding, SAS Institute Inc. makes the following recommendations in terms of how I should approach my data:

Reflecting on Your Teaching Practice: Learn to use your Teacher reports to reflect on the effectiveness of your instructional delivery.

The Teacher Value Added report displays value-added data across multiple years for the same subject and grade or course. As you review the report, you’ll want to ask these questions:

  • Looking at the Growth Index for the most recent year, were you effective at helping students to meet or exceed the Growth Standard?
  • If you have multiple years of data, are the Growth Index values consistent across years? Is there a positive or negative trend?
  • If there is a trend, what factors might have contributed to that trend?
  • Based on this information, what strategies and instructional practices will you replicate in the current school year? What strategies and instructional practices will you change or refine to increase your success in helping students make academic growth?

Yet my growth index values are not consistent across years, as also noted above. Rather, my “trends” are baffling to me.  When I compare those three instructional years in my mind, nothing stands out to me in terms of differences in instructional strategies that would explain the fluctuations in growth measures, either.

So let’s take a closer look at my data for last year (i.e., 2016-2017).  I teach 7th grade English/language arts (ELA), so my numbers are based on my students reading grade 7 scores in the table below.

What jumps out for me here is the contradiction in “my” data for achievement Levels 3 and 4 (achievement levels start at Level 1 and top out at Level 5, whereas levels 3 and 4 are considered proficient/middle of the road).  There is moderate evidence that my grade 7 students who scored a Level 4 on the state reading test exceeded the Growth Standard.  But there is also moderate evidence that my same grade 7 students who scored Level 3 did not meet the Growth Standard.  At the same time, the number of students I had demonstrating proficiency on the same reading test (by scoring at least a 3) increased from 71% in 2015-2016 (when I exceeded expected growth) to 76% in school year 2016-2017 (when my growth declined significantly). This makes no sense, right?

Hence, and after considering my data above, the question I’m left with is actually really important:  Are the instructional strategies I’m using for my students whose achievement levels are in the middle working, or are they not?

I’d love to hear from other teachers on their interpretations of these data.  A tool that costs taxpayers this much money and impacts teacher evaluations in so many states should live up to its claims of being useful for informing our teaching.

More of Kane’s “Objective” Insights on Teacher Evaluation Measures

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You might recall from a series of prior posts (see, for example, here, here, and here), the name of Thomas Kane — an economics professor from Harvard University who directed the $45 million worth of Measures of Effective Teaching (MET) studies for the Bill & Melinda Gates Foundation, who also testified as an expert witness in two lawsuits (i.e., in New Mexico and Houston) opposite me (and in the case of Houston, also opposite Jesse Rothstein).

He, along with Andrew Bacher-Hicks (PhD Candidate at Harvard), Mark Chin (PhD Candidate at Harvard), and Douglas Staiger (Economics Professor of Dartmouth), just released yet another National Bureau of Economic Research (NBER) “working paper” (i.e., not peer-reviewed, and in this case not internally reviewed by NBER for public consumption and use either) titled “An Evaluation of Bias in Three Measures of Teacher Quality: Value-Added, Classroom Observations, and Student Surveys.” I review this study here.

Using Kane’s MET data, they test whether 66 mathematics teachers’ performance measured (1) by using teachers’ student test achievement gains (i.e., calculated using value-added models (VAMs)), classroom observations, and student surveys, and (2) under naturally occurring (i.e., non-experimental) settings “predicts performance following random assignment of that teacher to a class of students” (p. 2). More specifically, researchers “observed a sample of fourth- and fifth-grade mathematics teachers and collected [these] measures…[under normal conditions, and then in]…the third year…randomly assigned participating teachers to classrooms within their schools and then again collected all three measures” (p. 3).

They concluded that “the test-based value-added measure—is a valid predictor of teacher impacts on student achievement following random assignment” (p. 28). This finding “is the latest in a series of studies” (p. 27) substantiating this not-surprising, as-oft-Kane-asserted finding, or as he might assert it, fact. I should note here that no other studies substantiating “the latest in a series of studies” (p. 27) claim are referenced or cited, but a quick review of the 31 total references included in this report include 16/31 (52%) references conducted by only econometricians (i.e., not statisticians or other educational researchers) on this general topic, of which 10/16 (63%) are not peer reviewed and of which 6/16 (38%) are either authored or co-authored by Kane (1/6 being published in a peer-reviewed journal). The other articles cited are about the measurements used, the geenral methods used in this study, and four other articles written on the topic not authored by econometricians. Needless to say, there is clearly a slant that is quite obvious in this piece, and unfortunately not surprising, but that had it gone through any respectable vetting process, this sh/would have been caught and addressed prior to this study’s release.

I must add that this reminds me of Kane’s New Mexico testimony (see here) where he, again, “stressed that numerous studies [emphasis added] show[ed] that teachers [also] make a big impact on student success.” He stated this on the stand while expressly contradicting the findings of the American Statistical Association (ASA). While testifying otherwise, and again, he also only referenced (non-representative) studies in his (or rather defendants’ support) authored by primarily him (e.g, as per his MET studies) and some of his other econometric friends (e.g. Raj Chetty, Eric Hanushek, Doug Staiger) as also cited within this piece here. This was also a concern registered by the court, in terms of whether Kane’s expertise was that of a generalist (i.e., competent across multi-disciplinary studies conducted on the matter) or a “selectivist” (i.e., biased in terms of his prejudice against, or rather selectivity of certain studies for confirmation, inclusion, or acknowledgment). This is also certainly relevant, and should be taken into consideration here.

Otherwise, in this study the authors also found that the Mathematical Quality of Instruction (MQI) observational measure (one of two observational measures they used in this study, with the other one being the Classroom Assessment Scoring System (CLASS)) was a valid predictor of teachers’ classroom observations following random assignment. The MQI also, did “not seem to be biased by the unmeasured characteristics of students [a] teacher typically teaches” (p. 28). This also expressly contradicts what is now an emerging set of studies evidencing the contrary, also not cited in this particular piece (see, for example, here, here, and here), some of which were also conducted using Kane’s MET data (see, for example, here and here).

Finally, authors’ evidence on the predictive validity of student surveys was inconclusive.

Needless to say…

Citation: Bacher-Hicks, A., Chin, M. J., Kane, T. J., & Staiger, D. O. (2017). An evaluation of bias in three measures of teacher quality: Value-added, classroom observations, and student surveys. Cambridge, MA: ational Bureau of Economic Research (NBER). Retrieved from http://www.nber.org/papers/w23478

The More Weight VAMs Carry, the More Teacher Effects (Will Appear to) Vary

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Matthew A. Kraft — an Assistant Professor of Education & Economics at Brown University and co-author of an article published in Educational Researcher on “Revisiting The Widget Effect” (here), and another of his co-authors Matthew P. Steinberg — an Assistant Professor of Education Policy at the University of Pennsylvania — just published another article in this same journal on “The Sensitivity of Teacher Performance Ratings to the Design of Teacher Evaluation Systems” (see the full and freely accessible, at least for now, article here; see also its original and what should be enduring version here).

In this article, Steinberg and Kraft (2017) examine teacher performance measure weights while conducting multiple simulations of data taken from the Bill & Melinda Gates Measures of Effective Teaching (MET) studies. They conclude that “performance measure weights and ratings” surrounding teachers’ value-added, observational measures, and student survey indicators play “critical roles” when “determining teachers’ summative evaluation ratings and the distribution of teacher proficiency rates.” In other words, the weighting of teacher evaluation systems’ multiple measures matter, matter differently for different types of teachers within and across school districts and states, and matter also in that so often these weights are arbitrarily and politically defined and set.

Indeed, because “state and local policymakers have almost no empirically based evidence [emphasis added, although I would write “no empirically based evidence”] to inform their decision process about how to combine scores across multiple performance measures…decisions about [such] weights…are often made through a somewhat arbitrary and iterative process, one that is shaped by political considerations in place of empirical evidence” (Steinberg & Kraft, 2017, p. 379).

This is very important to note in that the consequences attached to these measures, also given the arbitrary and political constructions they represent, can be both professionally and personally, career and life changing, respectively. How and to what extent “the proportion of teachers deemed professionally proficient changes under different weighting and ratings thresholds schemes” (p. 379), then, clearly matters.

While Steinberg and Kraft (2017) have other key findings they also present throughout this piece, their most important finding, in my opinion, is that, again, “teacher proficiency rates change substantially as the weights assigned to teacher performance measures change” (p. 387). Moreover, the more weight assigned to measures with higher relative means (e.g., observational or student survey measures), the greater the rate by which teachers are rated effective or proficient, and vice versa (i.e., the more weight assigned to teachers’ value-added, the higher the rate by which teachers will be rated ineffective or inadequate; as also discussed on p. 388).

Put differently, “teacher proficiency rates are lowest across all [district and state] systems when norm-referenced teacher performance measures, such as VAMs [i.e., with scores that are normalized in line with bell curves, with a mean or average centered around the middle of the normal distributions], are given greater relative weight” (p. 389).

This becomes problematic when states or districts then use these weighted systems (again, weighted in arbitrary and political ways) to illustrate, often to the public, that their new-and-improved teacher evaluation systems, as inspired by the MET studies mentioned prior, are now “better” at differentiating between “good and bad” teachers. Thereafter, some states over others are then celebrated (e.g., by the National Center of Teacher Quality; see, for example, here) for taking the evaluation of teacher effects more seriously than others when, as evidenced herein, this is (unfortunately) more due to manipulation than true changes in these systems. Accordingly, the fact remains that the more weight VAMs carry, the more teacher effects (will appear to) vary. It’s not necessarily that they vary in reality, but the manipulation of the weights on the back end, rather, cause such variation and then lead to, quite literally, such delusions of grandeur in these regards (see also here).

At a more pragmatic level, this also suggests that the teacher evaluation ratings for the roughly 70% of teachers who are not VAM eligible “are likely to differ in systematic ways from the ratings of teachers for whom VAM scores can be calculated” (p. 392). This is precisely why evidence in New Mexico suggests VAM-eligible teachers are up to five times more likely to be ranked as “ineffective” or “minimally effective” than their non-VAM-eligible colleagues; that is, “[also b]ecause greater weight is consistently assigned to observation scores for teachers in nontested grades and subjects” (p. 392). This also causes a related but also important issue with fairness, whereas equally effective teachers, just by being VAM eligible, may be five-or-so times likely (e.g., in states like New Mexico) of being rated as ineffective by the mere fact that they are VAM eligible and their states, quite literally, “value” value-added “too much” (as also arbitrarily defined).

Finally, it should also be noted as an important caveat here, that the findings advanced by Steinberg and Kraft (2017) “are not intended to provide specific recommendations about what weights and ratings to select—such decisions are fundamentally subject to local district priorities and preferences. (p. 379). These findings do, however, “offer important insights about how these decisions will affect the distribution of teacher performance ratings as policymakers and administrators continue to refine and possibly remake teacher evaluation systems” (p. 379).

Related, please recall that via the MET studies one of the researchers’ goals was to determine which weights per multiple measure were empirically defensible. MET researchers failed to do so and then defaulted to recommending an equal distribution of weights without empirical justification (see also Rothstein & Mathis, 2013). This also means that anyone at any state or district level who might say that this weight here or that weight there is empirically defensible should be asked for the evidence in support.

Citations:

Rothstein, J., & Mathis, W. J. (2013, January). Review of two culminating reports from the MET Project. Boulder, CO: National Educational Policy Center. Retrieved from http://nepc.colorado.edu/thinktank/review-MET-final-2013

Steinberg, M. P., & Kraft, M. A. (2017). The sensitivity of teacher performance ratings to the design of teacher evaluation systems. Educational Researcher, 46(7), 378–
396. doi:10.3102/0013189X17726752 Retrieved from http://journals.sagepub.com/doi/abs/10.3102/0013189X17726752

Breaking News: The End of Value-Added Measures for Teacher Termination in Houston

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Recall from multiple prior posts (see, for example, here, here, here, here, and here) that a set of teachers in the Houston Independent School District (HISD), with the support of the Houston Federation of Teachers (HFT) and the American Federation of Teachers (AFT), took their district to federal court to fight against the (mis)use of their value-added scores derived via the Education Value-Added Assessment System (EVAAS) — the “original” value-added model (VAM) developed in Tennessee by William L. Sanders who just recently passed away (see here). Teachers’ EVAAS scores, in short, were being used to evaluate teachers in Houston in more consequential ways than any other district or state in the nation (e.g., the termination of 221 teachers in one year as based, primarily, on their EVAAS scores).

The case — Houston Federation of Teachers et al. v. Houston ISD — was filed in 2014 and just one day ago (October 10, 2017) came the case’s final federal suit settlement. Click here to read the “Settlement and Full and Final Release Agreement.” But in short, this means the “End of Value-Added Measures for Teacher Termination in Houston” (see also here).

More specifically, recall that the judge notably ruled prior (in May of 2017) that the plaintiffs did have sufficient evidence to proceed to trial on their claims that the use of EVAAS in Houston to terminate their contracts was a violation of their Fourteenth Amendment due process protections (i.e., no state or in this case district shall deprive any person of life, liberty, or property, without due process). That is, the judge ruled that “any effort by teachers to replicate their own scores, with the limited information available to them, [would] necessarily fail” (see here p. 13). This was confirmed by the one of the plaintiffs’ expert witness who was also “unable to replicate the scores despite being given far greater access to the underlying computer codes than [was] available to an individual teacher” (see here p. 13).

Hence, and “[a]ccording to the unrebutted testimony of [the] plaintiffs’ expert [witness], without access to SAS’s proprietary information – the value-added equations, computer source codes, decision rules, and assumptions – EVAAS scores will remain a mysterious ‘black box,’ impervious to challenge” (see here p. 17). Consequently, the judge concluded that HISD teachers “have no meaningful way to ensure correct calculation of their EVAAS scores, and as a result are unfairly subject to mistaken deprivation of constitutionally protected property interests in their jobs” (see here p. 18).

Thereafter, and as per this settlement, HISD agreed to refrain from using VAMs, including the EVAAS, to terminate teachers’ contracts as long as the VAM score is “unverifiable.” More specifically, “HISD agree[d] it will not in the future use value-added scores, including but not limited to EVAAS scores, as a basis to terminate the employment of a term or probationary contract teacher during the term of that teacher’s contract, or to terminate a continuing contract teacher at any time, so long as the value-added score assigned to the teacher remains unverifiable. (see here p. 2; see also here). HISD also agreed to create an “instructional consultation subcommittee” to more inclusively and democratically inform HISD’s teacher appraisal systems and processes, and HISD agreed to pay the Texas AFT $237,000 in its attorney and other legal fees and expenses (State of Texas, 2017, p. 2; see also AFT, 2017).

This is yet another big win for teachers in Houston, and potentially elsewhere, as this ruling is an unprecedented development in VAM litigation. Teachers and others using the EVAAS or another VAM for that matter (e.g., that is also “unverifiable”) do take note, at minimum.

Much of the Same in Louisiana

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As I wrote into a recent post: “…it seems that the residual effects of the federal governments’ former [teacher evaluation reform policies and] efforts are still dominating states’ actions with regards to educational accountability.” In other words, many states are still moving forward, more specifically in terms of states’ continued reliance on the use of value-added models (VAMs) for increased teacher accountability purposes, regardless of the passage of the Every Student Succeeds Act (ESSA).

Related, three articles were recently published online (here, here, and here) about how in Louisiana, the state’s old and controversial teacher evaluation system as based on VAMs is resuming after a four-year hiatus. It was put on hold when the state was in the process of adopting The Common Core.

This, of course, has serious implications for the approximately 50,000 teachers throughout the state, or the unknown proportion of them who are now VAM-eligible, believed to be around 15,000 (i.e., approximately 30% which is inline with other state trends).

While the state’s system has been partly adjusted, whereas 50% of a teacher’s evaluation was to be based on growth in student achievement over time using VAMs, and the new system has reduced this percentage down to 35%, now teachers of mathematics, English, science, and social studies are also to be held accountable using VAMs. The other 50% of these teachers’ evaluation scores are to be assessed using observations with 15% based on student learning targets (a.k.a., student learning objectives (SLOs)).

Evaluation system output are to be used to keep teachers from earning tenure, or to cause teachers to lose the tenure they might already have.

Among other controversies and issues of contention noted in these articles (see again here, here, and here), one of note (highlighted here) is also that now, “even after seven years”… the state is still “unable to truly explain or provide the actual mathematical calculation or formula’ used to link test scores with teacher ratings. ‘This obviously lends to the distrust of the entire initiative among the education community.”

A spokeswoman for the state, however, countered the transparency charge noting that the VAM formula has been on the state’s department of education website, “and updated annually, since it began in 2012.” She did not provide a comment about how to adequately explain the model, perhaps because she could not either.

Just because it might be available does not mean it is understandable and, accordingly, usable. This we have come to know from administrators, teachers, and yes, state-level administrators in charge of these models (and their adoption and implementation) for years. This is, indeed, one of the largest criticisms of VAMs abound.

“Virginia SGP” Overruled

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You might recall from a post I released approximately 1.5 years ago a story about how a person who self-identifies as “Virginia SGP,” who is also now known as Brian Davison — a parent of two public school students in the affluent Loudoun, Virginia area (hereafter referred to as Virginia SGP), sued the state of Virginia in an attempt to force the release of teachers’ student growth percentile (SGP) data for all teachers across the state.

More specifically, Virginia SGP “pressed for the data’s release because he thinks parents have a right to know how their children’s teachers are performing, information about public employees that exists but has so far been hidden. He also want[ed] to expose what he sa[id was] Virginia’s broken promise to begin [to use] the data to evaluate how effective the state’s teachers are.” The “teacher data should be out there,” especially if taxpayers are paying for it.

In January of 2016, a Richmond, Virginia judge ruled in Virginia SGP’s favor. The following April, a Richmond Circuit Court judge ruled that the Virginia Department of Education was to also release Loudoun County Public Schools’ SGP scores by school and by teacher, including teachers’ identifying information. Accordingly, the judge noted that the department of education and the Loudoun school system failed to “meet the burden of proof to establish an exemption’ under Virginia’s Freedom of Information Act [FOIA]” preventing the release of teachers’ identifiable information (i.e., beyond teachers’ SGP data). The court also ordered VDOE to pay Davison $35,000 to cover his attorney fees and other costs.

As per an article published last week, the Virginia Supreme Court overruled this former ruling, noting that the department of education did not have to provide teachers’ identifiable information along with teachers’ SGP data, after all.

See more details in the actual article here, but ultimately the Virginia Supreme Court concluded that the Richmond Circuit Court “erred in ordering the production of these documents containing teachers’ identifiable information.” The court added that “it was [an] error for the circuit court to order that the School Board share in [Virginia SGP’s] attorney’s fees and costs,” pushing that decision (i.e., the decision regarding how much to pay, if anything at all, in legal fees) back down to the circuit court.

Virginia SGP plans to ask for a rehearing of this ruling. See also his comments on this ruling here.

On Conditional Bias and Correlation: A Guest Post

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After I posted about “Observational Systems: Correlations with Value-Added and Bias,” a blog follower, associate professor, and statistician named Laura Ring Kapitula (see also a very influential article she wrote on VAMs here) posted comments on this site that I found of interest, and I thought would also be of interest to blog followers. Hence, I invited her to write a guest post, and she did.

She used R (i.e., a free software environment for statistical computing and graphics) to simulate correlation scatterplots (see Figures below) to illustrate three unique situations: (1) a simulation where there are two indicators (e.g., teacher value-added and observational estimates plotted on the x and y axes) that have a correlation of r = 0.28 (the highest correlation coefficient at issue in the aforementioned post); (2) a simulation exploring the impact of negative bias and a moderate correlation on a group of teachers; and (3) another simulation with two indicators that have a non-linear relationship possibly induced or caused by bias. She designed simulations (2) and (3) to illustrate the plausibility of the situation suggested next (as written into Audrey’s post prior) about potential bias in both value-added and observational estimates:

If there is some bias present in value-added estimates, and some bias present in the observational estimates…perhaps this is why these low correlations are observed. That is, only those teachers teaching classrooms inordinately stacked with students from racial minority, poor, low achieving, etc. groups might yield relatively stronger correlations between their value-added and observational scores given bias, hence, the low correlations observed may be due to bias and bias alone.

Laura continues…

Here, Audrey makes the point that a correlation of r = 0.28 is “weak.” It is, accordingly, useful to see an example of just how “weak” such a correlation is by looking at a scatterplot of data selected from a population where the true correlation is r = 0.28. To make the illustration more meaningful the points are colored based on their quintile scores as per simulated teachers’ value-added divided into the lowest 20%, next 20%, etc.

In this figure you can see by looking at the blue “least squares line” that, “on average,” as a simulated teacher’s value-added estimate increases the average of a teacher’s observational estimate increases. However, there is a lot of variability (or scatter points) around the (scatterplot) line. Given this variability, we can make statements about averages, such as “on average” teachers in the top 20% for VAM scores will likely have on average higher observed observational scores; however, there is not nearly enough precision to make any (and certainly not any good) predictions about the observational score from the VAM score for individual teachers. In fact, the linear relationship between teachers’ VAM and observational scores only accounts for about 8% of the variation in VAM score. Note: we get 8% by squaring the aforementioned r = 0.28 correlation (i.e., an R squared). The other 92% of the variance is due to error and other factors.

What this means in practice is that when correlations are this “weak,” it is reasonable to say statements about averages, for example, that “on average” as one variable increases the mean of the other variable increases, but it would not be prudent or wise to make predictions for individuals based on these data. See, for example, that individuals in the top 20% (quintile 5) of VAM have a very large spread in their scores on the observational score, with 95% of the scores in the top quintile being in between the 7th and 98th percentiles for their observational scores. So, here if we observe a VAM for a specific teacher in the top 20%, and we do not know their observational score, we cannot say much more than their observational score is likely to be in the top 90%. Similarly, if we observe a VAM in the bottom 20%, we cannot say much more than their observational score is likely to be somewhere in the bottom 90%. That’s not saying a lot, in terms of precision, but also in terms of practice.

The second scatterplot I ran to test how bias that only impacts a small group of teachers might theoretically impact an overall correlation, as posited by Audrey. Here I simulated a situation where, again, there are two values present in a population of teachers: a teacher’s value-added and a teacher’s observational score. Then I insert a group of teachers (as Audrey described) who represent 20% of a population and teach a disproportionate number of students who come from relatively lower socioeconomic, high racial minority, etc. backgrounds, and I assume this group is measured with negative bias on both indicators and this group has a moderate correlation between indicators of r = 0.50. The other 80% of the population is assumed to be uncorrelated. Note: for this demonstration I assume that this group includes 20% of teachers from the aforementioned population, these teachers I assume to be measured with negative bias (by one standard deviation on average) on both measures, and, again, I set their correlation at r = 0.50 with the other 80% of teachers at a correlation of zero.

What you can see is that if there is bias in this correlation that impacts only a certain group on the two instrument indicators; hence, it is possible that this bias can result in an observed correlation overall. In other words, a strong correlation noted in just one group of teachers (i.e., teachers scoring the lowest on their value-added and observational indicators in this case) can be relatively stronger than the “weak” correlation observed on average or overall.

Another, possible situation is that there might be a non-linear relationship between these two measures. In the simulation below, I assume that different quantiles on VAM have a different linear relationship with the observational score. For example, in the plot there is not a constant slope, but teachers who are in the first quintile on VAM I assume to have a correlation of r = 0.50 with observational scores, the second quintile I assume to have a correlation of r = 0.20, and the other quintiles I assume to be uncorrelated. This results in an overall correlation in the simulation of r = 0.24, with a very small p-value (i.e. a very small chance that a correlation of this size would be observed by random chance alone if the true correlation was zero).

What this means in practice is that if, in fact, there is a non-linear relationship between teachers’ observational and VAM scores, this can induce a small but statistically significant correlation. As evidenced, teachers in the lowest 20% on the VAM score have differences in the mean observational score depending on the VAM score (a moderate correlation of r = 0.50), but for the other 80%, knowing the VAM score is not informative as there is a very small correlation for the second quintile and no correlation for the upper 60%. So, if quintile cut-off scores are used, teachers can easily be misclassified. In sum, Pearson Correlations (the standard correlation coefficient) measure the overall strength of  linear relationships between X and Y, but if X and Y have a non-linear relationship (like as illustrated in the above), this statistic can be very misleading.

Note also that for all of these simulations very small p-values are observed (e.g., p-values <0.0000001 which, again, mean these correlations are statistically significant or that the probability of observing correlations this large by chance if the true correlation is zero, is nearly 0%). What this illustrates, again, is that correlations (especially correlations this small) are (still) often misleading. While they might be statistically significant, they might mean relatively little in the grand scheme of things (i.e., in terms of practical significance; see also “The Difference Between”Significant’ and ‘Not Significant’ is not Itself Statistically Significant” or posts on Andrew Gelman’s blog for more discussion on these topics if interested).

At the end of the day r = 0.28 is still a “weak” correlation. In addition, it might be “weak,” on average, but much stronger and statistically and practically significant for teachers in the bottom quintiles (e.g., teachers in the bottom 20%, as illustrated in the final figure above) typically teaching the highest needs students. Accordingly, this might be due, at least in part, to bias.

In conclusion, one should always be wary of claims based on “weak” correlations, especially if they are positioned to be stronger than industry standards would classify them (e.g., in the case highlighted in the prior post). Even if a correlation is “statistically significant,” it is possible that the correlation is the result of bias, and that the relationship is so weak that it is not meaningful in practice, especially when the goal is to make high-stakes decisions about individual teachers. Accordingly, when you see correlations this small, keep these scatterplots in mind or generate some of your own (see, for example, here to dive deeper into what these correlations might mean and how significant these correlations might really be).

*Please contact Dr. Kapitula directly at kapitull@gvsu.edu if you want more information or to access the R code she used for the above.

Observational Systems: Correlations with Value-Added and Bias

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A colleague recently sent me a report released in November of 2016 by the Institute of Education Sciences (IES) division of the U.S. Department of Education that should be of interest to blog followers. The study is about “The content, predictive power, and potential bias in five widely used teacher observation instruments” and is authored by affiliates of Mathematica Policy Research.

Using data from the Bill & Melinda Gates Foundation’s Measures of Effective Teaching (MET) studies, researchers examined five widely used teacher observation instruments. Instruments included the more generally popular Classroom Assessment Scoring System (CLASS) and Danielson Framework for Teaching (of general interest in this post), as well as the more subject-specific instruments including the Protocol for Language Arts Teaching Observations (PLATO), the Mathematical Quality of Instruction (MQI), and the UTeach Observational Protocol (UTOP) for science and mathematics teachers.

Researchers examined these instruments in terms of (1) what they measure (which is not of general interest in this post), but also (2) the relationships of observational output to teachers’ impacts on growth in student learning over time (as measured using a standard value-added model (VAM)), and (3) whether observational output are biased by the characteristics of the students non-randomly (or in this study randomly) assigned to teachers’ classrooms.

As per #2 above, researchers found that the instructional practices captured across these instruments modestly [emphasis added] correlate with teachers’ value-added scores, with an adjusted (and likely, artificially inflated; see Note 1 below) correlation coefficient between observational and value added indicators at: 0.13 ≤ r ≤ 0.28 (see also Table 4, p. 10). As per the higher, adjusted r (emphasis added; see also Note 1 below), they found that these instruments’ classroom management dimensions most strongly (r = 0.28) correlated with teachers’ value-added.

Related, also at issue here is that such correlations are not “modest,” but rather “weak” to “very weak” (see Note 2 below). While all correlation coefficients were statistically significant, this is much more likely due to the sample size used in this study versus the actual or practical magnitude of these results. “In sum” this hardly supports the overall conclusion that “observation scores predict teachers’ value-added scores” (p. 11); although, it should also be noted that this summary statement, in and of itself, suggests that the value-added score is the indicator around which all other “less objective” indicators are to revolve.

As per #3 above, researchers found that students randomly assigned to teachers’ classrooms (as per the MET data, although there was some noncompliance issues with the random assignment employed in the MET studies) do bias teachers’ observational scores, for better or worse, and more often in English language arts than in mathematics. More specifically, they found that for the Danielson Framework and CLASS (the two more generalized instruments examined in this study, also of main interest in this post), teachers with relatively more racial/ethnic minority and lower-achieving students (in that order, although these are correlated themselves) tended to receive lower observation scores. Bias was observed more often for the Danielson Framework versus the CLASS, but it was observed in both cases. An “alternative explanation [may be] that teachers are providing less-effective instruction to non-White or low-achieving students” (p. 14).

Notwithstanding, and in sum, in classrooms in which students were randomly assigned to teachers, teachers’ observational scores were biased by students’ group characteristics, which also means that  bias is also likely more prevalent in classrooms to which students are non-randomly assigned (which is common practice). These findings are also akin to those found elsewhere (see, for example, two similar studies here), as this was also evidenced in mathematics, which may also be due to the random assignment factor present in this study. In other words, if non-random assignment of students into classrooms is practice, a biasing influence may (likely) still exist in English language arts and mathematics.

The long and short of it, though, is that the observational components of states’ contemporary teacher systems certainly “add” more “value” than their value-added counterparts (see also here), especially when considering these systems’ (in)formative purposes. But to suggest that because these observational indicators (artificially) correlate with teachers’ value-added scores at “weak” and “very weak” levels (see Notes 1 and 2 below), that this means that these observational systems might “add” more “value” to the summative sides of teacher evaluations (i.e., their predictive value) is premature, not to mention a bit absurd. Adding import to this statement is the fact that, as s duly noted in this study, these observational indicators are oft-to-sometimes biased against teachers who teacher lower-achieving and racial minority students, even when random assignment is present, making such bias worse when non-random assignment, which is very common, occurs.

Hence, and again, this does not make the case for the summative uses of really either of these indicators or instruments, especially when high-stakes consequences are to be attached to output from either indicator (or both indicators together given the “weak” to “very weak” relationships observed). On the plus side, though, remain the formative functions of the observational indicators.

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Note 1: Researchers used the “year-to-year variation in teachers’ value-added scores to produce an adjusted correlation [emphasis added] that may be interpreted as the correlation between teachers’ average observation dimension score and their underlying value added—the value added that is [not very] stable [or reliable] for a teacher over time, rather than a single-year measure (Kane & Staiger, 2012)” (p. 9). This practice or its statistic derived has not been externally vetted. Likewise, this also likely yields a correlation coefficient that is falsely inflated. Both of these concerns are at issue in the ongoing New Mexico and Houston lawsuits, in which Kane is one of the defendants’ expert witnesses in both cases testifying in support of his/this practice.

Note 2: As is common with social science research when interpreting correlation coefficients: 0.8 ≤ r ≤ 1.0 = a very strong correlation; 0.6 ≤ r ≤ 0.8 = a strong correlation; 0.4 ≤ r ≤ 0.6 = a moderate correlation; 0.2 ≤ r ≤ 0.4 = a weak correlation; and 0 ≤ r ≤ 0.2 = a very weak correlation, if any at all.

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Citation: Gill, B., Shoji, M., Coen, T., & Place, K. (2016). The content, predictive power, and potential bias in five widely used teacher observation instruments. Washington, DC: U.S. Department of Education, Institute of Education Sciences. Retrieved from https://ies.ed.gov/ncee/edlabs/regions/midatlantic/pdf/REL_2017191.pdf