Special Issue of “Educational Researcher” (Paper #9 of 9): Amidst the “Blooming Buzzing Confusion”

Recall that the peer-reviewed journal Educational Researcher (ER) – published a “Special Issue” including nine articles examining value-added measures (VAMs). I have reviewed the last of nine articles (#9 of 9), which is actually a commentary titled “Value Added: A Case Study in the Mismatch Between Education Research and Policy.” This commentary is authored by Stephen Raudenbush – Professor of Sociology and Public Policy Studies at the University of Chicago.

Like with the last two commentaries reviewed here and here, Raudenbush writes of the “Special Issue” that, in this topical area, “[r]esearchers want their work to be used, so we flirt with the idea that value-added research tells us how to improve schooling…[Luckily, perhaps] this volume has some potential to subdue this flirtation” (p. 138).

Raudenbush positions the research covered in this “Special Issue,” as well as the research on teacher evaluation and education in general, as being conducted amidst the “blooming buzzing confusion” (p. 138) surrounding the messy world through which we negotiate life. This is why “specific studies don’t tell us what to do, even if they sometimes have large potential for informing expert judgment” (p. 138).

With that being said, “[t]he hard question is how to integrate the new research on teachers with other important strands of research [e.g., effective schools research] in order to inform rather than distort practical judgment” (p. 138). Echoing Susan Moore Johnson’s sentiments, reviewed as article #6 here, this is appropriately hard if we are to augment versus undermine “our capacity to mobilize the “social capital” of the school to strengthen the human capital of the teacher” (p. 138).

On this note, and “[i]n sum, recent research on value added tells us that, by using data from student perceptions, classroom observations, and test score growth, we can obtain credible evidence [albeit weakly related evidence, referring to the Bill & Melinda Gates Foundation’s MET studies] of the relative effectiveness of a set of teachers who teach similar kids [emphasis added] under similar conditions [emphasis added]…[Although] if a district administrator uses data like that collected in MET, we can anticipate that an attempt to classify teachers for personnel decisions will be characterized by intolerably high error rates [emphasis added]. And because districts can collect very limited information, a reliance on district-level data collection systems will [also] likely generate…distorted behavior[s]..in which teachers attempt to “game” the
comparatively simple indicators,” or system (p. 138-139).

Accordingly, “[a]n effective school will likely be characterized by effective ‘distributed’ leadership, meaning that expert teachers share responsibility for classroom observation, feedback, and frequent formative assessments of student learning. Intensive professional development combined with classroom follow-up generates evidence about teacher learning and teacher improvement. Such local data collection efforts [also] have some potential to gain credibility among teachers, a virtue that seems too often absent” (p. 140).

This, might be at least a significant part of the solution.

“If the school is potentially rich in information about teacher effectiveness and teacher improvement, it seems to follow that key personnel decisions should be located firmly at the school level..This sense of collective efficacy [accordingly] seems to be a key feature of…highly effective schools” (p. 140).

*****

If interested, see the Review of Article #1 – the introduction to the special issue here; see the Review of Article #2 – on VAMs’ measurement errors, issues with retroactive revisions, and (more) problems with using standardized tests in VAMs here; see the Review of Article #3 – on VAMs’ potentials here; see the Review of Article #4 – on observational systems’ potentials here; see the Review of Article #5 – on teachers’ perceptions of observations and student growth here; see the Review of Article (Essay) #6 – on VAMs as tools for “egg-crate” schools here; see the Review of Article (Commentary) #7 – on VAMs situated in their appropriate ecologies here; and see the Review of Article #8, Part I – on a more research-based assessment of VAMs’ potentials here and Part II on “a modest solution” provided to us by Linda Darling-Hammond here.

Article #9 Reference: Raudenbush, S. W. (2015). Value added: A case study in the mismatch between education research and policy. Educational Researcher, 44(2), 138-141. doi:10.3102/0013189X15575345

 

 

 

Special Issue of “Educational Researcher” (Paper #8 of 9, Part I): A More Research-Based Assessment of VAMs’ Potentials

Recall that the peer-reviewed journal Educational Researcher (ER) – published a “Special Issue” including nine articles examining value-added measures (VAMs). I have reviewed the next of nine articles (#8 of 9), which is actually a commentary titled “Can Value-Added Add Value to Teacher Evaluation?” This commentary is authored by Linda Darling-Hammond – Professor of Education, Emeritus, at Stanford University.

Like with the last commentary reviewed here, Darling-Hammond reviews some of the key points taken from the five feature articles in the aforementioned “Special Issue.” More specifically, though, Darling-Hammond “reflect[s] on [these five] articles’ findings in light of other work in this field, and [she] offer[s her own] thoughts about whether and how VAMs may add value to teacher evaluation” (p. 132).

She starts her commentary with VAMs “in theory,” in that VAMs COULD accurately identify teachers’ contributions to student learning and achievement IF (and this is a big IF) the following three conditions were met: (1) “student learning is well-measured by tests that reflect valuable learning and the actual achievement of individual students along a vertical scale representing the full range of possible achievement measures in equal interval units” (2) “students are randomly assigned to teachers within and across schools—or, conceptualized another way, the learning conditions and traits of the group of students assigned to one teacher do not vary substantially from those assigned to another;” and (3) “individual teachers are the only contributors to students’ learning over the period of time used for measuring gains” (p. 132).

None of things are actual true (or near to true, nor will they likely ever be true) in educational practice, however. Hence, the errors we continue to observe that continue to prevent VAM use for their intended utilities, even with the sophisticated statistics meant to mitigate errors and account for the above-mentioned, let’s call them, “less than ideal” conditions.

Other pervasive and perpetual issues surrounding VAMs as highlighted by Darling-Hammond, per each of the three categories above, pertain to (1) the tests used to measure value-added is that the tests are very narrow, focus on lower level skills, and are manipulable. These tests in their current form cannot effectively measure the learning gains of a large share of students who are above or below grade level given a lack of sufficient coverage and stretch. As per Haertel (2013, as cited in Darling-Hammond’s commentary), this “translates into bias against those teachers working with the lowest-performing or the highest-performing classes’…and “those who teach in tracked school settings.” It is also important to note here that the new tests created by the Partnership for Assessing Readiness for College and Careers (PARCC) and Smarter Balanced, multistate consortia “will not remedy this problem…Even though they will report students’ scores on a vertical scale, they will not be able to measure accurately the achievement or learning of students who started out below or above grade level” (p.133).

With respect to (2) above, on the equivalence (or rather non-equivalence) of groups of student across teachers classrooms who are the ones whose VAM scores are relativistically compared, the main issue here is that “the U.S. education system is the one of most segregated and unequal in the industrialized world…[likewise]…[t]he country’s extraordinarily high rates of childhood poverty, homelessness, and food insecurity are not randomly distributed across communities…[Add] the extensive practice of tracking to the mix, and it is clear that the assumption of equivalence among classrooms is far from reality” (p. 133). Whether sophisticated statistics can control for all of this variation is one of most debated issues surrounding VAMs and their levels of outcome bias, accordingly.

And as per (3) above, “we know from decades of educational research that many things matter for student achievement aside from the individual teacher a student has at a moment in time for a given subject area. A partial list includes the following [that are also supposed to be statistically controlled for in most VAMs, but are also clearly not controlled for effectively enough, if even possible]: (a) school factors such as class sizes, curriculum choices, instructional time, availability of specialists, tutors, books, computers, science labs, and other resources; (b) prior teachers and schooling, as well as other current teachers—and the opportunities for professional learning and collaborative planning among them; (c) peer culture and achievement; (d) differential summer learning gains and losses; (e) home factors, such as parents’ ability to help with homework, food and housing security, and physical and mental support or abuse; and (e) individual student needs, health, and attendance” (p. 133).

“Given all of these influences on [student] learning [and achievement], it is not surprising that variation among teachers accounts for only a tiny share of variation in achievement, typically estimated at under 10%” (see, for example, highlights from the American Statistical Association’s (ASA’s) Position Statement on VAMs here). “Suffice it to say [these issues]…pose considerable challenges to deriving accurate estimates of teacher effects…[A]s the ASA suggests, these challenges may have unintended negative effects on overall educational quality” (p. 133). “Most worrisome [for example] are [the] studies suggesting that teachers’ ratings are heavily influenced [i.e., biased] by the students they teach even after statistical models have tried to control for these influences” (p. 135).

Other “considerable challenges” include: VAM output are grossly unstable given the swings and variations observed in teacher classifications across time, and VAM output are “notoriously imprecise” (p. 133) given the other errors observed as caused, for example, by varying class sizes (e.g., Sean Corcoran (2010) documented with New York City data that the “true” effectiveness of a teacher ranked in the 43rd percentile could have had a range of possible scores from the 15th to the 71st percentile, qualifying as “below average,” “average,” or close to “above average). In addition, practitioners including administrators and teachers are skeptical of these systems, and their (appropriate) skepticisms are impacting the extent to which they use and value their value-added data, noting that they value their observational data (and the professional discussions surrounding them) much more. Also important is that another likely unintended effect exists (i.e., citing Susan Moore Johnson’s essay here) when statisticians’ efforts to parse out learning to calculate individual teachers’ value-added causes “teachers to hunker down and focus only on their own students, rather than working collegially to address student needs and solve collective problems” (p. 134). Related, “the technology of VAM ranks teachers against each other relative to the gains they appear to produce for students, [hence] one teacher’s gain is another’s loss, thus creating disincentives for collaborative work” (p. 135). This is what Susan Moore Johnson termed the egg-crate model, or rather the egg-crate effects.

Darling-Hammond’s conclusions are that VAMs have “been prematurely thrust into policy contexts that have made it more the subject of advocacy than of careful analysis that shapes its use. There is [good] reason to be skeptical that the current prescriptions for using VAMs can ever succeed in measuring teaching contributions well (p. 135).

Darling-Hammond also “adds value” in one whole section (highlighted in another post forthcoming here), offering a very sound set of solutions, using VAMs for teacher evaluations or not. Given it’s rare in this area of research we can focus on actual solutions, this section is a must read. If you don’t want to wait for the next post, read Darling-Hammond’s “Modest Proposal” (p. 135-136) within her larger article here.

In the end, Darling-Hammond writes that, “Trying to fix VAMs is rather like pushing on a balloon: The effort to correct one problem often creates another one that pops out somewhere else” (p. 135).

*****

If interested, see the Review of Article #1 – the introduction to the special issue here; see the Review of Article #2 – on VAMs’ measurement errors, issues with retroactive revisions, and (more) problems with using standardized tests in VAMs here; see the Review of Article #3 – on VAMs’ potentials here; see the Review of Article #4 – on observational systems’ potentials here; see the Review of Article #5 – on teachers’ perceptions of observations and student growth here; see the Review of Article (Essay) #6 – on VAMs as tools for “egg-crate” schools here; and see the Review of Article (Commentary) #7 – on VAMs situated in their appropriate ecologies here.

Article #8, Part I Reference: Darling-Hammond, L. (2015). Can value-added add value to teacher evaluation? Educational Researcher, 44(2), 132-137. doi:10.3102/0013189X15575346

Special Issue of “Educational Researcher” (Paper #7 of 9): VAMs Situated in Appropriate Ecologies

Recall that the peer-reviewed journal Educational Researcher (ER) – recently published a “Special Issue” including nine articles examining value-added measures (VAMs). I have reviewed the next of nine articles (#7 of 9), which is actually a commentary titled “The Value in Value-Added Depends on the Ecology.” This commentary is authored by Henry Braun – Professor of Education and Public Policy, Educational Research, Measurement, and Evaluation at Boston College (also the author of a previous post on this site here).

In this article Braun, importantly, makes explicit the assumptions on which this special issue of ER is based; that is, on assumptions that (1) too many students in America’s public schools are being inadequately educated, (2) evaluation systems as they currently exist “require radical overhaul,” and (3) it is therefore essential to use student test performance with low- and high-stakes attached to improve that which educators do (or don’t do) to adequately address the first assumption. There are counterarguments Braun also offers to readers on each of these assumptions (see p. 127), but more importantly he makes evident that the focus of this special issue is situated otherwise, as in line with current education policies. This special issue, overall, then “raise[s] important questions regarding the potential for high-stakes, test-driven educator accountability systems to contribute to raising student achievement” (p. 127).

Given this context, the “value-added” provided within this special issue, again according to Braun, is that the authors of each of the five main research articles included report on how VAM output actually plays out in practice, given “careful consideration to how the design and implementation of teacher evaluation systems could be modified to enhance the [purportedly, see comments above] positive impact of accountability and mitigate the negative consequences” at the same time (p. 127). In other words, if we more or less agree to the aforementioned assumptions, also given the educational policy context influence, perpetuating, or actually forcing these assumptions, these articles should help others better understand VAMs’ and observational systems’ potentials and perils in practice.

At the same time, Braun encourages us to note that “[t]he general consensus is that a set of VAM scores does contain some useful information that meaningfully differentiates among teachers, especially in the tails of the distribution [although I would argue bias has a role here]. However, individual VAM scores do suffer from high variance and low year-to-year stability as well as an undetermined amount of bias [which may be greater in the tails of the distribution]. Consequently, if VAM scores are to be used for evaluation, they should not be given inordinate weight and certainly not treated as the “gold standard” to which all other indicators must be compared” (p. 128).

Likewise, it’s important to note that IF consequences are to be attached to said indicators of teacher evaluation (i.e., VAM and observational data), there should be validity evidence made available and transparent to warrant the inferences and decisions to be made, and the validity evidence “should strongly support a causal [emphasis added] argument” (p. 128). However, both indicators still face major “difficulties in establishing defensible causal linkage[s]” as theorized, and desired (p. 128); hence, this prevents validity in inference. What does not help, either, is when VAM scores are given precedence over other indicators OR when principals align teachers’ observational scores with the same teachers’ VAM scores given the precedence often given to (what are often viewed as the superior, more objective) VAM-based measures. This sometimes occurs given external pressures (e.g., applied by superintendents) to artificially conflate, in this case, levels of agreement between indicators (i.e., convergent validity).

Related, in the section Braun titles his “Trio of Tensions,” (p. 129) he notes that (1) [B]oth accountability and improvement are undermined, as attested to by a number of the articles in this issue. In the current political and economic climate, [if possible] it will take thoughtful and inspiring leadership at the state and district levels to create contexts in which an educator evaluation system constructively fulfills its roles with respect to both public accountability and school improvement” (p. 129-130); (2) [T]he chasm between the technical sophistication of the various VAM[s] and the ability of educators to appreciate what these models are attempting to accomplish…sow[s] further confusion…[hence]…there must be ongoing efforts to convey to various audiences the essential issues—even in the face of principled disagreements among experts on the appropriate roles(s) for VAM[s] in educator evaluations” (p. 130); and finally (3) [H]ow to balance the rights of students to an adequate education and the rights of teachers to fair evaluations and due process [especially for]…teachers who have value-added scores and those who teach in subject-grade combinations for which value-added scores are not feasible…[must be addressed; this] comparability issue…has not been addressed but [it] will likely [continue to] rear its [ugly] head” (p. 130).

In the end, Braun argues for another “Trio,” but this one including three final lessons: (1) “although the concerns regarding the technical properties of VAM scores are not misplaced, they are not necessarily central to their reputation among teachers and principals. [What is central is]…their links to tests of dubious quality, their opaqueness in an atmosphere marked by (mutual) distrust, and the apparent lack of actionable information that are largely responsible for their poor reception” (p. 130); (2) there is a “very substantial, multiyear effort required for proper implementation of a new evaluation system…[related, observational] ratings are not a panacea. They, too, suffer from technical deficiencies and are the object of concern among some teachers because of worries about bias” (p. 130); and (3) “legislators and policymakers should move toward a more ecological approach [emphasis added; see also the Review of Article (Essay) #6 – on VAMs as tools for “egg-crate” schools here] to the design of accountability systems; that is, “one that takes into account the educational and political context for evaluation, the behavioral responses and other dynamics that are set in motion when a new regime of high-stakes accountability is instituted, and the long-term consequences of operating the system” (p. 130).

*****

If interested, see the Review of Article #1 – the introduction to the special issue here; see the Review of Article #2 – on VAMs’ measurement errors, issues with retroactive revisions, and (more) problems with using standardized tests in VAMs here; see the Review of Article #3 – on VAMs’ potentials here; see the Review of Article #4 – on observational systems’ potentials here; see the Review of Article #5 – on teachers’ perceptions of observations and student growth here; and see the Review of Article (Essay) #6 – on VAMs as tools for “egg-crate” schools here.

Article #7 Reference: Braun, H. (2015). The value in value-added depends on the ecology. Educational Researcher, 44(2), 127-131. doi:10.3102/0013189X15576341

Special Issue of “Educational Researcher” (Paper #6 of 9): VAMs as Tools for “Egg-Crate” Schools

Recall that the peer-reviewed journal Educational Researcher (ER) – published a “Special Issue” including nine articles examining value-added measures (VAMs). I have reviewed the next of nine articles (#6 of 9), which is actually an essay here, titled “Will VAMS Reinforce the Walls of the Egg-Crate School?” This essay is authored by Susan Moore Johnson – Professor of Education at Harvard and somebody who I in the past I had the privilege of interviewing as an esteemed member of the National Academy of Education (see interviews here and here).

In this article, Moore Johnson argues that when policymakers use VAMs to evaluate, reward, or dismiss teachers, they may be perpetuating an egg-crate model, which is (referencing Tyack (1974) and Lortie (1975)) a metaphor for the compartmentalized school structure in which teachers (and students) work, most often in isolation. This model ultimately undermines the efforts of all involved in the work of schools to build capacity school wide, and to excel as a school given educators’ individual and collective efforts.

Contrary to the primary logic supporting VAM use, however, “teachers are not inherently effective or ineffective” on their own. Rather, their collective effectiveness is related to their professional development that may be stunted when they work alone, “without the benefit of ongoing collegial influence” (p. 119). VAMs then, and unfortunately, can cause teachers and administrators to (hyper)focus “on identifying, assigning, and rewarding or penalizing individual [emphasis added] teachers for their effectiveness in raising students’ test scores [which] depends primarily on the strengths of individual teachers” (p. 119). What comes along with this, then, are a series of interrelated egg-crate behaviors including, but not limited to, increased competition, lack of collaboration, increased independence versus interdependence, and the like, all of which can lead to decreased morale and decreased effectiveness in effect.

Inversely, students are much “better served when human resources are deliberately organized to draw on the strengths of all teachers on behalf of all students, rather than having students subjected to the luck of the draw in their classroom assignment[s]” (p. 119). Likewise, “changing the context in which teachers work could have important benefits for students throughout the school, whereas changing individual teachers without changing the context [as per VAMs] might not [work nearly as well] (Lohr, 2012)” (p. 120). Teachers learning from their peers, working in teams, teaching in teams, co-planning, collaborating, learning via mentoring by more experienced teachers, learning by mentoring, and the like should be much more valued, as warranted via the research, yet they are not valued given the very nature of VAM use.

Hence, there are also unintended consequences that can also come along with the (hyper)use of individual-level VAMs. These include, but are not limited to: (1) Teachers who are more likely to “literally or figuratively ‘close their classroom door’ and revert to working alone…[This]…affect[s] current collaboration and shared responsibility for school improvement, thus reinforcing the walls of the egg-crate school” (p. 120); (2) Due to bias, or that teachers might be unfairly evaluated given the types of students non-randomly assigned into their classrooms, teachers might avoid teaching high-needs students if teachers perceive themselves to be “at greater risk” of teaching students they cannot grow; (3) This can perpetuate isolative behaviors, as well as behaviors that encourage teachers to protect themselves first, and above all else; (4) “Therefore, heavy reliance on VAMS may lead effective teachers in high-need subjects and schools to seek safer assignments, where they can avoid the risk of low VAMS scores[; (5) M]eanwhile, some of the most challenging teaching assignments would remain difficult to fill and likely be subject to repeated turnover, bringing steep costs for students” (p. 120); While (6) “using VAMS to determine a substantial part of the teacher’s evaluation or pay [also] threatens to sidetrack the teachers’ collaboration and redirect the effective teacher’s attention to the students on his or her roster” (p. 120-121) versus students, for example, on other teachers’ rosters who might also benefit from other teachers’ content area or other expertise. Likewise (7) “Using VAMS to make high-stakes decisions about teachers also may have the unintended effect of driving skillful and committed teachers away from the schools that need them most and, in the extreme, causing them to leave the profession” in the end (p. 121).

I should add, though, and in all fairness given the Review of Paper #3 – on VAMs’ potentials here, many of these aforementioned assertions are somewhat hypothetical in the sense that they are based on the grander literature surrounding teachers’ working conditions, versus the direct, unintended effects of VAMs, given no research yet exists to examine the above, or other unintended effects, empirically. “There is as yet no evidence that the intensified use of VAMS interferes with collaborative, reciprocal work among teachers and principals or sets back efforts to move beyond the traditional egg-crate structure. However, the fact that we lack evidence about the organizational consequences of using VAMS does not mean that such consequences do not exist” (p. 123).

The bottom line is that we do not want to prevent the school organization from becoming “greater than the sum of its parts…[so that]…the social capital that transforms human capital through collegial activities in schools [might increase] the school’s overall instructional capacity and, arguably, its success” (p. 118). Hence, as Moore Johnson argues, we must adjust the focus “from the individual back to the organization, from the teacher to the school” (p. 118), and from the egg-crate back to a much more holistic and realistic model capturing what it means to be an effective school, and what it means to be an effective teacher as an educational professional within one. “[A] school would do better to invest in promoting collaboration, learning, and professional accountability among teachers and administrators than to rely on VAMS scores in an effort to reward or penalize a relatively small number of teachers” (p. 122).

*****

If interested, see the Review of Article #1 – the introduction to the special issue here; see the Review of Article #2 – on VAMs’ measurement errors, issues with retroactive revisions, and (more) problems with using standardized tests in VAMs here; see the Review of Article #3 – on VAMs’ potentials here; see the Review of Article #4 – on observational systems’ potentials here; and see the Review of Article #5 – on teachers’ perceptions of observations and student growth here.

Article #6 Reference: Moore Johnson, S. (2015). Will VAMS reinforce the walls of the egg-crate school? Educational Researcher, 44(2), 117-126. doi:10.3102/0013189X15573351

Special Issue of “Educational Researcher” (Paper #4 of 9): Make Room VAMs for Observations

Recall that the peer-reviewed journal Educational Researcher (ER) – recently published a “Special Issue” including nine articles examining value-added measures (VAMs). I have reviewed the next of nine articles (#4 of 9) here, titled “Make Room Value-Added: Principals’ Human Capital Decisions and the Emergence of Teacher Observation Data. This one is authored by Ellen Goldring, Jason A. Grissom, Christine Neumerski, Marisa Cannata, Mollie Rubin, Timothy Drake, and Patrick Schuermann, all of whom are associated with Vanderbilt University.

This article is primarily about (1) the extent to which the data generated by “high-quality observation systems” can inform principals’ human capital decisions (e.g., teacher hiring, contract renewal, assignment to classrooms, professional development), and (2) the extent to which principals are relying less on test scores derived via value-added models (VAMs), when making the same decisions, and why. Here are some of their key (and most important, in my opinion) findings:

  • Principals across all school systems revealed major hesitations and challenges regarding the use of VAM output for human capital decisions. Barriers preventing VAM use included the timing of data availability (e.g., the fall), which is well after human capital decisions are made (p. 99).
  • VAM output are too far removed from the practice of teaching (p. 99), and this lack of instructional sensitivity impedes, if not entirely prevents their actual versus hypothetical use for school/teacher improvement.
  • “Principals noted they did not really understand how value-added scores were calculated, and therefore they were not completely comfortable using them” (p. 99). Likewise, principals reported that because teachers did not understand how the systems worked either, teachers did not use VAM output data either (p. 100).
  • VAM output are not transparent when used to determine compensation, and especially when used to evaluate teachers teaching nontested subject areas. In districts that use school-wide VAM output to evaluate teachers in nontested subject areas, in fact, principals reported regularly ignoring VAM output altogether (p. 99-100).
  • “Principals reported that they perceived observations to be more valid than value-added measures” (p. 100); hence, principals reported using observational output much more, again, in terms of human capital decisions and making such decisions “valid.” (p. 100).
  • “One noted exception to the use of value-added scores seemed to be in the area of assigning teachers to particular grades, subjects, and classes. Many principals mentioned they use value-added measures to place teachers in tested subjects and with students in grade levels that ‘count’ for accountability purpose…some principals [also used] VAM [output] to move ineffective teachers to untested grades, such as K-2 in elementary schools and 12th grade in high schools” (p. 100).

Of special note here is also the following finding: “In half of the systems [in which researchers investigated these systems], there [was] a strong and clear expectation that there be alignment between a teacher’s value-added growth score and observation ratings…Sometimes this was a state directive and other times it was district-based. In some systems, this alignment is part of the principal’s own evaluation; principals receive reports that show their alignment” (p. 101). In other words, principals are being evaluated and held accountable given the extent to which their observations of their teachers match their teachers’ VAM-based data. If misalignment is noticed, it is not to be the fault of either measure (e.g., in terms of measurement error), it is to be the fault of the principal who is critiqued for inaccuracy, and therefore (inversely) incentivized to skew their observational data (the only data over which the supervisor has control) to artificially match VAM-based output. This clearly distorts validity, or rather the validity of the inferences that are to be made using such data. Appropriately, principals also “felt uncomfortable [with this] because they were not sure if their observation scores should align primarily…with the VAM” output (p. 101).

“In sum, the use of observation data is important to principals for a number of reasons: It provides a “bigger picture” of the teacher’s performance, it can inform individualized and large group professional development, and it forms the basis of individualized support for remediation plans that serve as the documentation for dismissal cases. It helps principals provides specific and ongoing feedback to teachers. In some districts, it is beginning to shape the approach to teacher hiring as well” (p. 102).

The only significant weakness, again in my opinion, with this piece is that the authors write that these observational data, at focus in this study, are “new,” thanks to recent federal initiatives. They write, for example, that “data from structured teacher observations—both quantitative and qualitative—constitute a new [emphasis added] source of information principals and school systems can utilize in decision making” (p. 96). They are also “beginning to emerge [emphasis added] in the districts…as powerful engines for principal data use” (p. 97). I would beg to differ as these systems have not changed much over time, pre and post these federal initiatives as (without evidence or warrant) claimed by these authors herein. See, for example, Table 1 on p. 98 of the article to see if what they have included within the list of components of such new and “complex, elaborate teacher observation systems systems” is actually new or much different than most of the observational systems in use prior. As an aside, one such system in use and of issue in this examination is one with which I am familiar, in use in the Houston Independent School District. Click here to also see if this system is also more “complex” or “elaborate” over and above such systems prior.

Also recall that one of the key reports that triggered the current call for VAMs, as the “more objective” measures needed to measure and therefore improve teacher effectiveness, was based on data that suggested that “too many teachers” were being rated as satisfactory or above. The observational systems in use then are essentially the same observational systems still in use today (see “The Widget Effect” report here). This is in stark contradiction to authors’ claims throughout this piece, for example, when they write “Structured teacher observations, as integral components of teacher evaluations, are poised to be a very powerful lever for changing principal leadership and the influence of principals on schools, teachers, and learning.” This counters all that is and all that came from “The Widget Effect” report here.

*****

If interested, see the Review of Article #1 – the introduction to the special issue here; see the Review of Article #2 – on VAMs’ measurement errors, issues with retroactive revisions, and (more) problems with using standardized tests in VAMs here; and see the Review of Article #3 – on VAMs’ potentials here.

Article #4 Reference: Goldring, E., Grissom, J. A., Rubin, M., Neumerski, C. M., Cannata, M., Drake, T., & Schuermann, P. (2015). Make room value-added: Principals’ human capital decisions and the emergence of teacher observation data. Educational Researcher, 44(2), 96-104. doi:10.3102/0013189X15575031

Vanderbilt Researchers on Performance Pay, VAMs, and SLOs

Do higher paychecks translate into higher student test scores? That is the question two researchers at Vanderbilt – Ryan Balch (recent Graduate Research Assistant at Vanderbilt’s National Center on Performance Incentives) and Matthew Springer (Assistant Professor of Public Policy and Education and Director of Vanderbilt’s National Center on Performance Incentives) – attempted to answer in a recent study of the REACH pay-for-performance program in Austin, Texas (a nationally recognized performance program model with $62.3 million in federal support). The study published in Education Economics Review can be found here, but for a $19.95 fee; hence, I’ll do my best to explain this study’s contents so you all can save your money, unless of course you too want to dig deeper.

As background (and as explained on the first page of the full paper), the theory behind performance pay is that tying teacher pay to teacher performance provides “strong incentives” to improve outcomes of interest. “It can help motivate teachers to higher levels of performance and align their behaviors and interests with institutional goals.” I should note, however, that there is very mixed evidence from over 100 years of research on performance pay regarding whether it has ever worked. Economists tend to believe it works while educational researchers tend to disagree.

Regardless, in this study as per a ResearchNews@Vanderbilt post put out by Vanderbilt highlighting it, researchers found that teacher-level growth in student achievement in mathematics and reading in schools in which teachers were given monetary performance incentives was significantly higher during the first year of the program’s implementation (2007-2008), than was the same growth in the nearest matched, neighborhood schools where teachers were not given performance incentives. Similar gains were maintained the following year, yet (as per the full report) no additional growth or loss was noted otherwise.

As per the full report as well, researchers more specifically found that students who were enrolled in the REACH program gained between 0.13 and 0.17 standard deviations greater gains in mathematics, and (although not as evident or highlighted in the text of the actual report, but within a related table) students who were enrolled in the REACH program gained between 0.10 and 0.05 standard deviations greater gains in reading, although these gains were also less significant in statistical terms. Curious…

While the method by which schools were matched was well-detailed, and inter-school descriptive statistics were presented to help readers determine whether in fact the schools sampled for this study were comparable (although statistics that would also help us determine whether the inter-school differences noted were statistically significant enough to pay attention to), the statistics comparing the teachers in REACH schools versus those not in REACH schools to whom they were compared were completely missing. Hence, it is impossible to even begin to determine whether the matching methodology used actually yielded comparable samples down to the teacher level – the heart of this research study. This is a fatal flaw that in my opinion should have prevented this study from being published, at least as is, as without this information we have no guarantees that teachers within these schools were indeed comparable.

Regardless, researchers also examined teachers’ Student Learning Objectives (SLOs) – the incentive program’s “primary measure of individual teacher performance” given so many teachers are still VAM-ineligible (see a prior post about SLOs, here). They examined whether SLO scores correlated with VAM scores, for those teachers who had both.

They found, as per a quote by Springer in the above-mentioned post, that “[w]hile SLOs may serve as an important pedagogical tool for teachers in encouraging goal-setting for students, the format and guidance for SLOs within the specific program did not lead to the proper identification of high value-added teachers.” That is, more precisely and as indicated in the actual study, SLOs were “not significantly correlated with a teacher’s value-added student test scores;” hence, “a teacher is no more likely to meet his or her SLO targets if [his/her] students have higher levels of achievement [over time].” This has huge implications, in particular regarding the still lacking evidence of validity surrounding SLOs.

Rothstein, Chetty et al., and (Now) Kane on Bias

Here’s an update to a recent post about research conducted by Berkeley Associate Professor of Economics – Jesse Rothstein.

In Rothstein’s recently released study, he provides evidence that puts the aforementioned Chetty et al. results under a more appropriate light. Rothstein’s charge, again, is that Chetty et al. (perhaps unintentionally) masked evidence of bias in their now infamous VAM-based study, which in turn biased Chetty et al.’s (perpetual) claims that teachers caused effects in student achievement growth over time. These effects, rather, might have been more likely caused by bias given the types of students non-randomly assigned to teachers’ classrooms versus “true teacher effects.”

In addition, while in his study Rothstein replicated Chetty et al.’s overall results using a similar data set, so did Thomas Kane – a colleague of Chetty’s at Harvard who has also been the source of prior VAMboozled! posts here, here, and here. During the Vergara v. California case last summer, the prosecuting team actually used Kane’s (and colleagues’) replication-study results to validate Chetty et al.’s initial results.

However, Rothstein did not replicate Chetty et al.’s findings when it came to bias (the best evidence of this is offered in Rothstein’s study’s Appendix B). Inversely, Kane’s (and colleagues’) study did not, then, have any of the prior year score analyses needed to analyze and assess bias, so the extent to which Chetty et al.’s results were due to bias was then more or less moot.

But after Rothstein released his recent study effectively critiquing Chetty et al. on this point, Kane (and colleagues) released the results Kane presented at the Vergara trial (see here). However, Kane (and colleagues) seemingly released an updated version of “Kane’s” initial results to seemingly counter Rothstein, in support of Chetty. In other words, Kane seems to have released his study (perhaps) more in support of his colleague Chetty than in the name of conducting good, independent research.

Oh the tangled web Chetty and Kane (purportedly) continue to weave.

See also Chetty et al.’s direct response to Rothstein here.

Rothstein, Chetty et al., and VAM-Based Bias

Recall the Chetty et al. study at focus of many posts on this blog (see for example here, here, and here)? The study was cited in President Obama’ 2012 State of the Union address when Obama said, “We know a good teacher can increase the lifetime income of a classroom by over $250,000,” and this study was more recently the focus of attention when the judge in Vergara v. California cited Chetty et al.’s study as providing evidence that “a single year in a classroom with a grossly ineffective teacher costs students $1.4 million in lifetime earnings per classroom.” Well, this study is at the source of a new, and very interesting VAM-based debate, again.

This time, new research conducted by Berkeley Associate Professor of Economics – Jesse Rothstein – provides evidence that puts the aforementioned Chetty et al. results under another appropriate light. While Rothstein and others have written critiques of the Chetty et al. study prior (see prior reviews here, here, here, and here), what Rothstein recently found (in his working, not-yet-peer-reviewed-study here) is that by using “teacher switching” statistical procedures, Chetty et al. masked evidence of bias in their prior study. While Chetty et al. have repeatedly claimed bias was not an issue (see for example a series of emails on this topic here), it seems indeed it was.

While Rothstein replicated Chetty et al.’s overall results using a similar dataset, Rothstein did not replicate Chetty et al.’s findings when it came to bias. As mentioned, Chetty et al. used a process of “teacher switching” to test for bias in their study, and by doing so found, with evidence, that bias did not exist in their value-added output. Rothstein found that when “teacher switching” is appropriately controlled, however, “bias accounts for about 20% of the variance in [VAM] scores.” This makes suspect, more now than before, Chetty et al.’s prior assertions that their model, and their findings, were immune to bias.

What this means, as per Rothstein, is that “teacher switching [the process used by Chetty et al.] is correlated with changes in students’ prior grade scores that bias the key coefficient toward a finding of no bias.” Hence, there was a reason Chetty et al. did not find bias in their value-added estimates because they did not use the proper, statistical controls to control for bias in the first place. When properly controlled, or adjusted, estimates yield “evidence of moderate bias;” hence, “[t]he association between [value-added] and long-run outcomes is not robust and quite sensitive to controls.”

This has major implications in the sense that this makes suspect the causal statements also made by Chetty et al. and repeated by President Obama, the Vergara v. California judge, and others – that “high value-added” teachers caused students to ultimately realize higher long-term incomes, fewer pregnancies, etc. X years down the road. If Chetty et al. did not appropriately control for bias, which again Rothstein argues with evidence they did not, it is likely that students would have realized these “things” almost if not entirely regardless of their teachers or what “value” their teachers purportedly “added” to their learning X years prior.

In other words, students were likely not randomly assigned to classrooms in either the Chetty et al. or the Rothstein datasets (making these datasets comparable). So if the statistical controls used did not effectively “control for” the lack of non random assignment of students into classrooms, teachers may have been assigned high value-added scores not necessarily because they were high value-added teachers but because they were non-randomly assigned higher performing, higher aptitude, etc. students, in the first place and as a whole. Thereafter, they were given credit for the aforementioned long-term outcomes, regardless.

If the name Jesse Rothstein sounds familiar, it should. I have referenced his research in prior posts here, here, and here, as he is well-known in the area of VAM research, in particular, for a series of papers in which he provided evidence that students who are assigned to classrooms in non-random ways can create biased, teacher-level value-added scores. If random assignment was the norm (i.e., whereas students are randomly assigned to classrooms and, ideally, teachers are randomly assigned to teach those classrooms of randomly assigned students), teacher-level bias would not be so problematic. However, given research I also recently conducted on this topic (see here), random assignment (at least in the state of Arizona) occurs 2% of the time, at best. Principals otherwise outright reject the notion as random assignment is not viewed as in “students’ best interests,” regardless of whether randomly assigning students to classrooms might mean “more accurate” value-added output as a result.

So it seems, we either get the statistical controls right (which I doubt is possible) or we randomly assign (which I highly doubt is possible). Otherwise, we are left wondering whether value-added analyses will ever work as per their intended (and largely ideal) purposes, especially when it comes to evaluating and holding accountable America’s public school teachers for their effectiveness.

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In case you’re interested, Chetty et al have responded to Rothstein’s critique. Their full response can be accessed here. Not surprisingly, they first highlight that Rothstein (and another set of their colleagues at Harvard), replicated their results. That “value-added (VA) measures of teacher quality show very consistent properties across different settings” is that on which Chetty et al. focus first and foremost. What they dismiss, however, is whether the main concerns raised by Rothstein threaten the validity of their methods, and their conclusions. They also dismiss the fact that Rothstein addressed Chetty et al.’s counterpoints before they published them, in Appendix B of his paper given Chetty et al. shared their concerns with Rothstein prior to his study’s release.

Nonetheless, the concerns Chetty et al. attempt to counter are whether their “teacher-switching” approach was invalid, and whether the “exclusion of teachers with missing [value-added] estimates biased the[ir]conclusion[s]” as well. The extent to which missing data bias value-added estimates has also been discussed prior when statisticians force the assumption in their analyses that missing data are “missing at random” (MAR), which is a difficult (although for some like Chetty et al, necessary) assumption to swallow (see, for example, the Braun 2004 reference here).

A Major VAMbarrassment in New Orleans

Tulane University’s Cowen Institute for Education Initiatives, in what is now being called a “high-profile embarrassment,” is apologizing for releasing a high-profile report based on faulty research. Their report, that at one point seemed to have successfully VAMboozle folks representing numerous Louisiana governmental agencies and education non-profits, many of which emerged after Hurricane Katrina, has since been pulled from its once prevalent placement on the institute’s website.

It seems that on October 1, 2014 the institute released a report titled “Beating-the-Odds: Academic Performance and Vulnerable Student Populations in New Orleans Public High Schools.” The report was celebrated widely (see, for example, here) in that it “proved” that students in post-Hurricane Katrina (largely charter) schools, despite the disadvantages they faced prior to the school-based reforms triggered by Katrina, were now “beating the odds, while “posting better test scores and graduation rates than predicted by their populations,” thanks to these reforms. Demographics were no longer predicting students’ educational destinies, and the institute had the VAM-based evidence to prove it.

Institute researchers also “found” that New Orleans charter schools, in which over 80% of all New Orleans are now educated, were to have been substantively helping students “beat the odds,” accordingly. Let’s just say the leaders of the charter movement were all over this one, and also allegedly involved.

To some, however, the report’s initial findings (the findings that have now been retracted) did not come as much of a surprise. It seems the Cowen Institute and the city’s leading charter school incubator, New Schools for New Orleans, literally share office space; that is, they are literally “in office space” together.

To read more about this, as per the research of Kristen Buras (Associate Professor at Georgia State), click here. Mercedes Schneider on a recent post she also wrote about this noted that the “Cowen Institute at Tulane University has been promoting the New Orleans Charter Miracle [emphasis added]” and consistently trying “to sell the ‘transformed’ post-Katrina education system in New Orleans” since 2007 (two years post Katrina). Thanks also go out to Mercedes Schneider as before the report was brought down, she downloaded the report. This report can still be accessed there, or also directly here: Beating-the-Odds for those who want to dive into this further.

Anyhow, and as per another news article recently released about this mess, the Cowen Institute’s Executive Director John Ayers removed the report because the research within it was “inaccurate,” and institute “[o]fficials determined the report’s methodology was flawed, making its conclusions inaccurate.” The report is not be reissued. The institute also intends to “thoroughly examine and strengthen [the institute’s] internal protocols” because of this and to make sure that this does not happen again. The released report was not appropriately internally reviewed, as per the institute’s official response, although external review would have certainly been more appropriate here. Similar situations capturing why internal AND more importantly external review are so very important have been the focus of prior blog posts here, here, and here.

But in this report, that listed Debra Vaughan (the Institute’s Director of Research) and Patrick Sims (the Institute’s Senior Research Analysts) as the lead researchers, they used what they called a VAM – BUT what they did in terms of analyses was certainly not akin to an advanced or “sophisticated” VAM (not that using a VAM would have revealed entirely more accurate and/or less flawed results). Instead, they used a simpler regression approach to reach (or confirm) their conclusions. While Ayers “would not say what piece of the methodology was flawed,” we can all be quite certain it was the so-called “VAM” that was the cause of this serious case of VAMbarrassment.

As background, and from a related article about the Cowen Institute and one of its new affiliates – Douglas Harris, who has also written a book about VAMs but positioned VAMs in a more positive light than I did in my book, but who is also not listed as a direct or affiliated author on the report – the situation in New Orleans post Katrina is as follows:

Before the storm hit in August 2005, New Orleans public schools were like most cities and followed the 100-year-old “One Best System.” A superintendent managed all schools in the school district, which were governed by a locally-elected school board. Students were taught by certified and unionized teachers and attended schools mainly based on where they lived.

But that all changed when the hurricane shuttered most schools and scattered students around the nation. That opened the door for alternative forms of public education, such as charter schools that shifted control from the Orleans Parish School Board into the hands of parents and a state agency, the Recovery School District.

In the 2012-13 school year, 84 percent of New Orleans public school students attended charter schools…New Orleans [currently] leads the nation in the percentage of public school students enrolled in charter schools, with the next-highest percentages in Washington D.C. and Detroit (41 percent in each).

Moshe Adler on Chetty et al.

Recall the study at focus of now many posts on this blog? Written by Raj Chetty and two of his colleagues at Harvard, cited first in President Obama’ 2012 State of the Union address when Obama said, “We know a good teacher can increase the lifetime income of a classroom by over $250,000,” and more recently the focus of much attention when the judge in Vergara v. California cited Chetty’s testimony and the aforementioned team’s research as evidence that “a single year in a classroom with a grossly ineffective teacher costs students $1.4 million in lifetime earnings per classroom?”

Well, one of this study’s biggest critics, and most important critics as an expert in economics himself, has written a response to all of this. More specifically, Moshe Adler – economist affiliated with Columbia University and Empire State College, State University of New York and author of the 2010 book, Economics for the Rest of Us: Debunking the Science That Makes Life Dismal – first (in April 2014) wrote a critique of Chetty et al.’s studies for the National Education Policy Center (NEPC) during which he also compared this study from its first release in 2011 (cited by Obama) to its current 2014 version (cited by the judge in Vergara v. California) that can be found here, after which Chetty et al. wrote a response (in May 2014) that can be found at the bottom of the page here, after which Adler released a final response (in June 2014) that can also be found at the very bottom of the page here.

For those of you interested in reading all three pieces, all three are really quite critical to understanding Chetty et al.’s methods, as well as their methods’ shortcomings; their assumptions, as well as their feasibility in context; their results, as unfortunately overstated as they are; and the like. For those of you who want just the highlights, though (as also referenced in the press release just released here), here are the nine key points taken from now the two studies. These are the nine concerns made and established by Moshe in the first round, to which Chetty et al. responded, but seemingly still stand as solid criticisms.

As per Chetty et al.’s study #1:

  1. Value-added scores in this report and in general are unstable from year to year and from test to test, but the report ignores this instability.
  2. The report inflates the effect of teacher value-added by assuming that a child’s test scores can be improved endlessly [over time].
  3. The procedure that the report develops for calculating teacher value-added varies greatly between subjects within school levels (math or English in elementary/high school) and between schools within subjects (elementary or middle school math/English), indicating that the calculated teacher value-added may be random.
  4. The commonly used method for determining how well a model predicts results is through correlations and illustrated through scatterplot graphs. The report does not [however] present either the calculations or graphs and instead invents its own novel graph to show that the measurements of value-added produce good predictions of future teacher performance. But this is misleading. Notwithstanding the graph, it is possible that the quality of predictions in the report was poor.

As per Chetty et al.’s study #2:

  1. An earlier version of the report found that an increase in teacher value-added has no effect on income at age 30, but this result is not mentioned in this revised version. Instead, the authors state that they did not have a sufficiently large sample to investigate the relationship between teacher value-added and income at any age after 28, but this claim is untrue. They had 220,000 observations (p. 15), which is a more than sufficiently large sample for their analysis.
  2. The method used to calculate the 1.34% increase [that a 1 standard deviation increase in teacher VA raises wages at age 28 by 1.34% (p. 37)] is misleading, since observations with no reported income were included in the analysis, while high earners were excluded. If done properly, it is possible that the effect of teacher value-added is to decrease, not increase, income at age 28 (or 30).
  3. The increase in annual income at age 28 due to having a higher quality teacher “improved” dramatically from the first version of the report ($182 per year, report of December, 2011) to the next ($286 per year, report of September, 2013). Because the data sets are not identical, a slight discrepancy between estimates is to be expected. But since the discrepancy is so large, it suggests that the correlation between teacher value-added and income later in life is random.
  4. In order to achieve its estimate of a $39,000 income gain per student, the report makes the assumption that the 1.34% increase in income at age 28 will be repeated year after year. Because no increase in income was detected at age 30, [however] and because 29.6% of the observations consisted of non-filers, this assumption is unjustified.
  5. The effect of teacher value-added on test scores fades out rapidly. The report deals with this problem by citing two studies that it claims buttress the validity of its own results. This claim is [also] both wrong and misleading.