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

Teachers’ “Similar” Value-Added Estimates Yield “Different” Meanings Across “Different” Contexts

Some, particularly educational practitioners, might respond with a sense of “duh”-like sarcasm to the title of this post above, but as per a new research study recently released in the highly reputable, peer-reviewed American Educational Research Journal (AERJ), researchers evidenced this very headline via an extensive research study they conducted in the northeast United States. Hence, this title has now been substantiated with empirical evidence.

Researchers David Blazar (Doctoral Candidate at Harvard), Erica Litke (Assistant Professor at University of Delaware), and Johanna Barmore (Doctoral Candidate at Harvard) examined (1) the comparability of teachers’ value-added estimates within and across four urban districts and (2), given the extent to which variations observed, how and whether said value-added estimates consistently captured differences in teachers’ observed, videotaped, and scored classroom practices.

Regarding their first point of investigation, they found that teachers were categorized differently when compared within versus across districts (i.e., when compared to other similar teachers within districts versus across districts, which is a methodological choice that value-added modelers often make). Researchers found and asserted not that either approach yielded more valid interpretations, however. Rather, they evidenced that the differences they observed within and across districts were notable, and these differences had notable implications for validity, whereas a teacher classified as adding X value in one context could be categorized as adding Y value in another, given the context in which (s)he was teaching. In other words, the validity of the inferences to be drawn about potentially any teacher depended greatly on context in which the teacher taught, in that his/her value-added estimate did not necessarily generalize across contexts. Put in their words, “it is not clear whether the signal of teachers’ effectiveness sent by their value-added rankings retains a substantive interpretation across contexts” (p. 326). Inversely put, “it is clear that labels such as highly effective or ineffective based on value-added scores do not have fixed meaning” (p. 351).

Regarding their second point of investigation, they found “stark differences in instructional practices across districts among teachers who received similar within-district value-added rankings” (p. 324). In other words, “when comparing [similar] teachers within districts, value-added rankings signaled differences in instructional quality in some but not all instances” (p. 351), whereas similarly ranked teachers did not necessarily display effective or ineffective teachers. This has also been more loosely evidenced via those who have investigated the correlations between teachers’ value-added and observational scores, and have found weak to moderate correlations (see prior posts on this here, here, here, and here). In the simplest of terms, “value-added categorizations did not signal common sets of instructional practices across districts” (p. 352).

The bottom line here, then, is that those in charge of making consequential decisions about teachers, as based even if in part on teachers’ value-added estimates, need to be cautious when making particularly high-stakes decisions about teachers as based on said estimates. A teacher, as based on the evidence presented in this particular study could logically but also legally argue that had (s)he been teaching in a different district, even within the same state and using the same assessment instruments, (s)he could have received a substantively different value-added score given the teacher(s) to whom (s)he was compared when estimating his/her value-added elsewhere. Hence, the validity of the inferences and statements asserting that one teacher was effective or not as based on his/her value-added estimates is suspect, again, as based on the contexts in which teachers teach, as well as when compared to whatever other comparable teachers to whom teachers are compared when estimating teacher-level value-added. “Here, the instructional quality of the lowest ranked teachers was not particularly weak and in fact was as strong as the instructional quality of the highest ranked teachers in other districts” (p. 353).

This has serious implications, not only for practice but also for the lawsuits ongoing across the nation, especially in terms of those pertaining to teachers’ wrongful terminations, as charged.

Citation: Blazar, D., Litke, E., & Barmore, J. (2016). What does it mean to be ranked a ‘‘high’’ or ‘‘low’’ value-added teacher? Observing differences in instructional quality across districts. American Educational Research Journal, 53(2), 324–359.  doi:10.3102/0002831216630407

Everything is Bigger (and Badder) in Texas: Houston’s Teacher Value-Added System

Last November, I published a post about “Houston’s “Split” Decision to Give Superintendent Grier $98,600 in Bonuses, Pre-Resignation.” Thereafter, I engaged some of my former doctoral students to further explore some data from Houston Independent School District (HISD), and what we collectively found and wrote up was just published in the highly-esteemed Teachers College Record journal (Amrein-Beardsley, Collins, Holloway-Libell, & Paufler, 2016). To view the full commentary, please click here.

In this commentary we discuss HISD’s highest-stakes use of its Education Value-Added Assessment System (EVAAS) data – the value-added system HISD pays for at an approximate rate of $500,000 per year. This district has used its EVAAS data for more consequential purposes (e.g., teacher merit pay and termination) than any other state or district in the nation; hence, HISD is well known for its “big use” of “big data” to reform and inform improved student learning and achievement throughout the district.

We note in this commentary, however, that as per the evidence, and more specifically the recent release of the Texas’s large-scale standardized test scores, that perhaps attaching such high-stakes consequences to teachers’ EVAAS output in Houston is not working as district leaders have, now for years, intended. See, for example, the recent test-based evidence comparing the state of Texas v. HISD, illustrated below.

Figure 1

“Perhaps the district’s EVAAS system is not as much of an “educational-improvement and performance-management model that engages all employees in creating a culture of excellence” as the district suggests (HISD, n.d.a). Perhaps, as well, we should “ponder the specific model used by HISD—the aforementioned EVAAS—and [EVAAS modelers’] perpetual claims that this model helps teachers become more “proactive [while] making sound instructional choices;” helps teachers use “resources more strategically to ensure that every student has the chance to succeed;” or “provides valuable diagnostic information about [teachers’ instructional] practices” so as to ultimately improve student learning and achievement (SAS Institute Inc., n.d.).

The bottom line, though, is that “Even the simplest evidence presented above should at the very least make us question this particular value-added system, as paid for, supported, and applied in Houston for some of the biggest and baddest teacher-level consequences in town.” See, again, the full text and another, similar graph in the commentary, linked  here.



Amrein-Beardsley, A., Collins, C., Holloway-Libell, J., & Paufler, N. A. (2016). Everything is bigger (and badder) in Texas: Houston’s teacher value-added system. [Commentary]. Teachers College Record. Retrieved from http://www.tcrecord.org/Content.asp?ContentId=18983

Houston Independent School District (HISD). (n.d.a). ASPIRE: Accelerating Student Progress Increasing Results & Expectations: Welcome to the ASPIRE Portal. Retrieved from http://portal.battelleforkids.org/Aspire/home.html

SAS Institute Inc. (n.d.). SAS® EVAAS® for K–12: Assess and predict student performance with precision and reliability. Retrieved from www.sas.com/govedu/edu/k12/evaas/index.html

Report on the Stability of Student Growth Percentile (SGP) “Value-Added” Estimates

The Student Growth Percentiles (SGPs) model, which is loosely defined by value-added model (VAM) purists as a VAM, uses students’ level(s) of past performance to determine students’ normative growth over time, as compared to his/her peers. “SGPs describe the relative location of a student’s current score compared to the current scores of students with similar score histories” (Castellano & Ho, p. 89). Students are compared to themselves (i.e., students serve as their own controls) over time; therefore, the need to control for other variables (e.g., student demographics) is less necessary, although this is of debate. Nonetheless, the SGP model was developed as a “better” alternative to existing models, with the goal of providing clearer, more accessible, and more understandable results to both internal and external education stakeholders and consumers. For more information about the SGP please see prior posts here and here. See also an original source about the SGP here.

Related, in a study released last week, WestEd researchers conducted an “Analysis of the stability of teacher-level growth scores [derived] from the student growth percentile [SGP] model” in one, large school district in Nevada (n=370 teachers). The key finding they present is that “half or more of the variance in teacher scores from the [SGP] model is due to random or otherwise unstable sources rather than to reliable information that could predict future performance. Even when derived by averaging several years of teacher scores, effectiveness estimates are unlikely to provide a level of reliability desired in scores used for high-stakes decisions, such as tenure or dismissal. Thus, states may want to be cautious in using student growth percentile [SGP] scores for teacher evaluation.”

Most importantly, the evidence in this study should make us (continue to) question the extent to which “the learning of a teacher’s students in one year will [consistently] predict the learning of the teacher’s future students.” This is counter to the claims continuously made by VAM proponents, including folks like Thomas Kane — economics professor from Harvard University who directed the $45 million worth of Measures of Effective Teaching (MET) studies for the Bill & Melinda Gates Foundation. While faint signals of what we call predictive validity might be observed across VAMs, what folks like Kane overlook or avoid is that very often these faint signals do not remain constant over time. Accordingly, the extent to which we can make stable predictions is limited.

Worse is when folks falsely assume that said predictions will remain constant over time, and they make high-stakes decisions about teachers unaware of the lack of stability present, in typically 25-59% of teachers’ value-added (or in this case SGP) scores (estimates vary by study and by analyses using one to three years of data — see, for example, the studies detailed in Appendix A of this report; see also other research on this topic here, here, and here). Nonetheless, researchers in this study found that in mathematics, 50% of the variance in teachers’ value-added scores were attributable to differences among teachers, and the other 50% was random or unstable. In reading, 41% of the variance in teachers’ value-added scores were attributable to differences among teachers, and the other 59% was random or unstable.

In addition, using a 95% confidence interval (which is very common in educational statistics) researchers found that in mathematics, a teacher’s true score would span 48 points, “a margin of error that covers nearly half the 100 point score scale,” whereby “one would be 95 percent confident that the true math score of a teacher who received a score of 50 [would actually fall] between 26 and 74.” For reading, a teacher’s true score would span 44 points, whereby one would be 95 percent confident that the true reading score of a teacher who received a score of 50 would actually fall between 38 and 72. The stability of these scores would increase with three years of data, which has also been found by other researchers on this topic. However, they too have found that such error rates persist to an extent that still prohibits high-stakes decision making.

In more practical terms, what this also means is that a teacher who might be considered highly ineffective might be terminated, even though the following year (s)he could have been observed to be highly effective. Inversely, teachers who are awarded tenure might be observed as ineffective one, two, and/or three years following, not because their true level(s) of effectiveness change, but because of the error in the estimates that causes such instabilities to occur. Hence, examinations of the the stability of such estimates over time provides essential evidence of the validity, and in this case predictive validity, of the interpretations and uses of such scores over time. This is particularly pertinent when high-stakes decisions are to be based on (or in large part on) such scores, especially given some researchers are calling for reliability coefficients of .85 or higher to make such decisions (Haertel, 2013; Wasserman & Bracken, 2003).

In the end, researchers’ overall conclusion is that SGP-derived “growth scores alone may not be sufficiently stable to support high-stakes decisions.” Likewise, relying on the extant research on this topic, the overall conclusion can be broadened in that neither SGP- or VAM-based growth scores may be sufficiently stable to support high-stakes decisions. In other words, it is not just the SGP model that is yielding such issues with stability (or a lack thereof). Again, see the other literature in which researchers situated their findings in Appendix A. See also other similar studies here, here, and here.

Accordingly, those who read this report, and consequently seek to find a better or more stable model that yields more stable estimates, will unfortunately but likely fail in their search.


Castellano, K. E., & Ho, A. D. (2013). A practitioner’s guide to growth models. Washington, DC: Council of Chief State School Officers.

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).

Lash, A., Makkonen, R., Tran, L., & Huang, M. (2016). Analysis of the stability of teacher-level growth scores [derived] from the student growth percentile [SGP] model. (16–104). Washington, DC: U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance, Regional Educational Laboratory West.

Wasserman, J. D., & Bracken, B. A. (2003). Psychometric characteristics of assessment procedures. In I. B. Weiner, J. R. Graham, & J. A. Naglieri (Eds.), Handbook of psychology:
Assessment psychology (pp. 43–66). Hoboken, NJ: John Wiley & Sons.

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 #5 of 9): Teachers’ Perceptions of Observations and Student Growth

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 (#5 of 9) here, titled “Teacher Perspectives on Evaluation Reform: Chicago’s REACH [Recognizing Educators Advancing Chicago Students] Students.” This one is authored by Jennie Jiang, Susan Sporte, and Stuart Luppescu, all of whom are associated with The University of Chicago’s Consortium on Chicago School Research, and all of whom conducted survey- and interview-based research on teachers’ perceptions of the Chicago Public Schools (CPS) teacher evaluation system, twice since it was implemented in 2012–2013. They did this across CPS’s almost 600 schools and its more than 12,000 teachers, with high-stakes being recently attached to teacher evaluations (e.g., professional development plans, remediation, tenure attainment, teacher dismissal/contract non-renewal; p. 108).

Directly related to the Review of Article #4 prior (i.e., #4 of 9 on observational systems’ potentials here), these researchers found that Chicago teachers are, in general, positive about the evaluation system, primarily given the system’s observational component (i.e., the Charlotte Danielson Framework for Teaching, used twice per year for tenured teachers and that counts for 75% of teachers’ evaluation scores), and not given the inclusion of student growth in this evaluation system (that counts for the other 25%). Although researchers also found that overall satisfaction levels with the REACH system at large is declining at a statistically significant rate over time, as teachers get to know the system, perhaps, better.

This system, like the strong majority of others across the nation, is based on only these two components, although the growth measure includes a combination of two different metrics (i.e., value-added scores and growth on “performance tasks” as per the grades and subject areas taught). See more information about how these measures are broken down by teacher type in Table 1 (p. 107), and see also (p. 107) for the different types of measures used (e.g., the Northwest Evaluation Association’s Measures of Academic Progress assessment (NWEA-MAP), a Web-based, computer-adaptive, multiple-choice assessment, that is used to measure value-added scores for teachers in grades 3-8).

As for the student growth component, more specifically, when researchers asked teachers “if their evaluation relies too heavily on student growth, 65% of teachers agreed or strongly agreed” (p. 112); “Fifty percent of teachers disagreed or strongly disagreed that NWEA-MAP [and other off-the-shelf tests used to measure growth in CPS offered] a fair assessment of their student’s learning” (p. 112); “teachers expressed concerns about the narrow representation of student learning that is measured by standardized tests and the increase in the already heavy testing burden on teachers and students” (p. 112); and “Several teachers also expressed concerns that measures of student growth were unfair to teachers in more challenging schools [i.e., bias], because student growth is related to the supports that students may or may not receive outside of the classroom” (p. 112). “One teacher explained this concern [writing]: “I think the part that I find unfair is that so much of what goes on in these kids’ lives is affecting their academics, and those are things that a
teacher cannot possibly control” (p. 112).

As for the performance tasks meant to compliment (or serve as) the student growth or VAM measure, teachers were discouraged with this being so subjective, and susceptible to distortion because teachers “score their own students’ performance tasks at both the beginning and end of the year. Teachers noted that if they wanted to maximize their student growth score, they could simply give all students a low score on the beginning-of-year task and a higher score at the end of the year” (p. 113).

As for the observational component, however, researchers found that “almost 90% of teachers agreed that the feedback they were provided in post-observation conferences” (p. 111) was of highest value; the observational processes but more importantly the post-observational processes made them and their supervisors more accountable for their effectiveness, and more importantly their improvement. While in the conclusions section of this article authors stretch this finding out a bit, writing that “Overall, this study finds that there is promise in teacher evaluation reform in Chicago,” (p. 114) as primarily based on their findings about “the new observation process” (p. 114) being used in CPS, recall from the Review of Article #4 prior (i.e., #4 of 9 on observational systems’ potentials here), these observational systems are not “new and improved.” Rather, these are the same observational systems that, given their levels of subjectivity featured and highlighted in reports like “The Widget Effect” (here), brought us to our now (over)reliance on VAMs.

Researchers also found that teachers were generally confused about the REACH system, and what actually “counted” and for how much in their evaluations. The most confusion surrounded the student growth or value-added component, as (based on prior research) would be expected. Beginning teachers reported more clarity, than did relatively more experienced teachers, high school teachers, and teachers of special education students, and all of this was related to the extent to which a measure of student growth directly impacted teachers’ evaluations. Teachers receiving school-wide value-added scores were also relatively more critical.

Lastly, researchers found that in 2014, “79% of teachers reported that the evaluation process had increased their levels of stress and anxiety, and almost 60% of teachers
agreed or strongly agreed the evaluation process takes more effort than the results are worth.” Again, beginning teachers were “consistently more positive on all…measures than veteran teachers; elementary teachers were consistently more positive than high
school teachers, special education teachers were significantly more negative about student growth than general teachers,” and the like (p. 113). And all of this was positively and significantly related to teachers’ perceptions of their school’s leadership, perceptions of the professional communities at their schools, and teachers’ perceptions of evaluation writ large.


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; and see the Review of Article #4 – on observational systems’ potentials here.

Article #5 Reference: Jiang, J. Y., Sporte, S. E., & Luppescu, S. (2015). Teacher perspectives on evaluation reform: Chicago’s REACH students. Educational Researcher, 44(2), 105-116. doi:10.3102/0013189X15575517

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

The Public Release of Value-Added Scores Does Not (Yet) Impact Real Estate

As per ScienceDaily, a resource “for the latest research news,” research just conducted by economists at Michigan State and Cornell evidences that “New school-evaluation method fails to affect housing prices.” See also a press release about this study on Michigan State’s website here, and see what I believe is a pre-publication version of the full study here.

As asserted in both pieces, the study recently published in the Journal of Urban Economics, is the first to examine how the public release of such data is considered in housing prices. Researchers, more specifically, examined whether and to what extent the (very controversial) public release of teachers’ VAM data by the Los Angeles Times impacted housing prices in Los Angeles. To read a prior post on this release, click here.

While for some time now we have known from similar research studies, conducted throughout the pre-VAM era, that students’ test scores are correlated with (or cause) rises in housing prices, these researchers evidenced that, thus far, the same does not (yet) seem to be true in the case of VAMs. That is, the public consumption of publicly available value-added data, at least in Los Angeles, does not (yet) seem to be correlated with or causing really anything in the housing market.

“The implication: Either people don’t value the popular new measures or they don’t fully understand them.” Perhaps another implication is that it is just (unfortunately) a matter of time. I write this in consideration of the fact that while researchers included data from more than 63,000 home sales as per the Los Angeles County Assessor’s Office, they did so in only the eight-month period following the public release of the VAM data. True effects might be lagged; hence, readers might interpret these results as preliminary, for now.

Including Summers “Adds Considerable Measurement Error” to Value-Added Estimates

A new article titled “The Effect of Summer on Value-added Assessments of Teacher and School Performance” was recently released in the peer-reviewed journal Education Policy Analysis Archives. The article is authored by Gregory Palardy and Luyao Peng from the University of California, Riverside. 

Before we begin, though, here is some background so that you all understand the importance of the findings in this particular article.

In order to calculate teacher-level value added, all states are currently using (at minimum) the large-scale standardized tests mandated by No Child Left Behind (NCLB) in 2002. These tests were mandated for use in the subject areas of mathematics and reading/language arts. However, because these tests are given only once per year, typically in the spring, to calculate value-added statisticians measure actual versus predicted “growth” (aka “value-added”) from spring-to-spring, over a 12-month span, which includes summers.

While many (including many policymakers) assume that value-added estimations are calculated from fall to spring during time intervals under which students are under the same teachers’ supervision and instruction, this is not true. The reality is that the pre- to post-test occasions actually span 12-month periods, including the summers that often cause the nettlesome summer effects often observed via VAM-based estimates. Different students learn different things over the summer, and this is strongly associated (and correlated) with student’s backgrounds, and this is strongly associated (and correlated) with students’ out-of-school opportunities (e.g., travel, summer camps, summer schools). Likewise, because summers are the time periods over which teachers and schools tend to have little control over what students do, this is also the time period during which research  indicates that achievement gaps maintain or widen. More specifically, research indicates that indicates that students from relatively lower socio-economic backgrounds tend to suffer more from learning decay than their wealthier peers, although they learn at similar rates during the school year.

What these 12-month testing intervals also include are prior teachers’ residual effects, whereas students testing in the spring, for example, finish out every school year (e.g., two months or so) with their prior teachers before entering the classrooms of the teachers for whom value-added is to be calculated the following spring, although teachers’ residual effects were not of focus in this particular study.

Nonetheless, via the research, we have always known that these summer (and prior or adjacent teachers’ residual effects) are difficult if not impossible to statistically control. This in and of itself leads to much of the noise (fluctuations/lack of reliability, imprecision, and potential biases) we observe in the resulting value-added estimates. This is precisely what was of focus in this particular study.

In this study researchers examined “the effects of including the summer period on value-added assessments (VAA) of teacher and school performance at the [1st] grade [level],” as compared to using VAM-based estimates derived from a fall-to-spring test administration within the same grade and same year (i.e., using data derived via a nationally representative sample via the National Center for Education Statistics (NCES) with an n=5,034 children).

Researchers found that:

  • Approximately 40-62% of the variance in VAM-based estimates originates from the summer period, depending on the reading or math outcome;
  • When summer is omitted from VAM-based calculations using within year pre/post-tests, approximately 51-61% of the teachers change performance categories. What this means in simpler terms is that including summers in VAM-based estimates is indeed causing some of the errors and misclassification rates being observed across studies.
  • Statistical controls to control for student and classroom/school variables reduces summer effects considerably (e.g., via controlling for students’ prior achievement), yet 36-47% of teachers still fall into different quintiles when summers are included in the VAM-based estimates.
  • Findings also evidence that including summers within VAM-based calculations tends to bias VAM-based estimates against schools with higher relative concentrations of poverty, or rather higher relative concentrations of students who are eligible for the federal free-and-reduced lunch program.
  • Overall, results suggest that removing summer effects from VAM-based estimates may require biannual achievement assessments (i.e., fall and spring). If we want VAM-based estimates to be more accurate, we might have to double the number of tests we administer per year in each subject area for which teachers are to be held accountable using VAMs. However, “if twice-annual assessments are not conducted, controls for prior achievement seem to be the best method for minimizing summer effects.”

This is certainly something to consider in terms of trade-offs, specifically in terms of whether we really want to “double-down” on the number of tests we already require our public students to take (also given the time that testing and test preparation already takes away from students’ learning activities), and whether we also want to “double-down” on the increased costs of doing so. I should also note here, though, that using pre/post-tests within the same year is (also) not as simple as it may seem (either). See another post forthcoming about the potential artificial deflation/inflation of pre/post scores to manufacture artificial levels of growth.

To read the full study, click here.

*I should note that I am an Associate Editor for this journal, and I served as editor for this particular publication, seeing it through the full peer-reviewed process.

Citation: Palardy, G. J., & Peng, L. (2015). The effects of including summer on value-added assessments of teachers and schools. Education Policy Analysis Archives, 23(92). doi:10.14507/epaa.v23.1997 Retrieved from http://epaa.asu.edu/ojs/article/view/1997