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

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

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Professor of Dartmouth), just released yet another National Bureau of Economic Research (NBER) “working paper” (i.e., not peer-reviewed, and in this case not internally reviewed by NBER for public consumption and use either) titled “An Evaluation of Bias in Three Measures of Teacher Quality: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

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

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(i.e., not statisticians or other educational researchers) on this general topic, of which 10/16 (63%) are not peer reviewed and of which 6/16 (38%) are either authored or co-authored by Kane (1/6 being published in a peer-reviewed journal).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

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

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

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

Needless to say…

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

New Evidence that Developmental (and Formative) Approaches to Teacher Evaluation Systems Work

Susan Moore Johnson – Professor of Education at Harvard University and author of another important article regarding how value-added models (VAMs) oft-reinforce the walls of “egg-crate” schools (here) – recently published (along with two co-authors) an article in the esteemed, peer-reviewed Educational Evaluation and Policy Analysis. The article titled: Investing in Development: Six High-Performing, High-Poverty Schools Implement the Massachusetts Teacher Evaluation Policy can be downloaded here (in its free, pre-publication form).

In this piece, as taken from the abstract, they “studied how six high-performing, high-poverty [and traditional, charter, under state supervision] schools in one large Massachusetts city implemented the state’s new teacher evaluation policy” (p. 383). They aimed to learn how these “successful” schools, with “success” defined by the state’s accountability ranking per school along with its “public reputation,” approached the state’s teacher evaluation system and its system components (e.g., classroom observations, follow-up feedback, and the construction and treatment of teachers’ summative evaluation ratings). They also investigated how educators within these schools “interacted to shape the character and impact of [the state’s] evaluation” (p. 384).

Akin to Moore Johnson’s aforementioned work, she and her colleagues argue that “to understand whether and how new teacher evaluation policies affect teachers and their work, we must investigate [the] day-to-day responses [of] those within the schools” (p. 384). Hence, they explored “how the educators in these schools interpreted and acted on the new state policy’s opportunities and requirements and, overall, whether they used evaluation to promote greater accountability, more opportunities for development, or both” (p. 384).

They found that “despite important differences among the six successful schools [they] studied (e.g., size, curriculum and pedagogy, student discipline codes), administrators responded to the state evaluation policy in remarkably similar ways, giving priority to the goal of development over accountability [emphasis added]” (p. 385). In addition, “[m]ost schools not only complied with the new regulations of the law but also went beyond them to provide teachers with more frequent observations, feedback, and support than the policy required. Teachers widely corroborated their principal’s reports that evaluation in their school was meant to improve their performance and they strongly endorsed that priority” (p. 385).

Overall, and accordingly, they concluded that “an evaluation policy focusing on teachers’ development can be effectively implemented in ways that serve the interests of schools, students, and teachers” (p. 402). This is especially true when (1) evaluation efforts are “well grounded in the observations, feedback, and support of a formative evaluation process;” (2) states rely on “capacity building in addition to mandates to promote effective implementation;” and (3) schools also benefit from spillover effects from other, positive, state-level policies (i.e., states do not take Draconian approaches to other educational policies) that, in these cases included policies permitting district discretion and control over staffing and administrative support (p. 402).

Related, such developmental and formatively-focused teacher evaluation systems can work, they also conclude, when schools are lead by highly effective principals who are free to select high quality teachers. Their findings suggest that this “is probably the most important thing district officials can do to ensure that teacher evaluation will be a constructive, productive process” (p. 403). In sum, “as this study makes clear, policies that are intended to improve schooling depend on both administrators and teachers for their effective implementation” (p. 403).

Please note, however, that this study was conducted before districts in this state were required to incorporate standardized test scores to measure teachers’ effects (e.g., using VAMs); hence, the assertions and conclusions that authors set forth throughout this piece should be read and taken into consideration given that important caveat. Perhaps findings should matter even more in that here is at least some proof that teacher evaluation works IF used for developmental and formative (versus or perhaps in lieu of summative) purposes.

Citation: Reinhorn, S. K., Moore Johnson, S., & Simon, N. S. (2017). Educational Evaluation and Policy Analysis, 39(3), 383–406. doi:10.3102/0162373717690605 Retrieved from https://projectngt.gse.harvard.edu/files/gse-projectngt/files/eval_041916_unblinded.pdf

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

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

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

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

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

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

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

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

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

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

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

Citations:

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

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

The “Widget Effect” Report Revisited

You might recall that in 2009, The New Teacher Project published a highly influential “Widget Effect” report in which researchers (see citation below) evidenced that 99% of teachers (whose teacher evaluation reports they examined across a sample of school districts spread across a handful of states) received evaluation ratings of “satisfactory” or higher. Inversely, only 1% of the teachers whose reports researchers examined received ratings of “unsatisfactory,” even though teachers’ supervisors could identify more teachers whom they deemed ineffective when asked otherwise.

Accordingly, this report was widely publicized given the assumed improbability that only 1% of America’s public school teachers were, in fact, ineffectual, and given the fact that such ineffective teachers apparently existed but were not being identified using standard teacher evaluation/observational systems in use at the time.

Hence, this report was used as evidence that America’s teacher evaluation systems were unacceptable and in need of reform, primarily given the subjectivities and flaws apparent and arguably inherent across the observational components of these systems. This reform was also needed to help reform America’s public schools, writ large, so the logic went and (often) continues to go. While binary constructions of complex data such as these are often used to ground simplistic ideas and push definitive policies, ideas, and agendas, this tactic certainly worked here, as this report (among a few others) was used to inform the federal and state policies pushing teacher evaluation system reform as a result (e.g., Race to the Top (RTTT)).

Likewise, this report continues to be used whenever a state’s or district’s new-and-improved teacher evaluation systems (still) evidence “too many” (as typically arbitrarily defined) teachers as effective or higher (see, for example, an Education Week article about this here). Although, whether in fact the systems have actually been reformed is also of debate in that states are still using many of the same observational systems they were using prior (i.e., not the “binary checklists” exaggerated in the original as well as this report, albeit true in the case of the district of focus in this study). The real “reforms,” here, pertained to the extent to which value-added model (VAM) or other growth output were combined with these observational measures, and the extent to which districts adopted state-level observational models as per the centralized educational policies put into place at the same time.

Nonetheless, now eight years later, Matthew A. Kraft – an Assistant Professor of Education & Economics at Brown University and Allison F. Gilmour – an Assistant Professor at Temple University (and former doctoral student at Vanderbilt University), revisited the original report. Just published in the esteemed, peer-reviewed journal Educational Researcher (see an earlier version of the published study here), Kraft and Gilmour compiled “teacher performance ratings across 24 [of the 38, including 14 RTTT] states that [by 2014-2015] adopted major reforms to their teacher evaluation systems” as a result of such policy initiatives. They found that “the percentage of teachers rated Unsatisfactory remains less than 1%,” except for in two states (i.e., Maryland and New Mexico), with Unsatisfactory (or similar) ratings varying “widely across states with 0.7% to 28.7%” as the low and high, respectively (see also the study Abstract).

Related, Kraft and Gilmour found that “some new teacher evaluation systems do differentiate among teachers, but most only do so at the top of the ratings spectrum” (p. 10). More specifically, observers in states in which teacher evaluation ratings include five versus four rating categories differentiate teachers more, but still do so along the top three ratings, which still does not solve the negative skew at issue (i.e., “too many” teachers still scoring “too well”). They also found that when these observational systems were used for formative (i.e., informative, improvement) purposes, teachers’ ratings were lower than when they were used for summative (i.e., final summary) purposes.

Clearly, the assumptions of all involved in this area of policy research come into play, here, akin to how they did in The Bell Curve and The Bell Curve Debate. During this (still ongoing) debate, many fervently debated whether socioeconomic and educational outcomes (e.g., IQ) should be normally distributed. What this means in this case, for example, is that for every teacher who is rated highly effective there should be a teacher rated as highly ineffective, more or less, to yield a symmetrical distribution of teacher observational scores across the spectrum.

In fact, one observational system of which I am aware (i.e., the TAP System for Teacher and Student Advancement) is marketing its proprietary system, using as a primary selling point figures illustrating (with text explaining) how clients who use their system will improve their prior “Widget Effect” results (i.e., yielding such normal curves; see Figure below, as per Jerald & Van Hook, 2011, p. 1).

Evidence also suggests that these scores are also (sometimes) being artificially deflated to assist in these attempts (see, for example, a recent publication of mine released a few days ago here in the (also) esteemed, peer-reviewed Teachers College Record about how this is also occurring in response to the “Widget Effect” report and the educational policies that follows).

While Kraft and Gilmour assert that “systems that place greater weight on normative measures such as value-added scores rather than…[just]…observations have fewer teachers rated proficient” (p. 19; see also Steinberg & Kraft, forthcoming; a related article about how this has occurred in New Mexico here; and New Mexico’s 2014-2016 data below and here, as also illustrative of the desired normal curve distributions discussed above), I highly doubt this purely reflects New Mexico’s “commitment to putting students first.”

I also highly doubt that, as per New Mexico’s acting Secretary of Education, this was “not [emphasis added] designed with quote unquote end results in mind.” That is, “the New Mexico Public Education Department did not set out to place any specific number or percentage of teachers into a given category.” If true, it’s pretty miraculous how this simply worked out as illustrated… This is also at issue in the lawsuit in which I am involved in New Mexico, in which the American Federation of Teachers won an injunction in 2015 that still stands today (see more information about this lawsuit here). Indeed, as per Kraft, all of this “might [and possibly should] undercut the potential for this differentiation [if ultimately proven artificial, for example, as based on statistical or other pragmatic deflation tactics] to be seen as accurate and valid” (as quoted here).

Notwithstanding, Kraft and Gilmour, also as part (and actually the primary part) of this study, “present original survey data from an urban district illustrating that evaluators perceive more than three times as many teachers in their schools to be below Proficient than they rate as such.” Accordingly, even though their data for this part of this study come from one district, their findings are similar to others evidenced in the “Widget Effect” report; hence, there are still likely educational measurement (and validity) issues on both ends (i.e., with using such observational rubrics as part of America’s reformed teacher evaluation systems and using survey methods to put into check these systems, overall). In other words, just because the survey data did not match the observational data does not mean either is wrong, or right, but there are still likely educational measurement issues.

Also of issue in this regard, in terms of the 1% issue, is (a) the time and effort it takes supervisors to assist/desist after rating teachers low is sometimes not worth assigning low ratings; (b) how supervisors often give higher ratings to those with perceived potential, also in support of their future growth, even if current evidence suggests a lower rating is warranted; (c) how having “difficult conversations” can sometimes prevent supervisors from assigning the scores they believe teachers may deserve, especially if things like job security are on the line; (d) supervisors’ challenges with removing teachers, including “long, laborious, legal, draining process[es];” and (e) supervisors’ challenges with replacing teachers, if terminated, given current teacher shortages and the time and effort, again, it often takes to hire (ideally more qualified) replacements.

References:

Jerald, C. D., & Van Hook, K. (2011). More than measurement: The TAP system’s lessons learned for designing better teacher evaluation systems. Santa Monica, CA: National Institute for Excellence in Teaching (NIET). Retrieved from http://files.eric.ed.gov/fulltext/ED533382.pdf

Kraft, M. A, & Gilmour, A. F. (2017). Revisiting the Widget Effect: Teacher evaluation reforms and the distribution of teacher effectiveness. Educational Researcher, 46(5) 234-249. doi:10.3102/0013189X17718797

Steinberg, M. P., & Kraft, M. A. (forthcoming). The sensitivity of teacher performance ratings to the design of teacher evaluation systems. Educational Researcher.

Weisberg, D., Sexton, S., Mulhern, J., & Keeling, D. (2009). “The Widget Effect.” Education Digest, 75(2), 31–35.

Observational Systems: Correlations with Value-Added and Bias

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

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

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

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

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

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

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

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

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

*****

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

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

*****

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

The Tripod Student Survey Instrument: Its Factor Structure and Value-Added Correlations

The Tripod student perception survey instrument is a “research-based” instrument increasingly being used by states to add to state’s teacher evaluation systems as based on “multiple measures.” While there are other instruments also in use, as well as student survey instruments being developed by states and local districts, this one in particular is gaining in popularity, also in that it was used throughout the Bill & Melinda Gates Foundation’s ($43 million worth of) Measures of Effective Teaching (MET) studies. A current estimate (as per the study discussed in this post) is that during the 2015–2016 school year approximately 1,400 schools purchased and administered the Tripod. See also a prior post (here) about this instrument, or more specifically a chapter of a book about the instrument as authored by the instrument’s developer and lead researcher in a  research surrounding it – Ronald Ferguson.

In a study recently released in the esteemed American Educational Research Journal (AERJ), and titled “What Can Student Perception Surveys Tell Us About Teaching? Empirically Testing the Underlying Structure of the Tripod Student Perception Survey,” researchers found that the Tripod’s factor structure did not “hold up.” That is, Tripod’s 7Cs (i.e., seven constructs including: Care, Confer, Captivate, Clarify, Consolidate, Challenge, Classroom Management; see more information about the 7Cs here) and the 36 items that are positioned within each of the 7Cs did not fit the 7C framework as theorized by instrument developer(s).

Rather, using the MET database (N=1,049 middle school math class sections; N=25,423 students), researchers found that an alternative bi-factor structure (i.e., two versus seven constructs) best fit the Tripod items theoretically positioned otherwise. These two factors included (1) a general responsivity dimension that includes all items (more or less) unrelated to (2) a classroom management dimension that governs responses on items surrounding teachers’ classroom management. Researchers were unable to to distinguish across items seven separate dimensions.

Researchers also found that the two alternative factors noted — general responsivity and classroom management — were positively associated with teacher value-added scores. More specifically, results suggested that these two factors were positively and statistically significantly associated with teachers’ value-added measures based on state mathematics tests (standardized coefficients were .25 and .25, respectively), although for undisclosed reasons, results apparently suggested nothing about these two factors’ (cor)relationships with value-added estimates base on state English/language arts (ELA) tests. As per authors’ findings in the area of mathematics, prior researchers have also found low to moderate agreement between teacher ratings and student perception ratings; hence, this particular finding simply adds another source of convergent evidence.

Authors do give multiple reasons and plausible explanations as to why they found what they did that you all can read in more depth via the full article, linked to above and fully cited below. Authors also note that “It is unclear whether the original 7Cs that describe the Tripod instrument were intended to capture seven distinct dimensions on which students can reliably discriminate among teachers or whether the 7Cs were merely intended to be more heuristic domains that map out important aspects of teaching” (p. 1859); hence, this is also important to keep in mind given study findings.

As per study authors, and to their knowledge, “this study [was] the first to systematically investigate the multidimensionality of the Tripod student perception survey” (p. 1863).

Citation: Wallace, T. L., Kelcey, B., &  Ruzek, E. (2016). What can student perception surveys tell us about teaching? Empirically testing the underlying structure of the Tripod student perception survey.  American Educational Research Journal, 53(6), 1834–1868.
doiI:10.3102/0002831216671864 Retrieved from http://journals.sagepub.com/doi/pdf/10.3102/0002831216671864

New Article Published on Using Value-Added Data to Evaluate Teacher Education Programs

A former colleague, a current PhD student, and I just had an article released about using value-added data to (or rather not to) evaluate teacher education/preparation, higher education programs. The article is titled “An Elusive Policy Imperative: Data and Methodological Challenges When Using Growth in Student Achievement to Evaluate Teacher Education Programs’ ‘Value-Added,” and the abstract of the article is included below.

If there is anyone out there who might be interested in this topic, please note that the journal in which this piece was published (online first and to be published in its paper version later) – Teaching Education – has made the article free for its first 50 visitors. Hence, I thought I’d share this with you all first.

If you’re interested, do access the full piece here.

Happy reading…and here’s the abstract:

In this study researchers examined the effectiveness of one of the largest teacher education programs located within the largest research-intensive universities within the US. They did this using a value-added model as per current federal educational policy imperatives to assess the measurable effects of teacher education programs on their teacher graduates’ students’ learning and achievement as compared to other teacher education programs. Correlational and group comparisons revealed little to no relationship between value-added scores and teacher education program regardless of subject area or position on the value-added scale. These findings are discussed within the context of several very important data and methodological challenges researchers also made transparent, as also likely common across many efforts to evaluate teacher education programs using value-added approaches. Such transparency and clarity might assist in the creation of more informed value-added practices (and more informed educational policies) surrounding teacher education accountability.

Difficulties When Combining Multiple Teacher Evaluation Measures

A new study about multiple “Approaches for Combining Multiple Measures of Teacher Performance,” with special attention paid to reliability, validity, and policy, was recently published in the American Educational Research Association (AERA) sponsored and highly-esteemed Educational Evaluation and Policy Analysis journal. You can find the free and full version of this study here.

In this study authors José Felipe Martínez – Associate Professor at the University of California, Los Angeles, Jonathan Schweig – at the RAND Corporation, and Pete Goldschmidt – Associate Professor at California State University, Northridge and creator of the value-added model (VAM) at legal issue in the state of New Mexico (see, for example, here), set out to help practitioners “combine multiple measures of complex [teacher evaluation] constructs into composite indicators of performance…[using]…various conjunctive, disjunctive (or complementary), and weighted (or compensatory) models” (p. 738). Multiple measures in this study include teachers’ VAM estimates, observational scores, and student survey results.

While authors ultimately suggest that “[a]ccuracy and consistency are greatest if composites are constructed to maximize reliability,” perhaps more importantly, especially for practitioners, authors note that “accuracy varies across models and cut-scores and that models with similar accuracy may yield different teacher classifications.”

This, of course, has huge implications for teacher evaluation systems as based upon multiple measures in that “accuracy” means “validity” and “valid” decisions cannot be made as based on “invalid” or “inaccurate” data that can so arbitrarily change. In other words, what this means is that likely never will a decision about a teacher being this or that actually mean this or that. In fact, this or that might be close, not so close, or entirely wrong, which is a pretty big deal when the measures combined are assumed to function otherwise. This is especially interesting, again and as stated prior, that the third author on this piece – Pete Goldschmidt – is the person consulting with the state of New Mexico. Again, this is the state that is still trying to move forward with the attachment of consequences to teachers’ multiple evaluation measures, as assumed (by the state but not the state’s consultant?) to be accurate and correct (see, for example, here).

Indeed, this is a highly inexact and imperfect social science.

Authors also found that “policy weights yield[ed] more reliable composites than optimal prediction [i.e., empirical] weights” (p. 750). In addition, “[e]mpirically derived weights may or may not align with important theoretical and policy rationales” (p. 750); hence, the authors collectively referred others to use theory and policy when combining measures, while also noting that doing so would (a) still yield overall estimates that would “change from year to year as new crops of teachers and potentially measures are incorporated” (p. 750) and (b) likely “produce divergent inferences and judgments about individual teachers (p. 751). Authors, therefore, concluded that “this in turn highlights the need for a stricter measurement validity framework guiding the development, use, and monitoring of teacher evaluation systems” (p. 751), given all of this also makes the social science arbitrary, which is also a legal issue in and of itself, as also quasi noted.

Now, while I will admit that those who are (perhaps unwisely) devoted to the (in many ways forced) combining of these measures (despite what low reliability indicators already mean for validity, as unaddressed in this piece) might find some value in this piece (e.g., how conjunctive and disjunctive models vary, how principal component, unit weight, policy weight, optimal prediction approaches vary), I will also note that forcing the fit of such multiple measures in such ways, especially without a thorough background in and understanding of reliability and validity and what reliability means for validity (i.e., with rather high levels of reliability required before any valid inferences and especially high-stakes decisions can be made) is certainly unwise.

If high-stakes decisions are not to be attached, such nettlesome (but still necessary) educational measurement issues are of less importance. But any positive (e.g., merit pay) or negative (e.g., performance improvement plan) consequence that comes about without adequate reliability and validity should certainly cause pause, if not a justifiable grievance as based on the evidence provided herein, called for herein, and required pretty much every time such a decision is to be made (and before it is made).

Citation: Martinez, J. F., Schweig, J., & Goldschmidt, P. (2016). Approaches for combining multiple measures of teacher performance: Reliability, validity, and implications for evaluation policy. Educational Evaluation and Policy Analysis, 38(4), 738–756. doi: 10.3102/0162373716666166 Retrieved from http://journals.sagepub.com/doi/pdf/10.3102/0162373716666166

Note: New Mexico’s data were not used for analytical purposes in this study, unless any districts in New Mexico participated in the Bill & Melinda Gates Foundation’s Measures of Effective Teaching (MET) study yielding the data used for analytical purposes herein.

Value-Added for Kindergarten Teachers in Ecuador

In a study a colleague of mine recently sent me, authors of a study recently released in The Quarterly Journal of Economics and titled “Teacher Quality and Learning Outcomes in Kindergarten,” (nearly randomly) assigned two cohorts of more than 24,000 kindergarten students to teachers to examine whether, indeed and once again, teacher behaviors are related to growth in students’ test scores over time (i.e., value-added).

To assess this, researchers administered 12 tests to the Kindergarteners (I know) at the beginning and end of the year in mathematics and language arts (although apparently the 12 posttests only took 30-40 minutes to complete, which is a content validity and coverage issue in and of itself, p. 1424). They also assessed something they called the executive function (EF), and that they defined as children’s inhibitory control, working memory, capacity to pay attention, and cognitive flexibility, all of which they argue to be related to “Volumetric measures of prefrontal cortex size [when] predict[ed]” (p. 1424). This, along with the fact that teachers’ IQs were also measured (using the Spanish-speaking version of the Wechsler Adult Intelligence Scale) speaks directly to the researchers’ background theory and approach (e.g., recall our world’s history with craniometry, aptly captured in one of my favorite books — Stephen J. Gould’s best selling “The Mismeasure of Man”). Teachers were also observed using the Classroom Assessment Scoring System (CLASS), and parents were also solicited for their opinions about their children’s’ teachers (see other measures collected p. 1417-1418).

What should by now be some familiar names (e.g., Raj Chetty, Thomas Kane) served as collaborators on the study. Likewise, their works and the works of other likely familiar scholars and notorious value-added supporters (e.g., Eric Hanushek, Jonah Rockoff) are also cited throughout in support as evidence of “substantial research” (p. 1416) in support of value-added models (VAMs). Of course, this is unfortunate but important to point out in that this is an indicator of “researcher bias” in and of itself. For example, one of the authors’ findings really should come at no surprise: “Our results…complement estimates from [Thomas Kane’s Bill & Melinda Gates Measures of Effective Teaching] MET project” (p. 1419); although, the authors in a very interesting footnote (p. 1419) describe in more detail than I’ve seen elsewhere all of the weaknesses with the MET study in terms of its design, “substantial attrition,” “serious issue[s]” with contamination and compliance, and possibly/likely biased findings caused by self-selection given the extent to which teachers volunteered to be a part of the MET study.

Also very important to note is that this study took place in Ecuador. Apparently, “they,” including some of the key players in this area of research noted above, are moving their VAM-based efforts across international waters, perhaps in part given the Every Student Succeeds Act (ESSA) recently passed in the U.S., that we should all know by now dramatically curbed federal efforts akin to what is apparently going on now and being pushed here and in other developing countries (although the authors assert that Ecuador is a middle-income country, not a developing country, even though this categorization apparently only applies to the petroleum rich sections of the nation). Related, they assert that, “concerns about teacher quality are likely to be just as important in [other] developing countries” (p. 1416); hence, adopting VAMs in such countries might just be precisely what these countries need to “reform” their schools, as well.

Unfortunately, many big businesses and banks (e.g., the Inter-American Development Bank that funded this particular study) are becoming increasingly interested in investing in and solving these and other developing countries’ educational woes, as well, via measuring and holding teachers accountable for teacher-level value-added, regardless of the extent to which doing this has not worked in the U.S to improve much of anything. Needless to say, many who are involved with these developing nation initiatives, including some of those mentioned above, are also financially benefitting by continuing to serve others their proverbial Kool-Aid.

Nonetheless, their findings:

  • First, they “estimate teacher (rather than classroom) effects of 0.09 on language and math” (p. 1434). That is, just less than 1/10th of a standard deviation, or just over a 3% move in the positive direction away from the mean.
  • Similarly, the “estimate classroom effects of 0.07 standard deviation on EF” (p. 1433). That is, precisely 7/100th of a standard deviation, or about a 2% move in the positive direction away from the mean.
  • They found that “children assigned to teachers with a 1-standard deviation higher CLASS score have between 0.05 and 0.07 standard deviation higher end-of-year test scores” (p. 1437), or a 1-2% move in the positive direction away from the mean.
  • And they found that “that parents generally give higher scores to better teachers…parents are 15 percentage points more likely to classify a teacher who produces 1 standard deviation higher test scores as ‘‘very good’’ rather than ‘‘good’’ or lower” (p. 1442). This is quite an odd way of putting it, along with the assumption that the difference between “very good” and “good” is not arbitrary but empirically grounded, along with whatever reason a simple correlation was not more simply reported.
  • Their most major finding is that “a 1 standard deviation increase in classroom quality, corrected for sampling error, results in 0.11 standard deviation higher test scores in both language and math” (p. 1433; see also other findings from p. 1434-447).

Interestingly, the authors equivocate all of these effects to teacher or classroom “shocks,” although I’d hardly call them “shocks” that inherently imply a large, unidirectional, and causal impact. Moreover, this also implies how the authors, also as economists, still view this type of research (i.e., not correlational, even with close-to-random assignment, although they make a slight mention of this possibility on p. 1449).

Nonetheless, the authors conclude that in this article they effectively evidenced “that there are substantial differences [emphasis added] in the amount of learning that takes place in language, math, and executive function across kindergarten classrooms in Ecuador” (p. 1448). In addition, “These differences are associated with differences in teacher behaviors and practices,” as observed, and “that parents can generally tell better from worse teachers, but do not meaningfully alter their investments in children in response to random shocks [emphasis added] to teacher quality” (p. 1448).

Ultimately, they find that “value added is a useful summary measure of teacher quality in Ecuador” (p. 1448). Go figure…

They conclude “to date, no country in Latin America regularly calculates the value added of teachers,” yet “in virtually all countries in the region, decisions about tenure, in-service training, promotion, pay, and early retirement are taken with no regard for (and in most cases no knowledge about) a teacher’s effectiveness” (p. 1448). Also sound familiar??

“Value added is no silver bullet,” and indeed it is not as per much evidence now existent throughout the U.S., “but knowing which teachers produce more or less learning among equivalent students [is] an important step to designing policies to improve learning outcomes” (p. 1448), they also recognizably argue.

Citation: Araujo, M. C., Carneiro, P.,  Cruz-Aguayo, Y., & Schady, N. (2016). Teacher quality and learning outcomes in Kindergarten. The Quarterly Journal of Economics, 1415–1453. doi:10.1093/qje/qjw016  Retrieved from http://qje.oxfordjournals.org/content/131/3/1415.abstract

The “Value-Added” of Teacher Preparation Programs: New Research

The journal Education of Economics Review recently published a study titled “Teacher Quality Differences Between Teacher Preparation Programs: How Big? How Reliable? Which Programs Are Different?” The study was authored by researchers at the University of Texas – Austin, Duke University, and Tulane. The pre-publication version of this piece can be found here.

As the title implies, the purpose of the study was to “evaluate statistical methods for estimating teacher quality differences between TPPs [teacher preparation programs].” Needless to say, this research is particularly relevant, here, given “Sixteen US states have begun to hold teacher preparation programs (TPPs) accountable for teacher quality, where quality is estimated by teacher value-added to student test scores.” The federal government continues to support and advance these initiatives, as well (see, for example, here).

But this research study is also particularly important because while researchers found that “[t]he most convincing estimates [of TPP quality] [came] from a value-added model where confidence intervals [were] widened;” that is, the extent to which measurement errors were permitted was dramatically increased, and also widened further using statistical corrections. But even when using these statistical techniques and accomodations, they found that it was still “rarely possible to tell which TPPs, if any, [were] better or worse than average.”

They therefore concluded that “[t]he potential benefits of TPP accountability may be too small to balance the risk that a proliferation of noisy TPP estimates will encourage arbitrary and ineffective policy actions” in response. More specifically, and in their own words, they found that:

  1. Differences between TPPs. While most of [their] results suggest that real differences between TPPs exist, the differences [were] not large [or large enough to make or evidence the differentiation between programs as conceptualized and expected]. [Their] estimates var[ied] a bit with their statistical methods, but averaging across plausible methods [they] conclude[d] that between TPPs the heterogeneity [standard deviation (SD) was] about .03 in math and .02 in reading. That is, a 1 SD increase in TPP quality predict[ed] just [emphasis added] a [very small] .03 SD increase in student math scores and a [very small] .02 SD increase in student reading scores.
  2. Reliability of TPP estimates. Even if the [above-mentioned] differences between TPPs were large enough to be of policy interest, accountability could only work if TPP differences could be estimated reliably. And [their] results raise doubts that they can. Every plausible analysis that [they] conducted suggested that TPP estimates consist[ed] mostly of noise. In some analyses, TPP estimates appeared to be about 50% noise; in other analyses, they appeared to be as much as 80% or 90% noise…Even in large TPPs the estimates were mostly noise [although]…[i]t is plausible [although perhaps not probable]…that TPP estimates would be more reliable if [researchers] had more than one year of data…[although states smaller than the one in this study — Texs]…would require 5 years to accumulate the amount of data that [they used] from one year of data.
  3. Notably Different TPPs. Even if [they] focus[ed] on estimates from a single model, it remains hard to identify which TPPs differ from the average…[Again,] TPP differences are small and estimates of them are uncertain.

In conclusion, that researchers found “that there are only small teacher quality differences between TPPs” might seem surprising, but not really given the outcome variables they used to measure and assess TPP effects were students’ test scores. In short, students’ test scores are three times removed from the primary unit of analysis in studies like these. That is, (1) the TPP is to be measured by the effectiveness of its teacher graduates, and (2) teacher graduates are to be measured by their purported impacts on their students’ test scores, while (3) students’ test scores are to only and have only been validated for measuring student learning and achievement. These test scores have not been validated to assess and measure, in the inverse, teachers causal impacts on said achievements or on TPPs impacts on teachers on said achievements.

If this sounds confusing, it is, and also highly nonsensical, but this is also a reason why this is so difficult to do, and as evidenced in this study, improbable to do this well or as theorized in that TPP estimates are sensitive to error, insensitive given error, and, accordingly, highly uncertain and invalid.

Citation: von Hippela, P. T., Bellowsb, L., Osbornea, C., Lincovec, J. A., & Millsd, N. (2016). Teacher quality differences between teacher preparation programs: How big? How reliable? Which programs are different? Education of Economics Review, 53, 31–45. doi:10.1016/j.econedurev.2016.05.002