Does 1 teacher = 1 number? Some Questions About the Research on Composite Measures of Teacher Effectiveness

We are all familiar with approaches to combining VAM scores and other measures to generate a single measure that can be used to rate teachers for the purpose of personnel decisions. For example, as an alternative to using seniority as the basis for reducing the workforce, a school system may want to base such decisions—at least in part—on a ranking based on a number of measures of teacher effectiveness. One of the reports released January 8 by the Measures of Effective Teaching (MET) addressed approaches to creating a composite (i.e., a single number that averages various aspects of teacher performance) from multiple measures such as value-added modeling (VAM) scores, student surveys, and classroom observations. Working with the thousands of data points in the MET longitudinal database, the researchers were able to try out multiple statistical approaches to combining measures. The important recommendation from this research for practitioners is that, while there is no single best way to weight the various measures that are combined in the composite, balancing the weights more evenly tends to increase reliability.
While acknowledging the value of these analyses, we want to take a step back in this commentary. Here we ask whether agencies may sometimes be jumping to the conclusion that a composite is necessary when the individual measures (and even the components of these measures) may have greater utility than the composite for many purposes.
The basic premise behind creating a composite measure is the idea that there is an underlying characteristic that the composite can more or less accurately reflect. The criterion for a good composite is the extent to which the result accurately identifies a stable characteristic of the teacher’s effectiveness.
A problem with this basic premise is that in focusing on the common factor, the aspects of each measure that are unrelated to the common factor get left out—treated as noise in the statistical equation. But, what if observations and student surveys measure things that are unrelated to what the teacher’s students are able to achieve in a single year under her tutelage (the basis for a VAM score)? What if there are distinct domains of teacher expertise that have little relation to VAM scores? By definition, the multifaceted nature of teaching gets reduced to a single value in the composite.
This single value does have a use in decisions that require an unequivocal ranking of teachers, such as some personnel decisions. For most purposes, however, a multifaceted set of measures would be more useful. The single measure has little value for directing professional development, whereas the detailed output of the observation protocols are designed for just that. Consider a principal deciding which teachers to assign as mentors, or a district administrator deciding which teachers to move toward a principalship. Might it be useful, in such cases, to have several characteristics to represent different dimensions of abilities relevant to success in the particular roles?
Instead of collapsing the multitude of data points from achievement, surveys, and observations, consider an approach that makes maximum use of the data points to identify several distinct characteristics. In the usual method for constructing a composite (and in the MET research), the results for each measure (e.g., the survey or observation protocol) are first collapsed into a single number, and then these values are combined into the composite. This approach already obscures a large amount of information. The Tripod student survey provides scores on the seven Cs; an observation framework may have a dozen characteristics; and even VAM scores, usually thought of as a summary number, can be broken down (with some statistical limitations) into success with low-scoring vs. with high-scoring students (or any other demographic category of interest). Analyzing dozens of these data points for each teacher can potentially identify several distinct facets of a teacher’s overall ability. Not all facets will be strongly correlated with VAM scores but may be related to the teacher’s ability to inspire students in subsequent years to take more challenging courses, stay in school, and engage parents in ways that show up years later.
Creating a single composite measure of teaching has value for a range of administrative decisions. However, the mass of teacher data now being collected are only beginning to be tapped for improving teaching and developing schools as learning organizations.
— DN & VL

Can We Measure the Measures of Teaching Effectiveness

Teacher evaluation has become the hot topic in education. State and local agencies are quickly implementing new programs spurred by federal initiatives and evidence that teacher effectiveness is a major contributor to student growth. The Chicago teachers’ strike brought out the deep divisions over the issue of evaluations. There, the focus was on the use of student achievement gains, or value-added. But the other side of evaluation—systematic classroom observations by administrators—is also raising interest. Teaching is a very complex skill, and the development of frameworks for describing and measuring its interlocking elements is an area of active and pressing research. The movement toward using observations as part of teacher evaluation is not without controversy. A recent OpEd in Education Week by Mike Schmoker criticizes  the rapid implementation of what he considers overly complex evaluation templates “without any solid evidence that it promotes better teaching.”

There are researchers engaged in the careful study of evaluation systems, including the combination of value-added and observations. The Bill and Melinda Gates Foundation has funded a large team of researchers through its Measures of Effective Teaching (MET) project,, which has already produced an array of reports for both academic and practitioner audiences (with more to come). But research can be ponderous, especially when the question is whether such systems can impact teacher effectiveness. A year ago, the Institute of Education Sciences (IES) awarded an $18 million contract to AIR to conduct a randomized experiment to measure the impact of a teacher and leader evaluation system on student achievement, classroom practices, and teacher and principal mobility. The experiment is scheduled to start this school year and results will likely start appearing by 2015. However, at the current rate of implementation by education agencies, most programs will be in full swing by then.

Empirical Education is currently involved in teacher evaluation through our Observation Engine—a web-based tool that helps administrators make more reliable observations (see story about our work with Tulsa Public Schools).  This tool, along with our R&D on protocol validation, was initiated as part of the MET project. In our view, the complexity and time-consuming aspects of many of the observation systems that Schmoker criticizes arise from their intended use as supports for professional development. The initial motivation for developing observation frameworks was to provide better feedback and professional development for teachers. Their complexity is driven by the goal of providing detailed, specific feedback. Such systems can become cumbersome when applied to the goal of providing a single score for every teacher representing teaching quality that can be used administratively, for example, for personnel decisions. We suspect that a more streamlined and less labor-intensive evaluation approach could be used to identify the teachers in need of coaching and professional development. That subset of teachers would then receive the more resource-intensive evaluation and training services such as complex, detailed scales, interviews, and coaching sessions.

The other question Schmoker raises is: do these evaluation systems promote better teaching? While waiting for the IES study to be reported, some things can be done. First, look at correlations of the components of the observation rubrics with other measures of teaching such as value-added to student achievement (VAM) scores or student surveys. The idea is to see whether the behaviors valued and promoted by the rubrics are associated with improved achievement. The videos and data collected by the MET project are the basis for tools to do this  (see earlier story on our Validation Engine.) But school systems can conduct the same analysis using their own student and teacher data. Second, use quasi-experimental methods to look at the changes in achievement related to the system’s local implementation of evaluation systems. In both cases, many school systems are already collecting very detailed data that can be used to test the validity and effectiveness of their locally adopted approaches.

The Value of Looking at Local Results

The report we released today has an interesting history that shows the value of looking beyond the initial results of an experiment. Later this week, we are presenting a paper at AERA entitled "In School Settings, Are All RCTs Exploratory?" The findings we report from our experiment with an iPad application were part of the inspiration for this. If Riverside Unified had not looked at its own data, we would not, in the normal course of data analysis, have broken the results out by individual districts, and our conclusion would have been that there was no discernible impact of the app. We can cite many other cases where looking at subgroups leads us to conclusions different from the conclusion based on the result averaged across the whole sample. Our report on AMSTI is another case we will cite in our AERA paper.

We agree with the Institute of Education Sciences (IES) in taking a disciplined approach in requiring that researchers "call their shots" by naming the small number of outcomes considered most important in any experiment. All other questions are fine to look at but fall into the category of exploratory work. What we want to guard against, however, is the implication that answers to primary questions, which often are concerned with average impacts for the study sample as a whole, must apply to various subgroups within the sample, and therefore can be broadly generalized by practitioners, developers, and policy makers.

If we find an average impact but in exploratory analysis discover plausible, policy-relevant, and statistically strong differential effects for subgroups, then some doubt about completeness may be cast on the value of the confirmatory finding. We may not be certain of a moderator effect--for example--but once it comes to light, the value of the average impact can also be considered incomplete or misleading for practical purposes. If it is necessary to conduct an additional experiment to verify a differential subgroup impact, the same experiment may verify that the average impact is not what practitioners, developers, and policy makers should be concerned with.

In our paper at AERA, we are proposing that any result from a school-based experiment should be treated as provisional by practitioners, developers, and policy-makers. The results of RCTs can be very useful, but the challenges of generalizability of the results from even the most stringently designed experiment mean that the results should be considered the basis for a hypothesis that the intervention may work under similar conditions. For a developer considering how to improve an intervention, the specific conditions under which it appeared to work or not work is the critical information to have. For a school system decision maker, the most useful pieces of information are insight into subpopulations that appear to benefit and conditions that are favorable for implementation. For those concerned with educational policy, it is often the case that conditions and interventions change and develop more rapidly than research studies can be conducted. Using available evidence may mean digging through studies that have confirmatory results in contexts similar or different from their own and examining exploratory analyses that provide useful hints as to the most productive steps to take next. The practitioner in this case is in a similar position to the researcher considering the design of the next experiment. The practitioner also has to come to a hypothesis about how things work as the basis for action.
-- DN & AJ

Exploration in the World of Experimental Evaluation

Our 300+ page report marks a good start into this exploration. But IES, faced with limited time and resources to complete the many experiments being conducted within the Regional Education Lab system, put strict limits on the number of exploratory analyses researchers could conduct. We usually think of exploratory work as questions to follow up on puzzling or unanticipated results. However, in the case of the REL experiments, IES asked researchers to focus on a narrow set of “confirmatory” results and anything else was considered “exploratory,” even if the question was included in the original research design.

The strict IES criteria were based on the principle that when a researcher is using tests of statistical significance, the probability of erroneously concluding that there is an impact when there isn’t one increases with the frequency of the tests. In our evaluation of AMSTI, we limited ourselves to only four such “confirmatory” (i.e., not exploratory) tests of statistical significance. These were used to assess whether there was an effect on student outcomes for math problem-solving and for science, and the amount of time teachers spent on “active learning” practices in math and in science. (Technically, IES considered this two sets of two, since two were the primary student outcomes and two were the intermediate teacher outcomes.) The threshold for significance was made more stringent to keep the probability of falsely concluding that there was a difference for any of the outcomes at 5% (often expressed as p < .05).

While the logic for limiting the number of confirmatory outcomes is based on technical arguments about adjustments for multiple comparisons, the limit on the amount of exploratory work was based more on resource constraints. Researchers are notorious (and we don’t exempt ourselves) for finding more questions in any study than were originally asked. Curiosity-based exploration can indeed go on forever. In the case of our evaluation of AMSTI, however, there were a number of fundamental policy questions that were not answered either by the confirmatory or by the exploratory questions in our report. More research is needed.

Take the confirmatory finding that the program resulted in the equivalent of 28 days of additional math instruction (or technically an impact of 5% of a standard deviation). This is a testament to the hard work and ingenuity of the AMSTI team and the commitment of the school systems. From a state policy perspective, it gives a green light to continuing the initiative’s organic growth. But since all the schools in the experiment applied to join AMSTI, we don’t know what would happen if AMSTI were adopted as the state curriculum requiring schools with less interest to implement it. Our results do not generalize to that situation. Likewise, if another state with different levels of achievement or resources were to consider adopting it, we would say that our study gives good reason to try it but, to quote Lee Cronbach, a methodologist whose ideas increasingly resonate as we translate research into practice: “…positive results obtained with a new procedure for early education in one community warrant another community trying it. But instead of trusting that those results generalize, the next community needs its own local evaluation” (Cronbach, 1975, p. 125).

The explorations we conducted as part of the AMSTI evaluation did not take the usual form of deeper examinations of interesting or unexpected findings uncovered during the planned evaluation. All the reported explorations were questions posed in the original study plan. They were defined as exploratory either because they were considered of secondary interest, such as the outcome for reading, or because they were not a direct causal result of the randomization, such as the results for subgroups of students defined by different demographic categories. Nevertheless, exploration of such differences is important for understanding how and for whom AMSTI works. The overall effect, averaging across subgroups, may mask differences that are of critical importance for policy.

Readers interested in the issue of subgroup differences can refer to Table 6.11. Once differences are found in groups defined in terms of individual student characteristics, our real exploration is just beginning. For example, can the difference be accounted for by other characteristics or combinations of characteristics? Is there something that differentiates the classes or schools that different students attend? Such questions begin to probe additional factors that can potentially be addressed in the program or its implementation. In any case, the report just released is not the “final report.” There is still a lot of work necessary to understand how any program of this sort can continue to be improved.

–DN & AJ