blog posts and news stories

Putting Many Small Studies Together

This is the last of a four-part blog posting about changes needed to the legacy of NCLB to make research more useful to school decision makers. Here we show how lots of small studies can give better evidence to resolve achievement gaps. To read the the first 3 parts, use these links.

1. Ending a Two-Decade Research Legacy

2. ESSA Evidence Tiers and Potential for Bias

3. Validating Research that Helps Reduce Achievement Gaps

The NCLB-era of the single big study should be giving way to the analysis of the differential impacts for subgroups from multiple studies. This is the information that schools need in order to reduce achievement gaps. Today’s technology landscape is ready for this major shift in the research paradigm. The school shutdowns resulting from the COVID-19 pandemic have demonstrated that the value of edtech products goes beyond just the cost reduction of eliminating expensive print materials. Over the last decade digital learning products have collected usage data which provides rich and systematic evidence of how products are being used and by whom. At the same time, schools have accumulated huge databases of digital records on demographics and achievement history, with public data at a granularity down to the grade-level. Using today’s “big data” analytics, this wealth of information can be put to work for a radical reduction in the cost of showing efficacy.

Fast turnaround, low cost research studies will enable hundreds of studies to be conducted providing information to school decision-makers that answer their questions. Their questions are not just “which program, on average, produces the largest effect?” Their questions are “which program is most likely to work in my district, with my kids and teachers, and with my available resources, and which are most likely to reduce gaps of greatest concern?”

Meta-analysis is a method for combining multiple studies to increase generalizability (Shadish, Cook, & Campbell, 2002). With meta-analysis, we can test for stability of effects across sites and synthesize those results, where warranted, based on specific statistical criteria. While moderator analysis is considered merely exploratory in the NCLB-era, using meta-analysis, moderator results from multiple small studies, can in combination provide confirmation of a differential impact. Meta-analysis, or other approaches to research synthesis, combined with big data present new opportunities to move beyond the NCLB-era philosophy that prizes the single big study to prove the efficacy of a program.

While addressing WWC and ESSA standards, we caution, that a single study in one school district, or even several studies in several school districts, may not provide enough useful information to generalize to other school districts. For research to be the most effective, we need studies in enough districts to represent the full diversity of relevant populations. Studies need to systematically include moderator analysis for an effective way to generalize impact for subgroups.

The definitions provided in ESSA do not address how much information is needed to generalize from a particular study for implementation in other school districts. While we accept that well-designed Tier 2 or 3 studies are necessary to establish an appropriate level of rigor, we do not believe a single study is sufficient to declare a program will be effective across varied populations. We note that the Standards for Excellence in Education Research (SEER) recently adopted by the IES, call for facilitating generalizability.

After almost two decades of exclusive focus on the design of the single study we need to more effectively address achievement gaps with the specifics that school decision-makers need. Lowering the cost and turn-around time for research studies that break out subgroup results is entirely feasible. With enough studies qualified for meta-analysis, a new wealth of information will be available to educators who want to select the products that will best serve their students. This new order will democratize learning across the country, reducing inequities and raising student achievement in K-12 schools.

Sure, the edtech product is proven to work, but will it work in my district?

It’s a scenario not uncommon in your district administrators’ office. They’ve received sales pitches and demos of a slew of new education technology (edtech) products, each one accompanied with “evidence” of its general benefits for teachers and students. But underlying the administrator’s decision is a question often left unanswered: Will this work in our district?

In the conventional approach to research advocated, for example, by the U.S. Department of Education and the Every Student Succeeds Act (ESSA), the finding that is reported and used in the review of products is the overall average impact for any and all subgroups of students, teachers, or schools in the study sample. In our own research, we have repeatedly seen that who it works for and under what conditions can be more important than the average impact. There are products that are effective on average but don’t work for an important subgroup of students, or vice versa, work for some students but not all. Some examples:

• A math product, while found to be effective overall, was effective for white students but ineffective for minority students. This effect would be relevant to any district wanting to close (rather than further widen) an achievement gap.
• A product that did well on average performed very well in elementary grades but poorly in middle school. This has obvious relevance for a district, as well as for the provider who may modify its marketing target.
• A teacher PD product greatly benefitted uncertified teachers but didn’t help the veteran teachers do any better than their peers using the conventional textbook. This product may be useful for new teachers but a poor choice for others.

As a research organization, we have been looking at ways to efficiently answer these kinds of questions for products. Especially now, with the evidence requirements built into ESSA, school leaders can ask the edtech salesperson: “Does your product have evidence that ESSA calls for?” They may well hear an affirmative answer supported by an executive summary of a recent study. But, there’s a fundamental problem with what ESSA is asking for. ESSA doesn’t ask for evidence that the product is likely to work in your specific district. This is not the fault of ESSA’s drafters. The problem is built into the conventional design of research on “what works”. The U.S. Department of Education’s What Works Clearinghouse (WWC) bases its evidence rating only on an average; if there are different results for different subgroups of students, that difference is not part of the rating. Since ESSA adopts the WWC approach, that’s the law of the land. Hence, your district’s most pressing question is left unanswered: will this work for a district like mine?

Recently, the Software & Information Industry Association, the primary trade association of the software industry, released a set of guidelines for research explaining to its member companies the importance of working with districts to conduct research that will meet the ESSA standards. As the lead author of this report, I can say it was our goal to foster an improved dialog between the schools and the providers about the evidence that should be available to support buying these products. As an addendum to the guidelines aimed at arming educators with ways to look at the evidence and questions to ask the edtech salesperson, here are three suggestions:

1. It is better to have some information than no information. The fact that there’s research that found the product worked somewhere gives you a working hypothesis that it could be a better than average bet to try out in your district. In this respect, you can consider the WWC and newer sites such as Evidence for ESSA rating of the study as a screening tool—they will point you to valid studies about the product you’re interested in. But you should treat previous research as a working hypothesis rather than proof.
2. Look at where the research evidence was collected. You’ll want to know whether the research sites and populations in the study resemble your local conditions. WWC has gone to considerable effort to code the research by the population in the study and provides a search tool so you can find studies conducted in districts like yours. And if you download and read the original report, it may tell you whether it will help reduce or increase an achievement gap of concern.
3. Make a deal with the salesperson. In exchange for your help in organizing a pilot and allowing them to analyze your data, you get the product for a year at a steep discount and a good ongoing price if you decide to implement the product on a full scale. While you’re unlikely to get results from a pilot (e.g., based on spring testing) in time to support a decision, you can at least lower your cost for the materials, and you’ll help provide a neighboring district (with similar populations and conditions) with useful evidence to support a strong working hypothesis as to whether it is likely to work for them as well.
   

i3 Request for Proposals Calls for New Approaches to Rigorous Evaluation

In the strongest indication yet that the new administration is serious about learning from its multi-billion-dollar experience, the draft notice for the Invest in Innovation (i3) grants sets out new requirements for research and evaluation. While it is not surprising that the U.S. Department of Education requires scientific evidence for programs asking for funds for expansion and scaling up, it is important to note that strong evidence is now being defined not just in terms of rigorous methods but also in terms of “studies that in total include enough of the range of participants and settings to support scaling up to the State, regional, or national level.” This requirement for generalizability is a major step toward sponsoring research that has value for practical decisions. Along the same lines, high quality evaluations are those that include implementation data and performance feedback.

The draft notice also includes recognition of an important research design: “interrupted time series.” While not acceptable under the current What Works Clearinghouse criteria, this method—essentially looking for a change in a series of measures taken before and after implementing a new program—has enormous practical application for schools systems with solid longitudinal data systems.

Finally, we notice that ED is requiring that all evaluators cooperate with broader national efforts to combine evidence from multiple sources and will provide technical assistance to evaluators to assure consistency among researchers. They want to be sure at the end of the process they have useful evidence about what worked, what didn’t, and why.

Empirical Education Joins the What Works Clearinghouse Research Team

Mathematica Policy Research, Inc. has subcontracted with Empirical Education to serve as one of the research partners on the new What Works Clearinghouse (WWC) team. This week, Empirical research staff joined a seminar to talk through the latest policies and criteria for judging the quality and rigor of effectiveness research.

Last summer, the Department of Education granted leadership of the WWC to Mathematica, (formerly led by AIR), which put together a team consisting of Empirical, RAND, SRI, and a number of other research organizations. This round of work is expected to have a greater emphasis on outreach to schools, industry, and other stakeholders.