Timothy Lycurgus, Ben B. Hansen, and Mark White

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Many efficacy trials are conducted only after careful vetting in national funding competitions. As part of these competitions, applications must justify the intervention’s theory of change: how and why do the desired improvements in outcomes occur? In scenarios with repeated measurements on participants, some of the measurements may be more likely to manifest a treatment effect than others; the theory of change may provide guidance as to which of those observations are most likely to be affected by the treatment.

Figure 1: Power for the various methods across increasing effect sizes when the theory of change is correct.  

Ben Clarke, Christian Doabler, Marah Sutherland, Derek Kosty, Jessica Turtura, and Keith Smolkowski

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The importance of early mathematics

The importance of a successful start to learning mathematics has been a national priority for several decades. Mounting evidence indicates that trajectories of mathematics performance are established early and remain relatively stable across time. This may in part be due to substantial disparities in young students’ access to early mathematics experiences and instruction with preschool-aged students from upper- and middle-class backgrounds already outperforming their economically disadvantaged peers.

Summary by: Hang (Heather) Do

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What was this study about?

Chronic absenteeism (missing more than 10% of school days or more in one year) negatively impacts children’s school achievement and development. Yet, little is known about how absenteeism influences the effectiveness of interventions. In this study, the authors examined whether absenteeism affected the impacts of an intensive two-year professional development (PD) intervention aiming to improve the quality of Chilean public preschool and kindergarten and enhance the language and literacy outcomes of participating children (UBC (Un Buen Comienzo/A Good Start)).

Luke Miratrix, Jane Furey, Avi Feller, Todd Grindal, and Lindsay Page

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Estimating program effects for subgroups is hard. Estimating effects for types of people who exist in theory, but whom we can’t always identify in practice (i.e., latent subgroups) is harder. These challenges arise often, with noncompliance being a primary example. Another is estimating effects on groups defined by “counterfactual experience,” i.e., by what opportunities would have been available absent treatment access. This paper tackles this difficult problem. We find that if one can predict, with some accuracy, latent subgroup membership, then bounding is a nice evaluation approach, relying on weak assumptions. This is in contrast to many alternatives that are tricky, often unstable, and/or rely on heroic assumptions.

What are latent subgroups again?