  Sequential, multiple assignment randomized trials (SMARTs), which assist in
the optimization of adaptive interventions, are growing in popularity in
education and behavioral sciences. This is unsurprising, as adaptive
interventions reflect the sequential, tailored nature of learning in a
classroom or school. Nonetheless, as is true elsewhere in education research,
observed effect sizes in education-based SMARTs are frequently small. As a
consequence, statistical efficiency is of paramount importance in their
analysis. The contributions of this manuscript are two-fold. First, we provide
an overview of adaptive interventions and SMART designs for researchers in
education science. Second, we propose four techniques that have the potential
to improve statistical efficiency in the analysis of SMARTs. We demonstrate the
benefits of these techniques in SMART settings both through the analysis of a
SMART designed to optimize an adaptive intervention for increasing cognitive
behavioral therapy delivery in school settings and through a comprehensive
simulation study. Each of the proposed techniques is easily implementable,
either with over-the-counter statistical software or through R code provided in
an online supplement.
