DEVELOPING SOFTWARE-BASED STATISTICAL MODELS FOR EDUCATIONAL INCENTIVES IN MIDDLE SCHOOL CLASSROOMS

<doi>10.24250/jpe/2/2024/KD/</doi>

Abstract

There is a need for more statistical, computerized
representations in studies via fixed effects and mixed effects
models. This article gives meta-analytic examples of (a)
adequate literary, statistical, and conceptual coverage of token
reinforcement as defined within educational interventions and
(b) practical mixed-effects modeling that is relevant for
determining how treatment effect size fits with other
characteristics in literature on incentives. The findings from the
meta-analytic modeling indicate that sample size, grouping
options, timing, study type, and treatment effect size variation
have significant influences on the practical significance
(effectiveness) of incentives with middle school students.
Accounting for these variables helps stakeholders in education
develop supports that offer more standardization, versatility, and
appeal to students as a whole. A variety of treatment effects for
reinforcers may exist, but the overall effect of reinforcement can
be positive. This article is recommended for those interested in
developing better instructional practices for students, regardless
of academic abilities.