Underrepresentation in gifted education for ethnically diverse student groups has been widely recognized. Two recent federal district court decisions defined the lower limits of equitable participation using the 20% equity allowance formula proposed by Donna Ford. The purpose of this article was to evaluate the application of the 20% rule to identify the prevalence of inequity and associated variables in Texas gifted education programs. Using data from the Office of Civil Rights and Texas Education Agency, the authors applied the 20% rule to demographics of K-12 gifted education programs in Texas to identify inequity and used Bayesian regression with district characteristics to investigate contributing factors of inequity. Only 282 of 994 (28.4%) districts met equity standards for Hispanic students. Second, Bayesian regressions with district-level characteristics of students, teachers, and expenditures were used to identify factors associated with inequitable enrollment of Hispanic students. Overall, the model accounted for 12.9% variance ( R2 = 0.129, 95% highest density interval [0.095, 0.170]), with increasing variance explained by district subsets (i.e., city, suburb, town, rural). Furthermore, the results of the regression models revealed the percentage of Hispanic and White teachers were inversely associated with inequity across all district subsets. It is postulated that the mechanism of inequity is in the teacher referral process, frequently used as a determinant of gifted education enrollment. The authors suggest means of addressing this reality.

In psychology, researchers are often interested in the predictive classification of individuals. Various models exist for such a purpose, but which model is considered a best practice is conditional on attributes of the data. Under certain conditions, linear discriminant analysis (LDA) has been shown to perform better than other predictive methods, such as logistic regression, multinomial logistic regression, random forests, support-vector machines, and the K-nearest neighbor algorithm. The purpose of this Tutorial is to provide researchers who already have a basic level of statistical training with a general overview of LDA and an example of its implementation and interpretation. Decisions that must be made when conducting an LDA (e.g., prior specification, choice of cross-validation procedures) and methods of evaluating case classification (posterior probability, typicality probability) and overall classification (hit rate, Huberty’s I index) are discussed. LDA for prediction is described from a modern Bayesian perspective, as opposed to its original derivation. A step-by-step example of implementing and interpreting LDA results is provided. All analyses were conducted in R, and the script is provided; the data are available online.

Meta-analyses are conducted to synthesize the quantitative results of related studies. The random-effects meta-analysis model is based on the assumption that a distribution of true effects exists in the population. This distribution is often assumed to be normal with a mean and variance. The population variance, also called heterogeneity, can be estimated numerous ways. Research exists comparing subsets of heterogeneity estimators over limited conditions. Additionally, heterogeneity is a parameter estimated with uncertainty. Various methods exist for heterogeneity interval estimation, and similar to heterogeneity estimators, these evaluations are limited. The current simulation study examined the performance of Bayesian (with 5 prior specifications) and non-Bayesian estimators over conditions found after a review of meta-analyses of the standardized mean difference in education and psychology research. Three simulation conditions were varied: (a) number of effect sizes per meta-analysis, (b) true heterogeneity, and (c) sample size per effect size within each meta-analysis. Estimators were evaluated over average bias and means square error. Methods of interval estimation were then evaluated with the estimators found to operate optimally. Interval estimators were evaluated based on coverage probability, interval width, and coverage of the estimated value. Overall, the Paule and Mandel estimator, in conjunction with the Jackson method of interval estimation, is recommended if no knowledge exists with regards to the expected value of heterogeneity when synthesizing the standardized mean difference effect size. If heterogeneity is expected to be small (e.g., < .075), then REML with the profile likelihood interval estimator is recommended. Sensitivity analysis evaluating differences in substantive conclusions with a suite of heterogeneity estimators, such as Paule and Mandel, REML, and Hedges and Olkin, is recommended.

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Background It is recommended that school-aged children accrue 30 minutes of daily moderate-to-vigorous physical activity (MVPA) in school. Current literature is inconclusive about the long-term associations between school-based physical activity and academic achievement. In this study, we use a large sample and longitudinal design to rigorously evaluate whether school-day MVPA is associated with academic achievement. Methods In a diverse suburban public school district, 4936 Grade 4 students were recruited in 40 elementary schools. Students wore accelerometers to measure school-day MVPA for 15 days across three semesters. Academic performance data was collected across Grade 3 fall to Grade 5 spring, including teacher-assigned grades and standardized test scores. Multilevel modeling was conducted controlling for student demographics and school characteristics. Results Cross-sectional analyses found small negative associations in Grade 4. Grade 4 full-year mean daily school-day MVPA had β = −-0.066, β = −-0.063, β = −-0.066, and β = −-0.058 associations (p <  0.001) with Grade 4 math, reading, spelling, and writing grades respectively, and Grade 4 full-year mean daily school-day MVPA had β = −-0.206 and β = −-0.283 (p <  0.001) associations with Grade 4 math and English Language Arts (ELA) standardized test scores respectively out of approximately 500 points. Longitudinal analyses found no significant associations between Grade 4 full-year mean daily school-day MVPA and Grade 5 Fall course grades. Results also indicated small negative associations for students attaining 30+ minutes of daily school-day MVPA compared to those attaining less than 15 minutes, but only in Grade 4 Fall cross-sectional analyses where teacher-assigned reading, spelling, and writing grades were − 1.666, − 1.638, and − 1.993 points lower respectively (p <  0.001). Conclusion The cross-sectional findings, while statistically significant in a negative direction, have a negligible association when translated practically. For example, even if students attained twice the recommended amount of school-day MVPA – which would constitute an approximately 300% increase from current levels – results suggest that grades would only decrease by 2 points on a 100-point scale. Furthermore, longitudinal analyses suggest school-day MVPA does not have a predictive association with course grades or standardized test scores. Findings suggest school-based MVPA implemented in accordance with recommendations does not meaningfully detract from academic progress. Trial registration ClinicalTrials.gov, NCT03765047. Registered 05 December 2018 - Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT03765047

The intentional integration of physical activity in elementary school classrooms—including brief instructional breaks for activity, or integration into lessons—can benefit children’s physical activity and education outcomes. Teachers are key implementation agents, but despite physical activity in the classroom being an evidence-informed practice, many teachers do not regularly implement it. The aim of this study was to obtain updated nationally representative prevalence estimates in United States public elementary schools, regarding four key outcomes: (1) school adoption of physically active lessons (PA lessons); (2) school adoption of physical-activity breaks (PA breaks); (3) penetration in the classroom, defined as ≥50% of teachers using PA breaks; and (4) dose, defined as an average of at least 50 min per week of PA breaks. We examined variations in outcomes by school demographic characteristics, and by three factors hypothesized to be implementation facilitators (administrative support, financial resources, and presence of a wellness champion at the school). In the 2019–20 school year, surveys were distributed to a nationally representative sample of 1010 public elementary schools in the US; responses were obtained from 559 (55.3%). The weighted prevalence of schools reporting adoption of PA lessons was 77.9% (95% CI = 73.9% to 81.9%), and adoption of PA breaks was nearly universal at 91.2% (95% CI = 88.4% to 94.1%). Few demographic differences emerged, although adoption of PA lessons was less prevalent at higher-poverty schools (73.9%) and medium-poverty schools (77.0%) as compared to schools with lower poverty levels (87.1%; p < 0.01). Across all four outcomes, associations emerged with facilitators in multivariable logistic regression models. The prevalence of adoption of PA lessons, adoption of PA breaks, and dose of PA breaks were all significantly higher at schools where administrative encouragement occurred more frequently. Financial support was associated with implementation outcomes, including adoption of PA lessons, and penetration and dose of PA breaks. Presence of a champion was associated with higher prevalence of reporting adoption of PA lessons. School leaders can play a crucial role in supporting teachers’ implementation of PA breaks and lessons in the classroom, through providing financial resources, encouragement, and supporting champions. Effective school-leadership practices have the potential to positively impact students at a large-scale population level by supporting implementation of PA lessons and breaks.

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