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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.

Multivariate analysis of variance (MANOVA) is a statistical method used to examine group differences on multiple outcomes. This article reports results of a review of MANOVA in gifted education journals between 2011 and 2017 ( N = 56). Findings suggest a number of conceptual and procedural misunderstandings about the nature of MANOVA and its application, including pervasive use of univariate post hoc tests to interpret MANOVA results. Accordingly, this article aims to make MANOVA more accessible to gifted education scholars by clarifying its purpose and introducing descriptive discriminant analysis as a more appropriate post hoc technique. A heuristic data set is used to demonstrate the procedures for running a descriptive discriminant analysis, both in place of a one-way MANOVA and as a post hoc analysis to a factorial design. SPSS and R syntax are provided.

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