Single equation regression models have been used to test the effect of Supplemental Instruction (SI) on student retention. These models, however, fail to account for the two salient features of SI attendance and retention: (1) both SI attendance and retention are categorical variables, and (2) are jointly determined endogenous variables. Adopting primarily from the economics education literature, this article applies the bivariate probit model, a model appropriate for the case of simultaneous binary choice, to analyze the effect of SI attendance on retention. It is demonstrated that single equation methods are likely to overestimate this effect.

This paper investigates the impact of globalization on income inequality distribution in 60 developed, transitional, and developing countries. Using Kearney's (2002, 2003 and 2004) data and principal component analysis (PCA), two globalization indices are created. One of these indices is the equally weighted index. The other index is derived from the principal component analysis. The Gini coefficient of a country is regressed on each index, respectively, in all 60 test cases. The main contribution of this paper is its finding of a negative relationship between both globalization indices and the Gini coefficient for all 60 countries under investigation. Furthermore, test results indicate that this relationship is robust. Therefore, the empirical evidence presented in this paper supports the claim that globalization helps reduce income distribution inequality within countries.

Single equation regression models have been used rather extensively to test the effectiveness of Supplemental Instruction (SI). This approach, however, fails to account for the possibility that SI attendance and the outcome of SI attendance are jointly determined endogenous variables. Moreover, the standard approach fails to account for the fact that these two endogenous variables are categorical. This article presents and applies a simultaneous equation, limited dependent variable model of SI effectiveness. Our analysis suggests that results from applying this type of model may differ markedly from the traditional statistical models applied in SI research. Specifically, our results suggest that students with below average academic ability are more likely to attend SI and that common measures of student ability included in single equation models fail to adequately control for this characteristic. Therefore, single equation OLS models may underestimate SI effectiveness.

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