Schofield Lynne Steuerle
Swarthmore College, Department of Mathematics and Statistics.
Ann Appl Stat. 2015 Dec 1;9(4):2133-2152. doi: 10.1214/15-AOAS877.
This paper represents a methodological-substantive synergy. A new model, the Mixed Effects Structural Equations (MESE) model which combines structural equations modeling and item response theory is introduced to attend to measurement error bias when using several latent variables as predictors in generalized linear models. The paper investigates racial and gender disparities in STEM retention in higher education. Using the MESE model with 1997 National Longitudinal Survey of Youth data, I find prior mathematics proficiency and personality have been previously underestimated in the STEM retention literature. Pre-college mathematics proficiency and personality explain large portions of the racial and gender gaps. The findings have implications for those who design interventions aimed at increasing the rates of STEM persistence among women and under-represented minorities.
本文展示了一种方法与实质内容的协同作用。引入了一种新模型，即混合效应结构方程（MESE）模型，该模型结合了结构方程建模和项目反应理论，以解决在广义线性模型中使用多个潜在变量作为预测变量时的测量误差偏差问题。本文研究了高等教育中STEM领域留校率方面的种族和性别差异。使用MESE模型和1997年全国青年纵向调查数据，我发现先前在STEM留校率文献中，大学前数学能力和个性被低估了。大学前数学能力和个性解释了很大一部分种族和性别差距。这些发现对于那些设计旨在提高女性和代表性不足的少数族裔在STEM领域坚持率的干预措施的人具有启示意义。
