GSE/RES: Understanding women's career choices and occupational trajectories in physical science, engineering and IT fields

该提案涉及对密歇根青少年生活过渡研究（MSALT）和童年及以后研究（CAB）的数据进行分析，这两个纵向数据集最初由首席研究员创建。研究人员还将为这两个数据集收集额外的数据。该项目得到了期望值理论的理论支持，该理论也是由首席研究员开发的。研究人员将收集新的数据来扩展现有的数据集，以此来探索代际影响对STEM参与的问题，分析来自多个学科的非STEM和STEM专业，以及可能有助于STEM参与的其他社会心理变量。其他感兴趣的问题包括STEM职业轨迹的个体差异预测因素和劳动力市场经验作为STEM职业选择的预测因素。数据分析技术包括结构方程建模、层次线性建模和以人为中心的技术，如聚类分析和潜在类分析。该项目的主要目标是研究stem相关兴趣发展中的性别差异和性别内个体差异。女性在许多STEM领域的代表性不足仍然是一个引人注目的劳动力统计数据。本研究旨在提高我们对影响女性选择从事特定STEM领域的因素的理解。这些信息对于制定旨在改变性别角色刻板印象和STEM相关专业和职业价值中的性别差异的计划和政策至关重要。

This proposal involves the analysis of data from the Michigan Study of Adolescent Life Transitions (MSALT) and the Childhood and Beyond Study (CAB), two longitudinal datasets originally created by the principal investigator. The researchers will also collect additional waves of data collection for each of the two datasets. The project draws theoretical support from Expectancy Value Theory, also developed by the principal investigator. The researchers will collect new data to expand the existing datasets as a way to explore issues of generational effects on STEM engagement, analyses of non-STEM and STEM majors from across multiple disciplines, and additional psychosocial variables that may contribute to STEM engagement. Additional issues of interest include predictors of individual differences in STEM career trajectories and labor market experiences as predictors of STEM career choices. Data analytic techniques include structural equation modeling, hierarchical linear modeling, and person-centered techniques such as cluster analysis and latent class analysis. The major goal of this project is investigate gender differences and within-gender individual differences in the development of STEM-related interests. The under-representation of females in many STEM fields continues to be a striking labor force statistic. This study is designed to improve our understanding of the factors influencing females' choices to pursue specific STEM fields. Such information is vital to devising programs and policies aimed at changing gender-role stereotypes and gender differences in the value of STEM related majors and occupations.