ITW: Collaborative Research: Statistical Methodology for Studying Women and Minorities in Information Technology Careers Using CPS and SESTAT Data

ITW：协作研究：使用 CPS 和 SESTAT 数据研究信息技术职业中的女性和少数族裔的统计方法

• 批准号: 0089872
• 负责人: Elaine Zanutto
• 金额: $ 8.03万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-01-01 至 2003-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0089872&HistoricalAwards=false
• 关键词: ITW; Collaborative; Research; Statistical; Methodology

Institution: University of PennsylvaniaProposal Number: EIA 0089872PI: Elaine L. ZanuttoTitle: Collaborative Research: Statistical Methodology for Studying Women and Minorities in Information Technology Careers Using CPS and SESTAT DataThis CISE Information Technology Workforce (ITW) proposal requests funds to find a statistical methodology for dealing with the limitations of data sets with small sample sizes for special subgroups of interest. The data sets to be used for the study are the Current Population Survey (CPS) and the NSF's SESTAT database. The researchers propose to study, develop, and demonstrate methods of statistical inference that will provide improved inferential conclusions than currently appear in the literature. These methods will address questions of whether or not, after taking into account all the relevant background characteristics such as education and experience, there are disparities in salary, promotion rates, and job responsibilities among women and underrepresented minorities in IT. They will also attempt to identify unique characteristics of successful women and underrepresented minorities in IT careers. This project has the potential to provide improved methodology for researchers using the CPS and SESTAT data sets as well as insights about women and underrepresented minorities who pursue IT careers.

机构：宾夕法尼亚大学提案编号：EIA 0089872PI：Elaine L. Zanutto 标题：协作研究：使用 CPS 和 SESTAT 数据研究信息技术职业中的女性和少数族裔的统计方法此 CISE 信息技术劳动力 (ITW) 提案要求提供资金来寻找统计方法处理感兴趣的特殊子组的小样本数据集的局限性。 本研究使用的数据集是当前人口调查 (CPS) 和 NSF 的 SESTAT 数据库。 研究人员建议研究、开发和演示统计推断方法，这些方法将提供比目前文献中出现的改进的推断结论。 这些方法将解决以下问题：在考虑了所有相关背景特征（例如教育和经验）后，IT 行业中女性和代表性不足的少数群体在薪资、晋升率和工作职责方面是否存在差异。 他们还将尝试找出 IT 职业中成功女性和代表性不足的少数群体的独特特征。该项目有可能为使用 CPS 和 SESTAT 数据集的研究人员提供改进的方法，以及有关从事 IT 职业的女性和代表性不足的少数群体的见解。