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首页> 外文会议>International Triennial Calcutta Symposium on Probability and Statistics; 20031228-31; Calcutta(IN) >LATENT CLASS ANALYSIS OF 1997 NSF SURVEY DATA ON SCIENCE PHDS
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LATENT CLASS ANALYSIS OF 1997 NSF SURVEY DATA ON SCIENCE PHDS

机译：1997年NSF科学PHDS调查数据的最新分类分析

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摘要

The field of Information Technology (IT) has provided extraordinary job growth in the United States over the last two decades; however, women and some minority groups are severely underrepresented in IT occupations, especially in management positions. These groups also on average receive lower salaries than their counterparts. The U.S. National Science Foundation's SESTAT database is created from biennial nationally representative surveys of U.S. scientists and engineers. SESTAT provides detailed information, such as employment history, educational background, and demographic characteristics. These data are analyzed here using latent class analysis, which is an exploratory technique that can be used to cluster cases based on categorical variables. The data are from the 1997 Survey of Doctoral Recipients. The subset of respondents received Ph.D.'s between 1990 and 1996 in either than physical or biological sciences or in engineering and work at higher educational institutions. There are a few significant differences between men and women in desired work activities, job search resources, and adequacy of doctoral training. There are many large, significant differences in limitations when searching for a job, work activities, and family and career status. Latent class analysis helped identify important subgroups of females and males based on clustering simultaneously on several categorical variables.
机译：在过去的二十年中，信息技术（IT）领域在美国提供了非凡的就业增长；但是，妇女和一些少数族裔在信息技术职业中，特别是在管理职位中的代表性严重不足。这些群体的平均薪资也低于同龄人。美国国家科学基金会的SESTAT数据库是根据对美国科学家和工程师进行的两年一次的全国代表性调查创建的。 SESTAT提供详细信息，例如就业历史，学历和人口统计学特征。这些数据在这里使用潜在类分析进行分析，这是一种探索性技术，可用于基于分类变量对案例进行聚类。数据来自1997年的博士接受者调查。在1990年至1996年期间，一部分受访者获得了物理学或生物科学或高等教育机构的工程和工作博士学位。男性和女性在所需的工作活动，求职资源和医生培训的充分性方面存在一些显着差异。在寻找工作，工作活动以及家庭和职业状况时，限制方面存在许多重大差异。潜在类别分析可基于对几个类别变量的同时聚类，帮助确定男女重要的亚组。

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