Concerns about representation in computing within the U.S. have driven numerous activities to broaden participation. Assessment of the impact of these efforts and, indeed, a clear assessment of the actual "problem" being addressed are limited by the nature of the most common data analysis which looks at the representation of each population as a percentage of the number of students graduating with a degree in computing. This use of a single metric cannot adequately assess the impact of broadening participation efforts. First, this approach fails to account for changing demographics of the undergraduate population in terms of overall numbers and relative proportion of the Federally designated gender, race, and ethnicity groupings. A second issue is that the majority of literature on broadening participation in computing (BPC) reports data on gender or on race/ethnicity, omitting data on students' intersectional identities. This leads to an incorrect understanding of both the data and the challenges we face as a field. In this paper we present several different approaches to tracking the impact of BPC efforts. We make three recommendations: 1) cohort-based analysis should be used to accurately show student engagement in computing; 2) the field as a whole needs to adopt the norm of always reporting intersectional data; 3) university demographic context matters when looking at how well a CS department is doing to broaden participation in computing, including longitudinal analysis of university demographic shifts that impact the local demographics of computing.

翻译：摘要：美国对计算领域代表性问题的担忧推动了多项旨在扩大参与度的活动。然而，对这些努力效果的评估，乃至对所解决实际“问题”的清晰评估，都受限于最常见数据分析的性质——即仅将各人群在计算机专业毕业生中所占百分比作为指标。这种单一指标的使用无法充分评估扩大参与度工作的影响。首先，这种方法未能考虑本科生群体在整体数量及联邦指定的性别、种族和族裔分组相对比例方面的人口结构变化。其次，关于扩大计算机参与度（BPC）的大部分文献仅报告性别或种族/族裔数据，忽略了学生交叉身份的信息。这导致对数据以及我们领域所面临挑战的错误理解。本文提出了多种追踪BPC工作影响的方法。我们提出三点建议：1）应采用基于队列的分析来准确反映学生对计算领域的参与；2）整个领域需采纳常态化的交叉数据分析报告规范；3）在评估计算机科学系扩大参与度的成效时，大学人口结构背景至关重要，需结合影响本地计算机领域人口结构的大学人口结构纵向变化进行分析。