Screen Twice, Cut Once: Assessing the Predictive Validity of Applicant Selection Tools
Screen Twice, Cut Once: Assessing the Predictive Validity of Applicant Selection Tools
复制标题
筛选两次，剪切一次：评估申请人选择工具的预测有效性
DOI：
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发表时间：
2017
影响因子：
2.1
通讯作者：
Nick Huntington
中科院分区：
文献类型：
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作者：
Dan Goldhaber;Cyrus Grout;Nick Huntington
尽管它们被广泛使用，但很少有学术证据表明申请人选择工具是否能改善教师招聘。我们研究了斯波坎公立学校用来选择课堂教师的两种筛选工具和三种教师结果之间的关系：增加值、缺勤和流失。我们观察了该地区的所有申请人（不仅仅是那些被雇用的人），允许我们使用随机统计误差和不同职位竞争水平的变化作为工具来估计样本选择修正模型。筛选工具的评分显著地预测了数学的附加值和教师的流失率，但不能预测缺勤率——筛选分数每增加一个标准差，学生数学成绩就会增加0.06个标准差，教师流失率就会下降3个百分点。因此，选择工具的使用似乎是提高教师队伍质量的关键手段。
Despite their widespread use, there is little academic evidence on whether applicant selection instruments can improve teacher hiring. We examine the relationship between two screening instruments used by Spokane Public Schools to select classroom teachers and three teacher outcomes: value added, absences, and attrition. We observe all applicants to the district (not only those who are hired), allowing us to estimate sample selection-corrected models using random tally errors and variation in the level of competition across job postings as instruments. Ratings on the screening instruments significantly predict value added in math and teacher attrition, but not absences—an increase of one standard deviation in screening scores is associated with an increase of about 0.06 standard deviations of student math achievement, and a decrease in teacher attrition of 3 percentage points. Hence the use of selection instruments appears to be a key means of improving the quality of the teacher workforce.