Department of Psychology, Florida State University, 1107 West Call Street, Tallahassee, FL, 32306, USA.
Ann Dyslexia. 2022 Oct;72(3):445-460. doi: 10.1007/s11881-022-00261-5. Epub 2022 Jun 10.
Given the recent push for universal screening, it is important to take into account how well a screener identifies children at risk for reading problems as well as how screener and sample information contribute to this classification. Picking the best cut-point for a particular sample and screening goal can be challenging given that test manuals often report classification information for a specific cut-point and sample base rate which may not generalize to other samples. By assuming a bivariate normal distribution, it is possible to calculate all of the classification information for a screener based on the correlation between the screener and outcome, the cut-point on the outcome (i.e., the base rate in the sample), and the cut-point on the screener. We provide an example with empirical data to validate these estimation procedures. This information is the basis for a free online tool that provides classification information for a given correlation between screener and outcome and cut-points on each. Results show that the correlation between screener and outcome needs to be greater than .9 (higher than observed in practice) to obtain good classification. These findings are important for researchers, administrators, and practitioners because current screeners do not meet these requirements. Since a correlation is dependent on the reliability of the measures involved, we need screeners with better reliability and/or multiple measures to increase reliability. Additionally, we demonstrate the impact of base rate on positive predictive power and discuss how gated screening can be useful in samples with low base rates.
鉴于最近普遍推行筛查，考虑到筛查器识别有阅读问题风险的儿童的能力以及筛查器和样本信息对这种分类的贡献程度非常重要。鉴于测试手册通常会为特定的切点和样本基率报告分类信息，而这些信息可能无法推广到其他样本，因此为特定样本和筛查目标选择最佳切点可能具有挑战性。通过假设双变量正态分布，可以根据筛查器和结果之间的相关性、结果上的切点（即样本中的基率）以及筛查器上的切点，计算出筛查器的所有分类信息。我们提供了一个带有经验数据的示例来验证这些估计程序。这些信息是一个免费在线工具的基础，该工具可根据每个切点上的筛查器和结果之间的相关性以及切点提供分类信息。结果表明，要获得良好的分类，筛查器和结果之间的相关性需要大于.9（高于实践中观察到的水平）。这些发现对于研究人员、管理人员和从业者来说非常重要，因为当前的筛查器不符合这些要求。由于相关性取决于所涉及的测量的可靠性，我们需要具有更高可靠性的筛查器和/或多个测量来提高可靠性。此外，我们展示了基率对阳性预测力的影响，并讨论了在基率较低的样本中门控筛查如何有用。
