Screening Screeners: Calculating Classification Indices Using Correlations and Cut-points

Given the recent push for universal screening, it is important to take into account how well the screener identifies children as well as what contributes to this classification based on screener and sample information. If we assume a bivariate normal distribution, we can calculate all of the classification information for a screener based on the correlation between the screener and outcome, the cut-point on the outcome (base rate in sample), and the cut-point on the screener. We provide an example with empirical data to validate these estimation procedures. This information is put together in a free online tool that provides classification information for a given correlation between screener and outcome and cut-points on each. Through this we can see that the correlation between the screener and outcome needs to be very high, greater than .9 (higher than observed in practice) to obtain good classification. These findings are important for researchers, administrators, and practitioners because our current screeners don’t meet these requirements. Since a correlation is dependent on the reliability of the measures involved, we need screeners with better reliability and the use of 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.