Constructing Subscores That Add Validity: A Case Study of Identifying Students at Risk

Abstract

Prior research suggests that subscores from a single achievement test seldom add value over a single total score. Such scores typically correspond to subcontent areas in the total content domain, but content subdomains might not provide a sound basis for subscores. Using scores on an inferential reading comprehension test from 625 third, fourth, and fifth graders, two new methods of creating subscores were explored. Three subscores were based on the types of incorrect answers given by students. The fourth was based on temporal efficiency in giving correct answers. All four scores were reliable. The three subscores based on incorrect answers added value and validity. In logistic regression analyses predicting failure to reach proficiency on a statewide test, models including subscores fit better than the model with a single total score. Including the pattern of incorrect responses improved fit in all three grades, whereas including the comprehension efficiency score only modestly improved fit in fourth and fifth grades, but not third grade. Area under the curve (AUC) statistics from receiver operating characteristic (ROC) curves based on the various models were higher for models including subscores than those without subscores. Implications for using models with and without subscores are illustrated and discussed.