Psychometric and machine learning approaches for diagnostic assessment and tests of individual classification.

Abstract

Assessments are commonly used to make a decision about an individual, such as grade placement, treatment assignment, job selection, or to inform a diagnosis. A psychometric approach to classify respondents based on the assessment would aggregate items into a score, and then each respondent's score is compared to a cut score. In contrast, a machine learning approach to classify respondents would build a model to predict the probability of belonging to a specific class from assessment items, and then respondents are classified based on their predicted probability of belonging to that class. It remains unclear whether psychometric and machine learning methods have comparable classification accuracy or if 1 method is preferable in all or some situations. In the context of diagnostic assessment, this study used Monte Carlo simulation methods to compare the classification accuracy of psychometric and machine learning methods as a function of the diagnosis-test correlation, prevalence, sample size, and the structure of the diagnostic assessment. Results suggest that machine learning models using logistic regression or random forest could have comparable classification accuracy to the psychometric methods using estimated item response theory scores. Therefore, machine learning models could provide a viable alternative for classification when psychometric methods are not feasible. Methods are illustrated with an empirical example predicting an oppositional defiant disorder diagnosis from a behavior disorders scale in children of age seven. Strengths and limitations for each of the methods are examined, and the overlap between the field of machine learning and psychometrics is discussed. (PsycInfo Database Record (c) 2020 APA, all rights reserved).