Development and Initial Validation of Differential Diagnostic Indices for the MMPI-2-RF

Abstract

In this article, I discuss construction of a set of weighted indices for the Minnesota Multiphasic Personality Inventory-2 Restructured Form (MMPI-2-RF) designed to provide direct guidance in three specific differential diagnostic problems. I created a calibration data set using a combined sample of mental health patients (n = 2,043). Using the MMPI-2-RF’s Substantive Scales as a pool of potential predictors, I applied the lasso, a penalized regression technique, to derive three logistic regression equations differentiating three major diagnostic groups (schizophrenia, bipolar disorder, and major depressive disorder) from one another. Then, I extracted empirically derived beta weights from these equations and used them to create composite differential diagnostic indices, which I scored in a separate holdout validation data set (n = 873). The differential diagnostic indices performed well in the validation data set (schizophrenia vs. bipolar area under the curve [AUC] = .76; schizophrenia vs. major depression AUC = .90; bipolar vs. major depression AUC = .75). Moreover, they substantially outperformed any single existing MMPI-2-RF scale in the same differential diagnostic tasks. In addition to discussing the development and initial validation of these indices, I present methods for deriving clinically referenced standard scores and diagnostic classification probabilities for obtained raw index scores.