Many individuals diagnosed with an addictive disorder are members of disadvantaged groups and obtain a high school education or less, yet self-report questionnaires widely used to identify symptoms of addictive disorders do not use best practices to ensure item clarity and comprehension. In the present study, we explore how advanced text-analysis technology can be used to guide the development of a diagnostic questionnaire with an emphasis on maximizing its readability and then test the accuracy of this questionnaire. In Study 1, a self-report questionnaire for symptoms of gambling disorder was created using best practices for item clarity and comprehension. In study 2 an experimental design was used to test whether the measure with enhanced readability, compared to a commonly used screening instrument, improved diagnostic symptom accuracy among samples of high school and college educated individuals. Subsequent analyses revealed that education was positively related to item comprehension, and participants who completed the maximized readability questionnaire correctly identified more symptoms of gambling disorder than participants who completed the comparison questionnaire, regardless of educational attainment. These studies indicate that the rate at which individuals accurately identify symptoms of psychopathology is strongly related to their educational attainment and the readability of the questionnaire items themselves.
