ORCID

https://orcid.org/0009-0000-2465-0671

Date of Award

2025

Document Type

Dissertation

Degree Name

Psychology (Ph.D.)

Department

Psychology

First Advisor

William F. Chaplin

Second Advisor

Melissa Peckins

Third Advisor

Wilson McDermut

Abstract

Background: Assessment of mental health symptomology is essential for clinical research and patient care. Traditional measures of mental health symptoms typically use a Likert-type rating scale with scores and interpretations based on the sum of numeric Likert item responses. However, in addition to the psychometric difficulties posed by summing ordinal items, Likert sums are not directly linked to the proposed underlying construct of clinical distress and may not be clinically appropriate for patients in treatment due to a few specific symptoms rather than global distress across all symptoms. Aim & Method: The current study proposes an alternative binary scoring approach to reflect if a symptom is an issue for a patient. Symptoms are considered at two levels of occurrence: “present” (occurring at least sometimes), or “persistent” (occurring frequently or always). The study aims to empirically and conceptually compare item-level binary scoring with traditional Likert sum scoring using data from the OQ-45.2 (N = 433 adults) and the ARES—short form (N = 1019 children). The impact of binary scoring on the factor structure of each measure is examined. IRT models and SDT analyses are employed to differentiate symptoms based on how they relate to the underlying latent trait of distress. This information is used to create a binary scoring model based on optimal cut-points for each item. Results: Results indicate that binary symptom count scoring provides a psychometrically sound and clinically useful alternative to Likert scoring that is simple, easy to interpret, and can directly guide diagnostic decision making.

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