Abstract

In response to shortcomings with the current categorical diagnostic classification system for mental health disorders, such as reliance on fixed disorder definitions and heterogeneity within disorders, the National Institute of Mental Health proposed the Research Domain Criteria (RDoC) initiative to move towards a dimensional approach using translational research. The current study examined associations between widely used measures of behaviors, cognitions and mental health symptoms and how they overlap in the Negative Valence Systems (NVS) domain. Specifically, we examined how self-reports unit of analysis reflect the acute threat, potential threat, sustained threat, frustrative non-reward, and loss constructs proposed by RDoC.

Participants, comprised of two student samples (Sample 1, N = 368; Sample 2, N = 400) and two community samples (Sample 3, N = 774; Sample 4, N = 485;), completed self-reported measures about their experiences, emotions, and behaviors via a secured Qualtrics link for course credit or compensation.

Questionnaire total and subscale scores were submitted to a principal-axis factor analysis with Promax rotation separately for each sample. For Samples 1, 2, and 4, six factor solutions emerged reflecting most facets of the RDoC NVS: fear/anxiety, response to sustained threat, low well-being, inhibition/worry, frustration/anger, and behavioral activation. Sample 3 differed in that a five-factor solution emerged where fear/anxiety and frustration/anger were combined in a general distress factor. These identified factors reflect major aspects of RDoC positive and NVS domain, particularly anxiety/worry, chronic stress, frustrative non-reward, loss, and reduced behavioral activation.

Degree Date

Summer 8-4-2020

Document Type

Thesis

Degree Name

M.A.

Department

Psychology

Advisor

Thomas Ritz

Second Advisor

Alicia Meuret

Subject Area

Psychology, Clinical

Number of Pages

81

Format

.pdf

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

Available for download on Thursday, July 31, 2025

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