Abstract: Beyond ACEs: Development and Psychometric Testing of the Trace Measure (Trauma, Resilience, and Adversity in Cultural Experiences) (Society for Social Work and Research 31st Annual Conference)

Beyond ACEs: Development and Psychometric Testing of the Trace Measure (Trauma, Resilience, and Adversity in Cultural Experiences)

Schedule:
Friday, January 22, 2027
Golden Gate 2 (Hilton San Francisco Union Square)
* noted as presenting author
Jessica Pryce, PhD, Research Faculty, College of Social Work, Florida State University, Tallahassee, FL
Yaacov Petscher, PhD, Professor, Florida State University, Tallahassee, FL
Chelsey Bowman, PhD, Psychologist, George Washington University, Tallahassee, DC
Sarah O'dell, Research Faculty, Florida State University
Background and Purpose

The Adverse Childhood Experiences (ACE) framework has significantly shaped research on childhood trauma and long-term health outcomes; however, it has been widely critiqued for its limited attention to cultural context and its deficit-oriented focus (Bernard et al., 2020; Ellis & Dietz, 2017). Emerging scholarship highlights the need to account for structural inequities, racism, and culturally grounded resilience when assessing childhood adversity. In response, this study introduces the TRACE measure (Trauma, Resilience, and Adversity in Cultural Experiences), a culturally informed extension of the ACE framework that integrates general adversity, culturally specific adversities, and resilience factors within a single instrument. The purpose of this study is to examine the preliminary psychometric properties of the TRACE measure, including its factor structure, and assess its potential to advance more equitable and culturally informed approaches to understanding childhood experiences.

Methods

The TRACE measure was administered to parents (N = 122) of kindergarten students participating in a longitudinal literacy project across eight elementary schools. The instrument includes three domains: traditional ACE items, culturally informed ACE (C-ACE) items, and culturally informed resilience (C-Resilience) items. Confirmatory factor analyses (CFA) were conducted using weighted least squares estimation (WLSMV) appropriate for categorical data. Multiple competing models, including correlated factors and bifactor models, were tested to evaluate dimensionality. Model fit was assessed using CFI, TLI, and RMSEA. Reliability was estimated using coefficient omega (ω). Additionally, explained common variance (ECV) was calculated to assess the strength of a general adversity factor.

Results

Item-level analyses demonstrated strong psychometric properties for both ACE (ω = .95) and C-ACE (ω = .93) domains, with acceptable reliability for C-Resilience (ω = .78). The correlated factors model showed poor fit, whereas bifactor models demonstrated improved fit, supporting a general adversity factor (G-ACE) alongside domain-specific factors. The general factor accounted for approximately 55%–59% of the common variance across adversity indicators, suggesting that traditional and culturally informed adversities reflect overlapping but distinct constructs. Additionally, a moderate inverse relationship between adversity and resilience (r ≈ −.50) was observed, indicating that while related, these constructs remain empirically distinct.

Conclusions and Implications

Findings provide preliminary evidence supporting TRACE as a psychometrically viable and conceptually meaningful extension of the ACE framework. By integrating culturally informed adversity and resilience within a single measure, TRACE advances beyond deficit-focused models and offers a more comprehensive, asset-based approach to assessing childhood experiences. Implications for social work research and practice include improving the field’s ability to capture structural inequities, informing culturally responsive screening tools, and guiding interventions that leverage family and community strengths. This work aligns with calls to center racial equity and social justice in research and contributes to advancing measurement approaches that better reflect the lived realities of diverse populations.