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Eating Disorders

Examination of the Factor Structure and Age-Based Measurement Invariance of the Eating Disorder Quality of Life (EDQOL) Instrument in a Clinical Eating Disorder Sample

(PS7-34) Examination of the Factor Structure and Age-based Measurement Invariance of the Eating Disorder Quality of Life (EDQOL) Instrument in a Clinical Eating Disorder Sample

Saturday, November 16, 2024
8:30 AM – 9:30 AM EST
Location: Franklin Hall, Level 4, Marriott Downtown
Abstract Text: Current recommendations advocate for assessing quality of life (QOL) alongside core cognitive-behavioral features in eating disorder (ED) patients. Disease-specific health-related quality of life measures, like the Eating Disorders Quality of Life (EDQOL) instrument, offer higher sensitivity than generic tools when evaluating QOL. The EDQOL, which comprises 25-items across four subscales (psychological, physical/cognitive, financial, and work/school), demonstrated excellent psychometric properties in its initial validation. Studies of adapted/translated versions of the EDQOL have further supported its validity, reliability, and factor structure. To our knowledge, the factor structure of the EDQOL has not been empirically evaluated specifically in a clinical ED sample despite evidence that factor structures of other ED-related measures may differ between clinical and non-clinical ED populations. Furthermore, although the EDQOL has been used across the lifespan, its utility for different age groups remains unknown. Statistics suggest that approximately 18.6% of individuals with EDs in the U.S. are under 20-years-old while more than 50% are above 30. Yet the EDQOL was developed and validated using an all-adult, primarily college student sample. Given these estimated rates as well as research showing that QOL constructs may differ by age, the present study examined the factor structure of the EDQOL in a clinical sample and tested measurement invariance across age. Retrospective chart data were obtained from patients (N = 407; 84.7% White, 84.2% cisgender women) who completed the EDQOL upon admission to a remote ED treatment program. Responses were grouped by age as follows: adolescents (13 to 19; n = 116), young adults (20 to 29; n = 147), and mature adults (30+; n = 144). An exploratory factor analysis (EFA) revealed five items with communalities below 0.5 after extraction. After excluding these items, a second EFA yielded a five-factor model (i.e., work/school split into two factors), explaining 72.08% of the total variance. Good internal consistency was established for each factor. Results of confirmatory factor analyses (CFA) verified that the new model was superior based on good fit indices [χ2 (160) = 346.22, p < .001; CFI = 0.96; RMSEA = 0.04; SRMR = 0.04] compared to not acceptable/acceptable fit indices for the original model [χ2 (269) = 10,006.97, p < .001; CFI = 0.87; RMSEA = 0.08; SRMR = 0.09). Finally, a multi-group CFA of the new model supported configural [χ2 (480) = 743.35, p < .001; CFI = 0.94; RMSEA = 0.04; SRMR = 0.08] and metric (Δ CFI = .004) invariance but scalar noninvariance (Δ CFI = .018). Item-level analyses indicated significantly different intercepts across age groups for nine items, the majority of which belonged to the financial, work, and school factors. Findings suggest that a shortened, five-factor version of the EDQOL may be more appropriate for use with clinical ED samples; however, researchers should exercise caution when using the EDQOL across age groups due to developmental differences in scale properties that may confound mean comparisons. Upon substantiation of these findings, additional psychometric research on this revised EDQOL as well as age-based modifications may be warranted.