From measurement to modeling: a systematic factorial framework for comparing structural models of motivation for seeking treatment.

Authors

Paulo Sergio Panse Silveira, Beatriz Helena Tess, Sandra Regina da Silva, Yuan-Pang Wang, Jose Oliveira Siqueira

Published in

Revista brasileira de psiquiatria (Sao Paulo, Brazil : 1999). Nov 06, 2026. Epub Nov 06, 2026.

Abstract

Motivation for seeking treatment is a clinically relevant yet underexplored construct in mental health and behavioral research. This study proposes a systematic methodology to identify, compare, and rank structural models representing the construct of motivation, emphasizing structural plausibility and model comparison.
Using the European Obesity Academy Questionnaire: Expectations of Surgical Treatment (EOAQ) as a working example, we analyzed data from 161 treatment-seeking patients with severe obesity, exploring their motivations for undergoing bariatric surgery. Seventy-nine candidate structural models were developed, incorporating one general factor (G) and two or three specific factors. Multigroup confirmatory factor analysis was used to evaluate model fit and test configural invariance across menopausal groups.
Bifactor models with one general factor and three specific dimensions were the most plausible under the proposed comparison (RMSEA 90%CI=0.038-0.102, p-close = 0.1214). The dimensions of personal, social, and healthcare represented intrinsic, extrinsic, and healthcare-related aspects of treatment motivation. The selected bifactor model reached the configural level of measurement invariance across groups and McDonald's omega (ω) coefficients ranged from 0.13 to 0.62.
This study presents a comprehensive framework for comparing alternative CFA models of motivation for seeking treatment, including unidimensional, first-order, bifactor, and second-order structures. The approach can be flexibly adapted to other instruments and populations, but the empirical structure identified here is preliminary and requiring replication in larger samples.

PMID:
43002644
Bibliographic data and abstract were imported from PubMed on 10 Nov 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.