摘要
Background: The psychosocial consequences of obesity are important but often underrated. The Attitudes Toward Obese Persons (ATOP) and Beliefs About Obese Persons (BAOP) scales used to measure weight-related bias have little psychometric information, especially in East Asian contexts. The objective of this study was to use rigorous statistical methods to demonstrate the psychometric properties of these two instruments in Hong Kong and Taiwanese college students. Methods: A convenience sample of 707 students was recruited from the universities in Hong Kong and Taiwan. Several competing confirmatory factor analyses (CFAs) were conducted to confirm the factorial structure of the ATOP and BAOP. The best fit models for the ATOP and BAOP were chosen for the examination of the measurement invariance across subcultures. We then compared configurable models with or without loading and/or intercept constrained before correlating the latent constructs between the best models for the ATOP and BAOP. Results: The comparison in multiple CFAs found that the model with one factor and two correlated-wording-method factors outperformed the other models for both the ATOP and BOAP. However, the internal consistency was suboptimal (ATOP: α =.56 to.80; BTOP: α =.57 to.65) and the measurement invariance was somewhat unsupported among the Hong Kong and Taiwan samples. Moreover, after controlling wording effects, the latent construct of the ATOP was moderately associated with that of BAOP (r =.356; p <.001). Conclusion: Chinese versions of the ATOP and BAOP can be treated as a unidimensional factor for use in Hong Kong and Taiwan university students. However, further refinements of both instruments may be needed before using them to capture the social attitudes and beliefs toward obesity individuals, which is expected to advance our understanding of weight-related bias in East Asian contexts.
| 原文 | English |
|---|---|
| 文章編號 | 134 |
| 期刊 | Health and quality of life outcomes |
| 卷 | 17 |
| 發行號 | 1 |
| DOIs | |
| 出版狀態 | Published - 2019 7月 30 |
UN SDG
此研究成果有助於以下永續發展目標
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SDG 3 良好的健康和福祉
All Science Journal Classification (ASJC) codes
- 公共衛生、環境和職業健康
指紋
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