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Behavior, Psychology and Sociology

The Modified Weight Bias Internalization Scale: measurement invariance by weight status and race among undergraduate women

International Journal of Obesity (2024)Cite this article

Subjects

Abstract

Background

Internalized weight bias is the belief in negative, weight-based stereotypes and the application of these stereotypes to oneself. These negative stereotypes have harmful impacts on people with overweight/obesity, and weight-based discrimination is well-documented across a variety of settings. Given poor outcomes associated with internalized weight bias, particularly among individuals with obesity, it is necessary to validate measures assessing internalized weight bias among diverse samples. The present study sets out to investigate measurement invariance properties across weight status (women with vs. without overweight/obesity) and race (White vs. Asian; White vs. bi- or multi-racial) of the Modified Weight Bias Internalization Scale (WBIS-M), an 11 item self-report measure.

Methods

Participants were 746 racially/ethnically diverse women across the weight spectrum (24.9% with overweight/obesity). Confirmatory factor analyses of the WBIS-M were initially performed among the full sample, and all sub-samples. Each model showed good to excellent descriptive model fit. Subsequent analyses examined factor loadings and item thresholds of the WBIS-M to assess metric, threshold, and scalar invariance. Invariance was determined by assessing changes in Comparative Fit Index (ΔCFI \(\le\) −0.010), Root Mean Square Error of Approximation (ΔRMSEA \(\le\) 0.015), and Standardized Root Mean Square Residuals (ΔSRMR \(\le\) 0.030).

Results

Based on these previously established statistical cutoffs, the WBIS-M showed invariance across weight status and racial groups in the present sample. The current results lend support for use of the WBIS-M to measure internalized weight bias in women who do and do not have overweight/obesity, and among White, Asian, and bi- or multi-racial women.

Conclusion

This may inform future studies that wish to utilize the WBIS-M, such as investigations of mean level differences in internalized weight bias. These findings may have clinical applications in the treatment and prevention of obesity, given the heightened levels of internalized weight bias and weight-based discrimination faced by individuals with higher body weights.

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Data availability

The dataset analyzed during the current study are available from the corresponding author upon reasonable request.

References

  1. Puhl RM, Andreyeva T, Brownell KD. Perceptions of weight discrimination: prevalence and comparison to race and gender discrimination in America. Int J Obes. 2008;32:992–1000.

    Article  CAS  Google Scholar 

  2. Durso LE, Latner JD. Understanding self‐directed stigma: development of the weight bias internalization scale. Obesity. 2008;16:S80–6.

    Article  PubMed  Google Scholar 

  3. Durso LE, Latner JD, White MA, Masheb RM, Blomquist KK, Morgan PT, et al. Internalized weight bias in obese patients with binge eating disorder: associations with eating disturbances and psychological functioning. Int J Eat Disord. 2012;45:423–7.

    Article  PubMed  Google Scholar 

  4. Durso LE, Latner JD, Ciao AC. Weight bias internalization in treatment-seeking overweight adults: psychometric validation and associations with self-esteem, body image, and mood symptoms. Eat Behav. 2016;21:104–8.

    Article  PubMed  Google Scholar 

  5. Bevan N, O’Brien KS, Lin CY, Latner JD, Vandenberg B, Jeanes R, et al. The relationship between weight stigma, physical appearance concerns, and enjoyment and tendency to avoid physical activity and sport. Int J Environ Res Public Health. 2021;18:9957.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Latner JD, Barile JP, Durso LE, O’Brien KS. Weight and health-related quality of life: the moderating role of weight discrimination and internalized weight bias. Eat Behav. 2014;15:586–90.

    Article  PubMed  Google Scholar 

  7. Tomiyama AJ, Epel ES, McClatchey TM, Poelke G, Kemeny ME, McCoy SK, et al. Associations of weight stigma with cortisol and oxidative stress independent of adiposity. Health Psychol. 2014;33:862.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Romano KA, Heron KE, Henson JM. Examining associations among weight stigma, weight bias internalization, body dissatisfaction, and eating disorder symptoms: does weight status matter? Body Image. 2021;37:38–49.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Pearl RL, Puhl RM. Measuring internalized weight attitudes across body weight categories: validation of the modified weight bias internalization scale. Body Image. 2014;11:89–92.

    Article  PubMed  Google Scholar 

  10. Pakpour AH, Tsai MC, Lin YC, Strong C, Latner JD, Fung XC, et al. Psychometric properties and measurement invariance of the Weight Self-Stigma Questionnaire and Weight Bias Internalization Scale in children and adolescents. Int J Clin Health Psychol. 2019;19:150–9.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Lin CY, Imani V, Cheung P, Pakpour AH. Psychometric testing on two weight stigma instruments in Iran: weight self-stigma questionnaire and weight bias internalized scale. Eat Weight Disord. 2020;25:889–901.

    Article  PubMed  Google Scholar 

  12. Apay SE, Yılmaz E, Aksoy M, Akalın H. Validity and reliability study of modified Weight Bias Internalization Scale in Turkish. Int J Caring Sci. 2017;10:1341–7.

  13. Andrés A, Fornieles-Deu A, Sepúlveda AR, Beltrán-Garrayo L, Montcada-Ribera A, Bach-Faig A, et al. Spanish validation of the Modified Weight Bias Internalization Scale (WBIS-M) for adolescents. Eat Weight Disord. 2022;27:3245–56.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Marshall RD, Latner JD, Masuda A. Internalized weight bias and disordered eating: the mediating role of body image avoidance and drive for thinness. Front Psychol. 2020;10:2999.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Marshall RD. Examining a group cognitive-behavioral program for internalized weight stigma and eating disturbances among college women. Unpublished doctoral dissertation. University of Hawai’i at Mānoa; 2023.

  16. Purton T, Mond J, Cicero D, Wagner A, Stefano E, Rand-Giovannetti D, et al. Body dissatisfaction, internalized weight bias and quality of life in young men and women. Qual Life Res. 2019;28:1825–33.

    Article  PubMed  Google Scholar 

  17. Weir CB, Jan A. BMI classification percentile and cut off points. In: StatPearls. StatPearls Publishing, Treasure Island (FL); 2023.

  18. Garner DM, Olmsted MP, Bohr Y, Garfinkel PE. The eating attitudes test: psychometric features and clinical correlates. Psychol Med. 1982;12:871–8.

    Article  CAS  PubMed  Google Scholar 

  19. Mueller RO. Structural equation modeling: back to basics. Struct Equ Model Multidiscip J. 1997;4:353–69.

    Article  Google Scholar 

  20. Mindrila D. Maximum likelihood (ML) and diagonally weighted least squares (DWLS) estimation procedures: a comparison of estimation bias with ordinal and multivariate non-normal data. Int J Digital Soc. 2010;1:60–6.

    Article  Google Scholar 

  21. Rosseel Y. lavaan: an R package for structural equation modeling. J Stat Softw. 2012;48:1–36.

    Article  Google Scholar 

  22. Swami V, Barron D. Translation and validation of body image instruments: challenges, good practice guidelines, and reporting recommendations for test adaptation. Body Image. 2019;31:204–20.

    Article  PubMed  Google Scholar 

  23. Schumacker RE, Lomax RG. A beginner’s guide to structural equation modeling. Psychology Press; 2004. Mahwah, NJ: Lawrence Erlbaum.

  24. Hu LT, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ model Multidiscip J. 1999;6:1–55.

    Article  Google Scholar 

  25. Comrey AL, Lee HB. A first course in factor analysis. Psychology Press; 2013. Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.

  26. Chen FF. Sensitivity of goodness of fit indexes to lack of measurement invariance. Struct Equ Model Multidiscip J. 2007;14:464–504.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Psychology, University of Hawai’i at Manoa, Honolulu, HI, USA

    Kaitlin N. Rozzell-Voss, Rachel D. Marshall & Janet D. Latner

  2. Yale School of Medicine, New Haven, CT, USA

    Rachel D. Marshall

  3. Institute of Allied Health Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan

    Chung-Ying Lin

  4. University of Religions and Denominations, Qom, Iran

    Chung-Ying Lin

Authors
  1. Kaitlin N. Rozzell-Voss
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  2. Rachel D. Marshall
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  3. Chung-Ying Lin
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  4. Janet D. Latner
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Contributions

Conceptualization: KNRV and JDL. Methodology: KNRV, JDL, and RDM. Data collection: RDM. Formal analysis: KNRV and CYL. Writing—original draft: KNRV and JDL. Writing—review and editing: KNRV, RDM, JDL, and CYL. Supervision: JDL. Funding acquisition: RDM.

Corresponding author

Correspondence to Kaitlin N. Rozzell-Voss.

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Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

The present research was conducted on human subjects and in accordance with the Declaration of Helsinki. This study was approved by the University Review Board (#2020-00059). All participants gave informed consent electronically prior to participation. Participants who enrolled in the study were asked to complete an anonymous online survey including survey measures and demographic information.

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Rozzell-Voss, K.N., Marshall, R.D., Lin, CY. et al. The Modified Weight Bias Internalization Scale: measurement invariance by weight status and race among undergraduate women. Int J Obes (2024). https://doi.org/10.1038/s41366-024-01602-7

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