Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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    A reproduction of the results of Onyike et al. (2003)
    (2021) Brown NJL; van Rongen J; van der Velde J; Williams M; Schönbrodt, FD
    Onyike et al. (2003) analyzed data from a large-scale US-American data set, the Third National Health and Nutrition Examination Survey (NHANES-III), and reported an association between obesity and major depression, especially among people with severe obesity. Here, we report the results of a detailed replication of Onyike et al.’s analyses. While we were able to reproduce the majority of these authors’ descriptive statistics, this took a substantial amount of time and effort, and we found several minor errors in the univariate descriptive statistics reported in their Tables 1 and 2. We were able to reproduce most of Onyike et al.’s bivariate findings regarding the relationship between obesity and depression (Tables 3 and 4), albeit with some small discrepancies (e.g., with respect to the magnitudes of standard errors). On the other hand, we were unable to reproduce Table 5, containing Onyike et al.’s findings with respect to the relationship between obesity and depression when controlling for plausible confounding variables—arguably the paper’s most important results—because some of the included predictor variables appear to be either unavailable, or not coded in the way reported by Onyike et al., in the public NHANES-III data sets. We discuss the implications of our findings for the transparency of reporting and the reproducibility of published results.
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    Fat stigma and body objectification: A text analysis approach using social media content
    (SAGE Publications, 15/08/2022) Wanniarachchi V; Scogings C; Susnjak T; Mathrani A
    This study investigates how female and male genders are positioned in fat stigmatising discourses that are being conducted over social media. Weight-based linguistic data corpus, extracted from three popular social media (SM) outlets, Twitter, YouTube and Reddit, was examined for fat stigmatising content. A mixed-method analysis comprising sentiment analysis, word co-occurrences and qualitative analysis, assisted our investigation of the corpus for body objectification themes and gender-based differences. Objectification theory provided the underlying framework to examine the experiential consequences of being fat across both genders. Five objectifying themes, namely, attractiveness, physical appearance, lifestyle choices, health and psychological well-being, emerged from the analysis. A deeper investigation into more facets of the social interaction data revealed overall positive and negative attitudes towards obesity, which informed on existing notions of gendered body objectification and weight/fat stigmatisation. Our findings have provided a holistic outlook on weight/fat stigmatising content that is posted online which can further inform policymakers in planning suitable props to facilitate more inclusive SM spaces. This study showcases how lexical analytics can be conducted by combining a variety of data mining methods to draw out insightful subject-related themes that add to the existing knowledge base; therefore, has both practical and theoretical implications.