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    Standardization and other approaches to meta-analyze differences in means.
    (John Wiley and Sons Ltd, 2024-05-18) Hopkins WG; Rowlands DS
    Meta-analysts often use standardized mean differences (SMD) to combine mean effects from studies in which the dependent variable has been measured with different instruments or scales. In this tutorial we show how the SMD is properly calculated as the difference in means divided by a between-subject reference-group, control-group, or pooled pre-intervention SD, usually free of measurement error. When combining mean effects from controlled trials and crossovers, most meta-analysts have divided by either the pooled SD of change scores, the pooled SD of post-intervention scores, or the pooled SD of pre- and post-intervention scores, resulting in SMDs that are biased and difficult to interpret. The frequent use of such inappropriate standardizing SDs by meta-analysts in three medical journals we surveyed is due to misleading advice in peer-reviewed publications and meta-analysis packages. Even with an appropriate standardizing SD, meta-analysis of SMDs increases heterogeneity artifactually via differences in the standardizing SD between settings. Furthermore, the usual magnitude thresholds for standardized mean effects are not thresholds for clinically important differences. We therefore explain how to use other approaches to combining mean effects of disparate measures: log transformation of factor effects (response ratios) and of percent effects converted to factors; rescaling of psychometrics to percent of maximum range; and rescaling with minimum clinically important differences. In the absence of clinically important differences, we explain how standardization after meta-analysis with appropriately transformed or rescaled pre-intervention SDs can be used to assess magnitudes of a meta-analyzed mean effect in different settings.
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    Analysing underpinning patterns in social media posts that promote fat stigmatisation : a thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy (Ph.D) in Information Technology, Massey University, Auckland, New Zealand
    (Massey University, 2022) Wanniarachchi, Vajisha Udayangi
    Social media offers users an online platform to freely express themselves; however, when users post opinionated and offensive comments that target certain communities, this could instigate hatred towards them. With the global increase in obese/fat populations, social media discourses laced with fat hatred have become commonplace, leading to much fat stigmatising content being posted online. This research aims to investigate the patterns of fat stigma, and how female and male genders are positioned in fat stigmatising discourses that are being conducted over social media. To achieve this objective, a methodological framework is proposed for unearthing underlying stigmatising patterns prevalent in social media discussions, with specific focus on fat stigma. Methods incorporating natural language processing techniques such as sentiment analysis and topic modelling, along with discourse analysis have been described for classifying users’ emotions and comprehending the stigma patterns embedded in social big data. The framework has been applied to weight-based textual data, extracted from Twitter and Reddit, to identify emergent gender-based themes, emotions and word frequency patterns that underpin the fat stigmatising content posted online. The experiential consequences of being considered fat across both genders have been analysed with objectification theory. The findings from this study have provided a holistic outlook on fat stigmatising content that is posted online which can further inform policymakers in planning suitable props to facilitate more inclusive social media 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.