Data analysis
Data screening was conducted prior to inferential analyses to evaluate whether assumptions were met regarding the presence of outliers, multivariate normality, linearity, and homogeneity of variance. The Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy and Bartlett’s test of sphericity were also evaluated preceding any attempts to conduct exploratory factor analyses to ensure data fitness. Once all assumptions were satisfied, Exploratory Factor Analysis (EFA), with principal axis factor extraction and oblique rotation was performed. Screeplot identification, eigenvalue (> 1), and higher item loading greater than 0.30 were criteria to evaluate factor extraction, with the addition of a Monte Carlo PCA for parallel analysis indicative of rejecting or accepting factors [Data were further analysed using AMOS 24. “Sensory Attention” and “Non-judgmental Awareness” were first-order latent factors that loaded onto a second-order latent factor; that is, “Mindful Eating Behaviour Scale”. Structural equation modelling was run using the maximum-likelihood method, and Confirmatory Factor Analysis (CFA) goodness-of-fit was assessed for this one factor, second-order model, which included indexes of fit: a Chi-squared by the degree of freedom (