2.4. Statistical Analysis
REGRESSION
Data were analysed using linear and logistic mixed models. Mixed models retain all available data and provide an intention-to-treat estimate of change under the assumption of missingness at random. Models included a fixed effect of time and a random effect of participants to adjust for the correlation of responses within participants over time. Logistic regression was used to explore predictors of missingness at six-month follow-up, using participant demographics and pre-course outcomes as potential predictors of attrition. Age was associated with missingness at follow-up (The stigma variable was highly skewed; therefore, a linear model was deemed inappropriate. For this outcome, mean scores were dichotomised based on scoring 1 (the lowest possible score) or scoring greater than 1 (indicating some stigma). A mixed-effect logistic regression model was used to calculate the odds of scoring 1 on stigma (low stigma) after the course. Effect sizes were calculated and interpreted using Cohen’s d and Cohen’s criteria for small, medium, and large effects, where the difference between means was divided by their pooled standard deviation [Course satisfaction data were analysed using means and standard deviations. Content analysis was used to determine prominent themes in the free-text responses [