Quantitative analyses
anxiety
REGRESSION
Before conducting any ANCOVAs or ANOVAs, assumptions were tested for each DV by checking for significant outliers, producing QQ plots and running Shapiro–Wilk tests (to check normality assumptions), running Mauchly’s test of sphericity, conducting Levene’s tests for homogeneity of variance and running a Box’s M test to check for homogeneity of covariances. Additionally, we produced scatterplots to check for linear relationships between the covariates and each DV and ran tests for homogeneity of regression slopes. Most assumptions were met, but the results showed the existence of some outliers and violations of the normality and sphericity assumptions across almost all conditions of all DVs. Additionally, there were violations of the assumptions regarding linearity and homogeneity of regression slopes for covariates. Given our large sample size, we considered ANOVAs robust to normality violations. To account for violations of sphericity, we will report Greenhouse–Geisser adjusted values throughout. However, given the assumption violations pertaining to our covariates, we decided against conducting an ANCOVA. Instead, we transformed the continuous covariate scores for age, health anxiety (HAQ), health literacy (HLQ) & and health risk-taking (DOSPERT) into binary variables, in order to enter these as independent variables in additional ANOVA analyses. This was done by summing individual participant ratings for the respective questionnaire items. For the health literacy questionnaire, items 1 and 3 were reverse coded. Subsequently, the median score was determined and participants were re-coded as either high- or low-scoring depending on their score’s position relative to the median. Participants with a HAQ score of > 34 were coded as “high in health anxiety” (vs. low in health anxiety), participants with an HLQ score of > 25 were coded as “high in health literacy (vs. low in health literacy) and participants with a DOSPERT score of > 10 were coded as “high in health risk taking” (vs. low in health risk taking). Similarly, demographic variables were transformed into binary variables. Participants aged > 46 years were recoded as “high age” (vs. “low age). For analyses involving the variable “sex”, participants were excluded if they identified as “other” (