Data analysis
REGRESSION
To account for the nature of the alcohol use data which are often positively skewed and include many observations at zero, we used multilevel hurdle models. Hurdle models include a logistic regression to model the zeroes in the data as well as a count regression (in this case negative binomial) to model the counts. All the zeroes (not alcohol use occasions) are modeled with the logistic regression and nonzero-counts (alcohol use occasions) are modeled by a truncated negative binomial (i.e., truncated as it does not contain zero). Thus, these models allowed us to independently model whether a person drinks or not (logistic regression) at a given occasion and number of drinks when an individual drinks (count regression). We estimated hurdle modelsWe specified two models (one for Study 1 and another for Study 2) to assess whether drinking frequency and amount vary among participants in the mindfulness and perspective-taking conditions (within-person) on weeks when intervention reminders were present or absent. The main predictor of interest was active vs. inactive week and the outcomes were frequency of drinking occasions and drinking amount. To further explore if the effectiveness of the reminders varied by condition type (mindfulness vs. perspective-taking), we included an interaction term: active week (vs. inactive week) x condition type. Given that observations are nested within participants, who are in turn nested within non-independent social groups, all multilevel models accommodated the nested nature of the data, and intercepts were allowed to vary randomly across people. In models presented in the main manuscript, we controlled for the following covariates: social weekend (defined as Thursday, Friday, or Saturday), due to differences between weekend and weekday drinking among college students