The analysis will be conducted using mixed-effect logit model with binary indicator ("lucky choice") which takes value of 1 if respondent chooses side with the higher value and 0 otherwise. The model includes two random intercepts, one random slope and number of fixed effects. One random intercept captures variance in individual tendency to make lucky choices, another random intercept captures variance in likelihood of lucky choices over the 20 throws, and the random slope captures individual variance of tendency to make lucky choice when higher value is facing up (visibility effect). Main (fixed) effects of experimental condition indicators and environmental attitude are proportional to the average likelihood of lucky choices (i.e., tendency to dishonesty) in the two store conditions and to the average effect of environmental attitude on lucky choices (dishonesty). Interaction of the store conditions and environmental attitude captures moderating effect of environmental attitude on moral processes associated with experimental conditions. Finally, fixed effects of value difference (indicating difference between values on the two side of the die, that is 1 and 6, 2 and 5, and 3 and 4) capture average tendency to lucky choices (dishonesty) for each value difference. Note that we will use two specification of experimental condition indicators (these alternative specifications are labelled m1 and m2 in the R script), which differ in how the contrast between the experimental conditions (i.e., two store conditions and the control group) are coded. Whereas m1 uses control condition as the reference group for the experimental condition indicators, m2 uses green store condition as the reference group. The file “[die casting task analysis][1]” contains an R script that we will use for analysis of data and testing of hypotheses. [1]: https://osf.io/t7nhc/