We constructed an index of protective behavior by summing the answers to the questions regarding actions taken to avoid influenza infection. The index ranged from 0-9, with larger values indicating more protective measures taken. Using a binomial GLM with canonical logit-link, we modeled the protection index as a function of covariates. Our primary interest was the possible mediating effect of affective variables on action taken to protect against swine flu infection. To evaluate the hypothesis that respondents' affective state (subjective anxiety, fatalism about infection) predicts protective measures, we include in the model demographic (age, gender), epidemiological (household size, number of contacts, survey day), and media (source of information on the outbreak) conditioning variables. For the media variables, we constructed dummies with a value of 1 corresponding to answers of ''very often'' or ''often'' and a value of 0 for all other responses to the question of ''How often do you use the following sources to get information about swine flu?'' With such a large number of conditioning variables, in addition to the affective variables of greatest interest, there is a distinct danger of overfitting the GLM. To address this problem, we used likelihoodbased model selection [17] to search the model space set up by our conditioning variables [18] .