their disapproval of VAW through their Facebook profiles. This measure was operationalized by giving participants in the medium-term survey the opportunity to add a frame against VAW in their Facebook picture profile (frame in Appendix Figure B3).

Practically most attitudinal and behavioral measures were originally coded using a 5-point agreement scale. To facilitate the interpretation of the results, we transformed them into binary indicators, thus program impacts are reported as percentage point changes. To address the issue of multiple hypothesis testing, we group individual level outcomes into four topic indexes: (i) knowledge and awareness of existing gender norms and VAW, (ii) attitudes towards gender norms and roles, (iii) attitudes condoning VAW, and (iv) beliefs on others’ attitudes. We additionally constructed a general index that aggregates the first three outcome indexes. We excluded the fourth, what users perceive to be the attitudes of their closest Facebook friends, because this measure is not a final outcome but rather a potential mediator – – namely, the social channel of the effect of the intervention on users’ self-attitudes.

Individual items were aggregated into indexes following Kling et al. (2007), i.e., we constructed equally weighted averages of the z-scores of the variables that enter each index.<sup>17</sup> For robustness, we also used a second method based on principal component analysis. Appendix Tables A33 and A34 describe the individual items used per index, their factor loadings and the Cronbach’s alpha. Variables were oriented so that the intended impact of treatments on each component of the index should be positive. Therefore, consistent with the gender objectives of the videoclips, higher values of each index reflect more progressive views. To facilitate interpretation of impacts on the outcome indexes, we also report their standard deviations in the control group at the respective follow-up.<sup>18</sup>

# **3.4 Empirical specification**

We conducted two separate analyses for the short-term (n=606) and medium-term (n=619) follow-up samples. It is worth mentioning that individuals in these two samples were not necessarily the same. In particular, 42% of those who completed the medium-term survey also

> would count the entire difference between the two visits as a visit to the first website. To correct for the related measurement error we assumed that all visits that lasted more than 2 hours were capturing time spent away from our websites. Hence, we discarded them by replacing the values with missing data.

> 17For missing values, we also followed Kling et al. (2007). Particularly, if a respondent has a non-missing value for at least one of the variables in an index, we impute any missing values for the other variables using the random assignment group mean. This implies that differences between treatment and control means of an index coincide with the average of treatment and control means of the variables in that index (when divided by their standard deviations).

> 18Impact effects are divided by standard deviations derived from the control group at follow-up. An increase of two standard deviations, for example, would move someone from having average knowledge to being in the top 5 percent of the group.

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