Online Appendix for Moving Pictures? Experimental Evidence of Cinematic Influence on Political Attitudes.

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1 Online Appendix for Moving Pictures? Experimental Evidence of Cinematic Influence on Political Attitudes. In this appendix, we provide additional information on survey content and present the results of regression models that test the moderating influence of political ideology and political sophistication on attitude change. Appendix items are presented in order of appearance within the text and are cross-referenced accordingly. Tables are included at the end of the document. Note 3, Page 6: Sample Composition The sample was roughly balanced on gender. The sample was skewed towards higher family incomes, and (as noted in the paper) was skewed conservative and Republican. Finally, the sample was disproportionately white. See Table A1 for further details. In each case, the authors stress that, if anything, the non-representativeness of the sample would tend to skew the test away from our findings- those with higher family incomes, conservatives, and Republicans should be more resistant to liberal messages on healthcare compared to the general population. Note 5, Page 8: Survey Question Wording The following healthcare policy questions were embedded within a randomized sixteen question matrix format, with answer options ranging from strongly oppose (1) to strongly support (5). The other policy areas covered a wide range of issues, including gun control and environmental protection. The randomization was controlled so that a respondent would get a total of four policy questions per screen, with only one healthcare-related question at a time in order to minimize priming. Below are a few proposals for new laws, regulations, or programs. Please indicate whether you support, oppose, or are neutral towards each.

2 A government-run health care system to cover all Americans. An optional government health insurance program to compete with private insurance. Extension of Medicare to uninsured Americans. A new agency to oversee the insurance industry. Note 8, Page 15. Random Effects GLS Models of Political Ideology and Political Sophistication. An alternative theory might posit that ideology would be a moderating factor, and we examine that suggestion in Table A2. We maintain the same basic model design as Table 3 in the main section of the paper, but this time we create dummy variables for liberals (those who labeled themselves "extremely liberal," "liberal," and "slightly liberal" on a 7-point scale) and conservatives (those who described themselves as "extremely conservative," "conservative," and "slightly conservative" on a 7-point scale), with moderates as the comparison category. We interacted liberal and conservative with time, which we interpret as the change in the effect of ideology over the time series for the control group. We then set up a series of interactions with the experimental treatments, again culminating in a total of eight three-way interaction terms. The coefficients for the three-way interaction terms represent the additional effect of being liberal (or conservative) on attitude change for the treatment indicated, and statistically significant values for these interaction terms would suggest that ideology is indeed a moderating factor. Looking at the eight coefficients, we see that for the most part they are not statistically significant. The only exception is that liberals who watched The Rainmaker were significantly more liberal on the national insurance question at time 3 as compared to moderates. This might suggest that attitudinal effects from watching popular films have more "staying power" among

3 those in ideological agreement with the film, although we think it is important to note that this effect was not observed at time 2, or for any other variable. On the whole, the results from ideology lend further support to our assertion that part of the latent power of popular movies to effect political attitudes is that viewers often do not recognize that they are receiving political messages, and therefore the usual factors that moderate attitude change do not seem to have an effect. At the same time, however, it cannot be said that the political content of popular movies cannot be highly persuasive, just that it generally imparts political learning in a different way than other media sources. We also consider the potential that political knowledge is a moderating factor (e.g., Delli Carpini and Keeter 1996; Zaller 1992). We measure political knowledge is an additive scale, ranging from 0 to 1, made up of a total of nine survey questions: ability to name each the respondents' home state's senators as of February 2011 (one point for both), the party that controlled the Senate as of February 2010, and the party affiliations of Barack Obama, Harry Reid, Mitch McConnell, Nancy Pelosi, John Boehner, Rand Paul, and Jesse Jackson Jr. We present the results of our Random Effects GLS regression in Table A3. The coefficient for "knowledge" represents the effect of having higher knowledge at time 1 for the control group, and the knowledge X time interaction terms represent the change in the effect for high knowledge respondents over the time series for the control group. The three-way interactions between the treatments, knowledge, and time should be interpreted as the change in the effect of having high knowledge over the time series for those who watched The Rainmaker or As Good As It Gets. If our "no moderators" theory is correct, then we should see that none of these three-way interaction terms are significant. Looking at Table A2, we see that the only three-way interaction term that is significant is As Good X knowledge at time 3 for the question

4 regarding a new agency to regulate insurance. Given that those who watched As Good As It Gets did not show significant movement on this policy question (see Table 3), we believe that the significance of this interaction term does not impact our "no moderators" theory. None of the three-way interaction coefficients are significant for The Rainmaker, thereby indicating that having higher stores of political knowledge did not have a statistically significant impact on the effects from the treatments. This finding again lends support to our theory that the influence of popular films is due, at least in part, to the fact that respondents are unaware that they are receiving political messages when watching films created primarily for the purpose of entertainment.

5 Table A1. Select Statistics of Sample % N % N Gender Party Identification Male Strong Democrat Female Democrat Family Income Lean Democrat $0 to 25, Independent $25,001 to 50, Lean Republican $50,001 to 75, Republican $75,001 to 100, Strong Republican $100,001 to 200, Race Over $200, White DK/Not Sure Black Ideology Asian Extremely Liberal Native American Liberal Hispanic Slightly Liberal Other/Don't Know Moderate Slightly Conservative Conservative Extremely Conservative Note: N=268 unless otherwise noted. In addition to the statistics noted here, respondents are obviously relatively homogenous regarding age and level of education.

6 Table A2. Moderating Effects for Ideology? Medicare National Insurance Insurance Agency Time (.256) (.255) (.245) Time (.265) (.265) (.254) Rain (.380) (.403) (.246) Rain X time (.319) (.318) (.306) Rain X time (.329) (.326) (.313) As Good (.405) (.430) (.368) As Good X time (.340) (.339) (.326) As Good X time (.352) (.347) (.333) Liberal *.678* (.357) (.378) (.324) Liberal X t (.299) (.299) (.287) Liberal X t (.312) (.310) (.298) Conservative -.751* (.333) (.354) (.303) Cons X t (.280) (.279) (.268) Cons X t (.290) (.289) (.278) Rain X Liberal (.465) (.493) (.422) Rain X Conservative (.427) (.454) (.389) As Good X Liberal (.480) (.510) (.437) As Good X Conservative (.449) (.476) (.408) Rain X Liberal X t (.390) (.389) (.374) Rain X Liberal X t *.361 (.402) (.399) (.383) Rain X Conservative X t (.359) (.358) (.344) Rain X Conservative X t (.370) (.367) (.353) As Good X Liberal X t (.403) (.402) (.386) As Good X Liberal X t (.417) (.412) (.395) As Good X Cons. X t (.376) (.376) (.361) As Good X Cons. X t (.390) (.384) (.369) Constant 3.300*** 3.300*** 3.100*** (.305) (.323) (.277) N (groups) Wald Chi *** *** 89.42*** R2 within R2 between R2 overall Note: Entries are Random-Effects GLS coefficients. Standard errors are in parentheses. All independent variables are coded to range from 0 to 1. The dependent variable is coded 1=strongly oppose, 2=oppose, 3=neutral, 4=support, 5=strongly support. ***p<.001; **p<.01; *p<.05; p<.10 (two-tailed tests).

7 Table A3. Moderating Effects for Knowledge? Medicare National Insurance Insurance Agency Time (.269) (.264) (.254) Time (.282) (.274) (.263) Rain (.480) (.507) (.407) Rain X time ** (.359) (353) (.339) Rain X time * (.376) (.363) (.349) As Good (.477) (.503) (.404) As Good X time (.357) (.350) (.337) As Good X time * (.370) (.359) (.345) Knowledge (.480) (.506) (.406) Knowledge X t (.359) (.352) (.338) Knowledge X t (.372) (.362) (.347) Rain X Knowledge (.660) (.694) (.559) As Good X Knowledge (.648) (.682) (.548) Rain X Know X t (.493) (.484) (.465) Rain X Know X t (.509) (.495) (.475) As Good X Know X t (.484) (.475) (.457) As Good X Know X t * (.497) (.484) (.465) Constant 3.106*** 3.693*** 3.335*** (.359) (.379) (.304) N (groups) Wald Chi ** *** R2 within R2 between R2 overall Note: Entries are Random-Effects GLS coefficients. Standard errors are in parentheses. All independent variables are coded to range from 0 to 1. The dependent variable is coded 1=strongly oppose, 2=oppose, 3=neutral, 4=support, 5=strongly support. ***p<.001; **p<.01; *p<.05; p<.10 (two-tailed tests).