2014 20 15 10 0 Resist: 48% Support: 39% Frequency (%) Frequency (%) 20 5 2015 Security Purpose 15 10 5 Resist: 51% Support: 39% 0 1.00 2.00 3.00 4.00 5.00 6.00 7.00 Average Support and Approval for Technology 1.00 2.00 3.00 4.00 5.00 6.00 7.00 Average Support and Approval for Technology Economic Purpose 30 Frequency (%) Frequency (%) 30 20 10 0 Resist: 28% Support: 60% 20 10 Resist: 33% Support: 54% 0 1.00 2.00 3.00 4.00 5.00 6.00 7.00 Average Support and Approval for Technology 1.00 2.00 3.00 4.00 5.00 6.00 7.00 Average Support and Approval for Technology Environmental Purpose 30 Frequency (%) Frequency (%) 30 20 10 0 Resist: 17% Support: 68% 1.00 2.00 3.00 4.00 5.00 6.00 7.00 Average Support and Approval for Technology 20 10 Resist: 16% Support: 71% 0 1.00 2.00 3.00 4.00 5.00 6.00 7.00 Average Support and Approval for Technology Note: Support was assessed by averaging two items (see Table 2) resulting in a mean between 1 and 7. Percentages of resistors and supporters sum to less than 100 because a small percentage of persons' mean scores were at exactly "4" (neutral) and thus were not counted as resistors or supporters. Figure 3. Distribution of rated support or resistance for the development and use of UAVs by purpose and year. statistical interactions with time). These analyses indicated that the overall main effects did not change between 2014 and 2015. We therefore ignore the effect of time in most of our remaining analyses. Next, we examined a regression model in which the experimentally varied factors were entered on Step 1 and the measured variables were entered on Step 2. This allows us to see how important each variable is when it is competing with different combinations of other variables. Note that we standardized the measured predictor variables so that they would have a mean of zero (representing the average response) and a standard deviation of 1, in order to make results easier to 86 interpret. Table 4 shows the Step 1 and 2 models' effects, which we next discuss in relation to our research questions and hypotheses. Response to RQ1: U.S. Public Support is Impacted (Slightly) by Framing but not by Terminology Table 4 provides evidence supporting our hypothesis (H1) that terminology will have no impact on public support in the U.S., but framing will have a significant impact favoring prevention framing. Consistent with prior research in social psychology, prevention framing in terms of protecting people from harm was associated with slightly more support (predicting a 0.23 point IEEE Technology and Society Magazine ∕ march 2018