2. The statistical tests most appropriate for our data are paired t-tests. We chose these tests because we are comparing two sets of within-subjects data at a time to see if there are any differences between the groups. A t-test was conducted between the gain-framed questions and loss-framed questions as well as between the high-payoff-framed questions and the low-payoff-framed questions. For the interaction effect, a t-test was conducted between the mean difference of the answers for gain and loss-framed questions in the high payoff scenarios and the mean difference of the answers for gain and loss-framed questions in the low payoff scenarios. If the differences turn out to be statistically insignificant, the graphs will be parallel, suggesting no interaction (the level of payoff does not affect the gain or loss framed scenarios differently). Because our measure is a forced-choice survey and we, the experimenters, are entering the data into the computer, we do not expect errors or any other special case for which we need to discard or manipulate data.

3. In the survey, subjects answer 8 questions with one of two choices. One of the choices is a sure option (“this will happen”), and the other option is a risky option (“either this will happen or that will happen”). We have coded the answers from the subjects into an Excel spreadsheet. The answers to all 8 questions for each subject are recorded. Sure options are recorded as “1,” and risky options are recorded as “0.” This way, when performing t-tests, the means of the data per subject (or per type of question) will represent the total percentage of sure-option responses. (i.e. if subject A responds with a sure-option to question 1 and a risky option to answer 2, then the mean for those two questions will be .5). These percentages serve as our dependent variable.

4. Two-tailed, paired t-tests were implemented in each data analysis. Answers to loss-framed and gain framed-answers were compared; the mean percentage of sure-option responses for the gain-framed questions was .500 (standard deviation = 0, standard error =0). The mean percentage of sure options responses for the loss-framed questions was .333 (standard deviation = .144 and standard error = .083). Results are approaching significance with t(2) = 2.000 and p= 0.18. Concerning high-payoff and low-payoff scenarios, percentage of sure-option responses for the low-payoff questions was .333 (standard deviation = .144 and standard error = .083) and the percentage of sure options for the high-payoff questions was .417 (standard deviation = .144, standard error = .083). Results again approach significance with t(2) = .500 and p=.67. An interaction between payoff-type and gain/loss frame type is suggested; the mean difference between gain and loss frames for the low-payoff scenarios is .333 (standard deviation = .289, standard error = .167). The mean difference between gain and loss frames for the high-payoff scenarios is 0.00 (standard deviation = 0, standard error = 0). Results approach significance with t(2) = 2.000 and p=.18.