Answers to Test Yourself
(1a) The design contains two groups of unmatched participants. Thus, the appropriate test is an independent samples t test to compare the means for the video discussion and no video discussion groups. (1b) The null hypothesis tested in the analysis is that there is no mean difference between video discussion and no video discussion groups in the population. (1c) SPSS output indicates that the video discussion group had mean recognition accuracy of 72.7% (SD = 5.23), and the no video discussion group had a mean recognition accuracy of 82.4% (SD = 7.52). The test indicates that the no video discussion group has a significantly higher mean recognition score than the video discussion group, t(18) = –3.35, p = .004. (1d) Because the p value of .004 is less than alpha (.05), we can conclude that the null hypothesis is false and that there is a difference between video and no video groups in the population. (2a) The design contains a single sample of participants. The sample mean is compared with a known population mean when the participants have no poker ability (50% guessing accuracy). Thus, the appropriate test is a one-sample t test to compare the sample mean with the population mean of 50%. (2b) The null hypothesis tested in the analysis is that there is no difference between the population mean of participants claiming to have poker abilities and the population mean of 50%. (2c) SPSS output indicates that the sample mean is 50.4% (SD = 3.57). The test indicates that the sample mean does not significantly differ from 50%, t(9) = 0.36, p = .73. (2d) Because the p value of .73 is greater than alpha (.05), we do not have evidence against the null hypothesis and must retain it. (3a) There are two independent variables in this experiment: ad type (youthfulness, attractiveness) and product type (car, energy drink). (3b) This is a factorial design, and an ANOVA should be used to analyze the data. The main effect of each independent variable (ad type and product type) is tested to compare means for the levels (youthfulness vs. attractiveness and car vs. energy drink) of that variable on its own, and an interaction effect between the two variables is also tested to determine if the effect of one independent variable depends on the level of the other variable. (3c) Based on the p values reported in the output, the main effects of ad type, F(1, 16) = 6.23, p = .02, and product type, F(1, 16) = 27.77, p < .001, are both significant. However, the interaction between ad type and product type was not significant, F(1, 16) = 0.08, p = .785. (3d) We can conclude that youthfulness ads (M = 5.60, SD = 1.35) result in higher desire-to-buy ratings than attractiveness ads (M = 4.70, SD = 1.16). We can also conclude that energy drink ads (M = 6.10, SD = 0.74) result in higher desire-to-buy ratings than car ads (M = 4.20, SD = 1.03).