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SKIDMORE PS 217 - PS 217 Exam 3

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Page 1 of 7 Exam 3 PS 217, Fall 2008 1. OK, Zubin, this one’s for you (the promised question). In an independent groups ANOVA, the best estimate of population variance (σ2) is MSWithin (MSError). Is that also true for a repeated measures ANOVA? In other words, tell me whether or not MSError in a repeated measures design is a good estimate of population variance, along with your supporting logic. [5 pts] In an independent groups design, the MSWithin estimates variability due to individual differences and random variability. Those same two basic sources of variability underlie the population variance (σ2). However, in a repeated measures design, the appropriate error term (MSError) reflects only random variability. As a result, it would not be an appropriate estimate of population variance (which is due to both random variability and individual differences). 2. Kitamura (2005) was interested in the impact of mood on cognitive processes. Kitamura thought that a positive mood leads to more automatic processing than a negative mood, which leads to more controlled processing. In one study, half of the participants were placed in a positive mood and half in a negative mood (using a mood induction technique). Then they were all given a list of non-famous companies either once or four times. Two days later they were asked to judge the fame of a list of companies, some of which were new (Number = 0) and some that had been seen previously (Number = 1 or 4). Let’s pretend that the participants rated fame on a 7-point Likert-type scale (1 = “not famous” and 7 = “ famous”). Suppose that the data had produced the results seen below. Complete the analysis and interpret the results as completely as you can. [15 pts] 11.522.533.544.5Cell Mean0 1 4CellPositiveNegativeInteraction Line Plot for Mean FameEffect: Number * Mood Tests of Between-Subjects Effects Dependent Variable:Mean Fame Source Type III Sum of Squares df Mean Square F Sig. Partial Eta Squared Noncent. Parameter Observed Powerb Mood 39.9 1 39.9 105 .000 .614 105.055 1.000 Number 38.1 2 19.1 50.3 .000 .603 100.337 1.000 Mood * Number 21.0 2 10.5 27.6 .000 .456 55.320 1.000 Error 25.1 66 .38 Corrected Total 124.1 71Page 2 of 7 You could avoid computing Hartley’s FMax by noting that all effects are significant at .01 or lower. If you computed, you’d find FMax = 41. With FMax Crit about equal to 6, you would be concerned about violating the homogeneity of variance assumption, so you’d use α = .01. Thus, there is a significant main effect of Mood, a significant main effect of Number, and a significant interaction. (In each case, p < .001.) To interpret the interaction displayed above, you’d compute Tukey’s HSD: ! HSD = 4.16.3812= .74 The judged fame of new companies (0, or not seen previously) was the same whether the participant was in a positive (M = 1.23) or a negative mood (M = 1.21). However, if participants saw the company name one time, the company was judged to be more famous if people were in a positive mood (M = 3.4) than if people were in a negative mood (M = 1.58). The same was true if participants saw the company name four times. They judged the company to be more famous if people were in a positive mood (M = 4.23) than if people were in a negative mood (M = 1.63). 3. One area of psychology looks at factors that influence decision-making. One factor that people have studied is how a decision is influenced by the way in which the information is delivered. Even though the information is identical, people’s decisions will differ when the information is placed in a different context (frame). Suppose that a researcher was interested in looking at the impact of four different frames on people’s willingness to engage in risky behavior (or to be more protective). One scenario involves the participant’s willingness to smoke cigarettes. The four frames are: NF = No Frame (so it just asks the participant to imagine that he or she has been smoking for a while and enjoys doing so), AF = Analytical Frame (with statistical information about the scenario, such as how many people die of lung cancer each year), EF = Experiential Frame (which attempts to make the scenario personally relevant by asking the participant to think about a family member dying from lung cancer), and AEF = Analytical + Experiential Frames (which puts the two types of information together). Participants read a series of scenarios and then gave a response that indicated their willingness to engage in risky behavior. The dependent variable is called Protect-Risk, where a positive score indicates a more protective response and a negative score represents a willingness to engage in riskier behavior. Suppose that the researcher is also interested in looking at the impact of age (Young 18-23, Middle 38-43, and Older 58-63). Complete the source table below and interpret the results as completely as you can. [15 pts]Page 3 of 7 Tests of Between-Subjects Effects Dependent Variable:Protect-Risk Source Type III Sum of Squares df Mean Square F Sig. Partial Eta Squared Noncent. Parameter Observed Powerb Frame 46 3 15.339 57.4 .000 .782 172.346 1.000 Age 67.7 2 33.860 126.8 .000 .841 253.634 1.000 Frame * Age 2.4 6 .392 1.47 .210 .155 8.798 .515 Error 12.8 48 .267 Corrected Total 128.9 59 No need for Hartley’s FMax, because both main effects are significant with p < .01. However, in this case FMax = 13.64 and FMax Crit = 51.2, so there would be no concern about violating the homogeneity of variance assumption. FRAME ! HSD = 3.78.26715= .5 AE A E N AE -- A 1.77 -- E .26 1.5 -- N 1.96 .2 1.7 -- Thus, AE and E lead to more risk-averse responses than A or N. It appears that offering a scenario that has an experiential component leads people to avoid risk. AGE ! HSD = 3.43.26720= .4 Y M O Y -- M 1.97 -- O 2.45 .48 -- Older people are more risk averse than middle-aged people or young people. And middle-aged people are more risk averse than young people. 4. Two researchers were interested in studying the effects of reward magnitude on performance. Both researchers used introductory psychology students as participants, the same total number of participants (21), the same type of reward and reward magnitudes ($1, $5, $20), the same apparatus, the same task, and the same performance measure (DV). One researcher used an independent groups design and, on the basis of the results, cannot reject the


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