Putting Yourself in Their Shoes
1.1 CONFIDENCE INTERVALS, EFFECT SIZE, AND STATISTICAL POWER: PUTTING YOURSELF IN THEIR SHOES1 of 10
Welcome
Putting Yourself in Their Shoes
Authors:
Kelly M. Goedert, Seton Hall University
Susan A. Nolan, Seton Hall University
Kaylise D. Algrim, Seton Hall University
“Put yourself in their shoes:” Researcher Rhia Catapano and her colleagues asked participants to do just this (Catapano, Tormala, & Rucker, 2019). Participants were asked their opinions about universal health care, and then were randomly assigned to one of two conditions. In one condition, participants were asked to first imagine the perspective of someone with the opposite opinion, and then write an argument about universal health care from that different perspective. In the second condition, participants were asked to write an argument about universal health care that was opposite to their own, without explicitly considering another person’s perspective.
![Someone wearing what are obviously the wrong shoes.](asset/images/ch08/n485245011.jpg)
1.2 CONFIDENCE INTERVALS, EFFECT SIZE, AND STATISTICAL POWER: PUTTING YOURSELF IN THEIR SHOES2 of 10
The researchers wrote: “On the basis of a pilot test indicating a Cohen’s d of 0.175, we set a target sample size of 1,000 participants, yielding 75% power to detect a small effect size after allowing for 100 exclusions (10%)” (Catapano et al., 2019, p. 426). A “pilot test” is an initial, smaller study usually conducted to gather information to guide the actual, larger study. By “exclusions,” the researchers meant participant data that they didn’t use for some reason—for example, because the participant failed to answer all of the questions.
1.3 CONFIDENCE INTERVALS, EFFECT SIZE, AND STATISTICAL POWER: PUTTING YOURSELF IN THEIR SHOES3 of 10
The researchers wrote: “On the basis of a pilot test indicating a Cohen’s d of 0.175, we set a target sample size of 1,000 participants, yielding 75% power to detect a small effect size after allowing for 100 exclusions (10%)” (Catapano et al., 2019, p. 426).
1.4 CONFIDENCE INTERVALS, EFFECT SIZE, AND STATISTICAL POWER: PUTTING YOURSELF IN THEIR SHOES4 of 10
After outlining how they determined their sample size, the researchers wrote: “On the end date, 568 Reddit users had completed the survey. Following our preregistration plan, we excluded 84 participants (14.8%) who failed the attention check or did not follow instructions (final N = 484, 44.4% female; mean age = 30.57)” (Catapano et al., 2019, p. 426).
1.5 CONFIDENCE INTERVALS, EFFECT SIZE, AND STATISTICAL POWER: PUTTING YOURSELF IN THEIR SHOES5 of 10
After outlining how they determined their sample size, the researchers wrote: “On the end date, 568 Reddit users had completed the survey. Following our preregistration plan, we excluded 84 participants (14.8%) who failed the attention check or did not follow instructions (final N = 484, 44.4% female; mean age = 30.57)” (Catapano et al., 2019, p. 426).
1.6 CONFIDENCE INTERVALS, EFFECT SIZE, AND STATISTICAL POWER: PUTTING YOURSELF IN THEIR SHOES6 of 10
After outlining how they determined their sample size, the researchers wrote: “On the end date, 568 Reddit users had completed the survey. Following our preregistration plan, we excluded 84 participants (14.8%) who failed the attention check or did not follow instructions (final N = 484, 44.4% female; mean age = 30.57)” (Catapano et al., 2019, p. 426).
1.7 CONFIDENCE INTERVALS, EFFECT SIZE, AND STATISTICAL POWER: PUTTING YOURSELF IN THEIR SHOES7 of 10
After participants wrote arguments against their own opinions about universal health care, the researchers compared those who did so after taking the perspective of another person (perspective-taking condition) and those who did so without taking another’s perspective (control group). They reported that participants “showed less attitude change in the perspective-taking (M = 0.39, 95% CI = [−0.17, 0.94]) relative to the control (M = 1.52, 95% CI = [0.55, 2.50]) condition, t(482) = −1.98, p = .048, d = 0.18” (Catapano et al., 2019, p. 427).
1.8 CONFIDENCE INTERVALS, EFFECT SIZE, AND STATISTICAL POWER: PUTTING YOURSELF IN THEIR SHOES8 of 10
Once again, the researchers reported that, related to their beliefs about universal health care, participants “showed less attitude change in the perspective-taking (M = 0.39, 95% CI = [−0.17, 0.94]) relative to the control (M = 1.52, 95% CI = [0.55, 2.50]) condition, t(482) = −1.98, p = .048, d = 0.18” (Catapano et al., 2019, p. 427).
1.9 CONFIDENCE INTERVALS, EFFECT SIZE, AND STATISTICAL POWER: PUTTING YOURSELF IN THEIR SHOES9 of 10
Once again, the authors reported that, related to their beliefs about universal health care, participants “showed less attitude change in the perspective-taking (M = 0.39, 95% CI = [−0.17, 0.94]) relative to the control (M = 1.52, 95% CI = [0.55, 2.50]) condition, t(482) = −1.98, p = .048, d = 0.18” (Catapano et al., 2019, p. 427).
1.10 CONFIDENCE INTERVALS, EFFECT SIZE, AND STATISTICAL POWER: PUTTING YOURSELF IN THEIR SHOES10 of 10
The researchers concluded that putting yourself in someone else’s shoes allows you to attribute the arguments you generated against your own opinion about universal health care to that other person. The control group didn’t have that excuse, so the researchers suggest that their attitudes were more susceptible to change.
![Photo of a (non-white) doctor talking with a patient.](asset/images/ch08/n519516073.jpg)
REFERENCES
Catapano, R., Tormala, Z. L., & Rucker, D. D. (2019). Perspective taking and self-persuasion: Why “putting yourself in their shoes” reduces openness to attitude change. & Furukawa, T. A. (2017). Characteristic distribution of the total and individual item scores on the Kessler Screening Scale for Psychological Distress (K6) in US adults. Psychological Science, 30, 424-435. https://doi.org/10.1177/0956797618822697