Chapter 1. Confidence Intervals, Effect Size, and Statistical Power: Putting Yourself in Their Shoes

1.1 Confidence Intervals, Effect Size, and Statistical Power: Putting Yourself in Their Shoes

CONFIDENCE INTERVALS, EFFECT SIZE, AND STATISTICAL POWER: PUTTING YOURSELF IN THEIR SHOES
Putting Yourself in Their Shoes
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You must read each slide, and complete any questions on the slide, in sequence.

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.
Westend61/Getty Images

Question 1.1

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1.2

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.

Question 1.2

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Correct! Because effect size and sample size are so closely linked in terms of statistical power, if we have an estimate of the effect size, we can determine an appropriate sample size. The smaller the effect size, the larger the sample size necessary to achieve a high level of statistical power.
Actually, researchers need an estimate of the size of the effect they hope to detect so as to determine how many participants will be necessary to detect that effect for a given statistical power. The smaller the effect size, the larger the sample size necessary to achieve a high level of statistical power.

1.3

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).

Question 1.3

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Correct! To detect a small effect size at a given level of power, researchers would need a large sample size. Increasing the power to 80% would require even more participants than the already large sample size of 1000, and that may cost more money and take more time than the researchers have.
Actually, to detect a small effect size at a given level of power, researchers would need a large sample size. Increasing the power to 80% would require even more participants than the already large sample size of 1000, and that may cost more money and take more time than the researchers have.

1.4

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).

Question 1.4

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Correct! Reddit users would sign themselves up for the study, so they would form a volunteer sample; they would not have been randomly selected from all adults in the population. Also, preregistration and random assignment do not pertain to the recruitment of samples.
Actually, Reddit users would sign themselves up for the study, so they would form a volunteer sample; they would not have been randomly selected from all adults in the population. Also, preregistration and random assignment do not pertain to the recruitment of samples.

1.5

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).

Question 1.5

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Correct! Preregistration is a means for researchers to file their research plan online so that they cannot later change their hypotheses, research methods, or analyses. These researchers excluded only participants who met predefined criteria.
Actually, preregistration is a means for researchers to file their research plan online so that they cannot later change their hypotheses, research methods, or analyses. These researchers excluded only participants who met predefined criteria.

1.6

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).

Question 1.6

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Correct! The researchers ended up with 484 participants, far fewer than the 900 they had planned, and a smaller sample size is associated with less statistical power.
Actually, the researchers ended up with 484 participants, far fewer than the 900 they had planned, and a smaller sample size is associated with less statistical power.

1.7

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).

Question 1.7

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Correct! The confidence interval, [−0.17, 0.94], includes 0 in its range, so it is plausible that there was no mean attitude change; therefore, we cannot conclude that participants in this group changed their attitudes, on average.
Actually, the confidence interval, [−0.17, 0.94], includes 0 in its range, so it is plausible that there was no mean attitude change; therefore, we cannot conclude that participants in this group changed their attitudes, on average.

1.8

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).

Question 1.8

QzqTZPM81J1tp9KKCHh5ykzUJF1ouotzJ2mlSMAiqtMo3H0x10FbB3me07cTtLfEtXDYF/qUyqX1BdOcJ3eEXeLzeuzA1gki2zXgsOiaezpn2I0Ox1uJAoTdxtVdB3qbblTmXm8fSYlYXvuTtFuQzlTgnGs+4CQDeXsdEhcbDGdlT/VkoHMFi7l8qsmKn7tylHwMd5p23ln1He4J8aaTz761apiA+Dk5OWJYVolY3ZZ/3zjfv78x/B8GBbf9T4lZCwazT91ij/6t1n9n
Correct! Cohen’s d is 0.18, which is a small effect according to Cohen’s conventions.
Actually, Cohen’s d is 0.18, which is a small effect according to Cohen’s conventions.

1.9

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).

Question 1.9

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
Correct! The p value is 0.048, which is less than the alpha level of 0.05, so we can reject the null hypothesis; it appears that the mean attitude change among those in the perspective-taking condition is less than the mean attitude change in the control condition.
Actually, the p value is 0.048, which is less than the alpha level of 0.05, so we can reject the null hypothesis; it appears that the mean attitude change among those in the perspective-taking condition is less than the mean attitude change in the control condition.

1.10

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.
Jose Luis Pelaez Inc/DigitalVision/Getty Images

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