metadat (version 1.4-0)

dat.michael2013: The Non-Persuasive Power of a Brain Image

Description

Results from studies exploring how a superfluous fMRI brain image influences the persuasiveness of a scientific claim.

Usage

dat.michael2013

Format

The data frame contains the following columns:

Study	character	name of the study: Citation - Experiment - Subgroup
No_brain_n	numeric	sample size for no-brain-image condition
No_brain_m	numeric	mean agreement rating for no-brain-image condition
No_brain_s	numeric	standard deviation for no-brain-image condition
Brain_n	numeric	sample size for brain-image condition
Brain_m	numeric	mean agreement rating for brain-image condition
Brain_s	numeric	standard deviation for brain-image condition
Included_Critique	character	‘Critique’ if article included critical commentary on conclusions, otherwise ‘No_critique’
Medium	character	‘Paper’ if conducted in person; ‘Online’ if conducted online
Compensation	character	notes on compensation provided to participants
Participant_Pool	character	notes on where participants were recruited
yi	numeric	raw mean difference, calculated as Brain_m - No_brain_m
vi	numeric	corresponding sampling variance

Author

Robert Calin-Jageman, , https://calin-jageman.net

Concepts

psychology, persuasion, raw mean differences

Details

The dataset contains the data from the meta-analysis by Michael et al. (2013) of experiments on the persuasive power of a brain image. The meta-analysis analyzed an original study by McCabe and Castel (2008) as well as 10 replication attempts conducted by the authors of the meta-analysis.

In each study, participants read an article about using brain imaging as a lie detector. The article either included a superfluous fMRI image of a brain (brain) or not (no_brain). After reading the article, all participants responded to the statement “Do you agree or disagree with the conclusion that brain imaging can be used as a lie detector?” on a scale from 1 (strongly disagree) to 4 (strongly agree).

The original study by McCabe and Castel (2008) reported a relatively large increase in agreement due to the presence of brain images. Meta-analysis of the original study with the 10 replications suggests, however, a small, possibly null effect: an estimated average raw mean difference of 0.07 points, 95% CI [-0.00, 0.14], under a random-effects model.

In some studies, the article included a passage critiquing the primary claims made in the article; this is coded in the Included_Critique column for analysis as a possible moderator. Note that Experiment 3 by McCabe and Castel (2008) was a 2x2 between subjects design: brain image presence was manipulated as well as the inclusion of a critique. The two different critique conditions are recorded as separate rows in this dataset. Analysis of this dataset with metafor yields the same results (given rounding) reported in the manuscript.

References

McCabe, D. P., & Castel, A. D. (2008). Seeing is believing: The effect of brain images on judgments of scientific reasoning. Cognition, 107(1), 343--352. https://doi.org/10.1016/j.cognition.2007.07.017

Examples

### copy data into 'dat' and examine data
dat <- dat.michael2013
dat

if (FALSE) {
### load metafor package
library(metafor)

### Data prep
# yi and vi are already provided, but here's how you would use escalc() to obtain
# a raw-mean difference and its variance.
# Note the measure parameter is "MD" for 'raw mean difference'
dat <- metafor::escalc(
  measure = "MD",
  m1i = Brain_m,
  m2i = No_brain_m,
  sd1i = Brain_s,
  sd2i = No_brain_s,
  n1i = Brain_n,
  n2i = No_brain_n,
  data = dat
)

### meta-analysis using a random-effects model of the raw mean differences
res <- rma(yi, vi, data=dat)
print(res, digits=2)

### examine if Included_Critique is a potential moderator
res <- rma(yi, vi, mods = ~ Included_Critique, data=dat)
print(res, digits=2)
}