power_pose: Data Set: Power Pose Effect

In metaBMA: Bayesian Model Averaging for Random and Fixed Effects Meta-Analysis

Data Set: Power Pose Effect

Description

Includes six pre-registered replication studies testing whether participants feel more powerful if they adopt expansive as opposed to constrictive body postures. In the data set power_pose_unfamiliar, only those participants are included who were unfamiliar with the power pose effect.

Usage

power_pose

power_pose_unfamiliar

Format

A data frame with three variables:

study

Authors of original study

n_high_power

number of participants in high-power condition

n_low_power

number of participants in low-power condition

mean_high_power

mean rating in high-power condition on a 5-point Likert scale

mean_low_power

mean rating in low-power condition on a 5-point Likert scale

sd_high_power

standard deviation of ratings in high-power condition

sd_low_power

standard deviation of ratings in low-power condition

t_value

t-value for two-sample t-test

df

degrees of freedom for two-sample t-test

two_sided_p_value

two-sided p-value of two-sample t-test

one_sided_p_value

one-sided p-value of two-sample t-test

effectSize

Cohen's d, the standardized effect size (high vs. low power)

SE

Standard error of Cohen's d

Data frame with 6 rows and 13 variables

An object of class data.frame with 6 rows and 13 columns.

Details

See Carney, Cuddy, and Yap (2010) for more details.

References

Carney, D. R., Cuddy, A. J. C., & Yap, A. J. (2010). Power posing: Brief nonverbal displays affect neuroendocrine levels and risk tolerance. Psychological Science, 21, 1363–1368.

Gronau, Q. F., Erp, S. V., Heck, D. W., Cesario, J., Jonas, K. J., & Wagenmakers, E.-J. (2017). A Bayesian model-averaged meta-analysis of the power pose effect with informed and default priors: the case of felt power. Comprehensive Results in Social Psychology, 2(1), 123-138. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/23743603.2017.1326760")}

Examples

data(power_pose)
head(power_pose)

# Simple fixed-effects meta-analysis
mfix <- meta_fixed(effectSize, SE, study,
  data = power_pose
)
mfix
plot_posterior(mfix)

metaBMA documentation built on Sept. 13, 2023, 9:06 a.m.