Data Set: Power Pose Effect — power

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.

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. doi:10.1080/23743603.2017.1326760

Examples

data(power_pose)
head(power_pose)
#>                study n_high_power n_low_power mean_high_power mean_low_power
#> 1      Bailey et al.           46          48        2.619565       2.385417
#> 2       Ronay et al.           53          55        2.122642       1.945455
#> 3 Klaschinski et al.          101          99        3.001980       2.727273
#> 4     Bombari et al.           99         101        2.242424       1.990099
#> 5        Latu et al.          100         100        2.575000       2.445000
#> 6      Keller et al.          135         134        2.733333       2.578358
#>   sd_high_power sd_low_power  t_value  df two_sided_p_value one_sided_p_value
#> 1     0.9320514    0.9353551 1.215348  92        0.22734289        0.11367144
#> 2     0.7652655    0.7915803 1.182015 106        0.23984447        0.11992223
#> 3     0.8234051    0.9000928 2.252778 198        0.02537006        0.01268503
#> 4     0.9268215    0.8543424 2.002484 198        0.04659697        0.02329848
#> 5     0.7893707    0.9767370 1.035168 198        0.30185311        0.15092656
#> 6     1.0541319    0.9978444 1.238098 267        0.21676741        0.10838370
#>   effectSize        SE
#> 1  0.2507640 0.2071399
#> 2  0.2275180 0.1931046
#> 3  0.3186069 0.1423228
#> 4  0.2832082 0.1421356
#> 5  0.1463949 0.1416107
#> 6  0.1509773 0.1221166

# Simple fixed-effects meta-analysis
mfix <- meta_fixed(effectSize, SE, study,
  data = power_pose
)
mfix
#> ### Bayesian Fixed-Effects Meta-Analysis ### 
#>    Prior on d:    't' (location=0, scale=0.707, nu=1) with support on the interval [-Inf,Inf]. 
#> 
#> # Bayes factors:
#>            (denominator)
#> (numerator) fixed_H0 fixed_H1
#>    fixed_H0      1.0   0.0223
#>    fixed_H1     44.8   1.0000
#> 
#> # Posterior summary statistics of fixed-effects model:
#>    mean    sd 2.5%   50% 97.5% hpd95_lower hpd95_upper n_eff Rhat
#> d 0.219 0.061  0.1 0.219 0.339       0.098       0.337    NA   NA
plot_posterior(mfix)