For this exercise, please try to reproduce the results from Experiment 1 of the associated paper (Ko, Sadler & Galinsky, 2015). The PDF of the paper is included in the same folder as this Rmd file.
A sense of power has often been tied to how we perceive each other’s voice. Social hierarchy is embedded into the structure of society and provides a metric by which others relate to one another. In 1956, the Brunswik Lens Model was introduced to examine how vocal cues might influence hierarchy. In “The Sound of Power: Conveying and Detecting Hierarchical Rank Through Voice,” Ko and colleagues investigated how manipulation of hierarchal rank within a situation might impact vocal acoustic cues. Using the Brunswik Model, six acoustic metrics were utilized (pitch mean & variability, loudness mean & variability, and resonance mean & variability) to isolate a potential contribution between individuals of different hierarchal rank. In the first experiment, Ko, Sadler & Galinsky examined the vocal acoustic cues of individuals before and after being assigned a hierarchal rank in a sample of 161 subjects (80 male). Each of the six hierarchy acoustic cues were analyzed with a 2 (high vs. low rank condition) x 2 (male vs. female) analysis of covariance, controlling for the baseline of the respective acoustic cue.
Below is the specific result you will attempt to reproduce (quoted directly from the results section of Experiment 1):
The impact of hierarchical rank on speakers’ acoustic cues. Each of the six hierarchy-based (i.e., postmanipulation) acoustic variables was submitted to a 2 (condition: high rank, low rank) × 2 (speaker’s sex: female, male) between-subjects analysis of covariance, controlling for the corresponding baseline acoustic variable. Table 4 presents the adjusted means by condition. Condition had a significant effect on pitch, pitch variability, and loudness variability. Speakers’ voices in the high-rank condition had higher pitch, F(1, 156) = 4.48, p < .05; were more variable in loudness, F(1, 156) = 4.66, p < .05; and were more monotone (i.e., less variable in pitch), F(1, 156) = 4.73, p < .05, compared with speakers’ voices in the low-rank condition (all other Fs < 1; see the Supplemental Material for additional analyses of covariance involving pitch and loudness). (from Ko et al., 2015, p. 6; emphasis added)
The adjusted means for these analyses are reported in Table 4 (Table4_AdjustedMeans.png, included in the same folder as this Rmd file).
library(tidyverse) # for data munging
library(knitr) # for kable table formating
library(haven) # import and export 'SPSS', 'Stata' and 'SAS' Files
library(readxl) # import excel files
# #optional packages:
# library(psych)
# library(car) # for ANCOVA
# library(compute.es) # for ANCOVA
# library(lsmeans) # for ANCOVA# Just Experiment 1
d <-read_csv("data/S1_voice_level_Final.csv")
# DT::datatable(d)data_subset <-
d %>%
select(voice, plev, vsex, # grab voice, hierarchy rank, and speaker sex columns
starts_with("pitch"),
starts_with("intense"),
starts_with("form"))In the paper, the adjusted means by condition are reported (see Table 4, or Table4_AdjustedMeans.png, included in the same folder as this Rmd file). Reproduce these values below:
modelx <-
lm(pitch_smean ~
plev * vsex +
pitch_rmean,
data = data_subset)
summary(modelx)##
## Call:
## lm(formula = pitch_smean ~ plev * vsex + pitch_rmean, data = data_subset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.639 -4.521 0.351 4.804 36.825
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 13.16209 6.39517 2.058 0.0412 *
## plev 1.54353 0.72895 2.117 0.0358 *
## vsex 3.72992 1.72625 2.161 0.0322 *
## pitch_rmean 0.96214 0.04255 22.610 <2e-16 ***
## plev:vsex 1.18000 0.72961 1.617 0.1078
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.207 on 156 degrees of freedom
## Multiple R-squared: 0.9564, Adjusted R-squared: 0.9553
## F-statistic: 854.9 on 4 and 156 DF, p-value: < 2.2e-16es <- effectsize::eta_squared(modelx, partial = F)
round(es$Eta2[1], 2)## [1] 0es_partial <- effectsize::eta_squared(modelx, partial = T)
round(es_partial$Eta2_partial[1], 2)## [1] 0.02modely <-
lm(pitch_svar ~
plev * vsex +
pitch_rvar,
data = data_subset)
summary(modely)##
## Call:
## lm(formula = pitch_svar ~ plev * vsex + pitch_rvar, data = data_subset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1133.0 -428.0 -94.1 292.2 3651.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 934.54967 90.26713 10.353 < 2e-16 ***
## plev -111.67520 51.36458 -2.174 0.0312 *
## vsex 410.24911 52.20458 7.858 5.94e-13 ***
## pitch_rvar 0.34360 0.04246 8.092 1.55e-13 ***
## plev:vsex -15.40008 51.53361 -0.299 0.7655
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 651.1 on 156 degrees of freedom
## Multiple R-squared: 0.5111, Adjusted R-squared: 0.4985
## F-statistic: 40.77 on 4 and 156 DF, p-value: < 2.2e-16es <- effectsize::eta_squared(modely, partial = F)
round(es$Eta2[1], 2)## [1] 0.02es_partial <- effectsize::eta_squared(modely, partial = T)
round(es_partial$Eta2_partial[1], 3)## [1] 0.044modelz <-
lm(intense_smean ~
plev * vsex +
intense_rmean,
data = data_subset)
summary(modelz)##
## Call:
## lm(formula = intense_smean ~ plev * vsex + intense_rmean, data = data_subset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.3607 -1.7424 -0.1007 1.5687 6.8850
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.41837 3.43083 5.077 1.08e-06 ***
## plev 0.33391 0.22452 1.487 0.138977
## vsex 0.83344 0.23283 3.580 0.000459 ***
## intense_rmean 0.72368 0.05958 12.146 < 2e-16 ***
## plev:vsex -0.27067 0.22537 -1.201 0.231577
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.847 on 156 degrees of freedom
## Multiple R-squared: 0.4987, Adjusted R-squared: 0.4858
## F-statistic: 38.8 on 4 and 156 DF, p-value: < 2.2e-16es <- effectsize::eta_squared(modelz, partial = F)
round(es$Eta2[1], 2)## [1] 0.01modela <-
lm(intense_svar ~
plev * vsex +
intense_rvar,
data = data_subset)
summary(modela)##
## Call:
## lm(formula = intense_svar ~ plev * vsex + intense_rvar, data = data_subset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -126.73 -22.63 -5.05 20.21 130.57
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 71.33235 11.15481 6.395 1.78e-09 ***
## plev 6.62530 3.06844 2.159 0.0324 *
## vsex -13.76952 3.09507 -4.449 1.63e-05 ***
## intense_rvar 0.64991 0.05874 11.065 < 2e-16 ***
## plev:vsex 0.49652 3.06695 0.162 0.8716
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 38.86 on 156 degrees of freedom
## Multiple R-squared: 0.4709, Adjusted R-squared: 0.4573
## F-statistic: 34.71 on 4 and 156 DF, p-value: < 2.2e-16es <- effectsize::eta_squared(modela, partial = F)
round(es$Eta2[1], 2)## [1] 0.03modelf <-
lm(form_smean ~
plev * vsex +
form_rmean,
data = data_subset)
summary(modelf)##
## Call:
## lm(formula = form_smean ~ plev * vsex + form_rmean, data = data_subset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -112.665 -52.619 -25.835 -0.345 229.149
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 435.9936 193.9657 2.248 0.025992 *
## plev 0.2893 6.6030 0.044 0.965113
## vsex 22.6152 6.6564 3.398 0.000863 ***
## form_rmean 0.5362 0.1500 3.576 0.000466 ***
## plev:vsex -1.0279 6.5961 -0.156 0.876362
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 83.59 on 156 degrees of freedom
## Multiple R-squared: 0.1537, Adjusted R-squared: 0.132
## F-statistic: 7.082 on 4 and 156 DF, p-value: 2.892e-05es <- effectsize::eta_squared(modelf, partial = F)
round(es$Eta2[1], 2)## [1] 0modelJ <-
lm(form_svar ~
plev * vsex +
form_rvar,
data = data_subset)
summary(modelJ)##
## Call:
## lm(formula = form_svar ~ plev * vsex + form_rvar, data = data_subset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -17761 -8627 -4289 2159 59762
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.302e+04 6.151e+03 5.369 2.83e-07 ***
## plev -7.419e+02 1.082e+03 -0.686 0.494
## vsex 5.930e+02 1.202e+03 0.493 0.622
## form_rvar 1.542e-01 9.431e-02 1.635 0.104
## plev:vsex -3.092e+02 1.079e+03 -0.286 0.775
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 13650 on 156 degrees of freedom
## Multiple R-squared: 0.02184, Adjusted R-squared: -0.00324
## F-statistic: 0.8708 on 4 and 156 DF, p-value: 0.4829The impact of hierarchical rank on speakers’ acoustic cues. Each of the six hierarchy-based (i.e., postmanipulation) acoustic variables was submitted to a 2 (condition: high rank, low rank) × 2 (speaker’s sex: female, male) between-subjects analysis of covariance, controlling for the corresponding baseline acoustic variable. […] Condition had a significant effect on pitch, pitch variability, and loudness variability. Speakers’ voices in the high-rank condition had higher pitch, F(1, 156) = 4.48, p < .05; were more variable in loudness, F(1, 156) = 4.66, p < .05; and were more monotone (i.e., less variable in pitch), F(1, 156) = 4.73, p < .05, compared with speakers’ voices in the low-rank condition (all other Fs < 1; see the Supplemental Material for additional analyses of covariance involving pitch and loudness).
# reproduce the above results here
modela1 <-
lm(pitch_smean ~
plev * vsex +
pitch_rmean,
data = data_subset)
summary(modela1)##
## Call:
## lm(formula = pitch_smean ~ plev * vsex + pitch_rmean, data = data_subset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -28.639 -4.521 0.351 4.804 36.825
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 13.16209 6.39517 2.058 0.0412 *
## plev 1.54353 0.72895 2.117 0.0358 *
## vsex 3.72992 1.72625 2.161 0.0322 *
## pitch_rmean 0.96214 0.04255 22.610 <2e-16 ***
## plev:vsex 1.18000 0.72961 1.617 0.1078
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.207 on 156 degrees of freedom
## Multiple R-squared: 0.9564, Adjusted R-squared: 0.9553
## F-statistic: 854.9 on 4 and 156 DF, p-value: < 2.2e-16modelb1 <-
lm(pitch_svar ~
plev * vsex +
pitch_rvar,
data = data_subset)
summary(modelb1)##
## Call:
## lm(formula = pitch_svar ~ plev * vsex + pitch_rvar, data = data_subset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1133.0 -428.0 -94.1 292.2 3651.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 934.54967 90.26713 10.353 < 2e-16 ***
## plev -111.67520 51.36458 -2.174 0.0312 *
## vsex 410.24911 52.20458 7.858 5.94e-13 ***
## pitch_rvar 0.34360 0.04246 8.092 1.55e-13 ***
## plev:vsex -15.40008 51.53361 -0.299 0.7655
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 651.1 on 156 degrees of freedom
## Multiple R-squared: 0.5111, Adjusted R-squared: 0.4985
## F-statistic: 40.77 on 4 and 156 DF, p-value: < 2.2e-16modelc1 <-
lm(intense_svar ~
plev * vsex +
intense_rvar,
data = data_subset)
summary(modelc1)##
## Call:
## lm(formula = intense_svar ~ plev * vsex + intense_rvar, data = data_subset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -126.73 -22.63 -5.05 20.21 130.57
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 71.33235 11.15481 6.395 1.78e-09 ***
## plev 6.62530 3.06844 2.159 0.0324 *
## vsex -13.76952 3.09507 -4.449 1.63e-05 ***
## intense_rvar 0.64991 0.05874 11.065 < 2e-16 ***
## plev:vsex 0.49652 3.06695 0.162 0.8716
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 38.86 on 156 degrees of freedom
## Multiple R-squared: 0.4709, Adjusted R-squared: 0.4573
## F-statistic: 34.71 on 4 and 156 DF, p-value: < 2.2e-16modeld1 <-
lm(intense_smean ~
plev * vsex +
intense_rmean,
data = data_subset)
summary(modeld1)##
## Call:
## lm(formula = intense_smean ~ plev * vsex + intense_rmean, data = data_subset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.3607 -1.7424 -0.1007 1.5687 6.8850
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.41837 3.43083 5.077 1.08e-06 ***
## plev 0.33391 0.22452 1.487 0.138977
## vsex 0.83344 0.23283 3.580 0.000459 ***
## intense_rmean 0.72368 0.05958 12.146 < 2e-16 ***
## plev:vsex -0.27067 0.22537 -1.201 0.231577
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.847 on 156 degrees of freedom
## Multiple R-squared: 0.4987, Adjusted R-squared: 0.4858
## F-statistic: 38.8 on 4 and 156 DF, p-value: < 2.2e-16modele1 <-
lm(form_smean ~
plev * vsex +
form_rmean,
data = data_subset)
summary(modele1)##
## Call:
## lm(formula = form_smean ~ plev * vsex + form_rmean, data = data_subset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -112.665 -52.619 -25.835 -0.345 229.149
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 435.9936 193.9657 2.248 0.025992 *
## plev 0.2893 6.6030 0.044 0.965113
## vsex 22.6152 6.6564 3.398 0.000863 ***
## form_rmean 0.5362 0.1500 3.576 0.000466 ***
## plev:vsex -1.0279 6.5961 -0.156 0.876362
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 83.59 on 156 degrees of freedom
## Multiple R-squared: 0.1537, Adjusted R-squared: 0.132
## F-statistic: 7.082 on 4 and 156 DF, p-value: 2.892e-05Were you able to reproduce the results you attempted to reproduce? If not, what part(s) were you unable to reproduce?
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How difficult was it to reproduce your results?
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What aspects made it difficult? What aspects made it easy?
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