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.

Methods summary:

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.

Target outcomes:

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).

Step 1: Load packages

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

Step 2: Load data

# Just Experiment 1
d <-read_csv("data/S1_voice_level_Final.csv")
# DT::datatable(d)

Step 3: Tidy data

colnames(d)
##  [1] "voice"           "form_smean"      "form_svar"       "form_rmean"     
##  [5] "form_rvar"       "intense_smean"   "intense_svar"    "intense_rmean"  
##  [9] "intense_rvar"    "pitch_smean"     "pitch_svar"      "pitch_rmean"    
## [13] "pitch_rvar"      "pow"             "age"             "sex"            
## [17] "race"            "native"          "feelpower"       "plev"           
## [21] "vsex"            "pitch_rmeanMD"   "pitch_rvarMD"    "intense_rmeanMD"
## [25] "intense_rvarMD"  "formant_rmeanMD" "formant_rvarMD"  "pitch_smeanMD"  
## [29] "pitch_svarMD"    "intense_smeanMD" "intense_svarMD"  "formant_smeanMD"
## [33] "formant_svarMD"  "Zpitch_rmean"    "Zpitch_rvar"     "Zform_rmean"    
## [37] "Zform_rvar"      "Zintense_rmean"  "Zintense_rvar"   "Zpitch_smean"   
## [41] "Zpitch_svar"     "Zform_smean"     "Zform_svar"      "Zintense_smean" 
## [45] "Zintense_svar"

Step 4: Run analysis

Pre-processing

Descriptive statistics

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:

mean(d$pitch_smean)
## [1] 157.0815
mean(d$pitch_rmean)
## [1] 149.5674
mean(d$pitch_svar)
## [1] 1537.637
mean(d$pitch_rvar)
## [1] 1752.452
mean(d$intense_smean)
## [1] 59.02156
mean(d$intense_rmean)
## [1] 57.46067
mean(d$intense_svar)
## [1] 190.1273
mean(d$intense_rvar)
## [1] 182.7515
mean(d$form_smean)
## [1] 1129.273
mean(d$form_rmean)
## [1] 1292.607
mean(d$form_svar)
## [1] 42912.12
mean(d$form_rvar)
## [1] 64130.6

Inferential statistics

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. […] 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

Step 5: Reflection

I was able to reproduce the values of the descriptive statistics, but I wasn’t able to create the inferential statistics, primarily because I had difficulty re-structuring data and understanding the variables enough to run the analysis. A less clear mapping between the data structures and how the paper (in pdf) is written really poses a difficulty in reproducing the anlyses.