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)
d## # A tibble: 161 × 45
## voice form_smean form_svar form_rmean form_rvar intense_smean intense_svar
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1043. 38805. 1259. 68275. 65.5 157.
## 2 2 1083. 35609. 1278. 54612. 65.5 301.
## 3 3 1092. 25979. 1334. 55175. 53.6 71.2
## 4 4 1268. 71346. 1298. 74340. 60.7 201.
## 5 5 1071. 38246. 1256. 67846. 60.1 204.
## 6 6 1095. 40716. 1278. 76674. 60.1 150.
## 7 7 1060. 38855. 1310. 88947. 60.5 127.
## 8 8 1051. 28191. 1223. 52682. 57.3 207.
## 9 9 1038. 34105. 1256. 68508. 57.9 199.
## 10 10 1346. 69260. 1314. 55400. 60.3 149.
## # ℹ 151 more rows
## # ℹ 38 more variables: intense_rmean <dbl>, intense_rvar <dbl>,
## # pitch_smean <dbl>, pitch_svar <dbl>, pitch_rmean <dbl>, pitch_rvar <dbl>,
## # pow <dbl>, age <dbl>, sex <chr>, race <chr>, native <chr>, feelpower <dbl>,
## # plev <dbl>, vsex <dbl>, pitch_rmeanMD <dbl>, pitch_rvarMD <dbl>,
## # intense_rmeanMD <dbl>, intense_rvarMD <dbl>, formant_rmeanMD <dbl>,
## # formant_rvarMD <dbl>, pitch_smeanMD <dbl>, pitch_svarMD <dbl>, …d_tidy <- d %>%
pivot_longer(
cols = -c(voice, pow, age, sex, race, native, feelpower, plev, vsex),
names_to = "variable",
values_to = "value"
)
d_tidy## # A tibble: 5,796 × 11
## voice pow age sex race native feelpower plev vsex variable value
## <dbl> <dbl> <dbl> <chr> <chr> <chr> <dbl> <dbl> <dbl> <chr> <dbl>
## 1 1 1 19 M X Y 6 1 -1 form_smean 1.04e3
## 2 1 1 19 M X Y 6 1 -1 form_svar 3.88e4
## 3 1 1 19 M X Y 6 1 -1 form_rmean 1.26e3
## 4 1 1 19 M X Y 6 1 -1 form_rvar 6.83e4
## 5 1 1 19 M X Y 6 1 -1 intense_sm… 6.55e1
## 6 1 1 19 M X Y 6 1 -1 intense_sv… 1.57e2
## 7 1 1 19 M X Y 6 1 -1 intense_rm… 5.87e1
## 8 1 1 19 M X Y 6 1 -1 intense_rv… 1.74e2
## 9 1 1 19 M X Y 6 1 -1 pitch_smean 1.16e2
## 10 1 1 19 M X Y 6 1 -1 pitch_svar 8.58e2
## # ℹ 5,786 more rows# Verify that there are 161 participants and check distribution of plev and sex
nrow(d)## [1] 161table(d$plev, d$vsex)##
## -1 1
## -1 38 41
## 1 42 40In 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:
# Pitch Mean
model_pitch_mean <- lm(pitch_smean ~ pitch_rmean + plev * vsex, data = d)
lsm_pitch_mean <- lsmeans(model_pitch_mean, specs = "plev")
# Pitch Variability
model_pitch_var <- lm(pitch_svar ~ pitch_rvar + plev * vsex, data = d)
lsm_pitch_var <- lsmeans(model_pitch_var, specs = "plev")
# Loudness Mean
model_intensity_mean <- lm(intense_smean ~ intense_rmean + plev * vsex, data = d)
lsm_intensity_mean <- lsmeans(model_intensity_mean, specs = "plev")
# Loudness Variability
model_intensity_var <- lm(intense_svar ~ intense_rvar + plev * vsex, data = d)
lsm_intensity_var <- lsmeans(model_intensity_var, specs = "plev")
# Resonance Mean
model_formant_mean <- lm(form_smean ~ form_rmean + plev * vsex, data = d)
lsm_formant_mean <- lsmeans(model_formant_mean, specs = "plev")
# Resonance Variability
model_formant_var <- lm(form_svar ~ form_rvar + plev * vsex, data = d)
lsm_formant_var <- lsmeans(model_formant_var, specs = "plev")
print("====High Rank Condition====")## [1] "====High Rank Condition===="cat("Pitch mean:", summary(lsm_pitch_mean)$lsmean[2], "\n")## Pitch mean: 158.6098cat("Pitch variability:", summary(lsm_pitch_var)$lsmean[2], "\n")## Pitch variability: 1425.016cat("Loudness mean:", summary(lsm_intensity_mean)$lsmean[2], "\n")## Loudness mean: 59.33567cat("Loudness variability:", summary(lsm_intensity_var)$lsmean[2], "\n")## Loudness variability: 196.7301cat("Resonance mean:", summary(lsm_formant_mean)$lsmean[2], "\n")## Resonance mean: 1129.384cat("Resonance variability:", summary(lsm_formant_var)$lsmean[2], "\n")## Resonance variability: 42170.78print("====Low Rank Condition====")## [1] "====Low Rank Condition===="cat("Pitch mean:", summary(lsm_pitch_mean)$lsmean[1], "\n")## Pitch mean: 155.5227cat("Pitch variability:", summary(lsm_pitch_var)$lsmean[1], "\n")## Pitch variability: 1648.367cat("Loudness mean:", summary(lsm_intensity_mean)$lsmean[1], "\n")## Loudness mean: 58.66784cat("Loudness variability:", summary(lsm_intensity_var)$lsmean[1], "\n")## Loudness variability: 183.4795cat("Resonance mean:", summary(lsm_formant_mean)$lsmean[1], "\n")## Resonance mean: 1128.806cat("Resonance variability:", summary(lsm_formant_var)$lsmean[1], "\n")## Resonance variability: 43654.54The 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
print("=== Pitch Mean ===")## [1] "=== Pitch Mean ==="Anova(model_pitch_mean, type = 2)## Anova Table (Type II tests)
##
## Response: pitch_smean
## Sum Sq Df F value Pr(>F)
## pitch_rmean 43334 1 511.2208 < 2e-16 ***
## plev 379 1 4.4744 0.03599 *
## vsex 460 1 5.4218 0.02117 *
## plev:vsex 222 1 2.6156 0.10783
## Residuals 13223 156
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print("=== Pitch Variability ===")## [1] "=== Pitch Variability ==="Anova(model_pitch_var, type = 2)## Anova Table (Type II tests)
##
## Response: pitch_svar
## Sum Sq Df F value Pr(>F)
## pitch_rvar 27760551 1 65.4850 1.548e-13 ***
## plev 2006828 1 4.7340 0.03108 *
## vsex 26142260 1 61.6676 6.133e-13 ***
## plev:vsex 37857 1 0.0893 0.76546
## Residuals 66131879 156
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print("=== Loudness Variability ===")## [1] "=== Loudness Variability ==="Anova(model_intensity_var, type = 2)## Anova Table (Type II tests)
##
## Response: intense_svar
## Sum Sq Df F value Pr(>F)
## intense_rvar 184927 1 122.4323 < 2.2e-16 ***
## plev 7052 1 4.6687 0.03224 *
## vsex 29860 1 19.7693 1.651e-05 ***
## plev:vsex 40 1 0.0262 0.87160
## Residuals 235630 156
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Were you able to reproduce the results you attempted to reproduce? If not, what part(s) were you unable to reproduce?
Yes!
How difficult was it to reproduce your results?
Quite difficult.
What aspects made it difficult? What aspects made it easy?
The most difficult aspect was correctly identifying which variables were the baseline vs. post-manipulation measures. I looked through the codebook which helped me resolve the discrepancies and produce the correct results. I am also confused about tidying the data, since the way I did it ended up not being helpful for the computations needed.