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
library(effsize)
# #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>, …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"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:
vars <- c("pitch_smean", "pitch_svar",
"intense_smean", "intense_svar",
"form_smean", "form_svar")
effect_sizes <- sapply(vars, function(v){
cohen.d(d[[v]] ~ d$plev)$estimate
})
d %>%
group_by(plev) %>%
summarise(pitch = mean(pitch_smean, na.rm = TRUE),
pitch_var = mean(pitch_svar, na.rm = TRUE),
intense = mean(intense_smean, na.rm = TRUE),
intense_var = mean(intense_svar, na.rm = TRUE),
form = mean(form_smean, na.rm = TRUE),
form_var = mean(form_svar, na.rm = TRUE))## # A tibble: 2 × 7
## plev pitch pitch_var intense intense_var form form_var
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 -1 158. 1678. 58.7 181. 1131. 43837.
## 2 1 156. 1403. 59.4 198. 1127. 42021.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
vars <- c("pitch_smean", "pitch_svar", "intense_smean", "intense_svar", "form_smean", "form_svar")
results <- lapply(vars, function(v){
formula <- as.formula(paste(v, "~ plev * sex"))
model <- lm(formula, data = d)
anova_res <- Anova(model, type="III")
condition_row <- anova_res["plev", ]
data.frame(variable = v,
F_value = condition_row["F value"],
df = condition_row["Df"],
p_value = condition_row["Pr(>F)"])
})
# Combine into one table
results_df <- do.call(rbind, results)
results_df## variable F.value Df Pr..F.
## plev pitch_smean 0.02779386 1 0.86780891
## plev1 pitch_svar 4.04121862 1 0.04611406
## plev2 intense_smean 0.12186414 1 0.72748915
## plev3 intense_svar 1.71032511 1 0.19285491
## plev4 form_smean 0.01731022 1 0.89549421
## plev5 form_svar 0.51298279 1 0.47491446Were you able to reproduce the results you attempted to reproduce? If not, what part(s) were you unable to reproduce?
ANSWER HERE I’m not able to reproduce the results, especially the inferential statistics. For descriptive part, I cannot get exactly the same number, but got similar numbers. But for the second part, I cannot reproduce the stats at all.
How difficult was it to reproduce your results?
ANSWER HERE Yes, extremely.
What aspects made it difficult? What aspects made it easy?
ANSWER HERE I was barely able to figure out the var for condition, as well as search the neccessary variables needed in the model. For the second part, I don’t think the baseline (control variable) is included expressively in the data.