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 %>% mutate(rank_condition = factor(ifelse(plev == 1,"high","low"))) %>%
# group_by(rank_condition) %>% summarize_all(mean)#%>% select(contains("pitch") | contains("intense")) %>% mean()
table.4.vars <- d %>% mutate(rank_condition = factor(ifelse(plev == 1,"high","low"))) %>%
pivot_longer(cols = c("form_smean","form_rmean","form_svar","form_rvar"
,"intense_smean","intense_rmean","intense_svar",
"intense_rvar","pitch_smean","pitch_rmean",
"pitch_svar","pitch_rvar") , names_to = "script_type", values_to = "values") %>%
separate(script_type,sep = "_",into = c("cue","script_measure")) %>%
mutate(cue = factor(ifelse(cue == "form","resonance",ifelse(cue == "intense","loudness","pitch")))) %>%
mutate(measure = factor(str_extract(script_measure, "(mean)|(var)")) )%>%
select(rank_condition,cue,script_measure,values) %>%
group_by(rank_condition, cue, script_measure) %>%
summarize_all(mean)
table.4.vars## # A tibble: 24 × 4
## # Groups: rank_condition, cue [6]
## rank_condition cue script_measure values
## <fct> <fct> <chr> <dbl>
## 1 high loudness rmean 57.5
## 2 high loudness rvar 185.
## 3 high loudness smean 59.4
## 4 high loudness svar 198.
## 5 high pitch rmean 147.
## 6 high pitch rvar 1715.
## 7 high pitch smean 156.
## 8 high pitch svar 1403.
## 9 high resonance rmean 1290.
## 10 high resonance rvar 63202.
## # … with 14 more rows# d %>% mutate(rank_condition = factor(ifelse(plev == 1,"high","low"))) %>%
# group_by(rank_condition) %>% summarize_all(mean)#%>% select(contains("pitch") | contains("intense")) %>% mean()
t1 <- d %>% mutate(rank_condition = factor(ifelse(plev == 1,"high","low"))) %>%
# select(-c(voice, pow, age, sex, race, native, feelpower, plev, vsex, Zpitch_rmean, Zpitch_rvar, Zform_rmean, Zform_rvar,
# Zintense_rmean, Zintense_rvar, Zpitch_smean, Zpitch_svar, Zform_smean, Zform_svar, Zintense_smean, Zintense_svar)) %>%
select(-c("formant_smeanMD","formant_rmeanMD","formant_svarMD","formant_rvarMD"
,"intense_smeanMD","intense_rmeanMD","intense_svarMD",
"intense_rvarMD","pitch_smeanMD","pitch_rmeanMD",
"pitch_svarMD","pitch_rvarMD",)) %>%
pivot_longer(cols = c("form_smean","form_rmean","form_svar","form_rvar"
,"intense_smean","intense_rmean","intense_svar",
"intense_rvar","pitch_smean","pitch_rmean",
"pitch_svar","pitch_rvar", ) , names_to = "script_type", values_to = "values") %>%
separate(script_type,sep = "_",into = c("cue","script_measure")) %>%
mutate(cue = factor(ifelse(cue == "form","resonance",ifelse(cue == "intense","loudness","pitch")))) %>%
mutate(measure = factor(str_extract(script_measure, "(mean)|(var)")) )
# mutate(adj = factor(ifelse(str_ends(script_measure,"MD"),"Adj","Score"))) %>%
# pivot_wider(names_from = adj,values_from = values) %>%
# mutate(adj_values = Adj + Score) %>%
# select(rank_condition,cue,script_measure,adj_values) %>%
# group_by(rank_condition, cue, script_measure) %>%
# summarize_all(mean)
t2 <- d %>% mutate(rank_condition = factor(ifelse(plev == 1,"high","low"))) %>%
# select(-c(voice, pow, age, sex, race, native, feelpower, plev, vsex, Zpitch_rmean, Zpitch_rvar, Zform_rmean, Zform_rvar,
# Zintense_rmean, Zintense_rvar, Zpitch_smean, Zpitch_svar, Zform_smean, Zform_svar, Zintense_smean, Zintense_svar)) %>%
select(-c("form_smean","form_rmean","form_svar","form_rvar"
,"intense_smean","intense_rmean","intense_svar",
"intense_rvar","pitch_smean","pitch_rmean",
"pitch_svar","pitch_rvar", ) ) %>%
pivot_longer(cols = c("formant_smeanMD","formant_rmeanMD","formant_svarMD","formant_rvarMD"
,"intense_smeanMD","intense_rmeanMD","intense_svarMD",
"intense_rvarMD","pitch_smeanMD","pitch_rmeanMD",
"pitch_svarMD","pitch_rvarMD",) , names_to = "script_type", values_to = "values") %>%
separate(script_type,sep = "_",into = c("cue","script_measure")) %>%
mutate(cue = factor(ifelse(cue == "formant","resonance",ifelse(cue == "intense","loudness","pitch")))) %>%
mutate(measure = factor(str_extract(script_measure, "(mean)|(var)")) )
# mutate(adj = factor(ifelse(str_ends(script_measure,"MD"),"Adj","Score"))) %>%
# pivot_wider(names_from = adj,values_from = values) %>%
# mutate(adj_values = Adj + Score) %>%
# select(rank_condition,cue,script_measure,adj_values) %>%
# group_by(rank_condition, cue, script_measure) %>%
# summarize_all(mean)
t1 %>% inner_join(t2,by=names(t1)[1:24]) %>% group_by(rank_condition, cue, script_measure) %>% summarize_all(mean)## # A tibble: 0 × 28
## # Groups: rank_condition, cue [0]
## # … with 28 variables: rank_condition <fct>, cue <fct>, script_measure <chr>,
## # voice <dbl>, pow <dbl>, age <dbl>, sex <dbl>, race <dbl>, native <dbl>,
## # feelpower <dbl>, plev <dbl>, vsex <dbl>, Zpitch_rmean <dbl>,
## # Zpitch_rvar <dbl>, Zform_rmean <dbl>, Zform_rvar <dbl>,
## # Zintense_rmean <dbl>, Zintense_rvar <dbl>, Zpitch_smean <dbl>,
## # Zpitch_svar <dbl>, Zform_smean <dbl>, Zform_svar <dbl>,
## # Zintense_smean <dbl>, Zintense_svar <dbl>, values.x <dbl>, …# table.4.vars# d %>% mutate(rank_condition = factor(ifelse(plev == 1,"high","low"))) %>%
# group_by(rank_condition) %>% summarize_all(mean)#%>% select(contains("pitch") | contains("intense")) %>% mean()
t1 <- d %>% mutate(rank_condition = factor(ifelse(plev == 1,"high","low"))) %>%
mutate(resonance_s_mean = form_smean + formant_smeanMD,
resonance_r_mean = form_rmean + formant_rmeanMD,
resonance_s_var = form_svar + formant_svarMD,
resonance_r_var = form_rvar + formant_rvarMD,
loud_s_mean = intense_smean + intense_smeanMD,
loud_s_var = intense_svar + intense_svarMD,
loud_r_mean = intense_rmean + intense_rvarMD,
loud_r_var = intense_rvar + intense_rvarMD,
pitch_s_mean = pitch_smean + pitch_smeanMD,
pitch_s_var = pitch_svar + pitch_svarMD,
pitch_r_mean = pitch_rmean + pitch_rmeanMD,
pitch_r_var = pitch_rvar + pitch_rvarMD,
) %>%
# select(-c(voice, pow, age, sex, race, native, feelpower, plev, vsex, Zpitch_rmean, Zpitch_rvar, Zform_rmean, Zform_rvar,
# Zintense_rmean, Zintense_rvar, Zpitch_smean, Zpitch_svar, Zform_smean, Zform_svar, Zintense_smean, Zintense_svar)) %>%
select(-c("formant_smeanMD","formant_rmeanMD","formant_svarMD","formant_rvarMD"
,"intense_smeanMD","intense_rmeanMD","intense_svarMD",
"intense_rvarMD","pitch_smeanMD","pitch_rmeanMD",
"pitch_svarMD","pitch_rvarMD",)) %>%
pivot_longer(cols = c(resonance_s_mean,
resonance_r_mean,
resonance_s_var,
resonance_r_var,
loud_s_mean,
loud_s_var,
loud_r_mean,
loud_r_var,
pitch_s_mean,
pitch_s_var,
pitch_r_mean,
pitch_r_var,
) , names_to = "script_type", values_to = "values") %>%
separate(script_type,sep = "_",into = c("cue","script", "measure")) %>%
mutate(cue = factor(cue)) %>%
# mutate(measure = factor(str_extract(script_measure, "(mean)|(var)")) )
# mutate(adj = factor(ifelse(str_ends(script_measure,"MD"),"Adj","Score"))) %>%
# pivot_wider(names_from = adj,values_from = values) %>%
# mutate(adj_values = Adj + Score) %>%
select(rank_condition,cue,measure,values) %>%
group_by(rank_condition, cue, measure) %>%
summarize_all(mean)
# t2 <- d %>% mutate(rank_condition = factor(ifelse(plev == 1,"high","low"))) %>%
# # select(-c(voice, pow, age, sex, race, native, feelpower, plev, vsex, Zpitch_rmean, Zpitch_rvar, Zform_rmean, Zform_rvar,
# # Zintense_rmean, Zintense_rvar, Zpitch_smean, Zpitch_svar, Zform_smean, Zform_svar, Zintense_smean, Zintense_svar)) %>%
# select(-c("form_smean","form_rmean","form_svar","form_rvar"
# ,"intense_smean","intense_rmean","intense_svar",
# "intense_rvar","pitch_smean","pitch_rmean",
# "pitch_svar","pitch_rvar", ) ) %>%
# pivot_longer(cols = c("formant_smeanMD","formant_rmeanMD","formant_svarMD","formant_rvarMD"
# ,"intense_smeanMD","intense_rmeanMD","intense_svarMD",
# "intense_rvarMD","pitch_smeanMD","pitch_rmeanMD",
# "pitch_svarMD","pitch_rvarMD",) , names_to = "script_type", values_to = "values") %>%
# separate(script_type,sep = "_",into = c("cue","script_measure")) %>%
# mutate(cue = factor(ifelse(cue == "formant","resonance",ifelse(cue == "intense","loudness","pitch")))) %>%
# mutate(measure = factor(str_extract(script_measure, "(mean)|(var)")) )
# # mutate(adj = factor(ifelse(str_ends(script_measure,"MD"),"Adj","Score"))) %>%
# # pivot_wider(names_from = adj,values_from = values) %>%
# # mutate(adj_values = Adj + Score) %>%
# # select(rank_condition,cue,script_measure,adj_values) %>%
# # group_by(rank_condition, cue, script_measure) %>%
# # summarize_all(mean)
#
# t1 %>% inner_join(t2,by=names(t1)[1:23])
# table.4.vars
t1## # A tibble: 12 × 4
## # Groups: rank_condition, cue [6]
## rank_condition cue measure values
## <fct> <fct> <chr> <dbl>
## 1 high loud mean 59.7
## 2 high loud var 197.
## 3 high pitch mean 150.
## 4 high pitch var 1473.
## 5 high resonance mean 1206.
## 6 high resonance var 51701.
## 7 low loud mean 56.7
## 8 low loud var 176.
## 9 low pitch mean 157.
## 10 low pitch var 1824.
## 11 low resonance mean 1216.
## 12 low resonance var 55410.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:
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 hereWere you able to reproduce the results you attempted to reproduce? If not, what part(s) were you unable to reproduce?
I was not able to produce any of those results in the time.
How difficult was it to reproduce your results?
I needed https://osf.io/h6ywr?view_only=24a186cebd52441ea0170ed63caf70ab to just figure out what they were saying with the variables, but I couldn’t figure out the methods they used to get the adjusted data.
What aspects made it difficult? What aspects made it easy?
Terrible variable labels, unclear methods .