Summaries

library(tidyverse)
library(stringr)
library(ggplot2)

library(Hmisc)
library(DescTools)
library(weights)
library(boot)
library(papaja)

load(file='data/WEIRD_data.Rdata')

Descriptives and WEIRD

Compare descriptives across WEIRD and non-WEIRD countries across studies and samples (Table 1 in the manuscript).

source('scripts/table1.R')
Sample Size
sample_country_data_collected_WEOG median lwr.ci upr.ci
WEOG 48.0 44 53
Non-WEOG 54.5 41 71

[1] “\(W = 103,461.00\), \(p = .486\)”

Age Mean
wtd.avg CI.LL CI.UL
Non-WEIRD 19.93 17.06 22.01
WEIRD 27.63 25.18 30.03

[1] “t value = 9.37, df = 203.71, p-value = 0”

Age SD:

Age SD
wtd.avg CI.LL CI.UL
Non-WEIRD 3.72 2.50 4.59
WEIRD 7.12 6.21 8.09

[1] “t value = 8.31, df = 151.22, p-value = 0”

Gender balance (country data collected):

Gender balance (Primary Country Data Collected)
wtd.avg CI.LL CI.UL
Non-WEIRD 0.54 0.49 0.56
WEIRD 0.59 0.57 0.61

[1] “t value = 4.03, df = 139.18, p-value = 0”

Gender balance (based on first author country):

Gender balance (Primary Author Country)
wtd.avg CI.LL CI.UL
Non-WEIRD 0.54 0.49 0.56
WEIRD 0.59 0.57 0.60

[1] “t value = 3.71, df = 109, p-value = 0”

Solely musicians:

Musical expertise across samples
Expertise %
musicians 30.5
musicians; non-musicians 22.0
non-musicians 10.1
Not specified 37.4

[1] “musicians n=381: 30% [28% to 33%]” [1] “musicians n=48: 30% [23% to 37%]”

Solely musicians
var n label prop lwr.ci upr.ci
musicians 381 WEIRD 0.3 0.28 0.33
others 870 WEIRD 0.7 0.67 0.72
musicians 48 Non-WEIRD 0.3 0.23 0.37
others 112 Non-WEIRD 0.7 0.63 0.77

[1] “Chi = 0.01 , p-value = 0.919540229885057”

musicians and non-musicians: [1] “musicians n=662: 53% [50% to 56%]” [1] “musicians n=80: 50% [42% to 58%]”

musicians and non-musicians
var n label prop lwr.ci upr.ci
musicians 662 WEIRD 0.53 0.50 0.56
non-musicians 589 WEIRD 0.47 0.44 0.50
musicians 80 Non-WEIRD 0.50 0.42 0.58
non-musicians 80 Non-WEIRD 0.50 0.42 0.58

[1] “Chi = 0.48 , p-value = 0.493753123438281”

Solely Non-musicians: [1] “non-musicians n=131: 10% [9% to 12%]” [1] “non-musicians n=22: 14% [9% to 19%]”

non-musicians
var n label prop lwr.ci upr.ci
others 1120 WEIRD 0.90 0.88 0.91
non-musicians 131 WEIRD 0.10 0.09 0.12
others 138 Non-WEIRD 0.86 0.82 0.92
non-musicians 22 Non-WEIRD 0.14 0.09 0.19

[1] “Chi = 1.58 , p-value = 0.222888555722139”

University sample: [1] “university n=510: 41% [38% to 44%]” [1] “university n=69: 43% [36% to 51%]”

University samples
var n label prop lwr.ci upr.ci
others 741 WEIRD 0.59 0.56 0.62
university 510 WEIRD 0.41 0.38 0.44
others 91 Non-WEIRD 0.57 0.49 0.65
university 69 Non-WEIRD 0.43 0.36 0.51

[1] “Chi = 0.33 , p-value = 0.59720139930035”

Sample unspecified: [1] “unsp n=563: 45% [42% to 48%]” [1] “unsp n=58: 36% [29% to 44%]”

Unspecified samples
var n label prop lwr.ci upr.ci
others 688 WEIRD 0.55 0.52 0.58
unsp 563 WEIRD 0.45 0.42 0.48
others 102 Non-WEIRD 0.64 0.57 0.72
unsp 58 Non-WEIRD 0.36 0.29 0.44

[1] “Chi = 4.41 , p-value = 0.039480259870065”

Recruitment volunteers: [1] “volunteer n=370: 30% [27% to 32%]” [1] “volunteer n=54: 34% [27% to 42%]”

Volunteer samples
var n label prop lwr.ci upr.ci
others 881 WEIRD 0.70 0.68 0.73
volunteer 370 WEIRD 0.30 0.27 0.32
others 106 Non-WEIRD 0.66 0.59 0.74
volunteer 54 Non-WEIRD 0.34 0.27 0.42

[1] “Chi = 0.99 , p-value = 0.320865528891456”

Recruitment unspecified: [1] “Not specified n=496: 40% [37% to 42%]” [1] “Not specified n=77: 48% [41% to 56%]”

recruitment unspecified samples
var n label prop lwr.ci upr.ci
Not specified 496 WEIRD 0.40 0.37 0.42
others 755 WEIRD 0.60 0.58 0.63
Not specified 77 Non-WEIRD 0.48 0.41 0.56
others 83 Non-WEIRD 0.52 0.44 0.60

[1] “Chi = 3.88 , p-value = 0.0488020308237839”

Experimenter Created Music: [1] “experimenter n=270: 34% [31% to 38%]” [1] “experimenter n=59: 34% [27% to 41%]”

Experimenter selected. music
var n label prop lwr.ci upr.ci
other 514 WEIRD 0.66 0.62 0.69
experimenter 270 WEIRD 0.34 0.31 0.38
other 116 Non-WEIRD 0.66 0.59 0.73
experimenter 59 Non-WEIRD 0.34 0.27 0.41

[1] “Chi = 0.01 , p-value = 0.924726224159515”

Western music: [1] “western n=572: 73% [70% to 76%]” [1] “western n=104: 59% [53% to 67%]”

Western music
var n label prop lwr.ci upr.ci
other 212 WEIRD 0.27 0.24 0.30
western 572 WEIRD 0.73 0.70 0.76
other 71 Non-WEIRD 0.41 0.34 0.48
western 104 Non-WEIRD 0.59 0.53 0.67

[1] “Chi = 11.95 , p-value = 0.000546663726214618”

Music origin unspecified: [1] “Not specified n=186: 24% [21% to 27%]” [1] “Not specified n=35: 20% [15% to 26%]”

Music origin unspecified
var n label prop lwr.ci upr.ci
other 598 WEIRD 0.76 0.73 0.79
Not specified 186 WEIRD 0.24 0.21 0.27
other 140 Non-WEIRD 0.80 0.75 0.86
Not specified 35 Non-WEIRD 0.20 0.15 0.26

[1] “Chi = 0.92 , p-value = 0.337762079017047”

Produce numbers reported in the manuscript

source('scripts/report_numbers_for_MS.R')
[1] "Mean weighted age:"
[1] 27.06
[1] 0.7624021
[1] "gender % female prop. (weighted):"
[1] "61.1 %"
[1] "First Author Country Prop:"
[1] "US: 22 %"
[1] "UK: 15 %"
[1] "Aus: 11 %"
[1] "Ger: 9 %"
[1] "Can: 6 %"
[1] "Fin: 5 %"
[1] "WEIRD: 91 %"
[1] "Prop of human studies:"
[1] "94 %"
[1] "Country Data Collected Prop:"
[1] "US: 23 %"
[1] "UK: 12 %"
[1] "Aus: 8 %"
[1] "Ger: 7 %"
[1] "Can: 7 %"
[1] "Fin: 3 %"
[1] "online: 9 %"
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