02b_demog_explore
anjie
5/12/2022
knitr::opts_chunk$set(message=FALSE, warning = FALSE)library(tidyverse)
library(splithalf)
library(agreement)
library(here)
theme_set(theme_classic())
demog_d <- read_csv(here("data/4_processed/demog_df.csv"))
source(here("visualization_helper/R_rainclouds.R"))basic comparison in US/China samples
age
demog_d %>%
filter(demog_question == "age") %>%
mutate(demog_response = as.numeric(demog_response)) %>%
ggplot(aes(x = demog_response, fill = culture)) +
geom_density(alpha = .5) +
xlab("age")
demog_d %>%
filter(demog_question == "age") %>%
mutate(demog_response = as.numeric(demog_response)) %>%
ggplot(aes(x = demog_response)) +
geom_histogram() +
facet_wrap(~culture) +
xlab("age")
demog_d %>%
filter(demog_question == "age") %>%
filter(demog_response > 31) %>%
group_by(culture) %>%
count()## # A tibble: 2 × 2
## # Groups: culture [2]
## culture n
## <chr> <int>
## 1 CN 6
## 2 US 200gender
demog_d %>%
filter(demog_question == "gender") %>%
group_by(culture, demog_response) %>%
mutate(demog_response = case_when(
demog_response == "女性" ~ "Female",
demog_response == "男性" ~ "Male",
demog_response == "非二元性别者" ~ "Non-binary",
demog_response == "拒绝回答" ~ "Decline to answer",
TRUE ~ demog_response
)) %>%
count() ## # A tibble: 7 × 3
## # Groups: culture, demog_response [7]
## culture demog_response n
## <chr> <chr> <int>
## 1 CN Decline to answer 3
## 2 CN Female 88
## 3 CN Male 77
## 4 US Decline to answer 4
## 5 US Female 122
## 6 US Male 160
## 7 US Non-binary 6ethnic
US: majority white CN: majority Chinese
demog_d %>%
filter(demog_question == "ethnic") %>%
group_by(culture, demog_response) %>%
count()## # A tibble: 19 × 3
## # Groups: culture, demog_response [19]
## culture demog_response n
## <chr> <chr> <int>
## 1 CN "c(\"我是混血儿\", \"中国人（包括居住在中国大陆、香港、澳门、… 1
## 2 CN "中国人（包括居住在中国大陆、香港、澳门、台湾，以及任何亚洲国… 166
## 3 CN "非中国人的亚洲人" 1
## 4 US "Asian" 19
## 5 US "Black or African American" 12
## 6 US "c(\"American Indian or Alaska Native\", \"White\")" 1
## 7 US "c(\"Asian\", \"Black or African American\")" 1
## 8 US "c(\"Asian\", \"Hispanic or Latino\")" 2
## 9 US "c(\"Asian\", \"White\")" 1
## 10 US "c(\"Black or African American\", \"White\")" 1
## 11 US "c(\"Hispanic or Latino\", \"White\")" 4
## 12 US "c(\"White\", \"American Indian or Alaska Native\")" 1
## 13 US "c(\"White\", \"Asian\", \"Other\")" 1
## 14 US "c(\"White\", \"Asian\")" 1
## 15 US "c(\"White\", \"Hispanic or Latino\")" 3
## 16 US "c(\"White\", \"Other\", \"Asian\")" 1
## 17 US "Hispanic or Latino" 9
## 18 US "Other" 3
## 19 US "White" 232scales
- identity_local
- identity_global
- consumption_local
- consumption_global
- cosmopolitanism
demog_d %>%
mutate(scale_type = case_when(
grepl("identity_local", demog_question) ~ "identity_local",
grepl("identity_global", demog_question) ~ "identity_global",
grepl("consumption_local", demog_question) ~ "consumption_local",
grepl("consumption_global", demog_question) ~ "consumption_global",
grepl("cosmopolitanism", demog_question) ~ "cosmopolitanism",
TRUE ~ "non_scale"
)) %>%
filter(!(scale_type == "non_scale")) %>%
mutate(demog_response = as.numeric(demog_response) + 1) %>% # the scale is 1 to 7
ggplot(
aes(y = demog_response, x = culture, fill = culture)) +
geom_flat_violin(position = position_nudge(x = .1, y = 0), alpha = .8) +
geom_point(aes(y = demog_response, color = culture),
position = position_jitter(width = .15), size = .5, alpha = 0.2) +
geom_boxplot(width = .1, alpha = 0.3) +
scale_y_continuous(breaks = seq(1,7))+
scale_color_manual(values = c("red", "blue"))+
scale_fill_manual(values = c("red", "blue"))+
guides(fill = "none") +
guides(color = "none") +
ylab("Ratings on identity scales") +
xlab("") +
theme_classic() +
labs(title = "Scales") +
facet_wrap(~scale_type)
theme(plot.title = element_text(hjust = 0.5, size = 8),
plot.subtitle = element_text(hjust = 0.5, size = 6),
text = element_text(size=8))## List of 3
## $ text :List of 11
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## ..$ inherit.blank: logi FALSE
## ..- attr(*, "class")= chr [1:2] "element_text" "element"
## $ plot.title :List of 11
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## $ plot.subtitle:List of 11
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## ..- attr(*, "class")= chr [1:2] "element_text" "element"
## - attr(*, "class")= chr [1:2] "theme" "gg"
## - attr(*, "complete")= logi FALSE
## - attr(*, "validate")= logi TRUESES
demog_d %>%
filter(demog_question == "subjectiveses") %>%
mutate(demog_response = as.numeric(demog_response)) %>%
ggplot(aes(x = demog_response, fill = culture)) +
geom_density(alpha = .5) +
xlab("subjective ses")
demog_d %>%
filter(demog_question == "subjectiveses") %>%
mutate(demog_response = as.numeric(demog_response)) %>%
ggplot(aes(x = demog_response)) +
geom_histogram() +
facet_wrap(~culture) +
xlab("subjective ses")
Education
currently in college:
CN: majority in college, study STEM US: majority not in college, didn’t study STEM
demog_d %>%
filter(demog_question == "currentcollege") %>%
mutate(demog_response = case_when(
demog_response == "是" ~ "Yes",
demog_response == "否" ~ "No",
TRUE ~ demog_response
)) %>%
group_by(culture, demog_response) %>%
count()## # A tibble: 4 × 3
## # Groups: culture, demog_response [4]
## culture demog_response n
## <chr> <chr> <int>
## 1 CN No 42
## 2 CN Yes 126
## 3 US No 260
## 4 US Yes 32most college students
demog_d %>%
filter(demog_question == "selfeducation") %>%
group_by(culture, demog_response) %>%
count()## # A tibble: 13 × 3
## # Groups: culture, demog_response [13]
## culture demog_response n
## <chr> <chr> <int>
## 1 CN 一年或多年大学教育，无学位 6
## 2 CN 上过一部分高中 1
## 3 CN 四年/五年制大学本科学位 126
## 4 CN 大专学历 10
## 5 CN 普通高中或职业高中毕业/高中等级学历 2
## 6 CN 有过一些研究生经历 23
## 7 US 8th grade/junior high or less 1
## 8 US At least some graduate school 44
## 9 US Four-/Five-year college Bachelor's degree 110
## 10 US High school graduate/GED 53
## 11 US One or more years of college, no degree 47
## 12 US Some high school 3
## 13 US Two-year college degree/vocational school 34demog_d %>%
filter(demog_question == "stem_or_not") %>%
mutate(demog_response = case_when(
demog_response == "是" ~ "Yes",
demog_response == "否" ~ "No",
TRUE ~ demog_response
)) %>%
group_by(culture, demog_response) %>%
count()## # A tibble: 4 × 3
## # Groups: culture, demog_response [4]
## culture demog_response n
## <chr> <chr> <int>
## 1 CN No 48
## 2 CN Yes 120
## 3 US No 213
## 4 US Yes 79abroad experience
Have you lived in any other countries?
demog_d %>%
filter(demog_question == "abroadexp") %>%
mutate(demog_response = case_when(
demog_response == "是" ~ "Yes",
demog_response == "否" ~ "No",
TRUE ~ demog_response
)) %>%
group_by(culture, demog_response) %>%
count()## # A tibble: 4 × 3
## # Groups: culture, demog_response [4]
## culture demog_response n
## <chr> <chr> <int>
## 1 CN No 156
## 2 CN Yes 12
## 3 US No 270
## 4 US Yes 22demog_d %>%
filter(demog_question == "overseaexpnum") %>%
mutate(demog_response = case_when(
demog_response == "没有国际经历" ~ "No experiences",
demog_response == "一段国际经历" ~ "One experience",
demog_response == "两段国际经历" ~ "Two experience",
demog_response == "三到五段国际经历" ~ "Three to five experiences",
demog_response == "六段或更多国际经历" ~ "Six or more experiences",
TRUE ~ demog_response
)) %>%
mutate(overseaexp_rating = case_when(
demog_response == "No experiences" ~ 0,
demog_response == "One experience" ~ 1,
demog_response == "Two experience" ~ 2,
demog_response == "Three to five experiences" ~ 3,
demog_response == "Six or more experiences" ~ 4
)) %>%
ggplot(
aes(y = overseaexp_rating, x = culture, fill = culture)) +
geom_flat_violin(position = position_nudge(x = .1, y = 0), alpha = .8) +
geom_point(aes(y = overseaexp_rating, color = culture),
position = position_jitter(width = .15), size = .5, alpha = 0.2) +
geom_boxplot(width = .1, alpha = 0.3) +
scale_y_continuous(breaks = seq(1,7))+
scale_color_manual(values = c("red", "blue"))+
scale_fill_manual(values = c("red", "blue"))+
guides(fill = "none") +
guides(color = "none") +
ylab("Ratings on # oversea experiences") +
xlab("") +
theme_classic() +
labs(title = "Ratings on # oversea experiences")+
theme(plot.title = element_text(hjust = 0.5, size = 8),
plot.subtitle = element_text(hjust = 0.5, size = 6),
text = element_text(size=8))
Geographical distribution
toneless_dict <- pinyin::pydic("toneless")
demog_province_cleaned <- demog_d %>%
filter(demog_question == "state_grewup") %>%
rowwise() %>%
mutate(demog_response_clean = case_when(
culture == "CN" ~ as.character(pinyin::py(demog_response, toneless_dict,
sep = "",
other_replace = NULL)),
TRUE ~ demog_response
)) %>%
mutate(demog_response_clean = case_when(
demog_response_clean == "hena" ~ "Henan",
demog_response_clean == "jita" ~ "Others",
demog_response_clean == "haina" ~ "Hainan",
demog_response_clean == "huna" ~ "Hunan",
demog_response_clean == "andong" ~ "Guangdong",
demog_response_clean == "namenggu" ~ "Inner Mongolia",
demog_response_clean == "jinghai" ~ "Qinghai",
demog_response_clean == "angxi" ~ "Guangxi",
demog_response_clean == "anxi" ~ "Guangxi",
demog_response_clean == "yunna" ~ "Yunnan",
demog_response == "山西" ~ "Shanxi",
demog_response == "宁夏" ~ "Ningxia",
demog_response == "陕西" ~ "Shaanxi",
TRUE ~ demog_response_clean
)) %>%
mutate(demog_response_clean = tolower(demog_response_clean))
demog_province_cleaned %>%
group_by(demog_response_clean, culture) %>%
filter(culture == "CN") %>%
#count() %>%
ggplot(aes(x = demog_response_clean)) +
geom_bar() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
demog_province_cleaned %>%
group_by(demog_response_clean, culture) %>%
filter(culture == "US") %>%
#count() %>%
ggplot(aes(x = demog_response_clean)) +
geom_bar() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
us_regions <- read_csv(here("data/us_region.csv"))
cn_regions <- read_csv(here("data/cn_region.csv"))
demog_with_pronvices <- demog_province_cleaned %>%
left_join(us_regions %>%
mutate(demog_response_clean = tolower(State)) %>%
select(Region, Coast, demog_response_clean), by = "demog_response_clean"
) %>%
left_join(cn_regions %>%
mutate(demog_response_clean = tolower(Province)) %>%
select(PercentPaddy, RiceCat, demog_response_clean),
by = "demog_response_clean") %>%
janitor::clean_names()
write_csv(demog_with_pronvices, here("data/4_processed/demog_state.csv"))Scale predictors
demog_d_scale <- demog_d %>%
mutate(scale_type = case_when(
grepl("identity_local", demog_question) ~ "identity_local",
grepl("identity_global", demog_question) ~ "identity_global",
grepl("consumption_local", demog_question) ~ "consumption_local",
grepl("consumption_global", demog_question) ~ "consumption_global",
grepl("cosmopolitanism", demog_question) ~ "cosmopolitanism",
TRUE ~ "non_scale"
)) %>%
filter(!(scale_type == "non_scale")) %>%
mutate(demog_response = as.numeric(demog_response) + 1)Age
demog_age_d <- demog_d %>%
filter(demog_question == "age") %>%
mutate(demog_response = as.numeric(demog_response)) %>%
rename(age = demog_response) %>%
select(subject, culture, age)
demog_d_scale %>%
left_join(demog_age_d, by = c("subject", "culture")) %>%
ggplot(aes(x = age, y = demog_response, color = culture, group = culture)) +
geom_jitter(alpha = .1) +
geom_smooth(method = "lm") +
facet_wrap(~scale_type)
SES
demog_ses_d <- demog_d %>%
filter(demog_question == "subjectiveses") %>%
mutate(demog_response = as.numeric(demog_response)) %>%
rename(ses = demog_response) %>%
select(subject, culture, ses)
demog_d_scale %>%
left_join(demog_ses_d, by = c("subject", "culture")) %>%
ggplot(aes(x = ses, y = demog_response, color = culture,
group = culture)) +
geom_jitter(alpha = .1) +
geom_smooth(method = "lm") +
facet_wrap(~scale_type)
gender
demog_gender_d <- demog_d %>%
filter(demog_question == "gender") %>%
group_by(culture, demog_response) %>%
mutate(demog_response = case_when(
demog_response == "女性" ~ "Female",
demog_response == "男性" ~ "Male",
demog_response == "非二元性别者" ~ "Non-binary",
demog_response == "拒绝回答" ~ "Decline to answer",
TRUE ~ demog_response
)) %>%
rename(gender = demog_response) %>%
select(subject, culture, gender) %>%
ungroup()
demog_d_scale %>%
left_join(demog_gender_d, by = c("subject", "culture")) %>%
ggplot(aes(x = gender, y = demog_response, color = culture,
group = culture)) +
stat_summary(fun.data = "mean_cl_boot") +
facet_wrap(~scale_type) +
theme(axis.text.x = element_text(angle = 90,
vjust = .5,
hjust = 1))
education
the education level hard to compare because most CN in college, so currently just looking at STEm
demog_stem <- demog_d %>%
filter(demog_question == "stem_or_not") %>%
mutate(demog_response = case_when(
demog_response == "是" ~ "Yes",
demog_response == "否" ~ "No",
TRUE ~ demog_response
)) %>%
rename(stem = demog_response) %>%
select(subject, culture, stem)
demog_d_scale %>%
left_join(demog_stem, by = c("subject", "culture")) %>%
ggplot(aes(x = stem, y = demog_response, color = culture,
group = culture)) +
stat_summary(fun.data = "mean_cl_boot") +
facet_wrap(~scale_type)
oversea experience
most ppl didn’t live abroad, so looking at number of international traveling
demog_oversea_exp <- demog_d %>%
filter(demog_question == "overseaexpnum") %>%
mutate(demog_response = case_when(
demog_response == "没有国际经历" ~ "No experiences",
demog_response == "一段国际经历" ~ "One experience",
demog_response == "两段国际经历" ~ "Two experience",
demog_response == "三到五段国际经历" ~ "Three to five experiences",
demog_response == "六段或更多国际经历" ~ "Six or more experiences",
TRUE ~ demog_response
)) %>%
mutate(overseaexp_rating = case_when(
demog_response == "No experiences" ~ 0,
demog_response == "One experience" ~ 1,
demog_response == "Two experience" ~ 2,
demog_response == "Three to five experiences" ~ 3,
demog_response == "Six or more experiences" ~ 4
)) %>%
rename(oversea_exp = demog_response) %>%
select(subject, culture, oversea_exp)
level_order <- c("No experiences", "One experience",
"Two experience", "Three to five experiences",
"Six or more experiences")
demog_d_scale %>%
left_join(demog_oversea_exp, by = c("subject", "culture")) %>%
ggplot(aes(x = factor(oversea_exp, level = level_order), y = demog_response, color = culture,
group = culture)) +
stat_summary(fun.data = "mean_cl_boot") +
facet_wrap(~scale_type) +
theme(axis.text.x = element_text(angle = 90, vjust = .5, hjust = 1))
province info
cn_regions <- read_csv(here("data/cn_region.csv"))
demog_with_pronvices <- demog_province_cleaned %>%
filter(culture == "CN") %>%
left_join(cn_regions %>%
mutate(demog_response_clean = tolower(Province)) %>%
select(PercentPaddy, RiceCat, demog_response_clean,
ProvincePerCapitaGDP2012, ProvinceInternetPenetration2007,
HDI2008),
by = "demog_response_clean") %>%
janitor::clean_names() %>%
rename(province = demog_response) %>%
select(subject, culture, province, percent_paddy, rice_cat,
province_per_capita_gdp2012, province_internet_penetration2007,
hdi2008)
demog_d_scale %>%
left_join(demog_with_pronvices, by = c("subject", "culture")) %>%
pivot_longer(cols = c("percent_paddy",
"province_per_capita_gdp2012",
"province_internet_penetration2007",
"hdi2008"),
names_to = "province_info",
values_to = "province_info_value") %>%
ggplot(aes(x = province_info_value,
y = demog_response,)) +
geom_point(alpha = .1) +
geom_smooth(method = "lm") +
facet_grid(scale_type~province_info, scales = "free") +
theme(strip.text.x = element_text(size = 6),
strip.text.y = element_text(size = 6))
Scale factor analysis
demog_d_scale_f <- demog_d_scale %>%
select(-scale_type) %>%
mutate(demog_question_short = case_when(
demog_question == "identity_local_remainclose" ~ "il_rc",
demog_question == "identity_local_positiveimpact" ~ "il_pi",
demog_question == "identity_local_holiday" ~ "il_h",
demog_question == "identity_local_proud" ~ "il_p",
demog_question == "identity_global_feelconnected" ~ "ig_fc",
demog_question == "identity_global_thinkofmyself" ~ "ig_t",
demog_question == "identity_global_viewmyselfas" ~ "ig_v",
demog_question == "identity_global_feelapartof" ~ "ig_f",
demog_question == "identity_global_strongattachment" ~ "ig_sa",
demog_question == "identity_global_citizenoftheworld" ~ "ig_cw",
demog_question == "identity_global_describemyselfas" ~ "ig_d",
demog_question == "consumption_local_films" ~ "cl_f",
demog_question == "consumption_local_television_oftenwatch" ~ "cl_tv",
demog_question == "consumption_local_celebrity" ~ "cl_c",
demog_question == "consumption_local_movies" ~ "cl_mov",
demog_question == "consumption_local_magazine" ~ "cl_mag",
demog_question == "consumption_local_music" ~ "cl_mus",
demog_question == "consumption_local_television_donotlike" ~ "cl_tvd",
demog_question == "consumption_local_actors" ~ "cl_a",
demog_question == "consumption_local_dress" ~ "cl_d",
demog_question == "consumption_global_movies" ~ "cg_mov",
demog_question == "consumption_global_celebrity" ~ "cg_c",
demog_question == "consumption_global_actors" ~ "cg_a",
demog_question == "consumption_global_music" ~ "cg_mus",
demog_question == "consumption_global_dress" ~ "cg_d",
demog_question == "consumption_global_television_oftenwatch" ~ "cg_tv",
demog_question == "consumption_global_films" ~ "cg_f",
demog_question == "consumption_global_magazine" ~ "cg_mag",
demog_question == "consumption_global_television_donotlike" ~ "cg_tvd",
demog_question == "cosmopolitanism_observepeople" ~ "c_o",
demog_question == "cosmopolitanism_exchangeideas" ~ "c_ex",
demog_question == "cosmopolitanism_waysoflife" ~ "c_w",
demog_question == "cosmopolitanism_learnmoreabout" ~ "c_l",
demog_question == "cosmopolitanism_viewsandapproaches" ~ "c_v"
)) %>%
select(-demog_question) %>%
pivot_wider(names_from = demog_question_short, values_from = demog_response) %>%
select(-c("subject", "culture")) %>%
as.data.frame()datamatrix <- cor(demog_d_scale_f)
corrplot::corrplot(datamatrix, method = "circle",
addCoef.col = .5, # Change font size of text labels
tl.cex = 0.5)
psych::KMO(r = cor(demog_d_scale_f)) # cut off is .6## Kaiser-Meyer-Olkin factor adequacy
## Call: psych::KMO(r = cor(demog_d_scale_f))
## Overall MSA = 0.92
## MSA for each item =
## il_rc il_pi il_h il_p ig_fc ig_t ig_v ig_f ig_sa ig_cw ig_d
## 0.90 0.94 0.94 0.89 0.95 0.92 0.91 0.93 0.95 0.94 0.92
## cl_f cl_tv cl_c cl_mov cl_mag cl_mus cl_tvd cl_a cl_d cg_mov cg_c
## 0.92 0.85 0.92 0.90 0.92 0.95 0.87 0.93 0.95 0.90 0.93
## cg_a cg_mus cg_d cg_tv cg_f cg_mag cg_tvd c_o c_ex c_w c_l
## 0.93 0.95 0.92 0.93 0.91 0.93 0.94 0.94 0.91 0.92 0.92
## c_v
## 0.92psych::cortest.bartlett(demog_d_scale_f) # small p, facotr analysis useful ## $chisq
## [1] 13153.83
##
## $p.value
## [1] 0
##
## $df
## [1] 561library(psych)
fafitfree <- fa(demog_d_scale_f,nfactors = ncol(demog_d_scale_f), rotate = "none")
n_factors <- length(fafitfree$e.values)
scree <- data.frame(
Factor_n = as.factor(1:n_factors),
Eigenvalue = fafitfree$e.values)
ggplot(scree, aes(x = Factor_n, y = Eigenvalue, group = 1)) +
geom_point() + geom_line() +
xlab("Number of factors") +
ylab("Initial eigenvalue") +
labs( title = "Scree Plot",
subtitle = "(Based on the unreduced correlation matrix)")
library(nFactors)
parallel <- fa.parallel(demog_d_scale_f)
## Parallel analysis suggests that the number of factors = 5 and the number of components = 5fa.none <- fa(r= demog_d_scale_f,
nfactors = 5,
# covar = FALSE, SMC = TRUE,
fm="pa", # type of factor analysis we want to use (“pa” is principal axis factoring)
max.iter=100, # (50 is the default, but we have changed it to 100
rotate="varimax") # none rotation
fa.none## Factor Analysis using method = pa
## Call: fa(r = demog_d_scale_f, nfactors = 5, rotate = "varimax", max.iter = 100,
## fm = "pa")
## Standardized loadings (pattern matrix) based upon correlation matrix
## PA1 PA3 PA5 PA2 PA4 h2 u2 com
## il_rc -0.02 0.15 0.28 0.89 -0.02 0.89 0.11 1.3
## il_pi -0.03 0.19 0.29 0.83 -0.06 0.81 0.19 1.4
## il_h -0.03 0.10 0.33 0.78 -0.01 0.72 0.28 1.4
## il_p -0.02 0.14 0.26 0.88 -0.03 0.87 0.13 1.2
## ig_fc 0.83 0.08 0.03 0.08 0.23 0.76 0.24 1.2
## ig_t 0.88 0.12 0.07 -0.01 0.14 0.81 0.19 1.1
## ig_v 0.91 0.07 0.05 -0.06 0.11 0.84 0.16 1.1
## ig_f 0.83 0.05 0.05 0.02 0.27 0.77 0.23 1.2
## ig_sa 0.83 0.12 0.08 0.04 0.27 0.78 0.22 1.3
## ig_cw 0.86 0.08 0.06 0.02 0.23 0.80 0.20 1.2
## ig_d 0.89 0.08 0.06 -0.08 0.09 0.82 0.18 1.1
## cl_f 0.13 0.08 0.65 0.20 0.08 0.49 0.51 1.3
## cl_tv -0.01 -0.06 0.78 0.14 0.00 0.63 0.37 1.1
## cl_c 0.09 0.26 0.49 0.49 0.04 0.55 0.45 2.6
## cl_mov 0.01 -0.15 0.72 0.04 0.13 0.55 0.45 1.2
## cl_mag 0.12 0.17 0.52 0.49 0.05 0.56 0.44 2.4
## cl_mus 0.07 0.00 0.70 0.20 0.08 0.55 0.45 1.2
## cl_tvd -0.02 0.05 -0.74 -0.13 0.00 0.57 0.43 1.1
## cl_a 0.07 0.04 0.69 0.22 0.07 0.54 0.46 1.3
## cl_d 0.01 0.12 0.55 0.45 -0.05 0.52 0.48 2.1
## cg_mov 0.05 0.75 -0.04 0.10 0.10 0.59 0.41 1.1
## cg_c 0.10 0.73 0.09 0.13 0.14 0.59 0.41 1.2
## cg_a 0.07 0.74 0.04 0.12 0.13 0.58 0.42 1.1
## cg_mus 0.10 0.75 0.00 0.22 0.07 0.62 0.38 1.2
## cg_d 0.05 0.38 0.02 -0.25 0.31 0.30 0.70 2.8
## cg_tv 0.03 0.78 -0.03 0.12 0.06 0.64 0.36 1.1
## cg_f 0.14 0.78 0.01 0.08 0.09 0.64 0.36 1.1
## cg_mag 0.03 0.75 0.03 0.11 0.03 0.58 0.42 1.1
## cg_tvd -0.04 -0.63 0.05 0.05 -0.19 0.43 0.57 1.2
## c_o 0.27 0.21 0.10 0.07 0.81 0.79 0.21 1.4
## c_ex 0.25 0.17 0.07 -0.03 0.81 0.75 0.25 1.3
## c_w 0.24 0.13 0.11 -0.01 0.79 0.71 0.29 1.3
## c_l 0.23 0.15 0.04 -0.05 0.83 0.77 0.23 1.2
## c_v 0.28 0.19 0.06 -0.03 0.79 0.74 0.26 1.4
##
## PA1 PA3 PA5 PA2 PA4
## SS loadings 5.62 4.98 4.28 3.91 3.78
## Proportion Var 0.17 0.15 0.13 0.12 0.11
## Cumulative Var 0.17 0.31 0.44 0.55 0.66
## Proportion Explained 0.25 0.22 0.19 0.17 0.17
## Cumulative Proportion 0.25 0.47 0.66 0.83 1.00
##
## Mean item complexity = 1.4
## Test of the hypothesis that 5 factors are sufficient.
##
## The degrees of freedom for the null model are 561 and the objective function was 29.37 with Chi Square of 13153.83
## The degrees of freedom for the model are 401 and the objective function was 2.57
##
## The root mean square of the residuals (RMSR) is 0.02
## The df corrected root mean square of the residuals is 0.03
##
## The harmonic number of observations is 461 with the empirical chi square 311.81 with prob < 1
## The total number of observations was 461 with Likelihood Chi Square = 1143.58 with prob < 1.5e-72
##
## Tucker Lewis Index of factoring reliability = 0.917
## RMSEA index = 0.063 and the 90 % confidence intervals are 0.059 0.068
## BIC = -1315.91
## Fit based upon off diagonal values = 0.99
## Measures of factor score adequacy
## PA1 PA3 PA5 PA2 PA4
## Correlation of (regression) scores with factors 0.98 0.95 0.93 0.96 0.96
## Multiple R square of scores with factors 0.95 0.91 0.87 0.92 0.91
## Minimum correlation of possible factor scores 0.91 0.82 0.74 0.84 0.83fa.diagram(fa.none, cex=4, gap.size = 1)
scale.fa.none <- factanal(demog_d_scale_f, factors = 2, rotation = "none")
scale.fa.varimax <- factanal(demog_d_scale_f, factors = 2, rotation = "varimax")
scale.fa.promax <- factanal(demog_d_scale_f, factors = 2, rotation = "promax")
par(mfrow = c(1,3))
plot(scale.fa.none$loadings[,1],
scale.fa.none$loadings[,2],
xlab = "Factor 1",
ylab = "Factor 2",
ylim = c(-1,1),
xlim = c(-1,1),
main = "No rotation")
abline(h = 0, v = 0)
plot(scale.fa.none$loadings[,1],
scale.fa.none$loadings[,2],
xlab = "Factor 1",
ylab = "Factor 2",
ylim = c(-1,1),
xlim = c(-1,1),
main = "Varimax rotation")
text(scale.fa.none$loadings[,1]-0.08,
scale.fa.none$loadings[,2]+0.08,
colnames(demog_d_scale_f),
col="blue")
abline(h = 0, v = 0)
plot(scale.fa.none$loadings[,1],
scale.fa.none$loadings[,2],
xlab = "Factor 1",
ylab = "Factor 2",
ylim = c(-1,1),
xlim = c(-1,1),
main = "Promax rotation")
abline(h = 0, v = 0)
Scale reliability
splithalf
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|======================================================================| 100%[1] "condition all complete"
## [1] "Calculating split half estimates"
## [1] "split half estimates for 5000 random splits"
## condition n spearmanbrown SB_low SB_high
## 1 all 168 0.79 0.75 0.83
## [1] "this could be reported as: using 5000 random splits, the spearman-brown corrected reliability estimate for the all condition was 0.79, 95% CI [0.75, 0.83]"
##
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|======================================================================| 100%[1] "condition all complete"
## [1] "Calculating split half estimates"
## [1] "split half estimates for 5000 random splits"
## condition n spearmanbrown SB_low SB_high
## 1 all 293 0.61 0.55 0.66
## [1] "this could be reported as: using 5000 random splits, the spearman-brown corrected reliability estimate for the all condition was 0.61, 95% CI [0.55, 0.66]"
##
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|======================================================================| 100%[1] "condition all complete"
## [1] "Calculating split half estimates"
## [1] "split half estimates for 5000 random splits"
## condition n spearmanbrown SB_low SB_high
## 1 all 168 0.6 0.53 0.67
## [1] "this could be reported as: using 5000 random splits, the spearman-brown corrected reliability estimate for the all condition was 0.6, 95% CI [0.53, 0.67]"
##
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|======================================================================| 100%[1] "condition all complete"
## [1] "Calculating split half estimates"
## [1] "split half estimates for 5000 random splits"
## condition n spearmanbrown SB_low SB_high
## 1 all 293 0.55 0.48 0.62
## [1] "this could be reported as: using 5000 random splits, the spearman-brown corrected reliability estimate for the all condition was 0.55, 95% CI [0.48, 0.62]"
##
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|======================================================================| 100%[1] "condition all complete"
## [1] "Calculating split half estimates"
## [1] "split half estimates for 5000 random splits"
## condition n spearmanbrown SB_low SB_high
## 1 all 168 0.87 0.82 0.9
## [1] "this could be reported as: using 5000 random splits, the spearman-brown corrected reliability estimate for the all condition was 0.87, 95% CI [0.82, 0.9]"
##
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|======================================================================| 100%[1] "condition all complete"
## [1] "Calculating split half estimates"
## [1] "split half estimates for 5000 random splits"
## condition n spearmanbrown SB_low SB_high
## 1 all 293 0.96 0.95 0.97
## [1] "this could be reported as: using 5000 random splits, the spearman-brown corrected reliability estimate for the all condition was 0.96, 95% CI [0.95, 0.97]"
##
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|======================================================================| 100%[1] "condition all complete"
## [1] "Calculating split half estimates"
## [1] "split half estimates for 5000 random splits"
## condition n spearmanbrown SB_low SB_high
## 1 all 168 0.92 0.9 0.94
## [1] "this could be reported as: using 5000 random splits, the spearman-brown corrected reliability estimate for the all condition was 0.92, 95% CI [0.9, 0.94]"
##
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|======================================================================| 100%[1] "condition all complete"
## [1] "Calculating split half estimates"
## [1] "split half estimates for 5000 random splits"
## condition n spearmanbrown SB_low SB_high
## 1 all 293 0.98 0.97 0.98
## [1] "this could be reported as: using 5000 random splits, the spearman-brown corrected reliability estimate for the all condition was 0.98, 95% CI [0.97, 0.98]"
##
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|
|======================================================================| 100%[1] "condition all complete"
## [1] "Calculating split half estimates"
## [1] "split half estimates for 5000 random splits"
## condition n spearmanbrown SB_low SB_high
## 1 all 168 0.9 0.88 0.92
## [1] "this could be reported as: using 5000 random splits, the spearman-brown corrected reliability estimate for the all condition was 0.9, 95% CI [0.88, 0.92]"
##
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|======================================================================| 100%[1] "condition all complete"
## [1] "Calculating split half estimates"
## [1] "split half estimates for 5000 random splits"
## condition n spearmanbrown SB_low SB_high
## 1 all 293 0.93 0.92 0.94
## [1] "this could be reported as: using 5000 random splits, the spearman-brown corrected reliability estimate for the all condition was 0.93, 95% CI [0.92, 0.94]"splithalf_df <- scale_splithalfs_df %>%
pivot_longer(cols = c("splithalf",
"spearmanbrown"),
names_to = "value_type",
values_to = "value") %>%
filter(value_type == "splithalf") %>%
select(`95_high`, `95_low`, info_type, culture, value,value_type) %>%
rename(ub = `95_high`,
lb = `95_low`,
scale_type = info_type)
sb_df <- scale_splithalfs_df %>%
pivot_longer(cols = c("splithalf",
"spearmanbrown"),
names_to = "value_type",
values_to = "value") %>%
filter(value_type == "spearmanbrown") %>%
select(`SB_low`, `SB_high`, info_type, culture, value,value_type) %>%
rename(ub = `SB_high`,
lb = `SB_low`,
scale_type = info_type)
reliability_df <- bind_rows(splithalf_df, sb_df)
reliability_df %>%
ggplot(aes(x = scale_type, y = value, color = culture)) +
geom_pointrange(mapping = aes(ymin = lb, ymax = ub),
position = position_dodge(width = .2)) +
theme_classic() +
facet_wrap(~value_type) +
theme(axis.text.x = element_text(angle = 90, vjust = .5))
icc
Scale with task
tidy_d <- read_csv(here("data/4_processed/with_human_coded_main.csv"))
demog_d_summary <- demog_d_scale %>%
group_by(subject, culture, scale_type) %>%
summarise(scale_rating = mean(demog_response))FD
tidy_d %>%
filter(task_name == "FD") %>%
group_by(subject, culture) %>%
summarise(first_mention_rate = mean(as.numeric(resp))) %>%
left_join(demog_d_summary, by = c("subject", "culture")) %>%
filter(!is.na(scale_type))%>%
ggplot(aes(x = scale_rating,
y = first_mention_rate,
color = culture)) +
geom_jitter(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~scale_type) +
labs(title = "Free description with scale")
CA
tidy_d %>%
filter(task_name == "CA") %>%
filter(task_info == "situational") %>%
group_by(subject, culture) %>%
summarise(situational_rating = mean(as.numeric(resp, na.rm = TRUE))) %>%
left_join(demog_d_summary, by = c("subject", "culture")) %>%
filter(!is.na(scale_type))%>%
ggplot(aes(x = scale_rating,
y = situational_rating,
color = culture)) +
geom_jitter(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~scale_type) +
labs(title = "Causal Attribution (Situational) with scale")
SSI
tidy_d %>%
filter(task_name == "SSI") %>%
group_by(subject, culture) %>%
filter(resp_type == "task_score_ratio") %>%
mutate(ssi_ratio = as.numeric(resp)) %>%
left_join(demog_d_summary, by = c("subject", "culture")) %>%
ggplot(aes(x = scale_rating,
y = ssi_ratio,
color = culture)) +
geom_jitter(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~scale_type) +
labs(title = "symbolic self inflation ratio with scale")
triads
tidy_d %>%
filter(task_name == "TD") %>%
filter(task_info == "triads") %>%
group_by(subject, culture) %>%
summarise(tax_match_rate = mean(as.numeric(as.logical(resp)))) %>%
left_join(demog_d_summary, by = c("subject", "culture")) %>%
ggplot(aes(x = scale_rating,
y = tax_match_rate,
color = culture)) +
geom_jitter(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~scale_type) +
labs(title = "triads taxonomy ratio with scale")
Semantic Intuition
tidy_d %>%
filter(task_name == "SeI") %>%
filter(task_info == "critical") %>%
mutate(resp_score = if_else(resp == "causal_historical", 1, 0)) %>%
group_by(subject, culture) %>%
summarise(causal_historical_rate = mean(resp_score)) %>%
left_join(demog_d_summary, by = c("subject", "culture")) %>%
ggplot(aes(x = scale_rating,
y = causal_historical_rate,
color = culture)) +
geom_jitter(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~scale_type) +
labs(title = "causal historical rating with scale")
RMTS
tidy_d %>%
filter(task_name == "RMTS") %>%
group_by(subject, culture) %>%
summarise(relational_match = mean(as.numeric(resp))) %>%
left_join(demog_d_summary, by = c("subject", "culture")) %>%
ggplot(aes(x = scale_rating,
y = relational_match,
color = culture)) +
geom_jitter(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~scale_type) +
labs(title = "rmts with scale")
ravens
tidy_d %>%
filter(task_name == "RV") %>%
group_by(subject, culture) %>%
summarise(rv_score = mean(as.numeric(resp))) %>%
left_join(demog_d_summary, by = c("subject", "culture")) %>%
ggplot(aes(x = scale_rating,
y = rv_score,
color = culture)) +
geom_jitter(alpha = .3) +
geom_smooth(method = "lm") +
facet_wrap(~scale_type) +
labs(title = "rmts with scale")