library(tidyverse)
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## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.0 ✔ stringr 1.4.1
## ✔ readr 2.1.2 ✔ forcats 0.5.1
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## ✖ dplyr::filter() masks stats::filter()
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library(here)
## here() starts at /Users/caoanjie/Desktop/projects/CCRR_writeups
library(broom)
d1 <- read_csv(here("data/03_processed_data/exp1/tidy_main.csv"))
## New names:
## • `` -> `...1`
## Rows: 37595 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (6): subject, culture, task_name, task_info, trial_info, resp_type
## dbl (2): ...1, resp
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
d2 <- read_csv(here("data/03_processed_data/exp2/tidy_main.csv"))
## Warning: One or more parsing issues, see `problems()` for details
## Rows: 40257 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (7): subject, culture, task_name, task_info, trial_info, resp_type, resp
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
d1_demog <- read_csv(here("data/03_processed_data/exp1/tidy_demog.csv"))
## New names:
## Rows: 5468 Columns: 6
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (5): subject, which_regions, demog_question, demog_response, culture dbl (1):
## ...1
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`
d2_demog <- read_csv(here("data/03_processed_data/exp2/tidy_demog.csv"))
## Rows: 25417 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): subject, culture, demog_question, demog_response
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
us_regions <- read_csv(here("data/03_processed_data/us_regions.csv"))
## Rows: 51 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): State, State Code, Region, Coast, Division
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
cn_regions <- read_csv(here("data/03_processed_data/cn_regions.csv"))
## Rows: 33 Columns: 17
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Province, GrowProv
## dbl (15): PercentPaddy, RiceCat, UNFAORiceSuitabilityIndexAll, PerCapitaGDP1...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Task-global identity relationships
Since we only collected scales for study 2, here we are only running
the test with study 2
d2_with_scale <- d2 %>%
left_join(d2_demog %>%
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,
demog_response = case_when(
grepl("local", scale_type) ~ -demog_response,
TRUE ~ demog_response
)) %>%
group_by(subject, culture) %>%
summarise(scale_sum_score = sum(demog_response)),
by = c("subject", "culture"))
## `summarise()` has grouped output by 'subject'. You can override using the
## `.groups` argument.
# RMTS, FD, CD, SSI, CA, TD, SeI, RV
# cleaning
clean_d2_CD <- d2_with_scale %>%
filter(task_name == "CD", task_info == "context") %>%
group_by(subject, culture, task_name, scale_sum_score) %>%
filter(!resp == "null") %>%
summarise(resp_num = mean(as.numeric(resp)))
## `summarise()` has grouped output by 'subject', 'culture', 'task_name'. You can
## override using the `.groups` argument.
clean_d2_SSI <- d2_with_scale %>%
filter(task_name == "SSI", resp_type == "task_score_ratio") %>%
mutate(resp_num = as.numeric(resp))
clean_d2_CA <- d2_with_scale %>%
filter(task_name == "CA", task_info == "situational") %>%
group_by(subject, culture, task_name, scale_sum_score) %>%
filter(!is.na(resp)) %>%
summarise(resp_num = mean(as.numeric(resp)))
## `summarise()` has grouped output by 'subject', 'culture', 'task_name'. You can
## override using the `.groups` argument.
clean_d2_TD <- d2_with_scale %>%
filter(task_name == "TD", task_info == "triads") %>%
group_by(subject, culture, task_name, scale_sum_score) %>%
filter(!is.na(resp)) %>%
summarise(resp_num = mean(as.numeric(as.logical(resp))))
## `summarise()` has grouped output by 'subject', 'culture', 'task_name'. You can
## override using the `.groups` argument.
clean_d2_SeI <- d2_with_scale %>%
filter(task_name == "SeI", task_info == "critical") %>%
mutate(resp = case_when(
resp == "causal_historical" ~ 1,
TRUE ~ 0)) %>%
group_by(subject, culture, task_name, scale_sum_score) %>%
summarise(resp_num = mean(as.numeric(resp)))
## `summarise()` has grouped output by 'subject', 'culture', 'task_name'. You can
## override using the `.groups` argument.
d2_task_giscale <- d2_with_scale %>%
filter(task_name %in% c("RMTS","RV","FD")) %>%
mutate(resp_num = as.numeric(resp)) %>%
group_by(subject, culture, task_name, scale_sum_score) %>%
summarise(resp_num = mean(resp_num)) %>%
bind_rows(clean_d2_CD, clean_d2_SSI, clean_d2_CA,
clean_d2_TD, clean_d2_SeI) %>%
group_by(task_name) %>%
nest() |>
mutate(mod = map(data,
function (df) lm(resp_num ~ scale_sum_score * culture,
data = df)),
tidy = map(mod, tidy)) |>
select(-mod, -data) |>
unnest(cols = c(tidy)) |>
filter(term %in% c("scale_sum_score","scale_sum_score:cultureUS"))
## `summarise()` has grouped output by 'subject', 'culture', 'task_name'. You can
## override using the `.groups` argument.
d2_task_giscale$adjusted = p.adjust(d2_task_giscale$p.value, method = "bonferroni")
saveRDS(d2_task_giscale, here("cached_results/ea/task_giscale_df.RDS"))
d2_task_giscale|>
knitr::kable(digits = 3)
| RMTS |
scale_sum_score |
-0.003 |
0.002 |
-1.876 |
0.061 |
0.980 |
| RMTS |
scale_sum_score:cultureUS |
0.002 |
0.002 |
0.824 |
0.411 |
1.000 |
| RV |
scale_sum_score |
0.001 |
0.001 |
0.835 |
0.404 |
1.000 |
| RV |
scale_sum_score:cultureUS |
0.000 |
0.001 |
-0.327 |
0.744 |
1.000 |
| FD |
scale_sum_score |
0.000 |
0.001 |
0.212 |
0.832 |
1.000 |
| FD |
scale_sum_score:cultureUS |
0.000 |
0.001 |
-0.423 |
0.673 |
1.000 |
| CD |
scale_sum_score |
-16.199 |
18.595 |
-0.871 |
0.384 |
1.000 |
| CD |
scale_sum_score:cultureUS |
9.872 |
22.574 |
0.437 |
0.662 |
1.000 |
| SSI |
scale_sum_score |
-0.002 |
0.002 |
-0.627 |
0.531 |
1.000 |
| SSI |
scale_sum_score:cultureUS |
0.003 |
0.003 |
0.846 |
0.398 |
1.000 |
| CA |
scale_sum_score |
-0.005 |
0.005 |
-0.924 |
0.356 |
1.000 |
| CA |
scale_sum_score:cultureUS |
0.012 |
0.006 |
1.989 |
0.047 |
0.758 |
| TD |
scale_sum_score |
-0.001 |
0.001 |
-0.855 |
0.393 |
1.000 |
| TD |
scale_sum_score:cultureUS |
0.002 |
0.001 |
2.067 |
0.039 |
0.628 |
| SeI |
scale_sum_score |
-0.002 |
0.002 |
-0.929 |
0.353 |
1.000 |
| SeI |
scale_sum_score:cultureUS |
0.001 |
0.002 |
0.513 |
0.608 |
1.000 |
Regional differences
toneless_dict <- pinyin::pydic("toneless")
demog_province_cleaned <-
bind_rows(d1_demog %>% mutate(study = "d1"),
d2_demog %>% mutate(study = "d2")) %>%
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)) %>%
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() %>%
mutate(rice_cat = case_when(rice_cat == 1 ~ "wheat",
rice_cat == 2 ~ "rice"))
us_d1_region_d <- demog_province_cleaned %>%
filter(study == "d1", culture == "US") %>%
select(subject, study, culture, study, coast, region)
us_d2_region_d <- demog_province_cleaned %>%
filter(study == "d2", culture == "US") %>%
select(subject, study, culture, study, coast, region)
cn_d1_region_d <- demog_province_cleaned %>%
filter(study == "d1", culture == "CN") %>%
select(subject, study, culture, study, rice_cat)
cn_d2_region_d <- demog_province_cleaned %>%
filter(study == "d2", culture == "CN") %>%
select(subject, study, culture, study, rice_cat)
study 1
clean_d1_ssi <- d1 %>%
filter(task_name == "SI") %>%
filter(resp_type == "inflation_score_ratio") %>%
mutate(resp_num = as.numeric(resp)) %>%
select(subject, culture, task_name, resp_num)
clean_d1_ebb <- d1 %>%
filter(task_name == "EBB", task_info == "IL") %>%
group_by(subject, culture, task_name) %>%
summarise(resp_num = mean(resp))
## `summarise()` has grouped output by 'subject', 'culture'. You can override
## using the `.groups` argument.
clean_d1_rmts <- d1 %>%
filter(task_name == "RMTS") %>%
group_by(subject, culture, task_name) %>%
summarise(resp_num = mean(resp))
## `summarise()` has grouped output by 'subject', 'culture'. You can override
## using the `.groups` argument.
clean_d1_hz <- d1 %>%
filter(task_name == "HZ", resp_type == "hz_height") %>%
mutate(resp_num = resp) %>%
select(subject, culture, task_name, resp_num)
clean_d1_up <- d1 %>%
filter(task_name == "CP") %>%
mutate(resp_num = resp) %>%
select(subject, culture, task_name, resp_num)
clean_d1_rv <- d1 %>%
filter(task_name == "RV") %>%
group_by(subject, culture, task_name) %>%
summarise(resp_num = mean(resp)) %>%
select(subject, culture, task_name, resp_num)
## `summarise()` has grouped output by 'subject', 'culture'. You can override
## using the `.groups` argument.
clean_d1_fd <- d1 %>%
filter(task_name == "FD", resp_type == "first_mention_focal") %>%
group_by(subject, culture, task_name) %>%
summarise(resp_num = mean(resp)) %>%
select(subject, culture, task_name, resp_num)
## `summarise()` has grouped output by 'subject', 'culture'. You can override
## using the `.groups` argument.
clean_d1_ca <- d1 %>%
filter(task_name == "CA", resp_type == "situation_attribution")%>%
group_by(subject, culture, task_name) %>%
summarise(resp_num = mean(resp)) %>%
select(subject, culture, task_name, resp_num)
## `summarise()` has grouped output by 'subject', 'culture'. You can override
## using the `.groups` argument.
d1_region_df <- bind_rows(clean_d1_ca,
clean_d1_ebb,
clean_d1_fd,
clean_d1_hz,
clean_d1_rmts,
clean_d1_rv,
clean_d1_ssi,
clean_d1_up) %>%
left_join(us_d1_region_d %>% select(subject, culture, coast, region), by = c("subject", "culture")) %>%
left_join(cn_d1_region_d %>% select(subject, culture, rice_cat), by = c("subject", "culture")) %>%
select(subject, culture, task_name, resp_num, rice_cat, coast,region) %>%
pivot_longer(cols = c("rice_cat", "coast", "region"),
names_to = "region_type",
values_to = "region") %>%
mutate(region = as.factor(region)) %>%
group_by(culture, task_name, region_type) %>%
nest() %>%
filter(!(culture == "CN" & region_type %in% c("coast", "region")),
!(culture == "US"& region_type == "rice_cat")) %>%
mutate(mod = case_when(
region_type == "rice_cat" ~ map(data, function (df) lm(resp_num ~ relevel(region,
ref =
"wheat"),
data = df)),
region_type == "region" ~ map(data, function (df) lm(resp_num ~ relevel(region,
ref =
"West"),
data = df)),
region_type == "coast" ~ map(data, function (df) lm(resp_num ~ relevel(region,
ref =
"west_coast"),
data = df))
),
tidy = map(mod, tidy)) %>%
select(-mod, -data) |>
unnest(cols = c(tidy)) %>%
filter(grepl("region", term))
d1_region_df$p.adjusted = p.adjust(d1_region_df$p.value,
method = "bonferroni")
d1_region_df %>%
knitr::kable(digits = 3)
| US |
CA |
coast |
relevel(region, ref = “west_coast”)inland |
-0.002 |
0.108 |
-0.019 |
0.985 |
1 |
| US |
CA |
coast |
relevel(region, ref = “west_coast”)east_coast |
0.107 |
0.136 |
0.783 |
0.435 |
1 |
| US |
CA |
region |
relevel(region, ref = “West”)Midwest |
0.098 |
0.145 |
0.675 |
0.501 |
1 |
| US |
CA |
region |
relevel(region, ref = “West”)South |
-0.032 |
0.127 |
-0.253 |
0.800 |
1 |
| US |
CA |
region |
relevel(region, ref = “West”)Northeast |
0.152 |
0.166 |
0.916 |
0.361 |
1 |
| CN |
CA |
rice_cat |
relevel(region, ref = “wheat”)rice |
0.013 |
0.086 |
0.148 |
0.882 |
1 |
| US |
EBB |
coast |
relevel(region, ref = “west_coast”)inland |
-0.047 |
0.035 |
-1.334 |
0.184 |
1 |
| US |
EBB |
coast |
relevel(region, ref = “west_coast”)east_coast |
-0.035 |
0.045 |
-0.790 |
0.431 |
1 |
| US |
EBB |
region |
relevel(region, ref = “West”)Midwest |
-0.055 |
0.048 |
-1.158 |
0.249 |
1 |
| US |
EBB |
region |
relevel(region, ref = “West”)South |
0.007 |
0.042 |
0.167 |
0.868 |
1 |
| US |
EBB |
region |
relevel(region, ref = “West”)Northeast |
-0.019 |
0.055 |
-0.354 |
0.724 |
1 |
| CN |
EBB |
rice_cat |
relevel(region, ref = “wheat”)rice |
0.013 |
0.033 |
0.380 |
0.704 |
1 |
| US |
FD |
coast |
relevel(region, ref = “west_coast”)inland |
0.064 |
0.028 |
2.311 |
0.022 |
1 |
| US |
FD |
coast |
relevel(region, ref = “west_coast”)east_coast |
0.071 |
0.035 |
2.018 |
0.045 |
1 |
| US |
FD |
region |
relevel(region, ref = “West”)Midwest |
0.066 |
0.038 |
1.761 |
0.080 |
1 |
| US |
FD |
region |
relevel(region, ref = “West”)South |
0.063 |
0.033 |
1.911 |
0.058 |
1 |
| US |
FD |
region |
relevel(region, ref = “West”)Northeast |
0.065 |
0.043 |
1.505 |
0.134 |
1 |
| CN |
FD |
rice_cat |
relevel(region, ref = “wheat”)rice |
0.060 |
0.049 |
1.228 |
0.221 |
1 |
| US |
HZ |
coast |
relevel(region, ref = “west_coast”)inland |
-0.038 |
0.026 |
-1.458 |
0.147 |
1 |
| US |
HZ |
coast |
relevel(region, ref = “west_coast”)east_coast |
0.040 |
0.033 |
1.233 |
0.220 |
1 |
| US |
HZ |
region |
relevel(region, ref = “West”)Midwest |
-0.042 |
0.035 |
-1.181 |
0.239 |
1 |
| US |
HZ |
region |
relevel(region, ref = “West”)South |
0.004 |
0.031 |
0.116 |
0.908 |
1 |
| US |
HZ |
region |
relevel(region, ref = “West”)Northeast |
0.023 |
0.040 |
0.570 |
0.570 |
1 |
| CN |
HZ |
rice_cat |
relevel(region, ref = “wheat”)rice |
0.016 |
0.032 |
0.508 |
0.612 |
1 |
| US |
RMTS |
coast |
relevel(region, ref = “west_coast”)inland |
0.054 |
0.085 |
0.635 |
0.527 |
1 |
| US |
RMTS |
coast |
relevel(region, ref = “west_coast”)east_coast |
0.051 |
0.107 |
0.475 |
0.636 |
1 |
| US |
RMTS |
region |
relevel(region, ref = “West”)Midwest |
-0.061 |
0.114 |
-0.532 |
0.595 |
1 |
| US |
RMTS |
region |
relevel(region, ref = “West”)South |
0.105 |
0.100 |
1.049 |
0.296 |
1 |
| US |
RMTS |
region |
relevel(region, ref = “West”)Northeast |
0.012 |
0.130 |
0.089 |
0.929 |
1 |
| CN |
RMTS |
rice_cat |
relevel(region, ref = “wheat”)rice |
0.005 |
0.076 |
0.070 |
0.944 |
1 |
| US |
RV |
coast |
relevel(region, ref = “west_coast”)inland |
0.008 |
0.042 |
0.191 |
0.849 |
1 |
| US |
RV |
coast |
relevel(region, ref = “west_coast”)east_coast |
-0.002 |
0.053 |
-0.034 |
0.973 |
1 |
| US |
RV |
region |
relevel(region, ref = “West”)Midwest |
0.073 |
0.056 |
1.304 |
0.194 |
1 |
| US |
RV |
region |
relevel(region, ref = “West”)South |
0.032 |
0.049 |
0.658 |
0.511 |
1 |
| US |
RV |
region |
relevel(region, ref = “West”)Northeast |
0.039 |
0.064 |
0.610 |
0.543 |
1 |
| CN |
RV |
rice_cat |
relevel(region, ref = “wheat”)rice |
-0.029 |
0.027 |
-1.066 |
0.288 |
1 |
| CN |
SI |
rice_cat |
relevel(region, ref = “wheat”)rice |
-0.164 |
0.096 |
-1.709 |
0.090 |
1 |
| US |
SI |
coast |
relevel(region, ref = “west_coast”)inland |
0.025 |
0.057 |
0.441 |
0.660 |
1 |
| US |
SI |
coast |
relevel(region, ref = “west_coast”)east_coast |
0.039 |
0.070 |
0.554 |
0.581 |
1 |
| US |
SI |
region |
relevel(region, ref = “West”)Midwest |
0.023 |
0.073 |
0.314 |
0.754 |
1 |
| US |
SI |
region |
relevel(region, ref = “West”)South |
0.030 |
0.065 |
0.457 |
0.649 |
1 |
| US |
SI |
region |
relevel(region, ref = “West”)Northeast |
-0.022 |
0.091 |
-0.238 |
0.812 |
1 |
| US |
CP |
coast |
relevel(region, ref = “west_coast”)inland |
0.006 |
0.087 |
0.071 |
0.943 |
1 |
| US |
CP |
coast |
relevel(region, ref = “west_coast”)east_coast |
-0.116 |
0.110 |
-1.054 |
0.293 |
1 |
| US |
CP |
region |
relevel(region, ref = “West”)Midwest |
-0.057 |
0.118 |
-0.481 |
0.631 |
1 |
| US |
CP |
region |
relevel(region, ref = “West”)South |
0.011 |
0.103 |
0.104 |
0.917 |
1 |
| US |
CP |
region |
relevel(region, ref = “West”)Northeast |
-0.165 |
0.135 |
-1.224 |
0.223 |
1 |
| CN |
CP |
rice_cat |
relevel(region, ref = “wheat”)rice |
0.002 |
0.078 |
0.027 |
0.979 |
1 |
study 2
# here we are recycling the code snippet from above so that we can run batch models
d2_region_df <- d2_with_scale %>%
filter(task_name %in% c("RMTS","RV","FD")) %>%
mutate(resp_num = as.numeric(resp)) %>%
group_by(subject, culture, task_name, scale_sum_score) %>%
summarise(resp_num = mean(resp_num)) %>%
bind_rows(clean_d2_CD, clean_d2_SSI, clean_d2_CA,
clean_d2_TD, clean_d2_SeI) %>%
select(-scale_sum_score) %>%
# start combining the demographic information
left_join(cn_d2_region_d %>% select(subject,
culture,
rice_cat), by = c("subject", "culture")) %>%
left_join( us_d2_region_d %>% select(subject, culture, coast, region),
by = c("subject", "culture")) %>%
select(subject, culture, task_name, resp_num, rice_cat, coast,region) %>%
pivot_longer(cols = c("rice_cat", "coast", "region"),
names_to = "region_type",
values_to = "region") %>%
mutate(region = as.factor(region)) %>%
group_by(culture, task_name, region_type) %>%
nest() %>%
filter(!(culture == "CN" & region_type %in% c("coast", "region")),
!(culture == "US"& region_type == "rice_cat")) %>%
mutate(mod = case_when(
region_type == "rice_cat" ~ map(data, function (df) lm(resp_num ~ relevel(region,
ref =
"wheat"),
data = df)),
region_type == "region" ~ map(data, function (df) lm(resp_num ~ relevel(region,
ref =
"West"),
data = df)),
region_type == "coast" ~ map(data, function (df) lm(resp_num ~ relevel(region,
ref =
"west_coast"),
data = df))
),
tidy = map(mod, tidy)) %>%
select(-mod, -data) |>
unnest() %>%
filter(grepl("region", term))
## `summarise()` has grouped output by 'subject', 'culture', 'task_name'. You can
## override using the `.groups` argument.
## Warning: `cols` is now required when using unnest().
## Please use `cols = c(tidy)`
d2_region_df$p.adjusted = p.adjust(d2_region_df$p.value,
method = "bonferroni")
d2_region_df %>%
knitr::kable(digits = 3)
| CN |
RMTS |
rice_cat |
relevel(region, ref = “wheat”)rice |
-0.064 |
0.064 |
-0.993 |
0.322 |
1.000 |
| CN |
RV |
rice_cat |
relevel(region, ref = “wheat”)rice |
0.038 |
0.035 |
1.074 |
0.284 |
1.000 |
| CN |
FD |
rice_cat |
relevel(region, ref = “wheat”)rice |
0.088 |
0.046 |
1.903 |
0.059 |
1.000 |
| US |
FD |
coast |
relevel(region, ref = “west_coast”)inland |
-0.015 |
0.025 |
-0.591 |
0.555 |
1.000 |
| US |
FD |
coast |
relevel(region, ref = “west_coast”)east_coast |
-0.006 |
0.027 |
-0.234 |
0.815 |
1.000 |
| US |
FD |
region |
relevel(region, ref = “West”)Midwest |
-0.045 |
0.025 |
-1.789 |
0.075 |
1.000 |
| US |
FD |
region |
relevel(region, ref = “West”)Northeast |
0.015 |
0.026 |
0.585 |
0.559 |
1.000 |
| US |
FD |
region |
relevel(region, ref = “West”)South |
-0.028 |
0.025 |
-1.120 |
0.264 |
1.000 |
| US |
RMTS |
coast |
relevel(region, ref = “west_coast”)inland |
0.050 |
0.074 |
0.670 |
0.503 |
1.000 |
| US |
RMTS |
coast |
relevel(region, ref = “west_coast”)east_coast |
-0.075 |
0.081 |
-0.925 |
0.356 |
1.000 |
| US |
RMTS |
region |
relevel(region, ref = “West”)Midwest |
0.097 |
0.076 |
1.269 |
0.205 |
1.000 |
| US |
RMTS |
region |
relevel(region, ref = “West”)Northeast |
-0.106 |
0.080 |
-1.329 |
0.185 |
1.000 |
| US |
RMTS |
region |
relevel(region, ref = “West”)South |
-0.049 |
0.074 |
-0.660 |
0.509 |
1.000 |
| US |
RV |
coast |
relevel(region, ref = “west_coast”)inland |
0.138 |
0.045 |
3.056 |
0.002 |
0.118 |
| US |
RV |
coast |
relevel(region, ref = “west_coast”)east_coast |
0.097 |
0.049 |
1.978 |
0.049 |
1.000 |
| US |
RV |
region |
relevel(region, ref = “West”)Midwest |
0.148 |
0.046 |
3.193 |
0.002 |
0.075 |
| US |
RV |
region |
relevel(region, ref = “West”)Northeast |
0.075 |
0.049 |
1.530 |
0.127 |
1.000 |
| US |
RV |
region |
relevel(region, ref = “West”)South |
0.094 |
0.045 |
2.104 |
0.036 |
1.000 |
| CN |
CD |
rice_cat |
relevel(region, ref = “wheat”)rice |
-880.421 |
735.700 |
-1.197 |
0.233 |
1.000 |
| US |
CD |
coast |
relevel(region, ref = “west_coast”)inland |
1042.399 |
711.246 |
1.466 |
0.144 |
1.000 |
| US |
CD |
coast |
relevel(region, ref = “west_coast”)east_coast |
1766.761 |
773.739 |
2.283 |
0.023 |
1.000 |
| US |
CD |
region |
relevel(region, ref = “West”)Midwest |
649.866 |
742.361 |
0.875 |
0.382 |
1.000 |
| US |
CD |
region |
relevel(region, ref = “West”)Northeast |
2111.613 |
785.166 |
2.689 |
0.008 |
0.367 |
| US |
CD |
region |
relevel(region, ref = “West”)South |
898.406 |
714.938 |
1.257 |
0.210 |
1.000 |
| CN |
SSI |
rice_cat |
relevel(region, ref = “wheat”)rice |
-0.194 |
0.105 |
-1.842 |
0.067 |
1.000 |
| US |
SSI |
coast |
relevel(region, ref = “west_coast”)inland |
0.058 |
0.094 |
0.617 |
0.538 |
1.000 |
| US |
SSI |
coast |
relevel(region, ref = “west_coast”)east_coast |
0.014 |
0.101 |
0.135 |
0.893 |
1.000 |
| US |
SSI |
region |
relevel(region, ref = “West”)Midwest |
-0.026 |
0.098 |
-0.262 |
0.793 |
1.000 |
| US |
SSI |
region |
relevel(region, ref = “West”)Northeast |
0.166 |
0.101 |
1.643 |
0.102 |
1.000 |
| US |
SSI |
region |
relevel(region, ref = “West”)South |
-0.022 |
0.093 |
-0.233 |
0.816 |
1.000 |
| CN |
CA |
rice_cat |
relevel(region, ref = “wheat”)rice |
0.269 |
0.198 |
1.356 |
0.178 |
1.000 |
| US |
CA |
coast |
relevel(region, ref = “west_coast”)inland |
0.115 |
0.154 |
0.743 |
0.458 |
1.000 |
| US |
CA |
coast |
relevel(region, ref = “west_coast”)east_coast |
0.180 |
0.167 |
1.078 |
0.282 |
1.000 |
| US |
CA |
region |
relevel(region, ref = “West”)Midwest |
0.013 |
0.159 |
0.080 |
0.936 |
1.000 |
| US |
CA |
region |
relevel(region, ref = “West”)Northeast |
-0.004 |
0.167 |
-0.022 |
0.982 |
1.000 |
| US |
CA |
region |
relevel(region, ref = “West”)South |
0.191 |
0.154 |
1.242 |
0.215 |
1.000 |
| CN |
TD |
rice_cat |
relevel(region, ref = “wheat”)rice |
-0.008 |
0.042 |
-0.188 |
0.851 |
1.000 |
| US |
TD |
coast |
relevel(region, ref = “west_coast”)inland |
0.025 |
0.037 |
0.666 |
0.506 |
1.000 |
| US |
TD |
coast |
relevel(region, ref = “west_coast”)east_coast |
0.014 |
0.040 |
0.358 |
0.720 |
1.000 |
| US |
TD |
region |
relevel(region, ref = “West”)Midwest |
0.047 |
0.038 |
1.221 |
0.223 |
1.000 |
| US |
TD |
region |
relevel(region, ref = “West”)Northeast |
-0.011 |
0.040 |
-0.264 |
0.792 |
1.000 |
| US |
TD |
region |
relevel(region, ref = “West”)South |
-0.012 |
0.037 |
-0.332 |
0.740 |
1.000 |
| CN |
SeI |
rice_cat |
relevel(region, ref = “wheat”)rice |
-0.022 |
0.066 |
-0.326 |
0.745 |
1.000 |
| US |
SeI |
coast |
relevel(region, ref = “west_coast”)inland |
-0.049 |
0.067 |
-0.731 |
0.465 |
1.000 |
| US |
SeI |
coast |
relevel(region, ref = “west_coast”)east_coast |
-0.059 |
0.072 |
-0.815 |
0.416 |
1.000 |
| US |
SeI |
region |
relevel(region, ref = “West”)Midwest |
-0.023 |
0.069 |
-0.342 |
0.733 |
1.000 |
| US |
SeI |
region |
relevel(region, ref = “West”)Northeast |
-0.048 |
0.072 |
-0.665 |
0.507 |
1.000 |
| US |
SeI |
region |
relevel(region, ref = “West”)South |
-0.100 |
0.066 |
-1.514 |
0.131 |
1.000 |
Basic demog
first putting the two clean df together
d1_clean <- bind_rows(clean_d1_ca,
clean_d1_ebb,
clean_d1_fd,
clean_d1_hz,
clean_d1_rmts,
clean_d1_rv,
clean_d1_ssi,
clean_d1_up) %>%
select(subject, culture, task_name, resp_num)
d2_clean <- d2_with_scale %>%
filter(task_name %in% c("RMTS","RV","FD")) %>%
mutate(resp_num = as.numeric(resp)) %>%
group_by(subject, culture, task_name, scale_sum_score) %>%
summarise(resp_num = mean(resp_num)) %>%
bind_rows(clean_d2_CD, clean_d2_SSI, clean_d2_CA,
clean_d2_TD, clean_d2_SeI) %>%
select(subject, culture, task_name, resp_num)
## `summarise()` has grouped output by 'subject', 'culture', 'task_name'. You can
## override using the `.groups` argument.
d12_joint_df <- bind_rows(d1_clean %>% mutate(study = "d1"),
d2_clean %>% mutate(study = "d2"))
cleaned_basic_demog_df <-
bind_rows(d1_demog %>% mutate(study = "d1"),
d2_demog %>% mutate(study = "d2")) %>%
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 experiences",
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(demog_response = case_when(
demog_response == "No experiences" ~ "0",
demog_response == "One experience" ~ "1",
demog_response == "Two experiences" ~ "2",
demog_response == "Three to five experiences" ~ "3",
demog_response == "Six or more experiences" ~ "4",
demog_response %in% c('8th grade/junior high or less',
"九年级/初中及以下") ~ "0",
demog_response %in% c('Some high school',
"上过一部分高中") ~ "1",
demog_response %in% c('High school graduate/GED',
"普通高中或职业高中毕业/高中等级学历") ~"2",
demog_response %in% c('One or more years of college, no degree',
"一年或多年大学教育，无学位") ~ "3",
demog_response %in% c('Two-year college degree/vocational school',
"大专学历") ~ "4",
demog_response %in% c("Four-/Five-year college Bachelor's degree",
"四年/五年制大学本科学位") ~ "5",
demog_response %in% c("At least some graduate school",
"有过一些研究生经历") ~ "6",
TRUE ~ demog_response
))
numeric demog
demog_d12_numeric_df <- d12_joint_df %>%
left_join(cleaned_basic_demog_df,
by = c("subject", "culture", "study")) %>%
rename(demog_type = demog_question) %>%
filter(demog_type %in% c("overseaexpnum", "objectiveses","subjectiveses", "age")) %>%
mutate(demog_resp = as.numeric(demog_response))
demog_d12_numeric_mod <- demog_d12_numeric_df %>%
group_by(culture, task_name, study, demog_type) %>%
nest() %>%
mutate(mod = map(data, function (df) lm(resp_num ~ demog_resp, data = df)),
tidy = map(mod, tidy)) %>%
select(-mod, -data) %>%
unnest(cols = c(tidy))
demog_d12_numeric_mod$p.adjusted = p.adjust(demog_d12_numeric_mod$p.value,
method = "bonferroni")
demog_d12_numeric_mod %>%
filter(term != "(Intercept)") %>%
knitr::kable(digits = 3)
| US |
CA |
d1 |
overseaexpnum |
demog_resp |
0.050 |
0.039 |
1.278 |
0.203 |
1.000 |
| US |
CA |
d1 |
objectiveses |
demog_resp |
-0.016 |
0.035 |
-0.463 |
0.644 |
1.000 |
| US |
CA |
d1 |
subjectiveses |
demog_resp |
-0.019 |
0.025 |
-0.779 |
0.437 |
1.000 |
| US |
CA |
d1 |
age |
demog_resp |
0.010 |
0.008 |
1.225 |
0.222 |
1.000 |
| CN |
CA |
d1 |
overseaexpnum |
demog_resp |
0.013 |
0.033 |
0.384 |
0.702 |
1.000 |
| CN |
CA |
d1 |
objectiveses |
demog_resp |
-0.077 |
0.038 |
-2.004 |
0.047 |
1.000 |
| CN |
CA |
d1 |
subjectiveses |
demog_resp |
0.005 |
0.028 |
0.188 |
0.851 |
1.000 |
| CN |
CA |
d1 |
age |
demog_resp |
-0.014 |
0.011 |
-1.304 |
0.194 |
1.000 |
| US |
EBB |
d1 |
overseaexpnum |
demog_resp |
0.004 |
0.013 |
0.310 |
0.757 |
1.000 |
| US |
EBB |
d1 |
objectiveses |
demog_resp |
0.016 |
0.012 |
1.380 |
0.169 |
1.000 |
| US |
EBB |
d1 |
subjectiveses |
demog_resp |
0.010 |
0.008 |
1.184 |
0.238 |
1.000 |
| US |
EBB |
d1 |
age |
demog_resp |
0.002 |
0.003 |
0.653 |
0.515 |
1.000 |
| CN |
EBB |
d1 |
overseaexpnum |
demog_resp |
0.003 |
0.013 |
0.203 |
0.839 |
1.000 |
| CN |
EBB |
d1 |
objectiveses |
demog_resp |
0.007 |
0.015 |
0.441 |
0.660 |
1.000 |
| CN |
EBB |
d1 |
subjectiveses |
demog_resp |
-0.013 |
0.011 |
-1.193 |
0.234 |
1.000 |
| CN |
EBB |
d1 |
age |
demog_resp |
0.007 |
0.004 |
1.733 |
0.085 |
1.000 |
| US |
FD |
d1 |
overseaexpnum |
demog_resp |
-0.008 |
0.011 |
-0.721 |
0.472 |
1.000 |
| US |
FD |
d1 |
objectiveses |
demog_resp |
-0.001 |
0.010 |
-0.056 |
0.955 |
1.000 |
| US |
FD |
d1 |
subjectiveses |
demog_resp |
0.000 |
0.007 |
0.054 |
0.957 |
1.000 |
| US |
FD |
d1 |
age |
demog_resp |
-0.001 |
0.002 |
-0.555 |
0.580 |
1.000 |
| CN |
FD |
d1 |
overseaexpnum |
demog_resp |
0.007 |
0.019 |
0.370 |
0.712 |
1.000 |
| CN |
FD |
d1 |
objectiveses |
demog_resp |
-0.015 |
0.022 |
-0.678 |
0.499 |
1.000 |
| CN |
FD |
d1 |
subjectiveses |
demog_resp |
-0.008 |
0.016 |
-0.522 |
0.602 |
1.000 |
| CN |
FD |
d1 |
age |
demog_resp |
0.000 |
0.006 |
0.058 |
0.954 |
1.000 |
| US |
HZ |
d1 |
overseaexpnum |
demog_resp |
-0.016 |
0.010 |
-1.698 |
0.091 |
1.000 |
| US |
HZ |
d1 |
objectiveses |
demog_resp |
0.005 |
0.009 |
0.586 |
0.559 |
1.000 |
| US |
HZ |
d1 |
subjectiveses |
demog_resp |
0.001 |
0.006 |
0.201 |
0.841 |
1.000 |
| US |
HZ |
d1 |
age |
demog_resp |
0.001 |
0.002 |
0.755 |
0.451 |
1.000 |
| CN |
HZ |
d1 |
overseaexpnum |
demog_resp |
-0.022 |
0.012 |
-1.803 |
0.073 |
1.000 |
| CN |
HZ |
d1 |
objectiveses |
demog_resp |
-0.005 |
0.015 |
-0.318 |
0.751 |
1.000 |
| CN |
HZ |
d1 |
subjectiveses |
demog_resp |
-0.010 |
0.010 |
-0.950 |
0.344 |
1.000 |
| CN |
HZ |
d1 |
age |
demog_resp |
-0.005 |
0.004 |
-1.209 |
0.228 |
1.000 |
| US |
RMTS |
d1 |
overseaexpnum |
demog_resp |
0.041 |
0.031 |
1.324 |
0.187 |
1.000 |
| US |
RMTS |
d1 |
objectiveses |
demog_resp |
0.038 |
0.027 |
1.400 |
0.163 |
1.000 |
| US |
RMTS |
d1 |
subjectiveses |
demog_resp |
0.009 |
0.020 |
0.480 |
0.632 |
1.000 |
| US |
RMTS |
d1 |
age |
demog_resp |
0.005 |
0.006 |
0.825 |
0.411 |
1.000 |
| CN |
RMTS |
d1 |
overseaexpnum |
demog_resp |
0.034 |
0.029 |
1.176 |
0.241 |
1.000 |
| CN |
RMTS |
d1 |
objectiveses |
demog_resp |
0.019 |
0.034 |
0.560 |
0.576 |
1.000 |
| CN |
RMTS |
d1 |
subjectiveses |
demog_resp |
0.037 |
0.025 |
1.529 |
0.128 |
1.000 |
| CN |
RMTS |
d1 |
age |
demog_resp |
0.022 |
0.009 |
2.479 |
0.014 |
1.000 |
| US |
RV |
d1 |
overseaexpnum |
demog_resp |
-0.003 |
0.016 |
-0.223 |
0.824 |
1.000 |
| US |
RV |
d1 |
objectiveses |
demog_resp |
0.025 |
0.014 |
1.781 |
0.077 |
1.000 |
| US |
RV |
d1 |
subjectiveses |
demog_resp |
0.006 |
0.010 |
0.581 |
0.562 |
1.000 |
| US |
RV |
d1 |
age |
demog_resp |
0.005 |
0.003 |
1.753 |
0.081 |
1.000 |
| CN |
RV |
d1 |
overseaexpnum |
demog_resp |
0.010 |
0.010 |
1.007 |
0.316 |
1.000 |
| CN |
RV |
d1 |
objectiveses |
demog_resp |
-0.002 |
0.012 |
-0.134 |
0.893 |
1.000 |
| CN |
RV |
d1 |
subjectiveses |
demog_resp |
-0.006 |
0.009 |
-0.688 |
0.493 |
1.000 |
| CN |
RV |
d1 |
age |
demog_resp |
-0.006 |
0.003 |
-1.791 |
0.075 |
1.000 |
| CN |
SI |
d1 |
overseaexpnum |
demog_resp |
0.003 |
0.039 |
0.084 |
0.933 |
1.000 |
| CN |
SI |
d1 |
objectiveses |
demog_resp |
0.049 |
0.045 |
1.098 |
0.274 |
1.000 |
| CN |
SI |
d1 |
subjectiveses |
demog_resp |
0.034 |
0.031 |
1.096 |
0.275 |
1.000 |
| CN |
SI |
d1 |
age |
demog_resp |
0.026 |
0.013 |
2.091 |
0.038 |
1.000 |
| US |
SI |
d1 |
overseaexpnum |
demog_resp |
-0.003 |
0.021 |
-0.143 |
0.887 |
1.000 |
| US |
SI |
d1 |
objectiveses |
demog_resp |
0.008 |
0.017 |
0.477 |
0.634 |
1.000 |
| US |
SI |
d1 |
subjectiveses |
demog_resp |
-0.024 |
0.013 |
-1.866 |
0.065 |
1.000 |
| US |
SI |
d1 |
age |
demog_resp |
0.000 |
0.004 |
-0.070 |
0.944 |
1.000 |
| US |
CP |
d1 |
overseaexpnum |
demog_resp |
-0.053 |
0.032 |
-1.662 |
0.098 |
1.000 |
| US |
CP |
d1 |
objectiveses |
demog_resp |
-0.017 |
0.029 |
-0.602 |
0.548 |
1.000 |
| US |
CP |
d1 |
subjectiveses |
demog_resp |
-0.055 |
0.020 |
-2.748 |
0.007 |
1.000 |
| US |
CP |
d1 |
age |
demog_resp |
-0.005 |
0.006 |
-0.758 |
0.449 |
1.000 |
| CN |
CP |
d1 |
overseaexpnum |
demog_resp |
-0.039 |
0.030 |
-1.305 |
0.194 |
1.000 |
| CN |
CP |
d1 |
objectiveses |
demog_resp |
0.023 |
0.036 |
0.642 |
0.522 |
1.000 |
| CN |
CP |
d1 |
subjectiveses |
demog_resp |
-0.031 |
0.026 |
-1.222 |
0.223 |
1.000 |
| CN |
CP |
d1 |
age |
demog_resp |
0.006 |
0.009 |
0.617 |
0.538 |
1.000 |
| CN |
RMTS |
d2 |
overseaexpnum |
demog_resp |
-0.046 |
0.042 |
-1.095 |
0.275 |
1.000 |
| CN |
RMTS |
d2 |
subjectiveses |
demog_resp |
-0.040 |
0.020 |
-2.053 |
0.042 |
1.000 |
| CN |
RMTS |
d2 |
age |
demog_resp |
0.012 |
0.008 |
1.470 |
0.144 |
1.000 |
| CN |
RV |
d2 |
overseaexpnum |
demog_resp |
0.024 |
0.023 |
1.035 |
0.302 |
1.000 |
| CN |
RV |
d2 |
subjectiveses |
demog_resp |
-0.020 |
0.011 |
-1.890 |
0.060 |
1.000 |
| CN |
RV |
d2 |
age |
demog_resp |
-0.003 |
0.005 |
-0.644 |
0.521 |
1.000 |
| CN |
FD |
d2 |
overseaexpnum |
demog_resp |
0.007 |
0.030 |
0.237 |
0.813 |
1.000 |
| CN |
FD |
d2 |
subjectiveses |
demog_resp |
0.014 |
0.014 |
0.967 |
0.336 |
1.000 |
| CN |
FD |
d2 |
age |
demog_resp |
-0.006 |
0.006 |
-0.904 |
0.368 |
1.000 |
| US |
FD |
d2 |
overseaexpnum |
demog_resp |
0.007 |
0.006 |
1.301 |
0.194 |
1.000 |
| US |
FD |
d2 |
subjectiveses |
demog_resp |
0.002 |
0.005 |
0.395 |
0.693 |
1.000 |
| US |
FD |
d2 |
age |
demog_resp |
0.001 |
0.001 |
1.822 |
0.070 |
1.000 |
| US |
RMTS |
d2 |
overseaexpnum |
demog_resp |
-0.005 |
0.017 |
-0.314 |
0.754 |
1.000 |
| US |
RMTS |
d2 |
subjectiveses |
demog_resp |
0.015 |
0.016 |
0.965 |
0.335 |
1.000 |
| US |
RMTS |
d2 |
age |
demog_resp |
0.000 |
0.002 |
0.099 |
0.921 |
1.000 |
| US |
RV |
d2 |
overseaexpnum |
demog_resp |
0.008 |
0.011 |
0.774 |
0.440 |
1.000 |
| US |
RV |
d2 |
subjectiveses |
demog_resp |
-0.020 |
0.009 |
-2.140 |
0.033 |
1.000 |
| US |
RV |
d2 |
age |
demog_resp |
-0.002 |
0.001 |
-1.990 |
0.047 |
1.000 |
| CN |
CD |
d2 |
overseaexpnum |
demog_resp |
-1184.074 |
458.873 |
-2.580 |
0.011 |
1.000 |
| CN |
CD |
d2 |
subjectiveses |
demog_resp |
-735.858 |
221.148 |
-3.327 |
0.001 |
0.246 |
| CN |
CD |
d2 |
age |
demog_resp |
286.657 |
95.098 |
3.014 |
0.003 |
0.676 |
| US |
CD |
d2 |
overseaexpnum |
demog_resp |
-4.436 |
173.038 |
-0.026 |
0.980 |
1.000 |
| US |
CD |
d2 |
subjectiveses |
demog_resp |
209.453 |
155.242 |
1.349 |
0.179 |
1.000 |
| US |
CD |
d2 |
age |
demog_resp |
131.543 |
17.525 |
7.506 |
0.000 |
0.000 |
| CN |
SSI |
d2 |
overseaexpnum |
demog_resp |
0.077 |
0.067 |
1.144 |
0.254 |
1.000 |
| CN |
SSI |
d2 |
subjectiveses |
demog_resp |
0.029 |
0.032 |
0.915 |
0.362 |
1.000 |
| CN |
SSI |
d2 |
age |
demog_resp |
0.011 |
0.014 |
0.775 |
0.440 |
1.000 |
| US |
SSI |
d2 |
overseaexpnum |
demog_resp |
-0.005 |
0.022 |
-0.242 |
0.809 |
1.000 |
| US |
SSI |
d2 |
subjectiveses |
demog_resp |
0.013 |
0.020 |
0.662 |
0.508 |
1.000 |
| US |
SSI |
d2 |
age |
demog_resp |
0.000 |
0.002 |
-0.176 |
0.860 |
1.000 |
| CN |
CA |
d2 |
overseaexpnum |
demog_resp |
-0.204 |
0.123 |
-1.657 |
0.100 |
1.000 |
| CN |
CA |
d2 |
subjectiveses |
demog_resp |
0.044 |
0.058 |
0.756 |
0.451 |
1.000 |
| CN |
CA |
d2 |
age |
demog_resp |
-0.017 |
0.033 |
-0.497 |
0.620 |
1.000 |
| US |
CA |
d2 |
overseaexpnum |
demog_resp |
-0.008 |
0.036 |
-0.231 |
0.817 |
1.000 |
| US |
CA |
d2 |
subjectiveses |
demog_resp |
0.009 |
0.033 |
0.273 |
0.785 |
1.000 |
| US |
CA |
d2 |
age |
demog_resp |
-0.006 |
0.004 |
-1.595 |
0.112 |
1.000 |
| CN |
TD |
d2 |
overseaexpnum |
demog_resp |
-0.046 |
0.027 |
-1.687 |
0.093 |
1.000 |
| CN |
TD |
d2 |
subjectiveses |
demog_resp |
-0.026 |
0.013 |
-2.046 |
0.042 |
1.000 |
| CN |
TD |
d2 |
age |
demog_resp |
-0.007 |
0.006 |
-1.224 |
0.223 |
1.000 |
| US |
TD |
d2 |
overseaexpnum |
demog_resp |
0.015 |
0.009 |
1.701 |
0.090 |
1.000 |
| US |
TD |
d2 |
subjectiveses |
demog_resp |
0.000 |
0.008 |
-0.057 |
0.955 |
1.000 |
| US |
TD |
d2 |
age |
demog_resp |
0.001 |
0.001 |
1.252 |
0.211 |
1.000 |
| CN |
SeI |
d2 |
overseaexpnum |
demog_resp |
-0.023 |
0.043 |
-0.528 |
0.598 |
1.000 |
| CN |
SeI |
d2 |
subjectiveses |
demog_resp |
-0.019 |
0.020 |
-0.969 |
0.334 |
1.000 |
| CN |
SeI |
d2 |
age |
demog_resp |
-0.003 |
0.009 |
-0.379 |
0.705 |
1.000 |
| US |
SeI |
d2 |
overseaexpnum |
demog_resp |
0.033 |
0.015 |
2.146 |
0.033 |
1.000 |
| US |
SeI |
d2 |
subjectiveses |
demog_resp |
0.006 |
0.014 |
0.408 |
0.684 |
1.000 |
| US |
SeI |
d2 |
age |
demog_resp |
0.002 |
0.002 |
0.980 |
0.328 |
1.000 |
categorical demog
demog_d12_cat_df <- d12_joint_df %>%
left_join(cleaned_basic_demog_df,
by = c("subject", "culture", "study")) %>%
rename(demog_type = demog_question) %>%
filter(demog_type %in% c("gender", "stem_or_not"))
demog_d12_cat_mod <- demog_d12_cat_df %>%
group_by(culture, task_name, study, demog_type) %>%
nest() %>%
mutate(mod = map(data, function (df) lm(resp_num ~ demog_response, data = df)),
tidy = map(mod, tidy)) %>%
select(-mod, -data) %>%
unnest(cols = c(tidy))
demog_d12_cat_mod$p.adjusted = p.adjust(demog_d12_cat_mod$p.value,
method = "bonferroni")
demog_d12_cat_mod %>%
filter(term != "(Intercept)") %>%
knitr::kable(digits = 3)
| US |
CA |
d1 |
gender |
demog_responseFemale |
-0.333 |
0.434 |
-0.769 |
0.443 |
1.000 |
| US |
CA |
d1 |
gender |
demog_responseMale |
-0.443 |
0.440 |
-1.008 |
0.315 |
1.000 |
| US |
CA |
d1 |
gender |
demog_responseNon-binary |
-0.222 |
0.475 |
-0.468 |
0.641 |
1.000 |
| CN |
CA |
d1 |
gender |
demog_response拒绝回答 |
-0.342 |
0.305 |
-1.123 |
0.263 |
1.000 |
| CN |
CA |
d1 |
gender |
demog_response男性 |
-0.078 |
0.088 |
-0.881 |
0.380 |
1.000 |
| CN |
CA |
d1 |
gender |
demog_response非二元性别者 |
0.824 |
0.524 |
1.575 |
0.117 |
1.000 |
| US |
EBB |
d1 |
gender |
demog_responseFemale |
0.074 |
0.144 |
0.511 |
0.610 |
1.000 |
| US |
EBB |
d1 |
gender |
demog_responseMale |
0.069 |
0.146 |
0.474 |
0.636 |
1.000 |
| US |
EBB |
d1 |
gender |
demog_responseNon-binary |
0.183 |
0.158 |
1.161 |
0.247 |
1.000 |
| CN |
EBB |
d1 |
gender |
demog_response拒绝回答 |
0.075 |
0.122 |
0.617 |
0.538 |
1.000 |
| CN |
EBB |
d1 |
gender |
demog_response男性 |
0.005 |
0.035 |
0.134 |
0.894 |
1.000 |
| CN |
EBB |
d1 |
gender |
demog_response非二元性别者 |
-0.208 |
0.209 |
-0.994 |
0.322 |
1.000 |
| US |
FD |
d1 |
gender |
demog_responseFemale |
0.185 |
0.119 |
1.553 |
0.122 |
1.000 |
| US |
FD |
d1 |
gender |
demog_responseMale |
0.198 |
0.121 |
1.637 |
0.104 |
1.000 |
| US |
FD |
d1 |
gender |
demog_responseNon-binary |
0.206 |
0.131 |
1.577 |
0.117 |
1.000 |
| CN |
FD |
d1 |
gender |
demog_response拒绝回答 |
-0.244 |
0.175 |
-1.396 |
0.165 |
1.000 |
| CN |
FD |
d1 |
gender |
demog_response男性 |
-0.037 |
0.051 |
-0.735 |
0.463 |
1.000 |
| CN |
FD |
d1 |
gender |
demog_response非二元性别者 |
-0.578 |
0.301 |
-1.921 |
0.056 |
1.000 |
| US |
HZ |
d1 |
gender |
demog_responseFemale |
-0.076 |
0.108 |
-0.703 |
0.483 |
1.000 |
| US |
HZ |
d1 |
gender |
demog_responseMale |
-0.080 |
0.110 |
-0.727 |
0.468 |
1.000 |
| US |
HZ |
d1 |
gender |
demog_responseNon-binary |
-0.057 |
0.119 |
-0.482 |
0.630 |
1.000 |
| CN |
HZ |
d1 |
gender |
demog_response拒绝回答 |
0.107 |
0.115 |
0.924 |
0.357 |
1.000 |
| CN |
HZ |
d1 |
gender |
demog_response男性 |
0.058 |
0.033 |
1.733 |
0.085 |
1.000 |
| CN |
HZ |
d1 |
gender |
demog_response非二元性别者 |
0.008 |
0.198 |
0.040 |
0.968 |
1.000 |
| US |
RMTS |
d1 |
gender |
demog_responseFemale |
0.439 |
0.340 |
1.290 |
0.199 |
1.000 |
| US |
RMTS |
d1 |
gender |
demog_responseMale |
0.313 |
0.344 |
0.907 |
0.366 |
1.000 |
| US |
RMTS |
d1 |
gender |
demog_responseNon-binary |
0.333 |
0.372 |
0.895 |
0.372 |
1.000 |
| CN |
RMTS |
d1 |
gender |
demog_response拒绝回答 |
-0.048 |
0.275 |
-0.176 |
0.860 |
1.000 |
| CN |
RMTS |
d1 |
gender |
demog_response男性 |
-0.039 |
0.079 |
-0.486 |
0.627 |
1.000 |
| CN |
RMTS |
d1 |
gender |
demog_response非二元性别者 |
-0.382 |
0.471 |
-0.810 |
0.419 |
1.000 |
| US |
RV |
d1 |
gender |
demog_responseFemale |
-0.208 |
0.173 |
-1.202 |
0.231 |
1.000 |
| US |
RV |
d1 |
gender |
demog_responseMale |
-0.161 |
0.176 |
-0.916 |
0.361 |
1.000 |
| US |
RV |
d1 |
gender |
demog_responseNon-binary |
-0.153 |
0.190 |
-0.804 |
0.422 |
1.000 |
| CN |
RV |
d1 |
gender |
demog_response拒绝回答 |
-0.153 |
0.097 |
-1.576 |
0.117 |
1.000 |
| CN |
RV |
d1 |
gender |
demog_response男性 |
-0.011 |
0.028 |
-0.388 |
0.699 |
1.000 |
| CN |
RV |
d1 |
gender |
demog_response非二元性别者 |
0.153 |
0.167 |
0.915 |
0.361 |
1.000 |
| CN |
SI |
d1 |
gender |
demog_response拒绝回答 |
-0.016 |
0.389 |
-0.040 |
0.968 |
1.000 |
| CN |
SI |
d1 |
gender |
demog_response男性 |
0.081 |
0.101 |
0.796 |
0.427 |
1.000 |
| CN |
SI |
d1 |
gender |
demog_response非二元性别者 |
1.073 |
0.547 |
1.962 |
0.052 |
1.000 |
| US |
SI |
d1 |
gender |
demog_responseFemale |
-0.268 |
0.184 |
-1.460 |
0.147 |
1.000 |
| US |
SI |
d1 |
gender |
demog_responseMale |
-0.268 |
0.187 |
-1.429 |
0.156 |
1.000 |
| US |
SI |
d1 |
gender |
demog_responseNon-binary |
-0.108 |
0.209 |
-0.514 |
0.608 |
1.000 |
| US |
CP |
d1 |
gender |
demog_responseFemale |
0.605 |
0.353 |
1.716 |
0.088 |
1.000 |
| US |
CP |
d1 |
gender |
demog_responseMale |
0.545 |
0.358 |
1.525 |
0.129 |
1.000 |
| US |
CP |
d1 |
gender |
demog_responseNon-binary |
0.444 |
0.387 |
1.149 |
0.252 |
1.000 |
| CN |
CP |
d1 |
gender |
demog_response拒绝回答 |
-0.283 |
0.284 |
-0.995 |
0.321 |
1.000 |
| CN |
CP |
d1 |
gender |
demog_response男性 |
0.051 |
0.082 |
0.616 |
0.539 |
1.000 |
| CN |
CP |
d1 |
gender |
demog_response非二元性别者 |
0.384 |
0.488 |
0.787 |
0.433 |
1.000 |
| CN |
RMTS |
d2 |
stem_or_not |
demog_response是 |
-0.101 |
0.071 |
-1.431 |
0.154 |
1.000 |
| CN |
RMTS |
d2 |
gender |
demog_response拒绝回答 |
-0.042 |
0.243 |
-0.173 |
0.862 |
1.000 |
| CN |
RMTS |
d2 |
gender |
demog_response男性 |
-0.115 |
0.065 |
-1.789 |
0.075 |
1.000 |
| CN |
RV |
d2 |
stem_or_not |
demog_response是 |
-0.064 |
0.038 |
-1.674 |
0.096 |
1.000 |
| CN |
RV |
d2 |
gender |
demog_response拒绝回答 |
0.083 |
0.131 |
0.634 |
0.527 |
1.000 |
| CN |
RV |
d2 |
gender |
demog_response男性 |
-0.117 |
0.034 |
-3.435 |
0.001 |
0.113 |
| CN |
FD |
d2 |
stem_or_not |
demog_response是 |
0.109 |
0.050 |
2.171 |
0.032 |
1.000 |
| CN |
FD |
d2 |
gender |
demog_response拒绝回答 |
0.075 |
0.154 |
0.486 |
0.628 |
1.000 |
| CN |
FD |
d2 |
gender |
demog_response男性 |
0.014 |
0.048 |
0.289 |
0.773 |
1.000 |
| US |
FD |
d2 |
stem_or_not |
demog_responseYes |
0.009 |
0.019 |
0.456 |
0.649 |
1.000 |
| US |
FD |
d2 |
gender |
demog_responseFemale |
0.013 |
0.074 |
0.171 |
0.865 |
1.000 |
| US |
FD |
d2 |
gender |
demog_responseMale |
0.008 |
0.074 |
0.109 |
0.914 |
1.000 |
| US |
FD |
d2 |
gender |
demog_responseNon-binary |
0.043 |
0.097 |
0.440 |
0.660 |
1.000 |
| US |
RMTS |
d2 |
stem_or_not |
demog_responseYes |
-0.002 |
0.059 |
-0.035 |
0.972 |
1.000 |
| US |
RMTS |
d2 |
gender |
demog_responseFemale |
-0.037 |
0.227 |
-0.162 |
0.872 |
1.000 |
| US |
RMTS |
d2 |
gender |
demog_responseMale |
-0.033 |
0.226 |
-0.148 |
0.882 |
1.000 |
| US |
RMTS |
d2 |
gender |
demog_responseNon-binary |
0.163 |
0.299 |
0.543 |
0.587 |
1.000 |
| US |
RV |
d2 |
stem_or_not |
demog_responseYes |
0.098 |
0.035 |
2.805 |
0.005 |
0.815 |
| US |
RV |
d2 |
gender |
demog_responseFemale |
-0.045 |
0.138 |
-0.326 |
0.745 |
1.000 |
| US |
RV |
d2 |
gender |
demog_responseMale |
-0.015 |
0.138 |
-0.110 |
0.912 |
1.000 |
| US |
RV |
d2 |
gender |
demog_responseNon-binary |
0.113 |
0.182 |
0.617 |
0.538 |
1.000 |
| CN |
CD |
d2 |
stem_or_not |
demog_response是 |
-162.554 |
826.378 |
-0.197 |
0.844 |
1.000 |
| CN |
CD |
d2 |
gender |
demog_response拒绝回答 |
3969.185 |
2654.875 |
1.495 |
0.137 |
1.000 |
| CN |
CD |
d2 |
gender |
demog_response男性 |
-1010.071 |
738.325 |
-1.368 |
0.173 |
1.000 |
| US |
CD |
d2 |
stem_or_not |
demog_responseYes |
-224.383 |
567.500 |
-0.395 |
0.693 |
1.000 |
| US |
CD |
d2 |
gender |
demog_responseFemale |
5145.774 |
2328.988 |
2.209 |
0.028 |
1.000 |
| US |
CD |
d2 |
gender |
demog_responseMale |
4530.248 |
2321.603 |
1.951 |
0.052 |
1.000 |
| US |
CD |
d2 |
gender |
demog_responseNon-binary |
871.530 |
2904.759 |
0.300 |
0.764 |
1.000 |
| CN |
SSI |
d2 |
stem_or_not |
demog_response是 |
-0.072 |
0.118 |
-0.611 |
0.542 |
1.000 |
| CN |
SSI |
d2 |
gender |
demog_response拒绝回答 |
-0.161 |
0.385 |
-0.417 |
0.677 |
1.000 |
| CN |
SSI |
d2 |
gender |
demog_response男性 |
0.065 |
0.108 |
0.606 |
0.545 |
1.000 |
| US |
SSI |
d2 |
stem_or_not |
demog_responseYes |
-0.082 |
0.072 |
-1.139 |
0.256 |
1.000 |
| US |
SSI |
d2 |
gender |
demog_responseFemale |
0.186 |
0.299 |
0.621 |
0.535 |
1.000 |
| US |
SSI |
d2 |
gender |
demog_responseMale |
0.129 |
0.298 |
0.433 |
0.665 |
1.000 |
| US |
SSI |
d2 |
gender |
demog_responseNon-binary |
0.140 |
0.373 |
0.376 |
0.708 |
1.000 |
| CN |
CA |
d2 |
stem_or_not |
demog_response是 |
0.483 |
0.211 |
2.292 |
0.024 |
1.000 |
| CN |
CA |
d2 |
gender |
demog_response拒绝回答 |
-0.130 |
1.045 |
-0.124 |
0.902 |
1.000 |
| CN |
CA |
d2 |
gender |
demog_response男性 |
0.501 |
0.202 |
2.478 |
0.015 |
1.000 |
| US |
CA |
d2 |
stem_or_not |
demog_responseYes |
-0.171 |
0.120 |
-1.423 |
0.156 |
1.000 |
| US |
CA |
d2 |
gender |
demog_responseFemale |
-0.277 |
0.467 |
-0.594 |
0.553 |
1.000 |
| US |
CA |
d2 |
gender |
demog_responseMale |
-0.495 |
0.465 |
-1.065 |
0.288 |
1.000 |
| US |
CA |
d2 |
gender |
demog_responseNon-binary |
0.103 |
0.616 |
0.168 |
0.867 |
1.000 |
| CN |
TD |
d2 |
stem_or_not |
demog_response是 |
0.008 |
0.046 |
0.183 |
0.855 |
1.000 |
| CN |
TD |
d2 |
gender |
demog_response拒绝回答 |
-0.336 |
0.159 |
-2.111 |
0.036 |
1.000 |
| CN |
TD |
d2 |
gender |
demog_response男性 |
-0.061 |
0.042 |
-1.475 |
0.142 |
1.000 |
| US |
TD |
d2 |
stem_or_not |
demog_responseYes |
0.063 |
0.029 |
2.196 |
0.029 |
1.000 |
| US |
TD |
d2 |
gender |
demog_responseFemale |
0.188 |
0.112 |
1.673 |
0.095 |
1.000 |
| US |
TD |
d2 |
gender |
demog_responseMale |
0.189 |
0.112 |
1.690 |
0.092 |
1.000 |
| US |
TD |
d2 |
gender |
demog_responseNon-binary |
0.287 |
0.148 |
1.933 |
0.054 |
1.000 |
| CN |
SeI |
d2 |
stem_or_not |
demog_response是 |
-0.025 |
0.072 |
-0.345 |
0.730 |
1.000 |
| CN |
SeI |
d2 |
gender |
demog_response拒绝回答 |
0.161 |
0.256 |
0.630 |
0.529 |
1.000 |
| CN |
SeI |
d2 |
gender |
demog_response男性 |
0.051 |
0.067 |
0.765 |
0.445 |
1.000 |
| US |
SeI |
d2 |
stem_or_not |
demog_responseYes |
0.081 |
0.052 |
1.564 |
0.119 |
1.000 |
| US |
SeI |
d2 |
gender |
demog_responseFemale |
-0.165 |
0.202 |
-0.817 |
0.415 |
1.000 |
| US |
SeI |
d2 |
gender |
demog_responseMale |
-0.182 |
0.202 |
-0.904 |
0.367 |
1.000 |
| US |
SeI |
d2 |
gender |
demog_responseNon-binary |
-0.075 |
0.267 |
-0.281 |
0.779 |
1.000 |