Init
options(digits = 3)
library(pacman)
p_load(kirkegaard, readxl, rms, gganimate, lubridate, animation)
Data
National IQs
#dataset
active_data = "data/NIQ-DATASET-V1.3.2/N-IQ-DATA (V1.3.2).xlsx"
#which sheets?
readxl::excel_sheets(active_data)
## [1] "INF" "FAV" "REC" "SEL" "CAL"
## [6] "NAT" "GEO" "NORM" "FEC" "STAT(REC)"
## [11] "STAT(NAT)" "REF"
#load sheets to mainspace
for (s in readxl::excel_sheets(active_data)) {
assign(x = s %>% str_legalize(),
value = readxl::read_excel(active_data, sheet = s))
}
## New names:
## * `` -> ...4
## * `` -> ...5
## * `` -> ...6
## * `` -> ...7
## * `` -> ...8
## * … and 4 more problems
## New names:
## * `` -> ...2
## * `` -> ...3
## * `` -> ...4
## * `` -> ...5
## * `` -> ...7
## * … and 36 more problems
## New names:
## * `` -> ...2
## * `` -> ...3
## * `` -> ...4
## * `` -> ...5
## * `` -> ...7
## * … and 36 more problems
## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting logical in O1564 / R1564C15: got 'All'
## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting logical in N1565 / R1565C14: got 'Raw scores (WISC-
## R(DEU)1989)'
## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting logical in O1565 / R1565C15: got 'N'
## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting logical in P1565 / R1565C16: got 'Raw scores (WISC-
## R(DEU)1989)'
## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting logical in N2749 / R2749C14: got 'Mean SPM+ scores'
## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting logical in O2749 / R2749C15: got 'SD SPM+ scores'
## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting logical in P2749 / R2749C16: got 'LL SPM+ scores'
## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting logical in Q2749 / R2749C17: got 'UL SPM+ scores'
## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting logical in R2749 / R2749C18: got 'IQ (CPM(GBR)2007)'
## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting logical in N2765 / R2765C14: got 'Mean SPM+ scores'
## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting logical in O2765 / R2765C15: got 'SD SPM+ scores'
## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting logical in P2765 / R2765C16: got 'LL SPM+ scores'
## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting logical in Q2765 / R2765C17: got 'UL SPM+ scores'
## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting logical in R2765 / R2765C18: got 'IQ (CPM(GBR)2007)'
## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting logical in N2782 / R2782C14: got 'Mean SPM+ scores'
## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting logical in O2782 / R2782C15: got 'SD SPM+ scores'
## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting logical in P2782 / R2782C16: got 'LL SPM+ scores'
## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting logical in Q2782 / R2782C17: got 'UL SPM+ scores'
## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i =
## sheet, : Expecting logical in R2782 / R2782C18: got 'IQ (CPM(GBR)2007)'
## New names:
## * `` -> ...2
## * `` -> ...3
## * `` -> ...4
## * `` -> ...5
## * `` -> ...6
## * … and 12 more problems
## New names:
## * `` -> ...2
## * Demographics -> Demographics...3
## * `` -> ...5
## * `` -> ...7
## * `` -> ...8
## * … and 789 more problems
## New names:
## * `` -> ...2
## * `` -> ...6
## * `` -> ...7
## * `` -> ...8
## * `` -> ...9
## * … and 10 more problems
## New names:
## * `` -> ...2
## * `` -> ...4
## * `` -> ...5
## * `` -> ...6
## * `` -> ...7
## * … and 352 more problems
## New names:
## * `` -> ...2
## * `` -> ...3
## * `` -> ...4
## * `` -> ...5
## * `` -> ...6
## * … and 120 more problems
## New names:
## * `` -> ...5
## * `` -> ...7
## * `` -> ...8
## * `` -> ...10
## * `` -> ...11
## New names:
## * `` -> ...5
## * `` -> ...7
## * `` -> ...8
## * `` -> ...10
## * `` -> ...11
##FAV - national level results
colnames(FAV)[4:12] = FAV[1, 4:12] %>% str_replace_all("&", "") %>% str_legalize()
FAV = FAV[-1, ]
#remove non-country rows
FAV = FAV[-c(which(FAV$Identification == "M"):nrow(FAV)), ] %>% map_df(parse_guess) %>% df_legalize_names()
##REC - sample level results
colnames(REC) = REC[1, ] %>% str_legalize()
REC = REC[-1, ]
REC = REC[-c(which(REC$ID == "M"):nrow(REC)), ] %>% map_df(function(x) {
if (is.character(x)) return(parse_guess(x))
x
})
Other national data
#mega compilation
mega = read_csv2("data/Megadataset_v2.0m.csv")
## Using ',' as decimal and '.' as grouping mark. Use read_delim() for more control.
## Parsed with column specification:
## cols(
## .default = col_number(),
## ID = col_character(),
## MalePercentDenmark2012 = col_character(),
## EthnicHeterogenityVanhanen2012 = col_double(),
## EthnicConflictVanhanen2012 = col_double(),
## HDI1980 = col_character(),
## HDI1990 = col_character(),
## HDI2000 = col_character(),
## HDI2010 = col_character(),
## HDI2013 = col_character(),
## SlowTimePrefWangetal2011 = col_double(),
## NorwayPusnishments2011 = col_character(),
## NorwayPusnishments2012 = col_character(),
## NorwayPunishedPersons2011 = col_character(),
## NorwayPunishedPersons2012 = col_character(),
## AlcoholConsumptionPerCapitaWHO = col_character(),
## Math00Mean = col_double(),
## Math00SD = col_double(),
## Read00Mean = col_double(),
## Read00SD = col_double(),
## Sci00Mean = col_double()
## # ... with 209 more columns
## )
## See spec(...) for full column specifications.
#join to national IQs
FAV = left_join(FAV, mega %>% select(ID, HDI2013), by = c("Identification" = "ID"))
## Warning: Column `Identification`/`ID` has different attributes on LHS and
## RHS of join
FAV$Identification %>% duplicated() %>% any() #no dups!
## [1] FALSE
#HDI
#http://hdr.undp.org/en/data#
HDI = read_csv("data/Human Development Index (HDI).csv", skip = 1)
## Warning: Missing column names filled in: 'X4' [4], 'X6' [6], 'X8' [8],
## 'X10' [10], 'X12' [12], 'X14' [14], 'X16' [16], 'X18' [18], 'X20' [20],
## 'X22' [22], 'X24' [24], 'X26' [26], 'X28' [28], 'X30' [30], 'X32' [32],
## 'X34' [34], 'X36' [36], 'X38' [38], 'X40' [40], 'X42' [42], 'X44' [44],
## 'X46' [46], 'X48' [48], 'X50' [50], 'X52' [52], 'X54' [54], 'X56' [56]
## Parsed with column specification:
## cols(
## .default = col_double(),
## Country = col_character(),
## X4 = col_logical(),
## X6 = col_logical(),
## X8 = col_logical(),
## X10 = col_logical(),
## X12 = col_logical(),
## X14 = col_logical(),
## X16 = col_logical(),
## X18 = col_logical(),
## X20 = col_logical(),
## X22 = col_logical(),
## X24 = col_logical(),
## X26 = col_logical(),
## X28 = col_logical(),
## X30 = col_logical(),
## X32 = col_logical(),
## X34 = col_logical(),
## X36 = col_logical(),
## X38 = col_logical(),
## X40 = col_logical()
## # ... with 8 more columns
## )
## See spec(...) for full column specifications.
HDI = HDI[names(HDI)[!str_detect(names(HDI), "^X")]]
names(HDI)[3:ncol(HDI)] = "HDI_" + names(HDI)[3:ncol(HDI)]
HDI$ISO3 = pu_translate(HDI$Country)
#join to national IQs
FAV = left_join(FAV, HDI %>% select(ISO3, HDI_1990:HDI_2017), by = c("Identification" = "ISO3"))
## Warning: Column `Identification`/`ISO3` has different attributes on LHS and
## RHS of join
FAV$Identification %>% duplicated() %>% any() #no dups!
## [1] FALSE
#putterman
putterman = read_excel("data/Putterman matrix version 1p1.xls")
#calculate European
euro_homelands = c("Albania", "Austria", "Belarus", "Belgium", "Bosnia and Herzegovina", "Bulgaria", "Croatia", "Cyprus", "Czech Republic", "Denmark", "Estonia", "Finland", "France", "Germany", "Greece", "Hungary", "Iceland", "Ireland", "Italy", "Latvia", "Lithuania", "Luxembourg", "Macedonia", "Malta", "Moldova", "Netherlands", "Norway", "Poland", "Portugal", "Romania", "Russia", "Serbia and Montenegro", "Slovakia", "Slovenia", "Spain", "Sweden", "Switzerland", "Ukraine", "United Kingdom")
euro_homelands_abbr = putterman %>% filter(wbname %in% euro_homelands) %>% pull(wbcode)
#African
african_homelands = c("Angola", "Belize", "Benin", "Botswana", "Burkina Faso", "Burundi", "Cameroon", "Central African Republic", "Chad", "Comoros", "Congo, Dem. Rep.", "Congo, Rep.", "Cote d'Ivoire", "Equatorial Guinea", "Eritrea", "Ethiopia", "Gabon", "Gambia", "Ghana", "Guinea", "Guinea-Bissau", "Lesotho", "Liberia", "Malawi", "Mali", "Mauritania", "Mozambique", "Namibia", "Niger", "Nigeria", "Rwanda", "Senegal", "Sierra Leone", "Somalia", "South Africa", "Sudan", "Swaziland", "Tanzania", "Togo", "Uganda", "Zambia", "Zimbabwe")
african_homelands_abbr = putterman %>% filter(wbname %in% african_homelands) %>% pull(wbcode)
#calculate European fraction
putterman$european = putterman %>% select(!!str_to_lower(euro_homelands_abbr)) %>% rowSums()
putterman$african = putterman %>% select(!!str_to_lower(african_homelands_abbr)) %>% rowSums()
#make new ISO, some of existing are wrong
putterman$wbname[76] = "Israel" #matching is incorrectly done to Palestine otherwise
putterman$ISO3 = pu_translate(putterman$wbname)
## No exact match: Hong Kong, China
## No exact match: Korea, Dem. Rep. (North)
## No exact match: Korea, Rep. (South)
## Best fuzzy match found: Hong Kong, China -> Hong Kong-China with distance 2.00
## Best fuzzy match found: Korea, Dem. Rep. (North) -> Korea, Dem. Rep. with distance 8.00
## Best fuzzy match found: Korea, Rep. (South) -> Korea, South with distance 7.00
putterman$ISO3 %>% duplicated() %>% any() #no dups!
## [1] FALSE
#join to national IQs
FAV = left_join(FAV, putterman %>% select(ISO3, european, african), by = c("Identification" = "ISO3"))
## Warning: Column `Identification`/`ISO3` has different attributes on LHS and
## RHS of join
FAV$Identification %>% duplicated() %>% any() #no dups!
## [1] FALSE
New and old values
#plot new and old
GG_scatter(FAV, "QNW_SAS_GEO", "LV12_GEO", case_names = "Identification") +
xlab("Lynn & Becker 2019") +
ylab("Lynn & Vanhanen 2012")
GG_save("figs/new_old.png")
#winsorize new
FAV$QNW_SAS_GEO_winsor = FAV$QNW_SAS_GEO %>% winsorise(lower = 60, upper = 999)
GG_scatter(FAV, "QNW_SAS_GEO_winsor", "LV12_GEO", case_names = "Identification")
#changes and ancestry
FAV$change = FAV$QNW_SAS_GEO - FAV$LV12_GEO
GG_scatter(FAV, "european", "change", case_names = "Identification")
wtd.cor(FAV$change, FAV$european)
## correlation std.err t.value p.value
## Y 0.11 0.0791 1.39 0.165
GG_scatter(FAV, "african", "change", case_names = "Identification")
wtd.cor(FAV$change, FAV$african)
## correlation std.err t.value p.value
## Y -0.0266 0.0795 -0.335 0.738
#HDI
GG_scatter(FAV, "HDI_2017", "change", case_names = "Identification")
wtd.cor(FAV$change, FAV$HDI_2017)
## correlation std.err t.value p.value
## Y 0.175 0.073 2.4 0.0174
#quantile regression
Rq(change ~ HDI_2017, data = FAV, tau = .75)
## Frequencies of Missing Values Due to Each Variable
## change HDI_2017
## 6 16
##
## Quantile Regression tau: 0.75
##
## Rq(formula = change ~ HDI_2017, tau = 0.75, data = FAV)
##
##
## Discrimination
## Index
## Obs 184 g 0.750
## p 2
## Residual d.f. 182
## mean |Y-Yhat| 5.56
##
## Coef S.E. t Pr(>|t|)
## Intercept 4.2862 3.1556 1.36 0.1760
## HDI_2017 -4.3335 3.6595 -1.18 0.2379
##
Rq(change ~ HDI_2017, data = FAV, tau = .25)
## Warning in quantreg::summary.rq(fit, covariance = TRUE, se = se, hs = hs):
## 1 non-positive fis
## Frequencies of Missing Values Due to Each Variable
## change HDI_2017
## 6 16
##
## Quantile Regression tau: 0.25
##
## Rq(formula = change ~ HDI_2017, tau = 0.25, data = FAV)
##
##
## Discrimination
## Index
## Obs 184 g 3.877
## p 2
## Residual d.f. 182
## mean |Y-Yhat| 5.84
##
## Coef S.E. t Pr(>|t|)
## Intercept -22.0741 3.5237 -6.26 <0.0001
## HDI_2017 22.3909 3.8684 5.79 <0.0001
##
HDI and IQ across time
#access data in ggplot
#new IQs
FAV %>%
gather(key = "year", value = "HDI", HDI_1990:HDI_2017) %>%
mutate(year = year %>% str_replace("HDI_", "") %>% as.Date("%Y")) %>%
ggplot(aes(QNW_SAS_GEO, HDI)) +
transition_time(year) +
geom_point() +
theme_bw() +
labs(x = 'National IQ\n(Lynn and Becker 2019)', y = 'HDI') +
ggtitle(
"Meritocracy of the world: national IQ and HDI",
subtitle = "Year: {format(frame_time, '%Y-%m')}"
)
## Warning: Removed 61 rows containing missing values (geom_point).
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gganimate::anim_save("figs/HDI_IQb.gif")
#seems not possible to do the correlation in each frame with gganimate
#but we can use oldschool approach
frames = list()
FAV_HDI_long = FAV %>%
gather(key = "year", value = "HDI", HDI_1990:HDI_2017) %>%
mutate(year = year %>% str_replace("HDI_", ""),
year_date = year %>% as.Date("%Y"))
for (y in unique(FAV_HDI_long$year)) {
y_data = frames[[y]] = FAV_HDI_long %>%
filter(year == y)
y_cor = wtd.cor(y_data$QNW_SAS_GEO, y_data$HDI)
y_plot = y_data %>%
ggplot(aes(QNW_SAS_GEO, HDI)) +
geom_point() +
geom_smooth(method = lm, se = F) +
theme_bw() +
ggtitle(
"National IQ and HDI over time",
str_glue("Year: {format(y_data$year_date[1], '%Y')}\nr = {format(round(y_cor, 3), digits = 3, nsmall = 3, scientific = F)}")
)
frames[[y]] = y_plot
}
#make a gif
saveGIF({
walk(frames, print)
}, interval = 0.5, movie.name = "IQ_HDI.gif")
## Warning: Removed 61 rows containing non-finite values (stat_smooth).
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## Warning: Removed 19 rows containing non-finite values (stat_smooth).
## Warning: Removed 19 rows containing missing values (geom_point).
## Warning: Removed 19 rows containing non-finite values (stat_smooth).
## Warning: Removed 19 rows containing missing values (geom_point).
## Warning: Removed 19 rows containing non-finite values (stat_smooth).
## Warning: Removed 19 rows containing missing values (geom_point).
## Warning: Removed 19 rows containing non-finite values (stat_smooth).
## Warning: Removed 19 rows containing missing values (geom_point).
## Warning: Removed 19 rows containing non-finite values (stat_smooth).
## Warning: Removed 19 rows containing missing values (geom_point).
## Warning: Removed 19 rows containing non-finite values (stat_smooth).
## Warning: Removed 19 rows containing missing values (geom_point).
## Warning: Removed 19 rows containing non-finite values (stat_smooth).
## Warning: Removed 19 rows containing missing values (geom_point).
## Warning: Removed 18 rows containing non-finite values (stat_smooth).
## Warning: Removed 18 rows containing missing values (geom_point).
## Output at: IQ_HDI.gif
## [1] TRUE
Missing/imputed data
#is missing
FAV$imputed = is.na(FAV$QNW)
FAV$imputed %>% table2()
#what predicts imputed
lrm(imputed ~ standardize(QNW_SAS_GEO), data = FAV)
## Frequencies of Missing Values Due to Each Variable
## imputed QNW_SAS_GEO
## 0 2
##
## Logistic Regression Model
##
## lrm(formula = imputed ~ standardize(QNW_SAS_GEO), data = FAV)
##
##
## Model Likelihood Discrimination Rank Discrim.
## Ratio Test Indexes Indexes
## Obs 201 LR chi2 6.57 R2 0.044 C 0.617
## FALSE 130 d.f. 1 g 0.430 Dxy 0.234
## TRUE 71 Pr(> chi2) 0.0104 gr 1.537 gamma 0.235
## max |deriv| 4e-08 gp 0.097 tau-a 0.108
## Brier 0.222
##
## Coef S.E. Wald Z Pr(>|Z|)
## Intercept -0.6238 0.1507 -4.14 <0.0001
## QNW_SAS_GEO -0.3816 0.1514 -2.52 0.0117
##
lrm(imputed ~ standardize(R), data = FAV)
## Frequencies of Missing Values Due to Each Variable
## imputed R
## 0 4
##
## Logistic Regression Model
##
## lrm(formula = imputed ~ standardize(R), data = FAV)
##
##
## Model Likelihood Discrimination Rank Discrim.
## Ratio Test Indexes Indexes
## Obs 199 LR chi2 17.75 R2 0.117 C 0.676
## FALSE 129 d.f. 1 g 0.757 Dxy 0.352
## TRUE 70 Pr(> chi2) <0.0001 gr 2.132 gamma 0.353
## max |deriv| 2e-11 gp 0.162 tau-a 0.161
## Brier 0.210
##
## Coef S.E. Wald Z Pr(>|Z|)
## Intercept -0.6718 0.1580 -4.25 <0.0001
## R -0.6586 0.1644 -4.00 <0.0001
##
Effect sizes
#OLS
(sample_ols_1 = ols(IQ_cor ~ Country_name, data = REC))
## Linear Regression Model
##
## ols(formula = IQ_cor ~ Country_name, data = REC)
##
## Model Likelihood Discrimination
## Ratio Test Indexes
## Obs 669 LR chi2 876.93 R2 0.730
## sigma8.7435 d.f. 129 R2 adj 0.666
## d.f. 539 Pr(> chi2) 0.0000 g 14.165
##
## Residuals
##
## Min 1Q Median 3Q Max
## -3.421e+01 -4.565e+00 3.527e-15 4.150e+00 2.801e+01
##
##
## Coef S.E. t
## Intercept 75.1000 8.7435 8.59
## Country_name=Argentina 19.8594 9.5781 2.07
## Country_name=Australia 23.6386 9.4441 2.50
## Country_name=Austria 25.0006 9.7756 2.56
## Country_name=Bahamas, The 9.1200 10.7086 0.85
## Country_name=Bahrain 11.7191 12.3652 0.95
## Country_name=Bangladesh 1.7104 9.5781 0.18
## Country_name=Barbados 16.2658 10.7086 1.52
## Country_name=Belarus 26.5000 10.7086 2.47
## Country_name=Belgium 20.0181 10.0962 1.98
## Country_name=Benin -4.7410 12.3652 -0.38
## Country_name=Bermuda 17.3475 9.7756 1.77
## Country_name=Bolivia 8.6867 10.0962 0.86
## Country_name=Bosnia and Herzegovina 15.8236 10.7086 1.48
## Country_name=Botswana 0.9616 12.3652 0.08
## Country_name=Brazil 12.7651 8.9316 1.43
## Country_name=Bulgaria 11.7386 10.7086 1.10
## Country_name=Burkina Faso -1.3000 12.3652 -0.11
## Country_name=Cambodia 4.8880 10.0962 0.48
## Country_name=Canada 16.0498 9.1703 1.75
## Country_name=Chile 12.7988 9.5781 1.34
## Country_name=China 29.2461 9.4441 3.10
## Country_name=Colombia 4.2728 10.0962 0.42
## Country_name=Congo, Democratic Republic of the -11.9531 9.2739 -1.29
## Country_name=Congo, Republic of the -12.1337 12.3652 -0.98
## Country_name=Costa Rica 12.3510 10.7086 1.15
## Country_name=Croatia 17.7766 10.0962 1.76
## Country_name=Cuba 7.7202 10.0962 0.76
## Country_name=Cyprus 20.4145 10.7086 1.91
## Country_name=Czechia 15.5200 12.3652 1.26
## Country_name=Denmark 19.1033 10.7086 1.78
## Country_name=Djibouti -22.9622 12.3652 -1.86
## Country_name=Dominica -9.0621 10.7086 -0.85
## Country_name=Dominican Republic 14.0500 12.3652 1.14
## Country_name=Ecuador 1.6420 9.7756 0.17
## Country_name=Egypt 5.6166 9.2165 0.61
## Country_name=Eritrea -3.2499 9.7756 -0.33
## Country_name=Estonia 24.0494 10.0962 2.38
## Country_name=Ethiopia -6.2715 9.4441 -0.66
## Country_name=Finland 21.0282 10.0962 2.08
## Country_name=France 23.4128 9.5781 2.44
## Country_name=Gambia, The -24.5286 9.4441 -2.60
## Country_name=Gaza Strip 6.0586 9.5781 0.63
## Country_name=Germany 25.5998 9.1006 2.81
## Country_name=Ghana -10.4404 9.5781 -1.09
## Country_name=Greece 11.3645 10.0962 1.13
## Country_name=Guatemala -24.3866 9.3472 -2.61
## Country_name=Haiti -4.0574 10.7086 -0.38
## Country_name=Hong Kong 35.2129 9.2739 3.80
## Country_name=Hungary 19.9435 10.7086 1.86
## Country_name=Iceland 24.8933 10.7086 2.32
## Country_name=India -0.5450 8.9594 -0.06
## Country_name=Indonesia 4.6032 8.9831 0.51
## Country_name=Iran 4.2992 10.0962 0.43
## Country_name=Iraq 12.7417 10.0962 1.26
## Country_name=Ireland 14.4907 10.0962 1.44
## Country_name=Israel 17.0813 9.4441 1.81
## Country_name=Italy 17.1852 9.5781 1.79
## Country_name=Jamaica -1.4095 9.0126 -0.16
## Country_name=Japan 33.2499 9.4441 3.52
## Country_name=Jordan 2.7514 9.7756 0.28
## Country_name=Kazakhstan 14.3917 10.0962 1.43
## Country_name=Kenya -1.2859 9.2739 -0.14
## Country_name=Korea, South 26.4094 9.7756 2.70
## Country_name=Kuwait 13.9260 9.7756 1.42
## Country_name=Kyrgyzstan 11.8356 12.3652 0.96
## Country_name=Laos 10.3438 9.7756 1.06
## Country_name=Latvia 16.0446 12.3652 1.30
## Country_name=Lebanon 8.0000 10.7086 0.75
## Country_name=Libya 3.3663 9.2165 0.37
## Country_name=Lithuania 18.3612 10.0962 1.82
## Country_name=Malawi -5.3960 12.3652 -0.44
## Country_name=Malaysia 10.9500 12.3652 0.89
## Country_name=Mali -15.3400 12.3652 -1.24
## Country_name=Malta 16.8341 10.0962 1.67
## Country_name=Marshall Isands 8.8600 12.3652 0.72
## Country_name=Mauritius 11.7200 12.3652 0.95
## Country_name=Mexico 15.2366 9.7756 1.56
## Country_name=Mongolia 24.2580 12.3652 1.96
## Country_name=Morocco -1.5988 9.3472 -0.17
## Country_name=Namibia -8.9056 12.3652 -0.72
## Country_name=Nepal -32.3089 9.2165 -3.51
## Country_name=Netherlands 25.7898 9.1323 2.82
## Country_name=Netherlands Antilles 4.9145 12.3652 0.40
## Country_name=New Zealand 21.2174 9.5781 2.22
## Country_name=Nicaragua -19.0708 10.0962 -1.89
## Country_name=Nigeria -2.4396 9.1703 -0.27
## Country_name=Norway 22.8251 10.0962 2.26
## Country_name=Oman 10.0610 9.4441 1.07
## Country_name=Pakistan 6.7564 9.2739 0.73
## Country_name=Peru 6.4905 9.2165 0.70
## Country_name=Philippines 15.0084 10.0962 1.49
## Country_name=Poland 22.1480 9.2165 2.40
## Country_name=Portugal 14.7881 10.0962 1.46
## Country_name=Puerto Rico 12.8731 8.9400 1.44
## Country_name=Qatar 10.4762 12.3652 0.85
## Country_name=Romania 13.3337 10.0962 1.32
## Country_name=Russia 17.1348 10.0962 1.70
## Country_name=Saint Vincent and the Grenadines -11.6791 12.3652 -0.94
## Country_name=Saudi Arabia 3.4728 9.2739 0.37
## Country_name=Serbia 12.1813 9.1006 1.34
## Country_name=Seychelles 3.6600 12.3652 0.30
## Country_name=Sierra Leone -29.5944 10.7086 -2.76
## Country_name=Singapore 26.8931 9.5781 2.81
## Country_name=Slovakia 20.2182 12.3652 1.64
## Country_name=Slovenia 22.4635 9.3472 2.40
## Country_name=Somalia -7.4251 12.3652 -0.60
## Country_name=South Africa -0.0567 9.0303 -0.01
## Country_name=South Sudan -15.4788 9.5781 -1.62
## Country_name=Spain 17.1934 9.3472 1.84
## Country_name=Sri Lanka 16.2137 10.0962 1.61
## Country_name=Sudan 1.9575 8.9831 0.22
## Country_name=Sweden 20.2800 10.7086 1.89
## Country_name=Switzerland 23.2715 9.5781 2.43
## Country_name=Syria -2.2455 9.4441 -0.24
## Country_name=Taiwan 31.8419 9.3472 3.41
## Country_name=Tajikistan 12.6148 12.3652 1.02
## Country_name=Tanzania -3.7693 9.5781 -0.39
## Country_name=Thailand 11.9458 8.9970 1.33
## Country_name=Turkey 11.5569 12.3652 0.93
## Country_name=Uganda -7.3680 10.0962 -0.73
## Country_name=Ukraine 13.5066 12.3652 1.09
## Country_name=United Arab Emirates 4.3752 12.3652 0.35
## Country_name=United Kingdom 21.8468 9.4441 2.31
## Country_name=United States 17.6365 8.8186 2.00
## Country_name=Uzbekistan 13.9064 12.3652 1.12
## Country_name=Venezuela 5.9467 10.0962 0.59
## Country_name=Vietnam 2.2943 10.0962 0.23
## Country_name=Yemen -1.9309 10.7086 -0.18
## Country_name=Zimbabwe -1.0938 12.3652 -0.09
## Pr(>|t|)
## Intercept <0.0001
## Country_name=Argentina 0.0386
## Country_name=Australia 0.0126
## Country_name=Austria 0.0108
## Country_name=Bahamas, The 0.3948
## Country_name=Bahrain 0.3437
## Country_name=Bangladesh 0.8583
## Country_name=Barbados 0.1294
## Country_name=Belarus 0.0136
## Country_name=Belgium 0.0479
## Country_name=Benin 0.7016
## Country_name=Bermuda 0.0765
## Country_name=Bolivia 0.3900
## Country_name=Bosnia and Herzegovina 0.1401
## Country_name=Botswana 0.9380
## Country_name=Brazil 0.1535
## Country_name=Bulgaria 0.2735
## Country_name=Burkina Faso 0.9163
## Country_name=Cambodia 0.6285
## Country_name=Canada 0.0807
## Country_name=Chile 0.1820
## Country_name=China 0.0021
## Country_name=Colombia 0.6723
## Country_name=Congo, Democratic Republic of the 0.1980
## Country_name=Congo, Republic of the 0.3269
## Country_name=Costa Rica 0.2493
## Country_name=Croatia 0.0789
## Country_name=Cuba 0.4448
## Country_name=Cyprus 0.0571
## Country_name=Czechia 0.2100
## Country_name=Denmark 0.0750
## Country_name=Djibouti 0.0639
## Country_name=Dominica 0.3978
## Country_name=Dominican Republic 0.2564
## Country_name=Ecuador 0.8667
## Country_name=Egypt 0.5425
## Country_name=Eritrea 0.7397
## Country_name=Estonia 0.0176
## Country_name=Ethiopia 0.5069
## Country_name=Finland 0.0377
## Country_name=France 0.0148
## Country_name=Gambia, The 0.0097
## Country_name=Gaza Strip 0.5273
## Country_name=Germany 0.0051
## Country_name=Ghana 0.2762
## Country_name=Greece 0.2608
## Country_name=Guatemala 0.0093
## Country_name=Haiti 0.7049
## Country_name=Hong Kong 0.0002
## Country_name=Hungary 0.0631
## Country_name=Iceland 0.0205
## Country_name=India 0.9515
## Country_name=Indonesia 0.6086
## Country_name=Iran 0.6704
## Country_name=Iraq 0.2075
## Country_name=Ireland 0.1518
## Country_name=Israel 0.0711
## Country_name=Italy 0.0733
## Country_name=Jamaica 0.8758
## Country_name=Japan 0.0005
## Country_name=Jordan 0.7785
## Country_name=Kazakhstan 0.1546
## Country_name=Kenya 0.8898
## Country_name=Korea, South 0.0071
## Country_name=Kuwait 0.1549
## Country_name=Kyrgyzstan 0.3389
## Country_name=Laos 0.2905
## Country_name=Latvia 0.1950
## Country_name=Lebanon 0.4554
## Country_name=Libya 0.7151
## Country_name=Lithuania 0.0695
## Country_name=Malawi 0.6627
## Country_name=Malaysia 0.3763
## Country_name=Mali 0.2153
## Country_name=Malta 0.0960
## Country_name=Marshall Isands 0.4740
## Country_name=Mauritius 0.3436
## Country_name=Mexico 0.1197
## Country_name=Mongolia 0.0503
## Country_name=Morocco 0.8643
## Country_name=Namibia 0.4717
## Country_name=Nepal 0.0005
## Country_name=Netherlands 0.0049
## Country_name=Netherlands Antilles 0.6912
## Country_name=New Zealand 0.0272
## Country_name=Nicaragua 0.0594
## Country_name=Nigeria 0.7903
## Country_name=Norway 0.0242
## Country_name=Oman 0.2872
## Country_name=Pakistan 0.4666
## Country_name=Peru 0.4816
## Country_name=Philippines 0.1377
## Country_name=Poland 0.0166
## Country_name=Portugal 0.1436
## Country_name=Puerto Rico 0.1505
## Country_name=Qatar 0.3972
## Country_name=Romania 0.1872
## Country_name=Russia 0.0902
## Country_name=Saint Vincent and the Grenadines 0.3453
## Country_name=Saudi Arabia 0.7082
## Country_name=Serbia 0.1813
## Country_name=Seychelles 0.7674
## Country_name=Sierra Leone 0.0059
## Country_name=Singapore 0.0052
## Country_name=Slovakia 0.1026
## Country_name=Slovenia 0.0166
## Country_name=Somalia 0.5484
## Country_name=South Africa 0.9950
## Country_name=South Sudan 0.1067
## Country_name=Spain 0.0664
## Country_name=Sri Lanka 0.1089
## Country_name=Sudan 0.8276
## Country_name=Sweden 0.0588
## Country_name=Switzerland 0.0154
## Country_name=Syria 0.8121
## Country_name=Taiwan 0.0007
## Country_name=Tajikistan 0.3081
## Country_name=Tanzania 0.6941
## Country_name=Thailand 0.1848
## Country_name=Turkey 0.3504
## Country_name=Uganda 0.4658
## Country_name=Ukraine 0.2752
## Country_name=United Arab Emirates 0.7236
## Country_name=United Kingdom 0.0211
## Country_name=United States 0.0460
## Country_name=Uzbekistan 0.2612
## Country_name=Venezuela 0.5561
## Country_name=Vietnam 0.8203
## Country_name=Yemen 0.8570
## Country_name=Zimbabwe 0.9295
##
(sample_ols_2 = ols(IQ_cor ~ Country_name + Test_meas, data = REC))
## Linear Regression Model
##
## ols(formula = IQ_cor ~ Country_name + Test_meas, data = REC)
##
## Model Likelihood Discrimination
## Ratio Test Indexes
## Obs 669 LR chi2 944.08 R2 0.756
## sigma8.5154 d.f. 154 R2 adj 0.683
## d.f. 514 Pr(> chi2) 0.0000 g 14.489
##
## Residuals
##
## Min 1Q Median 3Q Max
## -3.488e+01 -3.953e+00 -4.427e-14 4.064e+00 2.670e+01
##
##
## Coef S.E. t
## Intercept 76.7936 9.5271 8.06
## Country_name=Argentina 26.0304 10.0854 2.58
## Country_name=Australia 26.8296 9.9787 2.69
## Country_name=Austria 31.8982 10.2621 3.11
## Country_name=Bahamas, The 17.6920 12.8184 1.38
## Country_name=Bahrain 23.3611 12.8184 1.82
## Country_name=Bangladesh 1.4861 11.0445 0.13
## Country_name=Barbados 18.6384 11.6253 1.60
## Country_name=Belarus 23.4926 13.0876 1.80
## Country_name=Belgium 28.4513 10.6012 2.68
## Country_name=Benin -1.2225 12.8961 -0.09
## Country_name=Bermuda 25.6990 10.3854 2.47
## Country_name=Bolivia 15.2718 10.7830 1.42
## Country_name=Bosnia and Herzegovina 22.3529 11.1161 2.01
## Country_name=Botswana 7.4909 12.6421 0.59
## Country_name=Brazil 17.5012 9.5198 1.84
## Country_name=Bulgaria 21.5331 11.2662 1.91
## Country_name=Burkina Faso 6.4526 12.7301 0.51
## Country_name=Cambodia 11.5020 10.5957 1.09
## Country_name=Canada 20.4168 9.4441 2.16
## Country_name=Chile 21.6427 10.2068 2.12
## Country_name=China 31.8667 10.1068 3.15
## Country_name=Colombia 13.1708 10.6448 1.24
## Country_name=Congo, Democratic Republic of the -5.6455 10.0876 -0.56
## Country_name=Congo, Republic of the -5.6044 12.6421 -0.44
## Country_name=Costa Rica 17.9843 11.1133 1.62
## Country_name=Croatia 25.3943 10.5599 2.40
## Country_name=Cuba 14.2495 10.5585 1.35
## Country_name=Cyprus 26.9438 11.1161 2.42
## Country_name=Czechia 25.3145 12.7744 1.98
## Country_name=Denmark 27.2652 11.1364 2.45
## Country_name=Djibouti -19.4437 12.8961 -1.51
## Country_name=Dominica -2.5328 11.1161 -0.23
## Country_name=Dominican Republic 15.4616 12.9287 1.20
## Country_name=Ecuador 6.3795 10.2916 0.62
## Country_name=Egypt 11.4854 9.7567 1.18
## Country_name=Eritrea 2.8314 10.2632 0.28
## Country_name=Estonia 30.5787 10.5585 2.90
## Country_name=Ethiopia -0.3395 9.9643 -0.03
## Country_name=Finland 26.9679 10.6242 2.54
## Country_name=France 29.9534 9.8191 3.05
## Country_name=Gambia, The -19.1939 9.9723 -1.92
## Country_name=Gaza Strip 12.1158 10.0804 1.20
## Country_name=Germany 30.8357 9.6650 3.19
## Country_name=Ghana -4.3332 10.0900 -0.43
## Country_name=Greece 22.6902 10.5766 2.15
## Country_name=Guatemala -18.1133 9.8788 -1.83
## Country_name=Haiti 1.5760 11.1133 0.14
## Country_name=Hong Kong 41.1224 9.8096 4.19
## Country_name=Hungary 26.6000 11.2192 2.37
## Country_name=Iceland 31.4226 11.1161 2.83
## Country_name=India 5.4966 9.5221 0.58
## Country_name=Indonesia 9.7389 9.5657 1.02
## Country_name=Iran 10.2313 10.5534 0.97
## Country_name=Iraq 18.6737 10.5534 1.77
## Country_name=Ireland 22.3305 10.7043 2.09
## Country_name=Israel 23.3119 9.9654 2.34
## Country_name=Italy 22.9340 10.1014 2.27
## Country_name=Jamaica 5.2458 9.5729 0.55
## Country_name=Japan 38.4753 10.0076 3.84
## Country_name=Jordan 5.1693 10.2992 0.50
## Country_name=Kazakhstan 17.9102 10.8614 1.65
## Country_name=Kenya 3.4516 9.8414 0.35
## Country_name=Korea, South 34.4612 10.2928 3.35
## Country_name=Kuwait 20.0073 10.2632 1.95
## Country_name=Kyrgyzstan 18.3649 12.6421 1.45
## Country_name=Laos 19.5802 10.4322 1.88
## Country_name=Latvia 22.5739 12.6421 1.79
## Country_name=Lebanon 15.7526 11.2160 1.40
## Country_name=Libya 9.0992 9.7613 0.93
## Country_name=Lithuania 25.4002 10.5707 2.40
## Country_name=Malawi -0.6585 12.6610 -0.05
## Country_name=Malaysia 15.6875 12.6610 1.24
## Country_name=Mali -8.8107 12.6421 -0.70
## Country_name=Malta 22.7661 10.5534 2.16
## Country_name=Marshall Isands 18.6545 12.7744 1.46
## Country_name=Mauritius 24.2606 12.7500 1.90
## Country_name=Mexico 21.3179 10.2632 2.08
## Country_name=Mongolia 28.9954 12.6610 2.29
## Country_name=Morocco 4.9305 9.8828 0.50
## Country_name=Namibia -4.1682 12.6610 -0.33
## Country_name=Nepal -27.5714 9.7901 -2.82
## Country_name=Netherlands 32.1927 9.5391 3.37
## Country_name=Netherlands Antilles 11.4437 12.6421 0.91
## Country_name=New Zealand 28.7254 10.1480 2.83
## Country_name=Nicaragua -14.0451 10.6068 -1.32
## Country_name=Nigeria 3.1188 9.7709 0.32
## Country_name=Norway 30.5433 10.6030 2.88
## Country_name=Oman 14.3239 9.9690 1.44
## Country_name=Pakistan 12.8377 9.8117 1.31
## Country_name=Peru 11.7620 9.7711 1.20
## Country_name=Philippines 17.9241 11.3707 1.58
## Country_name=Poland 25.4310 9.7894 2.60
## Country_name=Portugal 21.8085 10.5596 2.07
## Country_name=Puerto Rico 20.2181 9.5148 2.12
## Country_name=Qatar 17.0054 12.6421 1.35
## Country_name=Romania 16.8521 10.8614 1.55
## Country_name=Russia 23.6641 10.5585 2.24
## Country_name=Saint Vincent and the Grenadines -6.9417 12.6610 -0.55
## Country_name=Saudi Arabia 9.6258 9.8190 0.98
## Country_name=Serbia 18.6887 9.6500 1.94
## Country_name=Seychelles 15.3020 12.8184 1.19
## Country_name=Sierra Leone -24.8569 11.1376 -2.23
## Country_name=Singapore 31.7778 10.0899 3.15
## Country_name=Slovakia 24.9557 12.6610 1.97
## Country_name=Slovenia 27.3505 9.8816 2.77
## Country_name=Somalia -3.9066 12.8961 -0.30
## Country_name=South Africa 6.1579 9.5875 0.64
## Country_name=South Sudan -9.3079 10.0854 -0.92
## Country_name=Spain 21.8258 9.6761 2.26
## Country_name=Sri Lanka 20.5448 10.5980 1.94
## Country_name=Sudan 8.4045 9.5483 0.88
## Country_name=Sweden 30.9982 11.1805 2.77
## Country_name=Switzerland 29.1933 10.1064 2.89
## Country_name=Syria 4.2837 9.9698 0.43
## Country_name=Taiwan 37.1731 9.8840 3.76
## Country_name=Tajikistan 16.1332 12.8961 1.25
## Country_name=Tanzania 0.1549 10.0995 0.02
## Country_name=Thailand 17.3562 9.5648 1.81
## Country_name=Turkey 18.0862 12.6421 1.43
## Country_name=Uganda -2.6305 10.5811 -0.25
## Country_name=Ukraine 20.0359 12.6421 1.58
## Country_name=United Arab Emirates 9.1127 12.6610 0.72
## Country_name=United Kingdom 26.6539 9.9683 2.67
## Country_name=United States 25.7713 9.3962 2.74
## Country_name=Uzbekistan 17.4249 12.8961 1.35
## Country_name=Venezuela 17.5887 10.7689 1.63
## Country_name=Vietnam 7.0317 10.5811 0.66
## Country_name=Yemen 2.1971 11.1892 0.20
## Country_name=Zimbabwe 5.4355 12.6421 0.43
## Test_meas=APM- -3.3124 9.2177 -0.36
## Test_meas=CFT -11.4881 2.8677 -4.01
## Test_meas=CPM -6.4310 2.1512 -2.99
## Test_meas=CRT-C2 -1.2165 9.4121 -0.13
## Test_meas=KABC -8.5244 3.8293 -2.23
## Test_meas=MMSE 3.7803 8.6686 0.44
## Test_meas=NNAT -1.3666 10.8997 -0.13
## Test_meas=OLSAT -7.1956 12.4579 -0.58
## Test_meas=SBIS -10.2448 3.1495 -3.25
## Test_meas=SON-R -0.2881 8.9276 -0.03
## Test_meas=SPM -8.2229 2.0903 -3.93
## Test_meas=SPM+ -5.2121 3.1817 -1.64
## Test_meas=WAIS -3.1052 3.6847 -0.84
## Test_meas=WAIS-III -1.6936 4.2724 -0.40
## Test_meas=WAIS-IV -3.1890 4.6313 -0.69
## Test_meas=WAIS-R -10.5062 4.1976 -2.50
## Test_meas=WASI 1.3138 7.5420 0.17
## Test_meas=WASI-II -1.4149 8.8951 -0.16
## Test_meas=WISC -14.2342 2.8473 -5.00
## Test_meas=WISC-III -13.3356 3.1898 -4.18
## Test_meas=WISC-IV -7.2957 3.7677 -1.94
## Test_meas=WISC-R -9.4462 2.7172 -3.48
## Test_meas=WPPSI -8.8893 5.7494 -1.55
## Test_meas=WPPSI-III -4.2381 5.2833 -0.80
## Test_meas=WPPSI-R -14.0094 4.7495 -2.95
## Pr(>|t|)
## Intercept <0.0001
## Country_name=Argentina 0.0101
## Country_name=Australia 0.0074
## Country_name=Austria 0.0020
## Country_name=Bahamas, The 0.1681
## Country_name=Bahrain 0.0690
## Country_name=Bangladesh 0.8930
## Country_name=Barbados 0.1095
## Country_name=Belarus 0.0732
## Country_name=Belgium 0.0075
## Country_name=Benin 0.9245
## Country_name=Bermuda 0.0137
## Country_name=Bolivia 0.1573
## Country_name=Bosnia and Herzegovina 0.0449
## Country_name=Botswana 0.5538
## Country_name=Brazil 0.0666
## Country_name=Bulgaria 0.0565
## Country_name=Burkina Faso 0.6125
## Country_name=Cambodia 0.2782
## Country_name=Canada 0.0311
## Country_name=Chile 0.0344
## Country_name=China 0.0017
## Country_name=Colombia 0.2165
## Country_name=Congo, Democratic Republic of the 0.5760
## Country_name=Congo, Republic of the 0.6577
## Country_name=Costa Rica 0.1062
## Country_name=Croatia 0.0165
## Country_name=Cuba 0.1777
## Country_name=Cyprus 0.0157
## Country_name=Czechia 0.0480
## Country_name=Denmark 0.0147
## Country_name=Djibouti 0.1322
## Country_name=Dominica 0.8199
## Country_name=Dominican Republic 0.2323
## Country_name=Ecuador 0.5356
## Country_name=Egypt 0.2397
## Country_name=Eritrea 0.7828
## Country_name=Estonia 0.0039
## Country_name=Ethiopia 0.9728
## Country_name=Finland 0.0114
## Country_name=France 0.0024
## Country_name=Gambia, The 0.0548
## Country_name=Gaza Strip 0.2299
## Country_name=Germany 0.0015
## Country_name=Ghana 0.6678
## Country_name=Greece 0.0324
## Country_name=Guatemala 0.0673
## Country_name=Haiti 0.8873
## Country_name=Hong Kong <0.0001
## Country_name=Hungary 0.0181
## Country_name=Iceland 0.0049
## Country_name=India 0.5640
## Country_name=Indonesia 0.3091
## Country_name=Iran 0.3328
## Country_name=Iraq 0.0774
## Country_name=Ireland 0.0375
## Country_name=Israel 0.0197
## Country_name=Italy 0.0236
## Country_name=Jamaica 0.5839
## Country_name=Japan 0.0001
## Country_name=Jordan 0.6159
## Country_name=Kazakhstan 0.0998
## Country_name=Kenya 0.7259
## Country_name=Korea, South 0.0009
## Country_name=Kuwait 0.0518
## Country_name=Kyrgyzstan 0.1469
## Country_name=Laos 0.0611
## Country_name=Latvia 0.0748
## Country_name=Lebanon 0.1608
## Country_name=Libya 0.3517
## Country_name=Lithuania 0.0166
## Country_name=Malawi 0.9585
## Country_name=Malaysia 0.2159
## Country_name=Mali 0.4862
## Country_name=Malta 0.0314
## Country_name=Marshall Isands 0.1448
## Country_name=Mauritius 0.0576
## Country_name=Mexico 0.0383
## Country_name=Mongolia 0.0224
## Country_name=Morocco 0.6181
## Country_name=Namibia 0.7421
## Country_name=Nepal 0.0050
## Country_name=Netherlands 0.0008
## Country_name=Netherlands Antilles 0.3658
## Country_name=New Zealand 0.0048
## Country_name=Nicaragua 0.1860
## Country_name=Nigeria 0.7497
## Country_name=Norway 0.0041
## Country_name=Oman 0.1514
## Country_name=Pakistan 0.1913
## Country_name=Peru 0.2292
## Country_name=Philippines 0.1156
## Country_name=Poland 0.0097
## Country_name=Portugal 0.0394
## Country_name=Puerto Rico 0.0341
## Country_name=Qatar 0.1792
## Country_name=Romania 0.1214
## Country_name=Russia 0.0254
## Country_name=Saint Vincent and the Grenadines 0.5837
## Country_name=Saudi Arabia 0.3274
## Country_name=Serbia 0.0533
## Country_name=Seychelles 0.2331
## Country_name=Sierra Leone 0.0261
## Country_name=Singapore 0.0017
## Country_name=Slovakia 0.0493
## Country_name=Slovenia 0.0058
## Country_name=Somalia 0.7621
## Country_name=South Africa 0.5210
## Country_name=South Sudan 0.3565
## Country_name=Spain 0.0245
## Country_name=Sri Lanka 0.0531
## Country_name=Sudan 0.3792
## Country_name=Sweden 0.0058
## Country_name=Switzerland 0.0040
## Country_name=Syria 0.6676
## Country_name=Taiwan 0.0002
## Country_name=Tajikistan 0.2115
## Country_name=Tanzania 0.9878
## Country_name=Thailand 0.0702
## Country_name=Turkey 0.1531
## Country_name=Uganda 0.8038
## Country_name=Ukraine 0.1136
## Country_name=United Arab Emirates 0.4720
## Country_name=United Kingdom 0.0077
## Country_name=United States 0.0063
## Country_name=Uzbekistan 0.1772
## Country_name=Venezuela 0.1030
## Country_name=Vietnam 0.5066
## Country_name=Yemen 0.8444
## Country_name=Zimbabwe 0.6674
## Test_meas=APM- 0.7195
## Test_meas=CFT <0.0001
## Test_meas=CPM 0.0029
## Test_meas=CRT-C2 0.8972
## Test_meas=KABC 0.0264
## Test_meas=MMSE 0.6630
## Test_meas=NNAT 0.9003
## Test_meas=OLSAT 0.5638
## Test_meas=SBIS 0.0012
## Test_meas=SON-R 0.9743
## Test_meas=SPM <0.0001
## Test_meas=SPM+ 0.1020
## Test_meas=WAIS 0.3998
## Test_meas=WAIS-III 0.6920
## Test_meas=WAIS-IV 0.4914
## Test_meas=WAIS-R 0.0126
## Test_meas=WASI 0.8618
## Test_meas=WASI-II 0.8737
## Test_meas=WISC <0.0001
## Test_meas=WISC-III <0.0001
## Test_meas=WISC-IV 0.0534
## Test_meas=WISC-R 0.0006
## Test_meas=WPPSI 0.1227
## Test_meas=WPPSI-III 0.4228
## Test_meas=WPPSI-R 0.0033
##
(sample_ols_3 = ols(IQ_cor ~ Country_name + Test_meas + rcs(Year_meas), data = REC))
## Linear Regression Model
##
## ols(formula = IQ_cor ~ Country_name + Test_meas + rcs(Year_meas),
## data = REC)
##
## Model Likelihood Discrimination
## Ratio Test Indexes
## Obs 669 LR chi2 949.40 R2 0.758
## sigma8.5148 d.f. 158 R2 adj 0.683
## d.f. 510 Pr(> chi2) 0.0000 g 14.518
##
## Residuals
##
## Min 1Q Median 3Q Max
## -3.624e+01 -4.083e+00 -1.521e-15 4.097e+00 2.716e+01
##
##
## Coef S.E. t
## Intercept -253.9245 207.9515 -1.22
## Country_name=Argentina 28.4796 10.2384 2.78
## Country_name=Australia 27.1242 10.0052 2.71
## Country_name=Austria 33.0137 10.3209 3.20
## Country_name=Bahamas, The 18.9911 12.9149 1.47
## Country_name=Bahrain 23.3680 12.8423 1.82
## Country_name=Bangladesh 3.0226 11.1306 0.27
## Country_name=Barbados 18.4407 11.6649 1.58
## Country_name=Belarus 24.6394 13.2220 1.86
## Country_name=Belgium 29.8457 10.6863 2.79
## Country_name=Benin -0.9759 12.9264 -0.08
## Country_name=Bermuda 24.8253 10.4663 2.37
## Country_name=Bolivia 16.1552 10.8633 1.49
## Country_name=Bosnia and Herzegovina 24.1696 11.2064 2.16
## Country_name=Botswana 8.7331 12.7467 0.69
## Country_name=Brazil 18.9989 9.5980 1.98
## Country_name=Bulgaria 22.2009 11.3193 1.96
## Country_name=Burkina Faso 7.7831 12.7798 0.61
## Country_name=Cambodia 12.3567 10.6700 1.16
## Country_name=Canada 21.2391 9.5325 2.23
## Country_name=Chile 23.3677 10.2803 2.27
## Country_name=China 32.0521 10.1631 3.15
## Country_name=Colombia 13.7917 10.6950 1.29
## Country_name=Congo, Democratic Republic of the -4.3284 10.1768 -0.43
## Country_name=Congo, Republic of the -4.5772 12.7391 -0.36
## Country_name=Costa Rica 19.4924 11.1813 1.74
## Country_name=Croatia 26.4891 10.5895 2.50
## Country_name=Cuba 15.5030 10.6405 1.46
## Country_name=Cyprus 28.7085 11.2033 2.56
## Country_name=Czechia 26.3692 12.8205 2.06
## Country_name=Denmark 28.7317 11.2123 2.56
## Country_name=Djibouti -19.1970 12.9264 -1.49
## Country_name=Dominica -0.6673 11.2093 -0.06
## Country_name=Dominican Republic 15.2984 12.9870 1.18
## Country_name=Ecuador 7.8609 10.3597 0.76
## Country_name=Egypt 12.1723 9.8184 1.24
## Country_name=Eritrea 3.9014 10.3791 0.38
## Country_name=Estonia 32.6269 10.6871 3.05
## Country_name=Ethiopia 1.0750 10.0448 0.11
## Country_name=Finland 28.0284 10.6862 2.62
## Country_name=France 31.1872 9.9139 3.15
## Country_name=Gambia, The -17.5975 10.0419 -1.75
## Country_name=Gaza Strip 12.8831 10.0984 1.28
## Country_name=Germany 31.8745 9.7425 3.27
## Country_name=Ghana -3.6977 10.1284 -0.37
## Country_name=Greece 24.3045 10.6522 2.28
## Country_name=Guatemala -16.4854 9.9737 -1.65
## Country_name=Haiti 1.8271 11.1855 0.16
## Country_name=Hong Kong 41.5498 9.8838 4.20
## Country_name=Hungary 28.0031 11.3297 2.47
## Country_name=Iceland 33.4293 11.2460 2.97
## Country_name=India 6.6840 9.6121 0.70
## Country_name=Indonesia 10.8415 9.6429 1.12
## Country_name=Iran 11.1603 10.6087 1.05
## Country_name=Iraq 19.7362 10.6050 1.86
## Country_name=Ireland 22.2134 10.7861 2.06
## Country_name=Israel 23.7792 10.0457 2.37
## Country_name=Italy 23.8572 10.1563 2.35
## Country_name=Jamaica 6.0737 9.6433 0.63
## Country_name=Japan 38.9698 10.0510 3.88
## Country_name=Jordan 6.1886 10.3207 0.60
## Country_name=Kazakhstan 18.7676 10.8934 1.72
## Country_name=Kenya 4.6427 9.9137 0.47
## Country_name=Korea, South 35.0068 10.3712 3.38
## Country_name=Kuwait 21.4614 10.3579 2.07
## Country_name=Kyrgyzstan 18.8565 12.6888 1.49
## Country_name=Laos 20.9896 10.5430 1.99
## Country_name=Latvia 23.6060 12.6603 1.86
## Country_name=Lebanon 17.4700 11.2961 1.55
## Country_name=Libya 10.3895 9.8038 1.06
## Country_name=Lithuania 27.1141 10.6675 2.54
## Country_name=Malawi 0.6981 12.7091 0.05
## Country_name=Malaysia 17.0441 12.7091 1.34
## Country_name=Mali -8.4550 12.7120 -0.67
## Country_name=Malta 23.5971 10.5853 2.23
## Country_name=Marshall Isands 21.6976 12.9029 1.68
## Country_name=Mauritius 23.9947 12.8073 1.87
## Country_name=Mexico 23.0491 10.3577 2.23
## Country_name=Mongolia 30.9483 12.7497 2.43
## Country_name=Morocco 6.2150 9.9266 0.63
## Country_name=Namibia -2.2153 12.7497 -0.17
## Country_name=Nepal -26.7145 9.8498 -2.71
## Country_name=Netherlands 33.0574 9.6100 3.44
## Country_name=Netherlands Antilles 13.4505 12.7563 1.05
## Country_name=New Zealand 29.4539 10.2112 2.88
## Country_name=Nicaragua -12.8549 10.6941 -1.20
## Country_name=Nigeria 3.8065 9.8046 0.39
## Country_name=Norway 32.1490 10.6728 3.01
## Country_name=Oman 15.8038 10.0286 1.58
## Country_name=Pakistan 14.3128 9.8814 1.45
## Country_name=Peru 13.4203 9.8680 1.36
## Country_name=Philippines 19.1657 11.4479 1.67
## Country_name=Poland 26.5647 9.8424 2.70
## Country_name=Portugal 23.0704 10.6321 2.17
## Country_name=Puerto Rico 21.0199 9.5816 2.19
## Country_name=Qatar 18.5440 12.7016 1.46
## Country_name=Romania 18.8462 11.0443 1.71
## Country_name=Russia 24.4453 10.6235 2.30
## Country_name=Saint Vincent and the Grenadines -4.9542 12.7552 -0.39
## Country_name=Saudi Arabia 10.6007 9.8525 1.08
## Country_name=Serbia 20.0461 9.7108 2.06
## Country_name=Seychelles 14.8047 12.8954 1.15
## Country_name=Sierra Leone -22.8722 11.2873 -2.03
## Country_name=Singapore 32.6614 10.1707 3.21
## Country_name=Slovakia 24.6385 12.7258 1.94
## Country_name=Slovenia 28.5025 9.9854 2.85
## Country_name=Somalia -3.3201 12.9087 -0.26
## Country_name=South Africa 7.5522 9.6703 0.78
## Country_name=South Sudan -8.5101 10.1092 -0.84
## Country_name=Spain 22.8658 9.7152 2.35
## Country_name=Sri Lanka 21.9469 10.6720 2.06
## Country_name=Sudan 9.8021 9.6024 1.02
## Country_name=Sweden 32.2222 11.2436 2.87
## Country_name=Switzerland 29.9829 10.1528 2.95
## Country_name=Syria 5.8612 10.0330 0.58
## Country_name=Taiwan 37.9058 9.9662 3.80
## Country_name=Tajikistan 16.5838 12.9110 1.28
## Country_name=Tanzania 1.3926 10.1751 0.14
## Country_name=Thailand 18.7997 9.6444 1.95
## Country_name=Turkey 18.7868 12.7277 1.48
## Country_name=Uganda -1.5682 10.6114 -0.15
## Country_name=Ukraine 21.5745 12.7016 1.70
## Country_name=United Arab Emirates 10.6839 12.7689 0.84
## Country_name=United Kingdom 27.2691 10.0172 2.72
## Country_name=United States 26.4096 9.4639 2.79
## Country_name=Uzbekistan 18.2823 12.9229 1.41
## Country_name=Venezuela 18.8350 10.8309 1.74
## Country_name=Vietnam 8.7104 10.6578 0.82
## Country_name=Yemen 3.2970 11.2259 0.29
## Country_name=Zimbabwe 8.1501 12.8182 0.64
## Test_meas=APM- -3.1102 9.2328 -0.34
## Test_meas=CFT -12.1260 3.0904 -3.92
## Test_meas=CPM -6.5124 2.1726 -3.00
## Test_meas=CRT-C2 0.2206 9.4986 0.02
## Test_meas=KABC -8.8839 3.8462 -2.31
## Test_meas=MMSE 3.4477 8.7085 0.40
## Test_meas=NNAT -0.9858 10.9390 -0.09
## Test_meas=OLSAT -8.7156 12.5119 -0.70
## Test_meas=SBIS -9.3396 3.2691 -2.86
## Test_meas=SON-R 0.0226 8.9382 0.00
## Test_meas=SPM -8.3652 2.1342 -3.92
## Test_meas=SPM+ -5.3133 3.2198 -1.65
## Test_meas=WAIS -3.2990 3.7884 -0.87
## Test_meas=WAIS-III -0.9374 4.2959 -0.22
## Test_meas=WAIS-IV -3.3180 4.7169 -0.70
## Test_meas=WAIS-R -10.7282 4.2174 -2.54
## Test_meas=WASI 2.0102 7.5936 0.26
## Test_meas=WASI-II -1.2970 8.9395 -0.15
## Test_meas=WISC -14.2240 2.9908 -4.76
## Test_meas=WISC-III -12.8573 3.2192 -3.99
## Test_meas=WISC-IV -6.8339 3.8592 -1.77
## Test_meas=WISC-R -9.4403 2.7524 -3.43
## Test_meas=WPPSI -7.2129 5.9793 -1.21
## Test_meas=WPPSI-III -3.8359 5.2951 -0.72
## Test_meas=WPPSI-R -14.1768 4.7668 -2.97
## Year_meas 0.1673 0.1050 1.59
## Year_meas' -0.6243 0.3692 -1.69
## Year_meas'' 2.9880 2.4173 1.24
## Year_meas''' -4.4203 7.9245 -0.56
## Pr(>|t|)
## Intercept 0.2226
## Country_name=Argentina 0.0056
## Country_name=Australia 0.0069
## Country_name=Austria 0.0015
## Country_name=Bahamas, The 0.1420
## Country_name=Bahrain 0.0694
## Country_name=Bangladesh 0.7861
## Country_name=Barbados 0.1145
## Country_name=Belarus 0.0630
## Country_name=Belgium 0.0054
## Country_name=Benin 0.9399
## Country_name=Bermuda 0.0181
## Country_name=Bolivia 0.1376
## Country_name=Bosnia and Herzegovina 0.0315
## Country_name=Botswana 0.4936
## Country_name=Brazil 0.0483
## Country_name=Bulgaria 0.0504
## Country_name=Burkina Faso 0.5428
## Country_name=Cambodia 0.2474
## Country_name=Canada 0.0263
## Country_name=Chile 0.0234
## Country_name=China 0.0017
## Country_name=Colombia 0.1978
## Country_name=Congo, Democratic Republic of the 0.6708
## Country_name=Congo, Republic of the 0.7195
## Country_name=Costa Rica 0.0819
## Country_name=Croatia 0.0127
## Country_name=Cuba 0.1457
## Country_name=Cyprus 0.0107
## Country_name=Czechia 0.0402
## Country_name=Denmark 0.0107
## Country_name=Djibouti 0.1381
## Country_name=Dominica 0.9526
## Country_name=Dominican Republic 0.2394
## Country_name=Ecuador 0.4483
## Country_name=Egypt 0.2156
## Country_name=Eritrea 0.7072
## Country_name=Estonia 0.0024
## Country_name=Ethiopia 0.9148
## Country_name=Finland 0.0090
## Country_name=France 0.0018
## Country_name=Gambia, The 0.0803
## Country_name=Gaza Strip 0.2026
## Country_name=Germany 0.0011
## Country_name=Ghana 0.7152
## Country_name=Greece 0.0229
## Country_name=Guatemala 0.0990
## Country_name=Haiti 0.8703
## Country_name=Hong Kong <0.0001
## Country_name=Hungary 0.0138
## Country_name=Iceland 0.0031
## Country_name=India 0.4871
## Country_name=Indonesia 0.2614
## Country_name=Iran 0.2933
## Country_name=Iraq 0.0633
## Country_name=Ireland 0.0400
## Country_name=Israel 0.0183
## Country_name=Italy 0.0192
## Country_name=Jamaica 0.5291
## Country_name=Japan 0.0001
## Country_name=Jordan 0.5490
## Country_name=Kazakhstan 0.0855
## Country_name=Kenya 0.6398
## Country_name=Korea, South 0.0008
## Country_name=Kuwait 0.0388
## Country_name=Kyrgyzstan 0.1379
## Country_name=Laos 0.0470
## Country_name=Latvia 0.0628
## Country_name=Lebanon 0.1226
## Country_name=Libya 0.2898
## Country_name=Lithuania 0.0113
## Country_name=Malawi 0.9562
## Country_name=Malaysia 0.1805
## Country_name=Mali 0.5063
## Country_name=Malta 0.0262
## Country_name=Marshall Isands 0.0933
## Country_name=Mauritius 0.0616
## Country_name=Mexico 0.0265
## Country_name=Mongolia 0.0156
## Country_name=Morocco 0.5315
## Country_name=Namibia 0.8621
## Country_name=Nepal 0.0069
## Country_name=Netherlands 0.0006
## Country_name=Netherlands Antilles 0.2922
## Country_name=New Zealand 0.0041
## Country_name=Nicaragua 0.2299
## Country_name=Nigeria 0.6980
## Country_name=Norway 0.0027
## Country_name=Oman 0.1157
## Country_name=Pakistan 0.1481
## Country_name=Peru 0.1744
## Country_name=Philippines 0.0947
## Country_name=Poland 0.0072
## Country_name=Portugal 0.0305
## Country_name=Puerto Rico 0.0287
## Country_name=Qatar 0.1449
## Country_name=Romania 0.0885
## Country_name=Russia 0.0218
## Country_name=Saint Vincent and the Grenadines 0.6979
## Country_name=Saudi Arabia 0.2825
## Country_name=Serbia 0.0395
## Country_name=Seychelles 0.2515
## Country_name=Sierra Leone 0.0432
## Country_name=Singapore 0.0014
## Country_name=Slovakia 0.0534
## Country_name=Slovenia 0.0045
## Country_name=Somalia 0.7971
## Country_name=South Africa 0.4352
## Country_name=South Sudan 0.4003
## Country_name=Spain 0.0190
## Country_name=Sri Lanka 0.0402
## Country_name=Sudan 0.3078
## Country_name=Sweden 0.0043
## Country_name=Switzerland 0.0033
## Country_name=Syria 0.5593
## Country_name=Taiwan 0.0002
## Country_name=Tajikistan 0.1996
## Country_name=Tanzania 0.8912
## Country_name=Thailand 0.0518
## Country_name=Turkey 0.1405
## Country_name=Uganda 0.8826
## Country_name=Ukraine 0.0900
## Country_name=United Arab Emirates 0.4031
## Country_name=United Kingdom 0.0067
## Country_name=United States 0.0055
## Country_name=Uzbekistan 0.1578
## Country_name=Venezuela 0.0826
## Country_name=Vietnam 0.4141
## Country_name=Yemen 0.7691
## Country_name=Zimbabwe 0.5252
## Test_meas=APM- 0.7364
## Test_meas=CFT <0.0001
## Test_meas=CPM 0.0029
## Test_meas=CRT-C2 0.9815
## Test_meas=KABC 0.0213
## Test_meas=MMSE 0.6923
## Test_meas=NNAT 0.9282
## Test_meas=OLSAT 0.4864
## Test_meas=SBIS 0.0045
## Test_meas=SON-R 0.9980
## Test_meas=SPM 0.0001
## Test_meas=SPM+ 0.0995
## Test_meas=WAIS 0.3843
## Test_meas=WAIS-III 0.8274
## Test_meas=WAIS-IV 0.4821
## Test_meas=WAIS-R 0.0113
## Test_meas=WASI 0.7913
## Test_meas=WASI-II 0.8847
## Test_meas=WISC <0.0001
## Test_meas=WISC-III <0.0001
## Test_meas=WISC-IV 0.0772
## Test_meas=WISC-R 0.0007
## Test_meas=WPPSI 0.2283
## Test_meas=WPPSI-III 0.4691
## Test_meas=WPPSI-R 0.0031
## Year_meas 0.1118
## Year_meas' 0.0915
## Year_meas'' 0.2170
## Year_meas''' 0.5772
##
(sample_ols_4 = ols(IQ_cor ~ Country_name + Test_meas + rcs(Year_meas) + SES, data = REC))
## Linear Regression Model
##
## ols(formula = IQ_cor ~ Country_name + Test_meas + rcs(Year_meas) +
## SES, data = REC)
##
## Model Likelihood Discrimination
## Ratio Test Indexes
## Obs 669 LR chi2 988.35 R2 0.772
## sigma8.2868 d.f. 160 R2 adj 0.700
## d.f. 508 Pr(> chi2) 0.0000 g 14.682
##
## Residuals
##
## Min 1Q Median 3Q Max
## -2.908e+01 -3.942e+00 8.330e-15 3.914e+00 2.733e+01
##
##
## Coef S.E. t
## Intercept -274.3886 203.2454 -1.35
## Country_name=Argentina 27.1012 9.9676 2.72
## Country_name=Australia 25.9787 9.7395 2.67
## Country_name=Austria 31.7423 10.0471 3.16
## Country_name=Bahamas, The 17.1910 12.5733 1.37
## Country_name=Bahrain 22.2643 12.5000 1.78
## Country_name=Bangladesh 1.7484 10.8351 0.16
## Country_name=Barbados 17.4515 11.3545 1.54
## Country_name=Belarus 24.1961 12.8682 1.88
## Country_name=Belgium 27.7086 10.4080 2.66
## Country_name=Benin -2.9594 12.5855 -0.24
## Country_name=Bermuda 22.1200 10.2011 2.17
## Country_name=Bolivia 14.8169 10.5781 1.40
## Country_name=Bosnia and Herzegovina 23.0050 10.9084 2.11
## Country_name=Botswana 6.8915 12.4099 0.56
## Country_name=Brazil 17.6224 9.3443 1.89
## Country_name=Bulgaria 20.6995 11.0197 1.88
## Country_name=Burkina Faso 6.2219 12.4421 0.50
## Country_name=Cambodia 10.3794 10.3906 1.00
## Country_name=Canada 22.0670 9.2785 2.38
## Country_name=Chile 21.2126 10.0127 2.12
## Country_name=China 30.7778 9.8936 3.11
## Country_name=Colombia 14.8140 10.4105 1.42
## Country_name=Congo, Democratic Republic of the -7.8678 9.9259 -0.79
## Country_name=Congo, Republic of the -6.3597 12.4021 -0.51
## Country_name=Costa Rica 18.4277 10.8836 1.69
## Country_name=Croatia 25.3234 10.3081 2.46
## Country_name=Cuba 14.3899 10.3576 1.39
## Country_name=Cyprus 27.5956 10.9051 2.53
## Country_name=Czechia 24.9601 12.4798 2.00
## Country_name=Denmark 27.6909 10.9138 2.54
## Country_name=Djibouti -21.1806 12.5855 -1.68
## Country_name=Dominica -1.8891 10.9113 -0.17
## Country_name=Dominican Republic 14.6450 12.6571 1.16
## Country_name=Ecuador 6.2616 10.0865 0.62
## Country_name=Egypt 12.4081 9.5567 1.30
## Country_name=Eritrea 2.0920 10.1065 0.21
## Country_name=Estonia 30.8986 10.4056 2.97
## Country_name=Ethiopia -0.4626 9.7797 -0.05
## Country_name=Finland 27.5387 10.4022 2.65
## Country_name=France 29.6877 9.6522 3.08
## Country_name=Gambia, The -18.9875 9.7762 -1.94
## Country_name=Gaza Strip 11.7011 9.8304 1.19
## Country_name=Germany 30.0356 9.4885 3.17
## Country_name=Ghana -5.0552 9.8603 -0.51
## Country_name=Greece 22.7205 10.3710 2.19
## Country_name=Guatemala -17.8526 9.7098 -1.84
## Country_name=Haiti 0.8866 10.8873 0.08
## Country_name=Hong Kong 40.3711 9.6215 4.20
## Country_name=Hungary 26.0206 11.0322 2.36
## Country_name=Iceland 31.6092 10.9498 2.89
## Country_name=India 5.1888 9.3587 0.55
## Country_name=Indonesia 11.9847 9.3905 1.28
## Country_name=Iran 10.2684 10.3259 0.99
## Country_name=Iraq 18.8573 10.3223 1.83
## Country_name=Ireland 20.5239 10.5018 1.95
## Country_name=Israel 22.6022 9.7989 2.31
## Country_name=Italy 22.4634 9.8876 2.27
## Country_name=Jamaica 5.2078 9.3865 0.55
## Country_name=Japan 37.1832 9.7874 3.80
## Country_name=Jordan 5.2669 10.0457 0.52
## Country_name=Kazakhstan 17.0523 10.6064 1.61
## Country_name=Kenya 3.0633 9.6525 0.32
## Country_name=Korea, South 33.9249 10.0976 3.36
## Country_name=Kuwait 19.9324 10.0843 1.98
## Country_name=Kyrgyzstan 17.6128 12.3510 1.43
## Country_name=Laos 17.9326 10.2759 1.75
## Country_name=Latvia 22.5879 12.3227 1.83
## Country_name=Lebanon 15.6545 10.9999 1.42
## Country_name=Libya 9.2624 9.5435 0.97
## Country_name=Lithuania 25.4604 10.3862 2.45
## Country_name=Malawi -0.4268 12.3704 -0.03
## Country_name=Malaysia 15.9192 12.3704 1.29
## Country_name=Mali -9.7595 12.3738 -0.79
## Country_name=Malta 22.1993 10.3049 2.15
## Country_name=Marshall Isands 20.4658 12.5595 1.63
## Country_name=Mauritius 21.7460 12.4721 1.74
## Country_name=Mexico 21.3824 10.0848 2.12
## Country_name=Mongolia 29.3010 12.4118 2.36
## Country_name=Morocco 5.0586 9.6630 0.52
## Country_name=Namibia -3.8626 12.4118 -0.31
## Country_name=Nepal -27.8990 9.5884 -2.91
## Country_name=Netherlands 31.6793 9.3565 3.39
## Country_name=Netherlands Antilles 11.6304 12.4191 0.94
## Country_name=New Zealand 27.9335 9.9420 2.81
## Country_name=Nicaragua -14.4282 10.4120 -1.39
## Country_name=Nigeria 2.4405 9.5453 0.26
## Country_name=Norway 29.9402 10.3949 2.88
## Country_name=Oman 14.4756 9.7630 1.48
## Country_name=Pakistan 12.8899 9.6202 1.34
## Country_name=Peru 15.4657 9.6165 1.61
## Country_name=Philippines 18.1448 11.1430 1.63
## Country_name=Poland 25.0832 9.5826 2.62
## Country_name=Portugal 21.5805 10.3509 2.08
## Country_name=Puerto Rico 18.8565 9.3344 2.02
## Country_name=Qatar 17.5620 12.3627 1.42
## Country_name=Romania 16.6494 10.7560 1.55
## Country_name=Russia 22.9397 10.3426 2.22
## Country_name=Saint Vincent and the Grenadines -6.7316 12.4178 -0.54
## Country_name=Saudi Arabia 9.4391 9.5910 0.98
## Country_name=Serbia 18.9229 9.4530 2.00
## Country_name=Seychelles 13.4638 12.5524 1.07
## Country_name=Sierra Leone -23.6957 10.9865 -2.16
## Country_name=Singapore 31.4249 9.9009 3.17
## Country_name=Slovakia 23.4555 12.3869 1.89
## Country_name=Slovenia 26.8166 9.7228 2.76
## Country_name=Somalia 3.1932 12.6406 0.25
## Country_name=South Africa 5.9550 9.4159 0.63
## Country_name=South Sudan -1.3397 9.9554 -0.13
## Country_name=Spain 21.7239 9.4573 2.30
## Country_name=Sri Lanka 19.9722 10.3924 1.92
## Country_name=Sudan 8.4860 9.3483 0.91
## Country_name=Sweden 30.8162 10.9455 2.82
## Country_name=Switzerland 28.8004 9.8834 2.91
## Country_name=Syria 4.5977 9.7670 0.47
## Country_name=Taiwan 36.5067 9.7026 3.76
## Country_name=Tajikistan 14.6914 12.5700 1.17
## Country_name=Tanzania 1.8771 9.9051 0.19
## Country_name=Thailand 18.9589 9.3873 2.02
## Country_name=Turkey 17.1145 12.3905 1.38
## Country_name=Uganda -2.6363 10.3290 -0.26
## Country_name=Ukraine 20.5925 12.3627 1.67
## Country_name=United Arab Emirates 8.6109 12.4326 0.69
## Country_name=United Kingdom 26.0604 9.7514 2.67
## Country_name=United States 25.0881 9.2152 2.72
## Country_name=Uzbekistan 16.5671 12.5808 1.32
## Country_name=Venezuela 17.5554 10.5434 1.67
## Country_name=Vietnam 9.8554 10.3775 0.95
## Country_name=Yemen 1.8377 10.9285 0.17
## Country_name=Zimbabwe 7.5498 12.4762 0.61
## Test_meas=APM- -2.5236 8.9864 -0.28
## Test_meas=CFT -11.2702 3.0139 -3.74
## Test_meas=CPM -6.1082 2.1166 -2.89
## Test_meas=CRT-C2 0.6207 9.2446 0.07
## Test_meas=KABC -6.2299 3.7799 -1.65
## Test_meas=MMSE 3.0953 8.4755 0.37
## Test_meas=NNAT -0.8391 10.6461 -0.08
## Test_meas=OLSAT -7.9797 12.1776 -0.66
## Test_meas=SBIS -7.1390 3.2242 -2.21
## Test_meas=SON-R 0.2896 8.6991 0.03
## Test_meas=SPM -8.1263 2.0781 -3.91
## Test_meas=SPM+ -4.4258 3.1390 -1.41
## Test_meas=WAIS -3.3835 3.7554 -0.90
## Test_meas=WAIS-III -1.7651 4.1836 -0.42
## Test_meas=WAIS-IV -4.8578 4.6478 -1.05
## Test_meas=WAIS-R -7.7957 4.1422 -1.88
## Test_meas=WASI 1.2773 7.3918 0.17
## Test_meas=WASI-II -0.8032 8.7017 -0.09
## Test_meas=WISC -12.6600 2.9335 -4.32
## Test_meas=WISC-III -12.6975 3.1335 -4.05
## Test_meas=WISC-IV -7.0393 3.7691 -1.87
## Test_meas=WISC-R -8.6079 2.6919 -3.20
## Test_meas=WPPSI -6.2382 5.8232 -1.07
## Test_meas=WPPSI-III -3.7151 5.1534 -0.72
## Test_meas=WPPSI-R -11.9923 4.6574 -2.57
## Year_meas 0.1817 0.1025 1.77
## Year_meas' -0.4930 0.3605 -1.37
## Year_meas'' 1.6416 2.3664 0.69
## Year_meas''' 0.1689 7.7581 0.02
## SES=low -15.8636 3.5619 -4.45
## SES=normal -7.5188 3.2169 -2.34
## Pr(>|t|)
## Intercept 0.1776
## Country_name=Argentina 0.0068
## Country_name=Australia 0.0079
## Country_name=Austria 0.0017
## Country_name=Bahamas, The 0.1721
## Country_name=Bahrain 0.0755
## Country_name=Bangladesh 0.8719
## Country_name=Barbados 0.1249
## Country_name=Belarus 0.0606
## Country_name=Belgium 0.0080
## Country_name=Benin 0.8142
## Country_name=Bermuda 0.0306
## Country_name=Bolivia 0.1619
## Country_name=Bosnia and Herzegovina 0.0354
## Country_name=Botswana 0.5789
## Country_name=Brazil 0.0599
## Country_name=Bulgaria 0.0609
## Country_name=Burkina Faso 0.6172
## Country_name=Cambodia 0.3183
## Country_name=Canada 0.0178
## Country_name=Chile 0.0346
## Country_name=China 0.0020
## Country_name=Colombia 0.1554
## Country_name=Congo, Democratic Republic of the 0.4283
## Country_name=Congo, Republic of the 0.6083
## Country_name=Costa Rica 0.0910
## Country_name=Croatia 0.0144
## Country_name=Cuba 0.1653
## Country_name=Cyprus 0.0117
## Country_name=Czechia 0.0460
## Country_name=Denmark 0.0115
## Country_name=Djibouti 0.0930
## Country_name=Dominica 0.8626
## Country_name=Dominican Republic 0.2478
## Country_name=Ecuador 0.5350
## Country_name=Egypt 0.1948
## Country_name=Eritrea 0.8361
## Country_name=Estonia 0.0031
## Country_name=Ethiopia 0.9623
## Country_name=Finland 0.0084
## Country_name=France 0.0022
## Country_name=Gambia, The 0.0527
## Country_name=Gaza Strip 0.2345
## Country_name=Germany 0.0016
## Country_name=Ghana 0.6084
## Country_name=Greece 0.0289
## Country_name=Guatemala 0.0666
## Country_name=Haiti 0.9351
## Country_name=Hong Kong <0.0001
## Country_name=Hungary 0.0187
## Country_name=Iceland 0.0041
## Country_name=India 0.5795
## Country_name=Indonesia 0.2024
## Country_name=Iran 0.3205
## Country_name=Iraq 0.0683
## Country_name=Ireland 0.0512
## Country_name=Israel 0.0215
## Country_name=Italy 0.0235
## Country_name=Jamaica 0.5793
## Country_name=Japan 0.0002
## Country_name=Jordan 0.6003
## Country_name=Kazakhstan 0.1085
## Country_name=Kenya 0.7511
## Country_name=Korea, South 0.0008
## Country_name=Kuwait 0.0486
## Country_name=Kyrgyzstan 0.1545
## Country_name=Laos 0.0816
## Country_name=Latvia 0.0674
## Country_name=Lebanon 0.1553
## Country_name=Libya 0.3322
## Country_name=Lithuania 0.0146
## Country_name=Malawi 0.9725
## Country_name=Malaysia 0.1987
## Country_name=Mali 0.4306
## Country_name=Malta 0.0317
## Country_name=Marshall Isands 0.1038
## Country_name=Mauritius 0.0818
## Country_name=Mexico 0.0345
## Country_name=Mongolia 0.0186
## Country_name=Morocco 0.6009
## Country_name=Namibia 0.7558
## Country_name=Nepal 0.0038
## Country_name=Netherlands 0.0008
## Country_name=Netherlands Antilles 0.3495
## Country_name=New Zealand 0.0052
## Country_name=Nicaragua 0.1664
## Country_name=Nigeria 0.7983
## Country_name=Norway 0.0041
## Country_name=Oman 0.1388
## Country_name=Pakistan 0.1809
## Country_name=Peru 0.1084
## Country_name=Philippines 0.1041
## Country_name=Poland 0.0091
## Country_name=Portugal 0.0376
## Country_name=Puerto Rico 0.0439
## Country_name=Qatar 0.1561
## Country_name=Romania 0.1223
## Country_name=Russia 0.0270
## Country_name=Saint Vincent and the Grenadines 0.5880
## Country_name=Saudi Arabia 0.3255
## Country_name=Serbia 0.0458
## Country_name=Seychelles 0.2840
## Country_name=Sierra Leone 0.0315
## Country_name=Singapore 0.0016
## Country_name=Slovakia 0.0588
## Country_name=Slovenia 0.0060
## Country_name=Somalia 0.8007
## Country_name=South Africa 0.5274
## Country_name=South Sudan 0.8930
## Country_name=Spain 0.0220
## Country_name=Sri Lanka 0.0552
## Country_name=Sudan 0.3644
## Country_name=Sweden 0.0051
## Country_name=Switzerland 0.0037
## Country_name=Syria 0.6380
## Country_name=Taiwan 0.0002
## Country_name=Tajikistan 0.2430
## Country_name=Tanzania 0.8498
## Country_name=Thailand 0.0439
## Country_name=Turkey 0.1678
## Country_name=Uganda 0.7986
## Country_name=Ukraine 0.0964
## Country_name=United Arab Emirates 0.4889
## Country_name=United Kingdom 0.0078
## Country_name=United States 0.0067
## Country_name=Uzbekistan 0.1885
## Country_name=Venezuela 0.0965
## Country_name=Vietnam 0.3427
## Country_name=Yemen 0.8665
## Country_name=Zimbabwe 0.5454
## Test_meas=APM- 0.7790
## Test_meas=CFT 0.0002
## Test_meas=CPM 0.0041
## Test_meas=CRT-C2 0.9465
## Test_meas=KABC 0.0999
## Test_meas=MMSE 0.7151
## Test_meas=NNAT 0.9372
## Test_meas=OLSAT 0.5126
## Test_meas=SBIS 0.0273
## Test_meas=SON-R 0.9735
## Test_meas=SPM 0.0001
## Test_meas=SPM+ 0.1592
## Test_meas=WAIS 0.3680
## Test_meas=WAIS-III 0.6733
## Test_meas=WAIS-IV 0.2964
## Test_meas=WAIS-R 0.0604
## Test_meas=WASI 0.8629
## Test_meas=WASI-II 0.9265
## Test_meas=WISC <0.0001
## Test_meas=WISC-III <0.0001
## Test_meas=WISC-IV 0.0624
## Test_meas=WISC-R 0.0015
## Test_meas=WPPSI 0.2846
## Test_meas=WPPSI-III 0.4713
## Test_meas=WPPSI-R 0.0103
## Year_meas 0.0769
## Year_meas' 0.1721
## Year_meas'' 0.4882
## Year_meas''' 0.9826
## SES=low <0.0001
## SES=normal 0.0198
##
#ANOVA variance decomposition
lm(IQ_cor ~ Country_name, data = REC) %>%
car::Anova() %>%
sjstats::anova_stats()
lm(IQ_cor ~ Country_name + Test_meas + rcs(Year_meas) + SES, data = REC) %>%
car::Anova() %>%
sjstats::anova_stats()
#using rms package plot
plot(anova(sample_ols_4), pl=T, what = "proportion R2") %>% print()
## Country_name Test_meas SES Year_meas
## 0.77981 0.02584 0.01773 0.00262
plot(anova(sample_ols_4), pl=T, what = "partial R2") %>% print()
## Country_name Test_meas SES Year_meas
## 0.60182 0.01995 0.01368 0.00203
References
REF$`Found?` %>% table2()