Clear Language, Clear Mind

March 2, 2016

The performance of immigrants from Yugoslavia in Denmark and Norway

Filed under: Sociology — Tags: , , , , — Emil O. W. Kirkegaard @ 07:08

This is actually an older post, but by accident I posted it on the Danish language sister blog.

I don’t know what study that is, however, I do have numbers for the performance of Yugoslavians in Denmark and Norway. There are both numbers for persons from Yugoslavia when that was one legal entity (actually multiple different with the same name) as well as for some of the constituent countries.

First a brief review. Many studies have looked at immigrant performance by macro-origin and recently country of origin. The country of origin studies are more useful because immigrants from e.g. “Asia” (which may or may not include the Muslim countries such as Afghanistan) are not very homogeneous. Cambodians and Chinese are different, but both are East Asians. Afghans are very different, but are sometimes included in the category. Mixing these together in a hodgepodge makes for uninterpretable results, especially when who is included changes over time and from study to study. For instance, whether a country is considered Western may depend on EU membership, which means that lots of non-Western became Western recently.

Because I was unsatisfied with the existing macro-origin studies for Denmark (most only found in Danish, but I guess I should do an English-language review some day) I began carrying out a series of such immigrants by country of origin studies with the underlying goal being to test the spatial transferability hypothesis (Fuerst’s name), which is that 1) when people move, they generally retain their mean levels of psychological traits, 2) and as a consequence, the effects of these traits follow them as well. Selective immigration and emigration makes this more difficult to test.

The two best such studies cover Denmark and Norway. They are the best because they include a large number of countries of origin and have data for many socioeconomic outcome variables. In brief, the S factors were extracted from available information regarding. income, educational attainment, crime, employment and use of social benefits. See the original papers for details (e.g. with regards to imputation).

The correlation between the S scores from DK and NO is .78 [CI .64 to .86], N=55. N’s by country are 70 and 67, DK and NO respectively. The table below shows all the data.

Abbrev. Country S in DK S in NO
AFG Afghanistan -1.38 -1.09
ARG Argentina 0.75
AUS Australia 1.131 1.03
AUT Austria 0.947 1.02
BDI Burundi -0.54
BEL Belgium 1.089 1.16
BGR Bulgaria 0.811 0.17
BIH Bosnia and Herzegovina -0.913 0.49
BRA Brazil 0.457 -0.34
CAN Canada 1.145 1.03
CHE Switzerland 1.119 1.13
CHL Chile 0.279 0.25
CHN China 0.627 0.61
COG Congo Rep. -1.07
COL Colombia 0.26
CZE Czech Republic 0.249 0.43
DEU Germany 0.846 1.04
DNK Denmark 1
DZA Algeria -0.775 -1.52
EGY Egypt Arab Rep. -0.241
ERI Eritrea -0.43
ESP Spain 0.788 0.52
EST Estonia 0.717 0.19
ETH Ethiopia -0.586 -0.16
FIN Finland 0.891 0.78
FRA France 1.098 0.97
GBR United Kingdom 0.848 1.14
GHA Ghana 0.162 0.03
GMB Gambia The -0.84
GRC Greece 0.613 0.61
HRV Croatia -0.12 0.54
HUN Hungary 0.837 0.45
IDN Indonesia 0.126 0.33
IND India 0.528 0.63
IRL Ireland 0.876
IRN Iran Islamic Rep. -0.688 -0.35
IRQ Iraq -1.654 -2.26
ISL Iceland 0.555 0.76
ISR Israel -0.061
ITA Italy 0.775 0.86
JOR Jordan -1.191
JPN Japan 1.018
KEN Kenya 0.088 -0.24
KSV Kosovo -0.43
KWT Kuwait -2.619
LBN Lebanon -2.027 -1.03
LKA Sri Lanka -0.749 -0.14
LTU Lithuania 0.897 -0.08
LVA Latvia 0.685 0.06
MAR Morocco -1.031 -0.63
MKD Macedonia FYR -0.439 -0.19
MMR Myanmar -1.812 -0.27
NGA Nigeria 0.336 -0.53
NLD Netherlands 1.118 1.11
NOR Norway 0.842
NPL Nepal 0.75
PAK Pakistan -0.679 -0.87
PER Peru 0.1
PHL Philippines 0.362 0.58
POL Poland 0.463 -0.02
PRT Portugal 0.631 0.54
PSE West Bank and Gaza -3.8
ROU Romania 0.703 0.31
RUS Russian Federation 0.447 -0.44
SDN Sudan -1.52
SOM Somalia -2.054 -3.06
SRB Serbia -1.931 0.46
SUN USSR 0.166
SVK Slovak Republic 0.42
SWE Sweden 0.766 1.03
SYR Syrian Arab Republic -1.997 -1.62
THA Thailand -0.233 -0.03
TUN Tunisia -0.825
TUR Turkey -1.42 -0.52
TZA Tanzania -0.254
UGA Uganda -0.341
UKR Ukraine 0.686 0.34
USA United States 1.259 0.97
VNM Vietnam -0.582 -0.11
YU2 Former Yugoslavia2 (Found in some Danish sources) -1.611
YUG Former Yugoslavia -1.247
ZAF South Africa 0.731

 

I have marked the Yugoslavian countries in italics above. The table below shows the Yugoslavian subset table:

Abbrev. Country S in DK S in NO
BIH Bosnia and Herzegovina -0.913 0.49
YUG Former Yugoslavia -1.247
YU2 Former Yugoslavia2 (Found in some Danish sources) -1.611
KSV Kosovo -0.43
MKD Macedonia FYR -0.439 -0.19
SRB Serbia -1.931 0.46

 

In both countries, the immigrants don’t perform well Well here means around native levels which is around +1. The natives are not found in the tables above because they are not immigrants. They perform worse in Denmark, in some cases by no small amount, which is somewhat puzzling. An S difference of 2.4 is case of Serbia is much larger than would be expected by sampling error (1.4 for BIH). Maybe differential selection. Looks like Denmark received more refugees than Norway despite similar population size, consistent with lower selection threshold for DK.

Studies

Crime, income, educational attainment and employment among immigrant groups in Norway and Finland

Educational attainment, income, use of social benefits, crime rate and the general socioeconomic factor among 71 immigrant groups in Denmark.

September 28, 2015

Crime by immigrant group by proportion of immigrants in the neighborhood in the Netherlands

Filed under: Sociology — Tags: , , , — Emil O. W. Kirkegaard @ 03:44

Just a quick analysis. When I read the Dutch crime report that forms the basis of this paper, I noticed one table that had crime rates by the proportion of immigrants in the neighborhood. Generally, one would expect r (immigrant% x S) to be negative and since r (S x crime) is negative, one would predict a positive r (immigrant% x crime). Is this the case? Well, mostly. The data are divided into 2 generation and 2 age groups, so there are 4 sub-datasets with lots of missing data and sampling error. If we just use all the cases as if they were independent and get rid of the data we get this result:

Immi% mean sd median trimmed mad min max range skew kurtosis
X0.5. 1.137 0.182 1.026 1.113 0.039 1 1.588 0.588 1.073 -0.148
X5.15. 1.284 0.292 1.162 1.258 0.24 1 1.938 0.938 0.809 -0.641
X15.50. 1.509 0.65 1.382 1.381 0.465 1 3.812 2.812 2.203 4.758
X.50. 1.769 1.154 1.435 1.526 0.471 1 5.812 4.812 2.36 4.937

 

In other words, within each group (N=28), the ones living in the areas with more immigrants are more crime-prone. There is however substantial variation. Sometimes the pattern is the reverse for no discernible reason. E.g. 12-17 year olds from Morocco have lower crime rates in the more immigrant heavy areas (7.4, 7.1, 6.5, 6.1).

The samples are too small for one to profitably dig more into it, I think.

R code & data

dutch_crime_area

library(pacman)
p_load(plyr, magrittr, readODS, kirkegaard, psych)

#load data from file
d_orig = read.ods("Z:/code/R/dutch_crime_area.ods")[[1]]
d_orig[d_orig=="" | d_orig=="0"] = NA

#headers
colnames(d_orig) = d_orig[1, ]
d_orig = d_orig[-1, ]

#remove cases with missing
d = na.omit(d_orig)

#remove names
origins = d$Origin
d$Origin = NULL

#remove unknown + total
d$Unknown = NULL
d$Total = NULL

#to numeric
d = lapply(d, as.numeric) %>% as.data.frame

#convert to standardized rates
d_std = adply(d, 1, function(x) {
  x_min = min(x)
  x_ret = x/x_min
})

describe(d_std) %>% write_clipboard

June 27, 2015

The performance of African immigrants in Europe: Some Danish and Norwegian data

Due to lengthy discussion over at Unz concerning the good performance of some African groups in the UK, it seems worth it to review the Danish and Norwegian results. Basically, some African groups perform better on some measures than native British. The author is basically arguing that this disproves global hereditarianism. I think not.

The over-performance relative to home country IQ of some African countries is not restricted to the UK. In my studies of immigrants in Denmark and Norway, I found the same thing. It is very clear that there are strong selection effects for some countries, but not others, and that this is a large part of the reason why the home country IQ x performance in host country are not higher. If the selection effect was constant across countries, it would not affect the correlations. But because it differs between countries, it essentially creates noise in the correlations.

Two plots:

NO_S_IQ DK_S_IQ

The codes are ISO-3 codes. SO e.g. NGA is Nigeria, GHA is Ghana, KEN = Kenya and so on. They perform fairly well compared to their home country IQ, both in Norway and Denmark. But Somalia does not and the performance of several MENAP immigrants is abysmal.

The scores on the Y axis are S factor scores for their performance in these countries. They are general factors extracted from measures of income, educational attainment, use of social benefits, crime and the like. The S scores correlate .77 between the countries. For details, see the papers concerning the data:

  • Kirkegaard, E. O. W. (2014). Crime, income, educational attainment and employment among immigrant groups in Norway and Finland. Open Differential Psychology. Retrieved from http://openpsych.net/ODP/2014/10/crime-income-educational-attainment-and-employment-among-immigrant-groups-in-norway-and-finland/
  • Kirkegaard, E. O. W., & Fuerst, J. (2014). Educational attainment, income, use of social benefits, crime rate and the general socioeconomic factor among 70 immigrant groups in Denmark. Open Differential Psychology. Retrieved from http://openpsych.net/ODP/2014/05/educational-attainment-income-use-of-social-benefits-crime-rate-and-the-general-socioeconomic-factor-among-71-immmigrant-groups-in-denmark/

I did not use the scores from the papers, I redid the analysis. The code is posted below for those curious. The kirkegaard package is my personal package. It is on github. The megadataset file is on OSF.


 

library(pacman)
p_load(kirkegaard, ggplot2)

M = read_mega("Megadataset_v2.0e.csv")

DK = M[111:135] #fetch danish data
DK = DK[miss_case(DK) <= 4, ] #keep cases with 4 or fewer missing
DK = irmi(DK, noise = F) #impute the missing
DK.S = fa(DK) #factor analyze
DK_S_scores = data.frame(DK.S = as.vector(DK.S$scores) * -1) #save scores, reversed
rownames(DK_S_scores) = rownames(DK) #add rownames

M = merge_datasets(M, DK_S_scores, 1) #merge to mega

#plot
ggplot(M, aes(LV2012estimatedIQ, DK.S)) + 
  geom_point() +
  geom_text(aes(label = rownames(M)), vjust = 1, alpha = .7) +
  geom_smooth(method = "lm", se = F)
ggsave("DK_S_IQ.png")


# Norway ------------------------------------------------------------------

NO_work = cbind(M["Norway.OutOfWork.2010Q2.men"], #for work data
                M["Norway.OutOfWork.2011Q2.men"],
                M["Norway.OutOfWork.2012Q2.men"],
                M["Norway.OutOfWork.2013Q2.men"],
                M["Norway.OutOfWork.2014Q2.men"],
                M["Norway.OutOfWork.2010Q2.women"],
                M["Norway.OutOfWork.2011Q2.women"],
                M["Norway.OutOfWork.2012Q2.women"],
                M["Norway.OutOfWork.2013Q2.women"],
                M["Norway.OutOfWork.2014Q2.women"])

NO_income = cbind(M["Norway.Income.index.2009"], #for income data
                  M["Norway.Income.index.2010"],
                  M["Norway.Income.index.2011"],
                  M["Norway.Income.index.2012"])

#make DF
NO = cbind(M["NorwayViolentCrimeAdjustedOddsRatioSkardhamar2014"],
           M["NorwayLarcenyAdjustedOddsRatioSkardhamar2014"],
           M["Norway.tertiary.edu.att.bigsamples.2013"])


#get 5 year means
NO["OutOfWork.2010to2014.men"] = apply(NO_work[1:5],1,mean,na.rm=T) #get means, ignore missing
NO["OutOfWork.2010to2014.women"] = apply(NO_work[6:10],1,mean,na.rm=T) #get means, ignore missing

#get means for income and add to DF
NO["Income.index.2009to2012"] = apply(NO_income,1,mean,na.rm=T) #get means, ignore missing

plot_miss(NO) #view is data missing?

NO = NO[miss_case(NO) <= 3, ] #keep those with 3 datapoints or fewer missing
NO = irmi(NO, noise = F) #impute the missing

NO_S = fa(NO) #factor analyze
NO_S_scores = data.frame(NO_S = as.vector(NO_S$scores) * -1) #save scores, reverse
rownames(NO_S_scores) = rownames(NO) #add rownames

M = merge_datasets(M, NO_S_scores, 1) #merge with mega

#plot
ggplot(M, aes(LV2012estimatedIQ, NO_S)) +
  geom_point() +
  geom_text(aes(label = rownames(M)), vjust = 1, alpha = .7) +
  geom_smooth(method = "lm", se = F)
ggsave("NO_S_IQ.png")

sum(!is.na(M$NO_S))
sum(!is.na(M$DK.S))

cor(M$NO_S, M$DK.S, use = "pair")

 

June 3, 2015

What is a good name? The S factor in Denmark at the name-level

Filed under: Sociology — Tags: , , , , , , — Emil O. W. Kirkegaard @ 20:50

Emil O. W. Kirkegaard

Bo Tranberg

Abstract
We present and analyze data from a dataset of 2358 Danish first names and socioeconomic outcomes not previously made available to the public (Navnehjulet, the Name Wheel). We visualize the data and show that there is a general socioeconomic factor with indicator loadings in the expected directions (positive: income, owning your own place; negative: having a criminal conviction, being without a job). This result holds after controlling for age and for each gender alone. It also holds when analyzing the data in age bins. The factor loading of being married depends on analysis method, so it is more difficult to interpret.

A pseudofertility is calculated based on the population size for the names for the years 2012 and 2015. This value is negatively correlated with the S factor score r = -.35 [95CI: -.39; -.31], but the relationship seems to be somewhat non-linear and there is an upward trend at the very high end of the S factor. The relationship is strongly driven by relatively uncommon names who have high pseudofertility and low to very low S scores. The n-weighted correlation is -.21 [95CI: -.25; -.17]. This dysgenic pseudofertility seems to be mostly driven by Arabic and African names.

All data and R code is freely available.

Key words: names, Denmark, Danish, social status, crime, income, education, age, scraping, S factor, general socioeconomic factor

Files: https://osf.io/t2h9c/

January 14, 2015

Predicting immigrant performance: Does inbreeding have incremental validity over IQ and Islam?

So, she came up with:

So I decided to try it out, since I’m taking a break from reading Lilienfeld which I had been doing that for 5 hours straight or so.

So the question is whether inbreeding measures have incremental validity over IQ and Islam, which I have previously used to examine immigrant performance in a number of studies.

So, to get the data into R, I OCR’d the PDF in Abbyy FineReader since this program allows for easy copying of table data by row or column. I only wanted column 1-2 and didn’t want to deal with the hassle of importing it with spreadsheet problems (which need a consistent separator, e.g. comma or space). Then I merged it with the megadataset to create a new version, 2.0d.

Then I created a subset of the data with variables of interest, and renamed them (otherwise results would be unwieldy). Intercorrelations are:

row.names Cousin% CoefInbreed IQ Islam S.in.DK
1 Cousin% 1.00 0.52 -0.59 0.78 -0.76
2 CoefInbreed 0.52 1.00 -0.28 0.40 -0.55
3 IQ -0.59 -0.28 1.00 -0.27 0.54
4 Islam 0.78 0.40 -0.27 1.00 -0.71
5 S.in.DK -0.76 -0.55 0.54 -0.71 1.00

 

Spearman’ correlations, which are probably better due to the non-normal data:

row.names Cousin% CoefInbreed IQ Islam S.in.DK
1 Cousin% 1.00 0.91 -0.63 0.67 -0.73
2 CoefInbreed 0.91 1.00 -0.55 0.61 -0.76
3 IQ -0.63 -0.55 1.00 -0.23 0.72
4 Islam 0.67 0.61 -0.23 1.00 -0.61
5 S.in.DK -0.73 -0.76 0.72 -0.61 1.00

 

The fairly high correlations of inbreeding measures with IQ and Islam mean that their contribution will likely be modest as incremental validity.

However, let’s try modeling them. I create 7 models of interest and compile the primary measure of interest from them, R2 adjusted, into an object. Looks like this:

row.names R2 adj.
1 S.in.DK ~ IQ+Islam 0.5472850
2 S.in.DK ~ IQ+Islam+CousinPercent 0.6701305
3 S.in.DK ~ IQ+Islam+CoefInbreed 0.7489312
4 S.in.DK ~ Islam+CousinPercent 0.6776841
5 S.in.DK ~ Islam+CoefInbreed 0.7438711
6 S.in.DK ~ IQ+CousinPercent 0.5486674
7 S.in.DK ~ IQ+CoefInbreed 0.4979552

 

So we see that either of them adds a fair amount of incremental validity to the base model (line 1 vs. 2-3). They are in fact better than IQ if one substitutes them in (1 vs. 4-5). They can also substitute for Islam, but only with about the same predictive power (1 vs 6-7).

Replication for Norway

Replication for science is important. Let’s try Norwegian data. The Finnish and Dutch data are well-suited for this (too few immigrant groups, few outcome variables i.e. only crime)

Pearson intercorrelations:

row.names CousinPercent CoefInbreed IQ Islam S.in.NO
1 CousinPercent 1.00 0.52 -0.59 0.78 -0.78
2 CoefInbreed 0.52 1.00 -0.28 0.40 -0.46
3 IQ -0.59 -0.28 1.00 -0.27 0.60
4 Islam 0.78 0.40 -0.27 1.00 -0.72
5 S.in.NO -0.78 -0.46 0.60 -0.72 1.00

 

Spearman:

row.names CousinPercent CoefInbreed IQ Islam S.in.NO
1 CousinPercent 1.00 0.91 -0.63 0.67 -0.77
2 CoefInbreed 0.91 1.00 -0.55 0.61 -0.71
3 IQ -0.63 -0.55 1.00 -0.23 0.75
4 Islam 0.67 0.61 -0.23 1.00 -0.47
5 S.in.NO -0.77 -0.71 0.75 -0.47 1.00

 

These look fairly similar to Denmark.

And the regression results:

row.names R2 adj.
1 S.in.NO ~ IQ+Islam 0.5899682
2 S.in.NO ~ IQ+Islam+CousinPercent 0.7053999
3 S.in.NO ~ IQ+Islam+CoefInbreed 0.7077162
4 S.in.NO ~ Islam+CousinPercent 0.6826272
5 S.in.NO ~ Islam+CoefInbreed 0.6222364
6 S.in.NO ~ IQ+CousinPercent 0.6080922
7 S.in.NO ~ IQ+CoefInbreed 0.5460777

 

Fairly similar too. If added, they have incremental validity (line 1 vs. 2-3). They perform better than IQ if substituted but not as much as in the Danish data (1 vs. 4-5). They can also substitute for Islam (1 vs. 6-7).

How to interpret?

Since inbreeding does not seem to have any direct influence on behavior that is reflected in the S factor, it is not so easy to interpret these findings. Inbreeding leads to various health problems and lower g in offspring, the latter which may have some effect. However, presumably, national IQs already reflect the lowered IQ from inbreeding, so there should be no additional effect there beyond national IQs. Perhaps inbreeding results in other psychological problems that are relevant.

Another idea is that inbreeding rates reflect non-g psychological traits that are relevant to adapting to life in Denmark. Perhaps it is a useful measure of clanishness, would be reflected in hostility towards integration in Danish society (such as getting an education, or lack of sympathy/antipathy towards ethnic Danes and resulting higher crime rates against them), which would be reflected in the S factor.

The lack of relatively well established causal routes for interpreting the finding makes me somewhat cautious about how to interpret this.


 

##Code for mergining cousin marriage+inbreeding data with megadataset
inbreed = read.table("clipboard", sep="\t",header=TRUE, row.names=1) #load data from clipboard
source("merger.R") #load mega functions
mega20d = read.mega("Megadataset_v2.0d.csv") #load latest megadataset
names = as.abbrev(rownames(inbreed)) #get abbreviated names
rownames(inbreed) = names #set them as rownames

#merge and save
mega20e = merge.datasets(mega20d,inbreed,1) #merge to create v. 2.0e
write.mega(mega20e,"Megadataset_v2.0e.csv") #save it

#select subset of interesting data
dk.data = subset(mega20e, selec=c("Weighted.mean.consanguineous.percentage.HobenEtAl2010",
                                  "Weighted.mean.coefficient.of.inbreeding.HobenEtAl2010",
                                  "LV2012estimatedIQ",
                                  "IslamPewResearch2010",
                                  "S.factor.in.Denmark.Kirkegaard2014"))
colnames(dk.data) = c("CousinPercent","CoefInbreed","IQ","Islam","S.in.DK") #shorter var names
rcorr = rcorr(as.matrix(dk.data)) #correlation object
View(round(rcorr$r,2)) #view correlations, round to 2
rcorr.S = rcorr(as.matrix(dk.data),type = "spearman") #spearman correlation object
View(round(rcorr.S$r,2)) #view correlations, round to 2

#Multiple regression
library(QuantPsyc) #for beta coef
results = as.data.frame(matrix(data = NA, nrow=0, ncol = 1)) #empty matrix for results
colnames(results) = "R2 adj."
models = c("S.in.DK ~ IQ+Islam", #base model,
           "S.in.DK ~ IQ+Islam+CousinPercent", #1. inbreeding var
           "S.in.DK ~ IQ+Islam+CoefInbreed", #2. inbreeding var
           "S.in.DK ~ Islam+CousinPercent", #without IQ
           "S.in.DK ~ Islam+CoefInbreed", #without IQ
           "S.in.DK ~ IQ+CousinPercent", #without Islam
           "S.in.DK ~ IQ+CoefInbreed") #without Islam

for (model in models){ #run all the models
  fit.model = lm(model, dk.data) #fit model
  sum.stats = summary(fit.model) #summary stats object
  summary(fit.model) #summary stats
  lm.beta(fit.model) #standardized betas
  results[model,] = sum.stats$adj.r.squared #add result to results object
}
View(results) #view results

##Let's try Norway too
no.data = subset(mega20e, selec=c("Weighted.mean.consanguineous.percentage.HobenEtAl2010",
                                  "Weighted.mean.coefficient.of.inbreeding.HobenEtAl2010",
                                  "LV2012estimatedIQ",
                                  "IslamPewResearch2010",
                                  "S.factor.in.Norway.Kirkegaard2014"))

colnames(no.data) = c("CousinPercent","CoefInbreed","IQ","Islam","S.in.NO") #shorter var names
rcorr = rcorr(as.matrix(no.data)) #correlation object
View(round(rcorr$r,2)) #view correlations, round to 2
rcorr.S = rcorr(as.matrix(no.data),type = "spearman") #spearman correlation object
View(round(rcorr.S$r,2)) #view correlations, round to 2

results = as.data.frame(matrix(data = NA, nrow=0, ncol = 1)) #empty matrix for results
colnames(results) = "R2 adj."
models = c("S.in.NO ~ IQ+Islam", #base model,
           "S.in.NO ~ IQ+Islam+CousinPercent", #1. inbreeding var
           "S.in.NO ~ IQ+Islam+CoefInbreed", #2. inbreeding var
           "S.in.NO ~ Islam+CousinPercent", #without IQ
           "S.in.NO ~ Islam+CoefInbreed", #without IQ
           "S.in.NO ~ IQ+CousinPercent", #without Islam
           "S.in.NO ~ IQ+CoefInbreed") #without Islam

for (model in models){ #run all the models
  fit.model = lm(model, no.data) #fit model
  sum.stats = summary(fit.model) #summary stats object
  summary(fit.model) #summary stats
  lm.beta(fit.model) #standardized betas
  results[model,] = sum.stats$adj.r.squared #add result to results object
}
View(results) #view results

May 12, 2014

New paper out: Educational attainment, income, use of social benefits, crime rate and the general socioeconomic factor among 71 immigrant groups in Denmark

Filed under: Differential psychology/psychometrics,Sociology — Tags: , , — Emil O. W. Kirkegaard @ 13:36

Educational attainment, income, use of social benefits, crime rate and the general socioeconomic factor among 71 immigrant groups in Denmark

http://openpsych.net/ODP/2014/05/educational-attainment-income-use-of-social-benefits-crime-rate-and-the-general-socioeconomic-factor-among-71-immmigrant-groups-in-denmark/

April 4, 2014

New paper out: Criminality among Norwegian immigrant populations

http://openpsych.net/ODP/2014/04/criminality-among-norwegian-immigrant-populations/

Abstract
A previous study found that criminality among immigrant groups in Denmark was highly predictable by their countries of origin’s prevalence of Muslims, IQ, GDP and height. This study replicates the study for Norway with similar results.

Keywords: Crime, national IQ, group differences, country of origin

Download paper.
Forum thread and supplementary material.

March 14, 2014

Paper published: Criminality and fertility among Danish immigrant populations

Filed under: Differential psychology/psychometrics — Tags: , , , — Emil O. W. Kirkegaard @ 19:06

Abstract
Criminality rates and fertility vary wildly among Danish immigrant populations by their country of origin. Correlational and regression analyses show that these are very predictable (R’s about .85 and .5) at the group level with national IQ, Islam belief, GDP and height as predictors.

Published in our new journal for psychology.

http://openpsych.net/index.php/diff/article/view/7

Peer review is here: http://openpsych.net/forum/showthread.php?tid=2&action=lastpost

January 17, 2014

Paper published: Predicting Immigrant IQ from their Countries of Origin, and Lynn’s National IQs: A Case Study from Denmark

Filed under: Differential psychology/psychometrics,intelligence / IQ / cognitive ability — Tags: — Emil O. W. Kirkegaard @ 09:54

I published my first peer reviewed paper in a journal.

Predicting Immigrant IQ from their Countries of Origin, and Lynn’s National IQs: A Case Study from Denmark

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