Clear Language, Clear Mind

September 26, 2018

XYY supermales and violence

Filed under: Criminology,intelligence / IQ / cognitive ability — Tags: , , , — Emil O. W. Kirkegaard @ 08:08

Nature has a review of Robert Plomin’s new book by some random lefty historian data-free reasoning type called Nathaniel Comfort (he follows the usual pattern of having a background in biology, as the usual suspects). Among the usual claims without any supporting references we find this one:

Crude hereditarianism often re-emerges after major advances in biological knowledge: Darwinism begat eugenics; Mendelism begat worse eugenics. The flowering of medical genetics in the 1950s led to the notorious, now-debunked idea that men with an extra Y chromosome (XYY genotype) were prone to violence. Hereditarian books such as Charles Murray and Richard Herrnstein’s The Bell Curve (1994) and Nicholas Wade’s 2014 A Troublesome Inheritance (see N. Comfort Nature513, 306–307; 2014) exploited their respective scientific and cultural moments, leveraging the cultural authority of science to advance a discredited, undemocratic agenda. Although Blueprint is cut from different ideological cloth, the consequences could be just as grave.

Our prior for this claim being wrong is strong because we know that large structural abnormalities including whole chromosome aneuploidies are usually associated with a lot of issues, including behavioral ones — many of which would be criminal if we held such people accountable. This is probably in part due to the link to lower intelligence. In any case, we can easily check the claim in the academic literature since people compile datasets about persons with large genetic abnormalities.

Stockholm et al 2012

Objective To investigate the criminal pattern in men between 15 and 70 years of age diagnosed with 47,XXY (Klinefelter’s syndrome (KS)) or 47,XYY compared to the general population.

Design Register-based cohort study comparing the incidence of convictions among men with KS and with 47,XYY with age- and calendar-matched samples of the general population. Crime was classified into eight types (sexual abuse, homicide, burglary, violence, traffic, drug-related, arson and ‘others’).

Setting Denmark 1978–2006.

Participants All men diagnosed with KS (N=934) or 47,XYY (N=161) at risk and their age- and calendar-time-matched controls (N=88 979 and 15 356, respectively).

Results The incidence of convictions was increased in men with KS (omitting traffic offenses) compared to controls with a HR of 1.40 (95% CI 1.23 to 1.59, p<0.001), with significant increases in sexual abuse, burglary, arson and ‘others’, but with a decreased risk of traffic and drug-related offenses. The incidence of convictions was significantly increased among men with 47,XYY compared to controls with a HR of 1.42 (95% CI 1.14 to 1.77, p<0.005) in all crime types, except drug-related crimes and traffic. Adjusting for socioeconomic variables (education, fatherhood, retirement and cohabitation) reduced the total HR for both KS and 47,XYY to levels similar to controls, while some specific crime types (sexual abuse, arson, etc) remained increased.

Conclusion The overall risk of conviction (excluding traffic offenses) was moderately increased in men with 47,XYY or KS; however, it was similar to controls when adjusting for socioeconomic parameters. Convictions for sexual abuse, burglary, arson and ‘others’ were significantly increased. The increased risk of convictions may be partly or fully explained by the poor socioeconomic conditions related to the chromosome aberrations.

This is more or less a perfect study for this purpose, the Everest regression of the authors notwithstanding.

Leggett et al 2010

Aim To review systematically the neurodevelopmental characteristics of individuals with sex chromosome trisomies (SCTs).

Method A bibliographic search identified English‐language articles on SCTs. The focus was on studies unbiased by clinical referral, with power of at least 0.69 to detect an effect size of 1.0.

Results We identified 35 articles on five neonatally identified samples that had adequate power for our review. An additional 11 studies were included where cases had been identified for reasons other than neurodevelopmental concerns. Individuals with an additional X chromosome had mean IQs that were within broadly normal limits but lower than the respective comparison groups, with verbal IQ most affected. Cognitive outcomes were poorest for females with XXX. Males with XYY had normal‐range IQs, but all three SCT groups (XXX, XXY, and XYY) had marked difficulties in speech and language, motor skills, and educational achievement. Nevertheless, most adults with SCTs lived independently. Less evidence was available for brain structure and for attention, social, and psychiatric outcomes. Within each group there was much variation.

Interpretation Individuals with SCTs are at risk of cognitive and behavioural difficulties. However, the evidence base is slender, and further research is needed to ascertain the nature, severity, and causes of these difficulties in unselected samples.

So, we know from the study above that crime rates are in fact higher. Do we also see the usual other types of problem behavior and related cognitive issues that would explain this? Yep, IQ scores are 10-15 points below comparison groups. Note that the authors’ discussion of the IQ gaps show a lack of understanding of the role of the Flynn effects since they keep remarking how the IQ means of affected groups are around 100 while matched comparisons are around 110-115 (failure to realize the need for renorming). This error was also seen in the Minnesota Transracial Adoption Study and was pointed out by Loehlin.

Money et al 1969

By now the reader might be wondering: maybe the historian’s view is based on something that was published before 2010? What does the older reviews say? Well, they say about the same thing.

It is possible that boys with the XYY syndrome are ill-equipped to cope with the ordinary stresses of learning and the academic environ- ment; 19 of the 35 patients are recorded as having had problems in school, and only 4 as having been free of problems. In five cases, the problems were specified as behavioral, in four as underachievement, and in ten as both. Behavioral problems included dislike of school, deficient attention span, restlessness, truancy and disruption of the classroom routine. In at least one case, behavior was so bizarre as to resemble both brain-damage symptoms and psychosis. This latter case is instructive because the boy’s behavior improved remarkably by the middle teenage years, under the influence of a planned, benign en- vironment. Simultaneously, there was an improvement of the abnormal, spike-wave EEG (there had been no clinical seizures), the spike being no longer in evidence. Also the IQ rose from a 6 2 year old low of 89, to 100 in teenage.

School difficulties did not especially correlate with IQ. They oc- curred over an IQ range from 63 to 125. The IQ was given in 18 cases, the median being 91, and the mean 89. The exact nature of the rela- tionship of the extra Y chromosome to IQ will remain uncertain until incidence studies have been completed. So far, there does not appear to be an excess of severe mental retardation, as in the XXY syndrome (though note that some individuals with XXY do have superior IQ’s above 120).

Unstable work histories closely relate to prison histories, so far as the 30 men over the age of 16 in the present series are concerned, for some of them were in and out of jail two or more times. Some were in special institutions for men considered to be poor risks as chronic offenders. One had been in both a jail and a mental hospital.

The present sample includes 24 men with a prison record; 2 others were in hospitals for the criminally insane and 1 was in a regular psychiatric hospital because of sex offenses. These total 27 in detention. There were three children, one of whom at age eight had been in trouble with the law. The other two and one of the teenagers had exhibited grossly deviant behavior. In only one case (an adult) was the necessary information lacking. In the whole sample, therefore, there were three, one teenager and two men whose behavior was relatively normal and law abiding.

The sample is, of course, deliberately biased in favor of law breakers, for investigators were, by design, screening tall men in jails, in the belief that there would be more XYY men among them than elsewhere. Thus, the exact relationship between XYY and imprisonment also cannot be ascertained until proper incidence studies have been completed.

The offenses that kept men in detention varied from robbery to murder (3 cases). In 7 cases crimes against both property and person were specified (in another 7, attacks on property and person were noted, though not in connection with detention). In 11 cases, the subjects were imprisoned for an offense against property only, and in 7 others, for an offense against persons only, 5 of them sex offenses. It should be noted that in the sum total of sex offenses, both homosexual and heterosexual assaults and/or approaches were represented

The history seems to be that prison workers discovered suspiciously high numbers of XXY and XYY men among high profile criminals. They not unreasonably then inferred that this had something to do with their predicament (essentially a case-control study). When data later became available for larger samples, the relationship between these disorders and problem behavior was confirmed. Somewhere along the way, some accounts presumably exaggerated the relationships which cause historians sensitive to social justice to produce counter-narratives which apparently they still believe in to this day despite the actual data.

February 11, 2017

Swedish immigrant crime data from the 1980s

Filed under: Criminology,Immigration — Tags: , , — Emil O. W. Kirkegaard @ 17:52

I was skimming a Wikipedia article related to immigrant crime and came across an obscure Swedish language report from the 1990s:

  • Ahlberg, J. (1996). Invandrares och invandrares barns brottslighet: En statistisk analys [Immigrants’ and immigrants’ children’s crime: a statistical analysis]. Brottsförebyggande rådet (BRÅ).

(file available on OSF)

This report is a goldmine. Briefly:

  • Data from 1985-1989.
  • Country of origin crime rates for 1st gen. Raw and adjusted for age and sex. n=38.
  • Country of origin crime rates for 2nd gen. Raw and adjusted for age and sex.
  • Stereotypes about immigrant crime levels. n=10 ethnicities.
  • Data for different crime types: violent, property, sex, etc.
  • Various other interesting things.

In this case, the old data are particularly useful because they allow us to examine whether recent wars etc. are responsible for poor performance of some groups. E.g. Iraqis usually do not perform well, but people will say it’s because their country got invaded by the US. Twice. And so they suffer from transgenerational epigenetic stress or whatever. And also had prolonged civil war. Similar things apply to Afghanistan and Yugoslavia.

The old data also allow for longitudinal analyses, which are very important to immigrant policy. I.e. if we can expect immigrants to acquire similar performance to natives after 20 years, then taking in poorly performing immigrants is only a temporary burden, not a permanent one.

Of the 38 cases, 34 are countries and 4 are unspecified remainder categories (e.g. “other European countries”). Of the country cases, some are combined, presumably due to small numbers. E.g. Argentina and Uruguay are combined. They are mostly combined sensibly by combining neighboring countries with similar cultures and genetics. E.g. there is a North African case with Algeria, Libya, Morocco and Tunisia. Perhaps the most problematic is the combination of Bangladesh and Pakistan. These are on different sides of India, which is included by itself. Both are mostly Muslim, but Bangladesh is mostly grouped with the other South Asian countries (Burma, Bhutan, Nepal) not with MENAP. In general, a sensible approach to these combined groups is to split them and use both countries as datapoints. This inflates the sample size a bit. One could weigh them accordingly when doing this to avoid this problem somewhat (i.e. weigh datapoints for Bangladesh and Pakistan by 0.5 vs. usual 1). After expanding the combined countries, we get a more respectable n=48. This still has a few former countries that are problematic: USSR, Yugoslavia, Czech Slovakia.

The stereotypes come from a large survey (n=1,362) and simple concern whether one thinks the 10 groups have more, same or fewer immigrants (5 options + don’t know). Similar to the data in this recent paper about the UK. Unfortunately, they don’t all match up with the immigrant groups, e.g. one group is gypsies. Gypsies have no country, so it is hard to get reliable data on them about any trait. However, if we match up 8 or 9 of the groups depending on our liberal we are. Depending on which, we get accuracy scores of r = .36 or .52. Not too bad. The groups are not random: all were above average, so there is variance reduction which reduces the observed correlation. But this is the best we can do.

[There is a meta-analysis of Roma IQ which found a mean of 74! Seems to be not published yet, but there’s an abstract from the talk.]

So, what are the basic findings? A scatterplot says a thousand words.

se_dk_rrse_de_rr

se_rape_robbery_rr

(all values adjusted for age and sex)

These subgroup rates should be taken very lightly because the samples must be very small indeed. Italians not known to be particularly crime prone.

More to come! We’re buying a large dataset for Sweden with immigrant performance data on 4 metrics: crime, education, income and social benefits. Then we will essentially replicate the Denmark and Norway analyses.

June 16, 2016

Measurement error and behavioral genetics in criminology

I am watching Brian Boutwell’s (Twitter, RG) talk at a recent conference and this got me thinking.

What are we measuring?

As far as I know, there are typically two outcome variables used in criminological studies:

  1. Official records convictions.
  2. Self-reported criminal or anti-social behavior.

But exactly what trait are we trying to measure? It seems to me that we (or I am!) are really interested in measuring something like tendency to break laws that are harmful to other people. Harmful is here used in a broad sense. Stealing something may not always cause someone harm, but it does deprive them (usually) unfairly of their property. Stealing is not always wrong, but it is usually wrong. Let’s call the construct we want to measure harmful criminal behavior.

Measurement error: two types

Before going on, it is necessary to distinguish between the two types of measurement error in classical test theory:

  1. Random measurement error.
  2. Systematic measurement error.

Random measurement error is by definition error in measurement that is not correlated with anything else at all (sampling error aside). Conceptually we can think of it as adding random noise to our measurements. A simple, every-day example of this would be a study where we examine the relationship between height and GPA for ground/elementary school students. Suppose we obtain access to a school and we measure the height of all the students using a measurement tape. Then we obtain their GPAs from the school administration. Random measurement error here would be if we used dice to pick random numbers and added/subtracted these to each student’s height.

Systematic measurement error (also called bias) is different. Suppose we are measuring the ability of persons to sneak past a guard post because we want to recruit a team of James Bond-type super spies. We conduct the experiment by having people try to sneak past a guard post. Because we have a lot of people to test, our experiment is carried out all day beginning in the early morning and ending in the evening. Each individual has to try three times to sneak past the guard post and we measure their ability as the number of times they sneaked past (so 0-3 are possible scores) We assign their trials in order of their birthdays: people born early in the year take their trials in the early morning. Because it is easier to see when the sun is higher in the sky, the individuals who happen to be born later or very early in the year have an advantage: it is more difficult for the guards to spot them when it is darker. Someone who successfully sneaked past the guards three times in the evening is not necessarily at the same skill level as someone who sneaked three times around noon. There is a systematic error in the measurement of sneaking ability related to the time of testing, and it is furthermore related to the persons’ birthday.

Problems with official records

Using official records as a measure of harmful criminal behavior has a big problem: they often include convictions for things that aren’t wrong (e.g. drug use or sex work). Ideally, we don’t care about convictions for things like smoking cannabis because in a sense, this isn’t a real crime: it’s just the government that is evil. In the same way that homosexual sex or even oral sex is not a crime anymore, and was not a real crime back when it was illegal (overview of US ‘sodomy’ laws). There is a moral dimension as to what to one is trying to measure if one does not just want to go with the construct of ‘any criminal behavior that the present day state in this country happen to have criminalized’.

Furthermore, official records are based on court decisions (and pleas). Court decisions are in turn the result of the police taking up a case. If the police are biased — rightly or wrongly — in their decision about which cases to pursue, this will give rise to systematic measurement error.

Since the police does not have infinite resources, they will not pursue every case they know of. They probably won’t even pursue every case they know of they think they can win in court. There is thus an inherent randomness in which cases they will pursue. i.e. random measurement error.

Worse, which cases the police pursues may depend on irrelevant things like whether the police leadership has set a goal for the number of cases of a given type that must be pursued every year. This practice seems to be fairly common, and yet it results in serious distortions in the use of police resources. In Denmark, the police often have these goals about biking violations (say, biking on the sidewalk). The result is that in December (if the goal is based on a year-to-year basis), if they are not close to meeting their goal, the police leadership will divert resources away from more important crimes, say, break-ins, to hand out fines for people breaking biking laws. They may also lower the bar as to what counts as a violation.

Even worse, they may focus on targeting violations that are not wrong they are easy to pursue. One police officer gave the following story (anonymously in order to prevent reprisals from the leaders!) in response to a parliament discussion of the topic:

“When we are told that we must write 120 bikers [hand out fines to] the next 14 days, then we don’t place ourselves in the pedestrian area while there are pedestrians, and when the bikers may cause problems. No, we take them in the morning when they bike thru the empty pedestrian area on their way to work, because then we get more quickly to the 120 number. In other words, we do it for the numbers’ sake and not for the sake of traffic safety.”

This kind of police behavior induce both random and systematic measurement error in the official records. For instance, people who happen to bike to work and whose work is on the opposite side of a pedestrian area are more likely to receive such fines.

Measurement error, self-rating and the heritability of personality traits

While personality is probably not really that simple to summarize, most research on personality use some variant of the big five/OCEAN model (use this test). Using such measures, it has generally been found that the heritability of OCEAN traits is around 40%. Lots of room for environmental effects, surely. Unfortunately, most of the non-heritable variance is in the everything else-category.

But, these results are based on self-rated personality and not even corrected for random measurement error which is usually easy enough to do. So, suppose we correct for random measurement error, then perhaps we get to 50% heritability. This is because (almost?) any kind of measurement error biases heritability downwards.

What about self-rating bias? Surely there are some personality traits that cause people to systematically rate themselves different from how other people rate themselves, i.e. systematic measurement error. Even for height — a very simple trait — using self-reported height deflated heritability by about 4% compared with clinical measurement (from 91 to 87%), and clinical measurement is not free of random measurement error either. Furthermore, human height varies somewhat within a given day — a kind of systematic measurement error.

So, are other-ratings of personality better? There is a large meta-analysis showing that other-ratings are better. They have stronger correlations with actual criteria outcomes than self-ratings:

Other_rating_strangersother_rating_academic other_rating_workperf

This suggests considerable systematic measurement error in the self-ratings. The counter-hypothesis: others’ ratings of one’s personality, while not actually more accurate than self-ratings, causally influences the chosen outcomes, such that it appears that other-ratings are better. E.g. teachers/supervisors give higher grades/performance ratings to those they incorrectly judge to be more open minded due to some kind of halo effect. I don’t know of any research on this question.

Still, what do we find if we analyze the heritability of personality using other-ratings and especially the combination of self- and other-ratings? We get this:

other_self_heritability

A mean heritability of 81% for the OCEAN traits. Like the height study, there was evidence of heritable influence on systematic self-rating error (53% in this study, the height study found 36% but had limited precision).

Conclusion: measurement error and criminology

Back to criminology. We have seen that:
  1. Official records have serious problems with measuring the right construct (criminal harmful behavior), probably suffer from lots of random measurement error and probably some systematic measurement error.
  2. Self-ratings suffer from systematic measurement error.
  3. Measurement error biases estimates of heritability downwards.
We combine them and derive the conclusion: heritabilities of harmful criminal behavior are probably seriously underestimated.
Questions for future research:
  • Locate or do behavioral genetic studies of crime based on multiple methods and other-ratings. What do they show?
  • Find evidence to determine whether the higher validity of other ratings is due to their higher precision or due to causal halo effects.

May 7, 2016

Crime/violence, gender, sexual orientation, cognitive ability

In a recent paper, Beaver et al looked at the relationships between crime, gender and sexual orientation:

This study examined the association between sexual orientation and nonviolent and violent delinquency across the life course. We analyzed self-reported nonviolent and violent delinquency
in a sample of heterosexual males (N=5220–7023) and females (N=5984–7875), bisexuals (N=34–73),gay males (N=145–189), and lesbians (N=115–150) from the National Longitudinal Study of Adolescent to Adult Health (Add Health). The analyses revealed, in general, that bisexuals were the most delinquent of the sexual orientation categories for both males and females. Additional analyses revealed that heterosexual males reported significantly higher levels of both violent and nonviolent
delinquency than gay males, whereas lesbians reported more involvement in nonviolent delinquency and, to a lesser extent, violent delinquency relative to heterosexual females. Analyses also revealed
that lesbians reported significantly more delinquent behavior, particularly for nonviolent delinquency, than gay males. Future research should explore the mechanisms that account for these observed patterns and how they can be used to more fully understand the etiology of delinquency.

I decided to see if this pattern held in the OKCupid dataset. I could find three questions about related matters, all of which are yes/no:

  1. Have you ever been arrested, even if just for a small crime or misdemeanor?
  2. Have you ever hit a significant other in anger?
  3. Excluding childhood fights, have you ever punched someone in the face?

Coding these so that “yes” = 1 and “no” = 0, we can use logistic regression. The second outcome variable had too few datapoints to give useful results, so I skipped it.

The raw associations are:

raw_means

I include only persons who self-identified as man or woman (>99% of the sample). I include age and cognitive ability as covariates.

Results:

> fit = glm(formula = "arrested ~ CA + gender_orientation + age", family = "binomial", data = d_main, subset = v_3bigorien & v_menwomen)
> MOD_summary(fit)
$coefs
                                     Beta   SE CI.lower CI.upper
CA                                  -0.29 0.02    -0.33    -0.24
gender_orientation: Hetero_female    0.00   NA       NA       NA
gender_orientation: Bisexual_female  0.65 0.12     0.41     0.88
gender_orientation: Gay_female       0.32 0.23    -0.13     0.78
gender_orientation: Gay_male         0.70 0.12     0.47     0.93
gender_orientation: Bisexual_male    1.13 0.15     0.83     1.43
gender_orientation: Hetero_male      1.04 0.06     0.92     1.17
age                                  0.19 0.02     0.15     0.23

$meta
        N pseudo-R2  deviance       AIC 
 11895.00      0.05  12237.69  12253.69 

> #punched in face
> fit = glm(formula = "punched_face ~ CA + gender_orientation + age", family = "binomial", data = d_main, subset = v_3bigorien & v_menwomen)
> MOD_summary(fit)
$coefs
                                     Beta   SE CI.lower CI.upper
CA                                  -0.29 0.02    -0.32    -0.26
gender_orientation: Hetero_female    0.00   NA       NA       NA
gender_orientation: Bisexual_female  0.58 0.08     0.42     0.74
gender_orientation: Gay_female       0.35 0.15     0.07     0.64
gender_orientation: Gay_male        -0.13 0.10    -0.32     0.06
gender_orientation: Bisexual_male    0.70 0.12     0.46     0.93
gender_orientation: Hetero_male      1.03 0.04     0.95     1.12
age                                 -0.11 0.02    -0.14    -0.07

$meta
        N pseudo-R2  deviance       AIC 
 17188.00      0.04  20926.46  20942.46

Observations:

  • Cognitive ability was consistently negatively related to crime/violence, standardized beta = -.29.
  • Female bisex. and homosex. were more criminal than heterosex.
  • Male homosex. were less violent/criminal, and male bisex. had inconsistent relationships in the controlled analyses, but lower levels in the raw analyses.

Project files: https://osf.io/zp8fx/

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 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 10, 2015

Intelligence, income inequality and prison rates: It’s complicated

There was some talk on Twitter around prison rates and inequality:

And IQ and inequality:

But then what about prison data beyond those given above? I have downloaded the newest data from here ICPS (rate data, not totals).

Now, what about all three variables?

#load mega20d as the datafile
ineqprisoniq = subset(mega20d, select=c("Fact1_inequality","LV2012estimatedIQ","PrisonRatePer100000ICPS2015"))
rcorr(as.matrix(ineqprisoniq),type = "spearman")
                            Fact1_inequality LV2012estimatedIQ PrisonRatePer100000ICPS2015
Fact1_inequality                        1.00             -0.51                        0.22
LV2012estimatedIQ                      -0.51              1.00                        0.16
PrisonRatePer100000ICPS2015             0.22              0.16                        1.00

n
                            Fact1_inequality LV2012estimatedIQ PrisonRatePer100000ICPS2015
Fact1_inequality                         275               119                         117
LV2012estimatedIQ                        119               275                         193
PrisonRatePer100000ICPS2015              117               193                         275

So IQ is slightly positively related to prison rates and so is equality. Positive? Isn’t it bad having people in prison? Well, if the alternative is having them dead… because the punishment for most crimes is death. Although one need not be excessive as the US is. Somewhere in the middle is perhaps best?

What if we combine them into a model?

model = lm(PrisonRatePer100000ICPS2015 ~ Fact1_inequality+LV2012estimatedIQ,ineqprisoniq)
summary = summary(model)
library(QuantPsyc)
lm.beta(model)
prediction = as.data.frame(predict(model))
colnames(prediction) = "Predicted"
ineqprisoniq = merge.datasets(ineqprisoniq,prediction,1)
scatterplot(PrisonRatePer100000ICPS2015 ~ Predicted, ineqprisoniq,
            smoother=FALSE,id.n=nrow(ineqprisoniq))
> summary

Call:
lm(formula = PrisonRatePer100000ICPS2015 ~ Fact1_inequality + 
    LV2012estimatedIQ, data = ineqprisoniq)

Residuals:
    Min      1Q  Median      3Q     Max 
-153.61  -75.05  -31.53   44.62  507.34 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)   
(Intercept)       -116.451     88.464  -1.316  0.19069   
Fact1_inequality    31.348     11.872   2.640  0.00944 **
LV2012estimatedIQ    3.227      1.027   3.142  0.00214 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 113.6 on 114 degrees of freedom
  (158 observations deleted due to missingness)
Multiple R-squared:  0.09434,	Adjusted R-squared:  0.07845 
F-statistic: 5.938 on 2 and 114 DF,  p-value: 0.003523

> lm.beta(model)
Fact1_inequality LV2012estimatedIQ 
        0.2613563         0.3110241

This is a pretty bad model (var%=8), but the directions held from before but were stronger. Standardized betas .25-.31. The R2 seems to be awkwardly low to me given the betas.

More importantly, the residuals are clearly not normal as can be seen above. The QQ-plot is:

QQ_plot

It is concave, so data distribution isn’t normal. To get diagnostic plots, simply use “plot(model)”.

Perhaps try using rank-order data:

ineqprisoniq = as.data.frame(apply(ineqprisoniq,2,rank,na.last="keep")) #rank order the data

And then rerunning model gives:

> summary

Call:
lm(formula = PrisonRatePer100000ICPS2015 ~ Fact1_inequality + 
    LV2012estimatedIQ, data = ineqprisoniq)

Residuals:
     Min       1Q   Median       3Q      Max 
-100.236  -46.753   -8.507   46.986  125.211 

Coefficients:
                  Estimate Std. Error t value Pr(>|t|)    
(Intercept)        1.08557   18.32052   0.059    0.953    
Fact1_inequality   0.84766    0.16822   5.039 1.78e-06 ***
LV2012estimatedIQ  0.50094    0.09494   5.276 6.35e-07 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 54.36 on 114 degrees of freedom
  (158 observations deleted due to missingness)
Multiple R-squared:  0.2376,	Adjusted R-squared:  0.2242 
F-statistic: 17.76 on 2 and 114 DF,  p-value: 1.924e-07

> lm.beta(model)
 Fact1_inequality LV2012estimatedIQ 
        0.4757562         0.4981808

Much better R2, directions the same but betas are stronger, and residuals look normalish from the above. QQ plot shows them not to be even now.

QQplot2

Prediction plots based off the models:

prison prison_rank

So is something strange going on with the IQ, inequality and prison rates? Perhaps something nonlinear. Let’s plot them by IQ bins:

bins = cut(unlist(ineqprisoniq["LV2012estimatedIQ"]),5) #divide IQs into 5 bins
ineqprisoniq["IQ.bins"] = bins
describeBy(ineqprisoniq["PrisonRatePer100000ICPS2015"],bins)
library(gplots)
plotmeans(PrisonRatePer100000ICPS2015 ~ IQ.bins, ineqprisoniq,
          main = "Prison rate by national IQ bins",
          xlab = "IQ bins (2012 data)", ylab = "Prison rate per 100000 (2014 data)")

prison_IQ_bins

That looks like “bingo!” to me. We found the pattern.

What about inequality? The trouble is that the inequality data is horribly skewed with almost all countries have a low and near identical inequality compared with the extremes. The above will (does not) work well. I tried with different bins numbers too. Results look something like this:

bins = cut(unlist(ineqprisoniq["Fact1_inequality"]),5) #divide IQs into 5 bins
ineqprisoniq["inequality.bins"] = bins
plotmeans(PrisonRatePer100000ICPS2015 ~ inequality.bins, ineqprisoniq,
          main = "Prison rate by national inequality bins",
          xlab = "inequality bins", ylab = "Prison rate per 100000 (2014 data)")

prison_inequality

So basically, the most equal countries to the left have low rates, somewhat higher in the unequal countries within the main group and varying and on average lowish among the very unequal countries (African countries without much infrastructure?).

Perhaps this is why the Equality Institute limited their analyses to the group on the left, otherwise they don’t get the nice clear pattern they want. One can see it a little bit if one uses a high number of bins and ignores the groups to the right. E.g. 10 bins:

prison_inequality_10bins

Among the 3 first groups, there is a slight upward trend.

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

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