Jeffrey Beall’s anti-open access tirade

I came across this one. On the one hand it seems written in a serious tone. On the other hand, the claims are so ridiculous that it is hard to believe it is sincere. Some quotes below.

Abstract: While the open-access (OA) movement purports to be about making scholarly content open-access, its true motives are much different. The OA movement is an anti-corporatist movement that wants to deny the freedom of the press to companies it disagrees with. The movement is also actively imposing onerous mandates on researchers, mandates that restrict individual freedom. To boost the open-access movement, its leaders sacrifice the academic futures of young scholars and those from developing countries, pressuring them to publish in lower-quality open-access journals. The open-access movement has fostered the creation of numerous predatory publishers and standalone journals, increasing the amount of research misconduct in scholarly publications and the amount of pseudo-science that is published as if it were authentic science.

The open-access movement is really about anti-corporatism. OA advocates want to make collective everything and eliminate private business, except for small businesses owned by the disadvantaged.

Similarly, a movement that tries to force out an existing technology and replace it with a purportedly better one also never succeeds. [now who can think of just a single counter-example to this bizarre claim?]

The open-access movement was born of political correctness, the dogma that unites and drives higher education. The open-access advocates have cleverly used and exploited political correctness in the academy to work towards achieving their goals and towards manipulating their colleagues into becoming open-access advocates. One of the ways they’ve achieved this is through the enactment of open-access mandates.

I’ll stop here… You can download it and read the rest if you desire more of the same.

Cancer rates: Part 2, does alcohol consumption have incremental predictive power?

Same guy proposed another idea. Wikipedia has data here. However, since i had previously seen that people fudge data on Wikipedia articles (e.g. this one), then maybe it was not a good idea to just rely on Wikipedia. So i did the best thing: fetched both the data from Wiki and the data from the primary source (WHO), and then compared them for accuracy. They were 100 identical for the “total rates”. I did not compare the other variables. But at least this dataset was not fudged. :)

So, then i loaded the data in R and plotted alcohol consumption per capita (age >=15) vs. cancer rates per capita.

source(“merger.R”) #load custom functions

DF.mega = read.mega(“Megadataset_v1.7b.csv”) #load megadataset

#load alcohol
alcohol = read.mega(“alcohol_consumption.csv”) #loads the data
short.names = as.abbrev(rownames(alcohol)) #gets the abbreviated names so it can be merged with megadataset
rownames(alcohol) = short.names #inserts the abbreviated names

DF.mega2= merge.datasets(alcohol,DF.mega) #merge datasets

scatterplot(CancerRatePer100000 ~ AlcoholConsumptionPerCapitaWHO, DF.mega2, #plot it
smoother=FALSE, #no moving average
labels = rownames(DF.mega),id.n=nrow(DF.mega)) #include datapoint names

alcohol_lifeexpectancy

There is no relationship there. However, it may work in multiple regression:

lm1 = lm(CancerRatePer100000 ~ AlcoholConsumptionPerCapitaWHO+X2012LifeExpectancyatBirth,
DF.mega2)
summary(lm1)

Call:
lm(formula = CancerRatePer100000 ~ AlcoholConsumptionPerCapitaWHO + 
    X2012LifeExpectancyatBirth, data = DF.mega2)

Residuals:
    Min      1Q  Median      3Q     Max 
-48.677 -26.569   0.717  28.486  61.631 

Coefficients:
                               Estimate Std. Error t value Pr(>|t|)    
(Intercept)                    -91.6149    79.2375  -1.156    0.254    
AlcoholConsumptionPerCapitaWHO   1.7712     1.6978   1.043    0.303    
X2012LifeExpectancyatBirth       4.2518     0.9571   4.442 6.13e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 31.56 on 43 degrees of freedom
  (227 observations deleted due to missingness)
Multiple R-squared:  0.3158,	Adjusted R-squared:  0.284 
F-statistic: 9.923 on 2 and 43 DF,  p-value: 0.0002861

There is seemingly no predictive power of alcohol consumption! But it does cause cancer, right? According to my skim of Wiki, yes, but only 3.5% of cancer cases, so the effect is too small to be seen here.

The data is in megadataset 1.7c.

Cancer rates and life expectancy?

An online acquaintance asked me me to find data about these two and look for a relationship. It stands to reason that if you have a country where people die of lots of other things (accidents, warfare, parasites/contagious disease, hunger/thirst), they don’t ling long enough to get cancer.

I already had life expectancy data from the UN when I imported the Human Development Index data. So where to find cancer rate data? I found 50 datapoints here. There are other sources such as this, but they have age standardized the data, which makes it useless for our purpose here. Furthermore, they are given by regions, where we want country-level.

So, I used the 50 datapoints from The Guardian. In R, I typed:

source(“merger.R”) #this loads my custom functions for working with the megadataset

DF.mega = read.mega(“Megadataset_v1.7b.csv”) #load data

#plot
library(car) #library needed for scatterplot function
scatterplot(CancerRatePer100000 ~ X2012LifeExpectancyatBirth, DF.mega,
smoother=FALSE, #no moving average
labels = rownames(DF.mega),id.n=nrow(DF.mega)) #add labels for all points

#correlation
cor(DF.mega["CancerRatePer100000"],DF.mega["X2012LifeExpectancyatBirth"],use=”pairwise”) #get correlation

Cancerrate_lifeexpectancy

The correlation is .55. The labels are ISO-3 or custom (full names can be found in the “Names” variable in the megadataset).

Does conscientiousness predict PISA scores at the national level? A cautious meta-analysis

Just a quick write-up before I write up a paper with this for ODP.

Introduction

Altho general cognitive ability (g) has received the most attention by differential psychologists, personality receives a fair share now a days. And just as g has been shown to have great predictive power in large meta-analyses in a variety of contexts (e.g. Gottfredson 1997 is still the best summary IMO), so has the personality trait of conscientiousness (C) (e.g. The Validity of Conscientiousness for Predicting Job Performance A meta-analytic test of two hypotheses A Meta-Analytic Investigation of Conscientiousness in the Prediction of Job Performance Examining the Intercorrelations and the Incremental Validity of Narrow Traits..asp(1) The Case for Conscientiousness Evidence and Implications for a Personality Trait Marker of Health and Longevity).

The ‘new’ thing in differential psych is to study national g estimates and how they correlate. This is the field ive been working mostly in with the spatial transferability hypothesis. The question then is, does C have predictive ability at the national level too? Well, maybe. There are some national estimates of the big five/OCEAN traits in Schmitt et al 2007. I added them to the Megdataset.

 

Partial correlations

The PISA x measured IQ (not the ones where scholastic ability have been factored in!) correlations were also of interest since no one apparently had calculated the mean PISA x measured IQ correlation. Well, it is .92. So, does C explain some of the remaining variance? One idea is to calculate the partial correlations of C and PISA with mIQ partialed out. However, this method seems to be wrong since some of the correlations are above 1! Ive never seen partial correlations above 1 before.

ConscientiousnessMeanSchmittEtAl2007
Math00Mean 1.4828725419
Read00Mean 1.1065080555
Sci00Mean 1.0012991174
Math03Mean 1.0742429148
Read03Mean 1.1147063889
Sci03Mean 1.2609157051
Sci06Mean 0.9137135525
Read06Mean 0.6593605051
Math06Mean 0.3923821506
Read09Mean 0.8607255528
Math09Mean 0.6409903363
Sci09Mean 0.843892485
Finance12Mean 0.3834897092
Math12Mean 0.3682415819
Read12Mean 0.5272534233
Sci12Mean 0.5563931581
CPS12Mean 0.1497008328

 

Multiple regression

So maybe another method is called for. I used multiple regression on all 17 PISA variables. One may be tempted to simply average them, but as Joost de Winter pointed out to me in an email, the PISA for the same year are not independent. So one cannot just count them as independent. One can get around this problem by doing the meta-analysis within test type, i.e. reading, math and science. Results:

Reading:
> IQ.betas.weighted.mean
[1] 0.9631086
> C.betas.weighted.mean
[1] 0.1673834
> sum(samples.sizes)
[1] 166Math:
> IQ.betas.weighted.mean
[1] 0.9621924
> C.betas.weighted.mean
[1] 0.02653771
> sum(samples.sizes)
[1] 167Science:
> IQ.betas.weighted.mean
[1] 0.9826468
> C.betas.weighted.mean
[1] 0.1080092
> sum(samples.sizes)
[1] 167

The results from reading have p=.03, so maybe. In 1-2 years, we will have more data from PISA15 to test with. There are plenty of reasons to be cautious: 1) The measured IQs are not perfectly reliably measured. This means that the true correlation between g and PISA scores is higher, leaving less variance to be explained by non-g factors. Maybe nothing? 2) The quality of the personality data is quite poor. Altho one may counter-argue that this is a reason to be more optimistic since the results (well, reading results) are still plausible.

The R sourcecode for the paper is here. The dataset is here.

What about measured IQ and PISA scores?

#the mean PISA x IQ correlation
DF.C.PISA.IQ.rcorr = rcorr(as.matrix(DF.C.PISA.IQ))
IQ.PISA.cors = DF.C.PISA.IQ.rcorr$r[19,] #get IQ row
IQ.PISA.cors = IQ.PISA.cors[2:18] #remove C and IQ-IQ
mean(IQ.PISA.cors) #the mean measured IQ x PISA correlation
round(IQ.PISA.cors,2)
#weighted mean
IQ.PISA.cors.n = DF.C.PISA.IQ.rcorr$n[19,] #get IQ row
IQ.PISA.cors.n = IQ.PISA.cors.n[2:18] #remove C and IQ-IQ
IQ.PISA.cors.weighted = IQ.PISA.cors*IQ.PISA.cors.n
IQ.PISA.cors.weighted.mean = sum(IQ.PISA.cors.weighted)/sum(IQ.PISA.cors.n)

The unweighted mean is 0.919, the weighted is 0.924.

 

Refs

Schmitt, D. P., Allik, J., McCrae, R. R., & Benet-Martinez, V. (2007). The Geographic Distribution of Big Five Personality Traits: Patterns and Profiles of Human Self-Description Across 56 Nations. Journal of Cross-Cultural Psychology, 38(2), 173–212. doi:10.1177/0022022106297299

Appendix – full output from MR

PISA test IQ.betas C.betas samples.sizes
Math00Mean 0.9895461 0.096764646 22
Read00Mean 0.977835 0.297191736 22
Sci00Mean 0.9759363 0.099720868 22
Math03Mean 0.9812832 0.016108517 27
Read03Mean 1.0141552 0.27851122 27
Sci03Mean 1.008251 0.104575077 27
Sci06Mean 0.9796918 0.125369373 38
Read06Mean 0.9346129 0.118300942 37
Math06Mean 0.9455623 0.010964361 38
Read09Mean 0.9596431 0.140295939 39
Math09Mean 0.9628133 0.035653129 39
Sci09Mean 0.977768 0.102601624 39
Finance12Mean 0.5286025 -0.144810379 14
Math12Mean 0.9497653 0.001486034 41
Read12Mean 0.9506026 0.094608558 41
Sci12Mean 0.9767656 0.103772057 41
CPS12Mean 0.8830054 -0.025983714 29

International general factor of personality? yes, but…

I merged the dataset from Schmitt et al (2007)’s paper about OCEAN traits in 56 countries with the rest of the megadataset. Then i extracted the first factor of the OCEAN means and SDs. These two are nearly uncorrelated (.07). As for factor strength, they are not too bad:

> DF.OCEAN.mean.omega
Omega 
Call: omega(m = DF.OCEAN.mean)
Alpha:                 0.73 
G.6:                   0.74 
Omega Hierarchical:    0.54 
Omega H asymptotic:    0.64 
Omega Total            0.84 

Schmid Leiman Factor loadings greater than  0.2 
                                        g   F1*   F2*   F3*   h2   u2   p2
ExtraversionMeanSchmittEtAl2007      0.44        0.66       0.64 0.36 0.30
AgreeablenessMeanSchmittEtAl2007     0.58  0.56             0.66 0.34 0.51
ConscientiousnessMeanSchmittEtAl2007 0.62  0.52             0.66 0.34 0.58
NeuroticismMeanSchmittEtAl2007      -0.66  0.28  0.36 -0.36 0.76 0.24 0.56
OpennessMeanSchmittEtAl2007          0.23        0.21  0.51 0.38 0.62 0.14

With eigenvalues of:
   g  F1*  F2*  F3* 
1.40 0.69 0.62 0.40 

general/max  2.04   max/min =   1.7
mean percent general =  0.42    with sd =  0.19 and cv of  0.46 
Explained Common Variance of the general factor =  0.45

 

and

> DF.OCEAN.SD.omega
Omega 
Call: omega(m = DF.OCEAN.SD)
Alpha:                 0.79 
G.6:                   0.78 
Omega Hierarchical:    0.72 
Omega H asymptotic:    0.86 
Omega Total            0.84 

Schmid Leiman Factor loadings greater than  0.2 
                                      g   F1*   F2*   F3*   h2   u2   p2
ExtraversionSDSchmittEtAl2007      0.80                   0.64 0.36 0.99
AgreeablenessSDSchmittEtAl2007     0.57        0.47       0.55 0.45 0.59
ConscientiousnessSDSchmittEtAl2007 0.57  0.35             0.48 0.52 0.68
NeuroticismSDSchmittEtAl2007       0.78  0.52             0.87 0.13 0.69
OpennessSDSchmittEtAl2007          0.43        0.24       0.25 0.75 0.74

With eigenvalues of:
   g  F1*  F2*  F3* 
2.08 0.41 0.31 0.00 

general/max  5.09   max/min =   136.11
mean percent general =  0.74    with sd =  0.15 and cv of  0.2 
Explained Common Variance of the general factor =  0.74

 

Compare with values in Table 5 in my just published paper. GFP-mean is clearly weaker than g factor at individual level, GFP-SD is about the same.

Dataset
Var% MR
Var% MR SL Omega h. Omega h. a. ECV R2
NO Complete cases 0.68 0.65 0.87 0.91 0.78 0.98
NO Impute 1 0.66 0.62 0.86 0.9 0.74 0.96
NO Impute 2 0.64 0.6 0.85 0.89 0.75 0.95
NO Impute 3 0.63 0.59 0.82 0.87 0.73 0.99
DK complete cases 0.57 0.51 0.83 0.85 0.68 0.99
DK impute 4 0.55 0.51 0.86 0.88 0.73 0.99
Int. S. Factor 0.43 0.35 0.76 0.77 0.51 0.81
Cognitive data 0.33 0.74 0.79 0.57 0.78
Personality data 0.16 0.37 0.48 0.34 0.41

Then i correlated these with national IQ, S factor and local S factors in Norway and Denmark.

> round(cor(DF.OCEAN.general.scores,use="pairwise.complete.obs"),2)
             GFP.mean GFP.SD S.in.Norway S.in.Denmark Islam S.Int    IQ
GFP.mean         1.00   0.07        0.09        -0.25  0.17 -0.21 -0.58
GFP.SD           0.07   1.00        0.39         0.26 -0.14  0.36  0.24
S.in.Norway      0.09   0.39        1.00         0.78 -0.72  0.73  0.60
S.in.Denmark    -0.25   0.26        0.78         1.00 -0.71  0.54  0.54
Islam            0.17  -0.14       -0.72        -0.71  1.00 -0.33 -0.27
S.Int           -0.21   0.36        0.73         0.54 -0.33  1.00  0.86
IQ              -0.58   0.24        0.60         0.54 -0.27  0.86  1.00

So strangely, the correlation of GFP-mean x national IQ is very negative. It correlates weakly with S factors. Let’s try partialing out national IQ:

> DF.OCEAN.general.scores.no.IQ = partial.r(DF.OCEAN.general.scores,c(1:6),7)
> DF.OCEAN.general.scores.no.IQ
partial correlations 
             GFP.mean GFP.SD S.in.Norway S.in.Denmark Islam S.Int
GFP.mean         1.00   0.26        0.68         0.09  0.02  0.72
GFP.SD           0.26   1.00        0.31         0.16 -0.08  0.32
S.in.Norway      0.68   0.31        1.00         0.67 -0.73  0.53
S.in.Denmark     0.09   0.16        0.67         1.00 -0.70  0.19
Islam            0.02  -0.08       -0.73        -0.70  1.00 -0.21
S.Int            0.72   0.32        0.53         0.19 -0.21  1.00

Even more strange. GFP-mean strongly correlates with 2 S factors, but not the one in Denmark. The Danish data are very good (25 variables) and so are the international data (42-54 variables). And all the S factors correlate strongly before partialing (.78, .73, .54) but mixed after removing IQ (.67, .53, .19). Again Denmark is odd. For GFP-SD, it is similar, but weaker (before: .39, .26, .36; after: .31, .16, .32).

What to make of this? So i emailed some colleagues:

Dear [NAMES]

Do you know if someone have looked at an international general factor of personality? Because I did it just now using a dataset of OCEAN trait scores (big five) from Schmitt et al 2007. There is indeed an international GFP in the data. It correlates negatively with national IQs (-.58). Strangely, partialing out national IQs, it correlates highly with general socioeconomic factors in Norway (.68) and internationally (.72), but not in Denmark (.09). Strange? Thoughts? I can send you the data+code if you like.

Regards,
Emil

One of them had insider info:

Emil,

There is a paper about to appear in Intelligence in which an international GFP has been computed and analyzed.

Best,

[NAME].
So i publish this here quickly so i establish priority and independence.

What about OCEAN traits themselves?

(sorry, tables apparently not easy to make smaller)
All correlations:
E mean E SD A mean A SD C mean C SD N mean N SD O mean O SD Mean SD S.NO S.DK Islam Int.S IQ
E mean 1 0.14 0.2 0.22 0.25 0.23 -0.49 0.17 0.27 0.09 0.23 0.06 -0.19 -0.02 0.09 -0.02
E sd 0.14 1 -0.08 0.47 -0.07 0.48 0.13 0.66 0.3 0.34 0.81 0.45 0.35 -0.35 0.53 0.39
A mean 0.2 -0.08 1 0.15 0.65 0.21 -0.48 0.21 0.26 -0.13 0.11 0.08 -0.26 0.26 -0.25 -0.53
A SD 0.22 0.47 0.15 1 0.23 0.43 0 0.45 0.22 0.35 0.71 0.18 0.23 -0.18 0.12 -0.04
C mean 0.25 -0.07 0.65 0.23 1 0.1 -0.57 0.07 0.2 -0.03 0.07 0.04 -0.19 0.14 -0.19 -0.6
C SD 0.23 0.48 0.21 0.43 0.1 1 0.11 0.62 0.41 0.25 0.78 0.34 -0.03 0.04 0.19 0.04
N mean -0.49 0.13 -0.48 0 -0.57 0.11 1 0.22 -0.09 0.25 0.19 -0.1 0.13 -0.06 0.12 0.38
N SD 0.17 0.66 0.21 0.45 0.07 0.62 0.22 1 0.41 0.28 0.83 0.23 0.19 0 0.24 0.18
O mean 0.27 0.3 0.26 0.22 0.2 0.41 -0.09 0.41 1 0.07 0.4 -0.01 -0.07 0.04 -0.02 -0.06
O sd 0.09 0.34 -0.13 0.35 -0.03 0.25 0.25 0.28 0.07 1 0.56 0.22 0.14 -0.07 0.25 0.37
Mean SD 0.23 0.81 0.11 0.71 0.07 0.78 0.19 0.83 0.4 0.56 1 0.41 0.25 -0.15 0.36 0.25
S.factor.in.Norway 0.06 0.45 0.08 0.18 0.04 0.34 -0.1 0.23 -0.01 0.22 0.41 1 0.78 -0.72 0.73 0.6
S.factor.in.Denmark -0.19 0.35 -0.26 0.23 -0.19 -0.03 0.13 0.19 -0.07 0.14 0.25 0.78 1 -0.71 0.54 0.54
IslamPewResearch2010 -0.02 -0.35 0.26 -0.18 0.14 0.04 -0.06 0 0.04 -0.07 -0.15 -0.72 -0.71 1 -0.33 -0.27
International.S.Factor 0.09 0.53 -0.25 0.12 -0.19 0.19 0.12 0.24 -0.02 0.25 0.36 0.73 0.54 -0.33 1 0.86
LV2012estimatedIQ -0.02 0.39 -0.53 -0.04 -0.6 0.04 0.38 0.18 -0.06 0.37 0.25 0.6 0.54 -0.27 0.86 1
With IQ partialed out:
E mean E sd A mean A SD C mean C SD N mean N SD O mean O SD Mean SD S.NO S.DK Islam Int.S
E mean 1 0.17 0.22 0.22 0.3 0.23 -0.52 0.18 0.27 0.1 0.24 0.09 -0.21 -0.02 0.21
E sd 0.17 1 0.16 0.53 0.22 0.51 -0.02 0.65 0.35 0.23 0.8 0.29 0.18 -0.28 0.42
A mean 0.22 0.16 1 0.15 0.49 0.28 -0.36 0.36 0.27 0.07 0.29 0.58 0.03 0.15 0.48
A SD 0.22 0.53 0.15 1 0.25 0.43 0.02 0.47 0.21 0.4 0.74 0.26 0.3 -0.2 0.3
C mean 0.3 0.22 0.49 0.25 1 0.15 -0.46 0.23 0.21 0.25 0.29 0.63 0.2 -0.02 0.82
C SD 0.23 0.51 0.28 0.43 0.15 1 0.1 0.62 0.41 0.26 0.79 0.39 -0.05 0.06 0.31
N mean -0.52 -0.02 -0.36 0.02 -0.46 0.1 1 0.17 -0.07 0.13 0.11 -0.45 -0.1 0.05 -0.44
N SD 0.18 0.65 0.36 0.47 0.23 0.62 0.17 1 0.43 0.23 0.83 0.16 0.11 0.05 0.18
O mean 0.27 0.35 0.27 0.21 0.21 0.41 -0.07 0.43 1 0.1 0.42 0.03 -0.04 0.03 0.06
O sd 0.1 0.23 0.07 0.4 0.25 0.26 0.13 0.23 0.1 1 0.52 0.01 -0.07 0.03 -0.14
Mean SD 0.24 0.8 0.29 0.74 0.29 0.79 0.11 0.83 0.42 0.52 1 0.33 0.15 -0.09 0.3
S.factor.in.Norway 0.09 0.29 0.58 0.26 0.63 0.39 -0.45 0.16 0.03 0.01 0.33 1 0.67 -0.73 0.53
S.factor.in.Denmark -0.21 0.18 0.03 0.3 0.2 -0.05 -0.1 0.11 -0.04 -0.07 0.15 0.67 1 -0.7 0.19
IslamPewResearch2010 -0.02 -0.28 0.15 -0.2 -0.02 0.06 0.05 0.05 0.03 0.03 -0.09 -0.73 -0.7 1 -0.21
International.S.Factor 0.21 0.42 0.48 0.3 0.82 0.31 -0.44 0.18 0.06 -0.14 0.3 0.53 0.19 -0.21 1
R code (load in the megadataset as DF.mega3 first):
DF.interest = cbind(DF.mega3[2:12],
                    DF.mega3[14],
                    DF.mega3[40],
                    DF.mega3[42],
                    DF.mega3[64],
                    DF.mega3[76])
DF.interest.cor = rcorr(as.matrix(DF.interest))
round(DF.interest.cor$r,2)
write.csv(round(DF.interest.cor$r,2),file="OCEANCors.csv")

#remove IQ
DF.interest.cor.without.IQ = partial.r(DF.interest, c(1:15),16)
write.csv(round(DF.interest.cor.without.IQ,2), file="OCEANCors_no_g.csv")

DF.OCEAN.full = cbind(DF.mega3[2:12])
DF.OCEAN.full.omega = omega(DF.OCEAN.full)
DF.OCEAN.full.mr = fa(DF.OCEAN.full)

DF.OCEAN.mean = cbind(DF.mega3[c(2,4,6,8,10)])
DF.OCEAN.mean.omega = omega(DF.OCEAN.mean)
DF.OCEAN.mean.mr = fa(DF.OCEAN.mean)

DF.OCEAN.SD = cbind(DF.mega3[c(3,5,7,9,11)])
DF.OCEAN.SD.omega = omega(DF.OCEAN.SD)
DF.OCEAN.SD.mr = fa(DF.OCEAN.SD)

DF.OCEAN.general.scores = cbind(DF.OCEAN.mean.mr$scores,DF.OCEAN.SD.mr$scores,
                                DF.mega3[14],DF.mega3[40],DF.mega3[42],DF.mega3[64],DF.mega3[76])
colnames(DF.OCEAN.general.scores) = c("GFP.mean","GFP.SD","S.in.Norway","S.in.Denmark","Islam","S.Int","IQ")
round(cor(DF.OCEAN.general.scores,use="pairwise.complete.obs"),2)
DF.OCEAN.general.scores.no.IQ = partial.r(DF.OCEAN.general.scores,c(1:6),7)

Megadataset is in the OSF repository, version 1.6b.

New paper out: Crime, income, educational attainment and employment among immigrant groups in Norway and Finland

openpsych.net/ODP/2014/10/crime-income-educational-attainment-and-employment-among-immigrant-groups-in-norway-and-finland/

Abstract

I present new predictive analyses for crime, income, educational attainment and employment among immigrant groups in Norway and crime in Finland. Furthermore I show that the Norwegian data contains a strong general socioeconomic factor (S) which is highly predictable from country-level variables (National IQ .59, Islam prevalence -.71, international general socioeconomic factor .72, GDP .55), and correlates highly (.78) with the analogous factor among immigrant groups in Denmark. Analyses of the prediction vectors show very high correlations (generally ±.9) between predictors which means that the same variables are relatively well or weakly predicted no matter which predictor is used. Using the method of correlated vectors shows that it is the underlying S factor that drives the associations between predictors and socioeconomic traits, not the remaining variance (all correlations near unity).

All data and source files are at the OSF repository: osf.io/emfag/

Paper out (some time ago): Are Headstart gains on the g factor?: A meta-analysis

Jan te Nijenhuis, Birthe Jongeneel-Grimen and Emil O. W. Kirkegaard. Are Headstart gains on the g factor?: A meta-analysis. Intelligence. Volume 46, September–October 2014, Pages 209–215

 

If all goes well, it may be the only paper i publish in Intelligence.

 

Primary author is a cool guy: www.uva.nl/over-de-uva/organisatie/medewerkers/content/n/i/j.tenijenhuis/j.te-nijenhuis.html The second author is his former grad student i think.

Causal effect of education on IQ scores using the discordant twin design?

Recently, the twin-control design has been used to test causal models (e.g. exercise→happiness, exercise→¬depression/anxiety symptoms, casual sex→depression/suicidal thoughts). The theory is simple. Suppose we do a standard cross-sectional design study and find that X and Y are correlated. Suppose we suspect that X causes Y. Then, if X causes Y, then one would expect to see a relationship within identical twin pairs for X and Y. If the correlation between X and Y is due to shared genetics, then it will not be correlated within identical twin pairs (baring any de novo mutation being responsible for it). If it is found to be correlated within identical twins, then the education model may be true but also some developmental models relying on non-education environmentally caused differences as well as de novo mutation genetic models.

Did anyone do a study like this? I haven’t seen it, but it is quite simple to do. The only thing needed is a dataset with identical twins, educational attainment/years in school and some g proxy. Maybe NLSY? If you know of a dataset, contact me and we will try.

Predatory journals

I had my first Twitter controversy. So:

I pointed out in the reply to this, that they don’t actually charge that much normally. The comparison is here. The prices are around 500-3000 USD, with an average (eyeballed) around 2500 USD.

Now, this is just a factual error, so not so bad. However…

If anyone is wondering why he is so emotional, he gave the answer himself:

A very brief history of journals and science

  • Science starts out involving few individuals.
  • They need a way to communicate ideas.
  • They set up journals to distribute the ideas on paper.
  • Printing costs money, so they cost money to buy.
  • Due to limitations of paper space, there needs to be some selection in what gets printed, which falls on the editor. Fast forward to perhaps 1950’s, now there are too many papers for the editors to handle, and so they delegate the job of deciding what to accept to other academics (reviewers). In the system, academics write papers, they edit them, and review them. All for free.
  • Fast forward to perhaps 1990 and what happens is that big business takes over the running of the journals so academics can focus on science. As it does, the prices rise becus of monetary interests.
  • Academics are reluctant to give up publishing in and buying journals becus their reputation system is built on publishing in said journals. I.e. the system is inherently conservatively biased (Status quo bias). It is perfect for business to make money from.
  • Now along comes the internet which means that publishing does not need to rely on paper. This means that marginal printing cost is very close to 0. Yet the journals keep demanding high prices becus academia is reliant on them becus they are the source of the reputation system.
  • There is a growing movement in academia that this is a bad situation for science, and that publications shud be openly available (open access movement). New OA journals are set up. However, since they are also either for-profit or crypto for-profit, in order to make money they charge outrageous amounts of money (say, anything above 100 USD) to publish some text+figures on a website. Academics still provide nearly all the work for free, yet they have to pay enormous amounts of money to publish, while the publisher provides a mere website (and perhaps some copyediting etc.).

Who thinks that is a good solution? It is clearly a smart business move. For instance, popular OA metajournal Frontiers are owned by Nature Publishing Group. This company thus very neatly both makes money off their legacy journals and the new challenger journals.

The solution is to set up journals run by academics again now that the internet makes this rather easy and cheap. The profit motive is bad for science and just results in even worse journals.

As for my claim, I stand by it. Altho in retrospect, the more correct term is parasitic. Publishers are a middleman exploiting the the fact that academia relies on established journals for reputation.

Review: The Digital Scholar: How Technology is Transforming Academic Practice (Martin Weller)

www.goodreads.com/book/show/12582388-the-digital-scholar

gen.lib.rus.ec/book/index.php?md5=5343D586EEEFB7BB24DE5B71FBD07C32

Someone posted a nice collection of books dealing with the on-going revolution in science:

So i decided to read some of them. Ironically, many of them are not available for free (contrary to the general idea of openness in them).

The book is short at 200 pages, with 14 chapters covering most aspects of changing educational system. It is at times long-winded. It shud probably have been 20-50 pages shorter. However, it seems fine as a general introduction to the area. The author shud have used more grafs, figures etc. to make points. There are plenty of good figures for these things (e.g. journal revenue increases).