Emil O. W. Kirkegaard

Recently Published

Understanding and testing for heteroscedasticity
A reviewer asked me to look at possible heteroscedasticity (HS) in some models I ran. It took me on a detour that ended with making some new convenience functions for resting for HS, whether linear or nonlinear. HS is when the variance of the residual depends on predictor values; i.e., the amount of error is a function of the predictors. Errors are supposed to be totally random normal values. HS biases the standard errors of the model, though does not normally result in bias in the effect sizes/slopes (I think).
Pupil size and intelligence: A large-scale replication study
R notebook for
Corona study results
Robust effect sizes
Dutch immigrant crime 2015
Dutch immigrant crime 2019
Immigrant crime in Germany 2012-2015
Ancient dysgenics?
ANES 2012 wordsum example
GSS skin color, wordsum study
Who wants to live forever?
EU incoming migrants analysis
More plots from: Racial/Ethnic Standards for Fetal Growth, the NICHD Fetal Growth Studies
plot data from
Vietnam regional analysis
Moscow districts analysis
What is a good name? The S factor in Denmark at the name-level
R notebook for
Are there any effects of active video games on cognitive functioning? a reanalysis of Stanmore et al (2017)
Orig. study:
Indvandring: definitioner, mønstre, forklaringer, socioøkonomiske konsekvenser
Baggrund for
Analyze own personality data
Admixture in Argentina
The effects of excluding students from PISA tests
Visual explanation of the effects of excluding lower scoring students from PISA samples.
Human Accomplishment
Visualizations based on Charles Murray's great book, Human Accomplishment.
Using classical test theory statistics with Jensen’s method
Commentary on Wicherts 2016.
ggplot2 functions in kirkegaard package
Examples of ggplot2 helpers functions in kirkegaard package.
Joining datasets of political units using ISO names
An example of a system for translating names of political units to ISO names, and using them to join datasets.
Optimal string joining
Joining tables based on fuzzy string matches in an optimal way.
Acedemic fields and gender
It has been claimed that gender differences in academic fields results from stereotypes about how difficult the fields are (Leslie, 2015). This is based on the finding that perceptions of brilliance requirement of different fields correlated strongly with the gender distribution. It apparently did not dawn on the people making this claim that the stereotypes were probably very accurate: people who study physics really are smarter than those who study psychology. This knitr visualizes data from a paper by Templer and Tomeo from 2002.
How to add names to choroplethr ggplot2 maps
A brief tutorial for the choroplethr package.
String functions in Kirkegaard package
A showcasing of some string functions in the Kirkegaard package.
Embryo selection and genetic correlated traits
A comment on Gwern's analysis.