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Author: demetriodor

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Obama Analytics Not only Nate Silver crunches numbers Marijuana Use and Driving Incidents More of these weird correlations Registry for Research Designs Why it’s not such a great idea The oldest town in Europe Unearthed   Bubbles. Jason Tozer [via Colossal]

Science is like sex…

‘Science is like sex – it might have practical consequences but that’s not why you do it!’ This seems to be a modified version of a quote by the physicist Richard Feynman that I heard last week at a meeting organized by the Dutch Organization for Scientific Research (the major research funding agency in the Netherlands). It kind of sums up the attitudes of natural scientists to the increasing pressures all researchers face to justify their grant applications in terms of the possible practical use (utilization, or valorization) of their research results. Which is totally fine by me. I perfectly understand that it is impossible to anticipate all the possible future practical consequences of fundmental research. On the other hand, I see no harm in forcing researchers to, at the very least, think about the possible real-world applications of their work. The current equilibrium  in which reflection on possible practical applications is required, but ‘utilization’ is neither necessary nor sufficient for getting a grant, seems like a good compromise. Of course, I come from a field (public administration) where demonstrating the scientific contribution is usually more difficult than showing the practical applicability of the results: so my view might be biased. I am not even sure what fundamental research in the social sciences looks like. Even rather esoteric work on non-cooperative game theory has been directly spurred by practical concerns related to the Cold War (and sponsored by the RAND corporation) and has rather directly led to the design of real-world social instituions (like the…

Hedging the bets: The US election outcome in the Dutch press

This is a guest post by Markus Haverland, Professor at Erasmus University Rotterdam and author of a recent book on research methods. *** Causal knowledge about the world proceeds by testing hypotheses. The context of discovery precedes the context of justification. We all know that journalists and pundits often do it the other way around: providing for an explanation after the fact. A particularly hilarious example can be found in today’s issue of “Spits”, a Dutch daily newspaper. Anticipating that the result of the elections for the president of the US would arrive after the newspapers went to press, the newspaper prepared for both situations. It has turned the backpage into a second frontpage. Depending on the results the reader is advised to either read the frontpage or the backpage. On both pages the well-known  Dutch journalists, a former correspondent in Washington, Charles Groenhuijsen analyses the results. On the “Obama wins” page he explains that it was evident that Obama would win, because he is a better campaigner and Romney’s economic program is inconsistent. On the “Romney  wins”  page he explains this outcome, by stating that, ultimately, the US is a conservative country, that voters were afraid of a turn to the left, laws against gun possession, and tolerance towards gay marriage, and that voters thought he was not effectively dealing with the economic crisis.

Network visualization in R with the igraph package

In this post I showed a visualization of the organizational network of my department. Since several people asked for details how the plot has been produced, I will provide the code and some extensions below. The plot has been done entirely in R (2.14.01) with the help of the igraph package. It is a great package but I found the documentation somewhat difficult to use, so hopefully this post can be a helpful introduction to network visualization with R. Here we go: # Load the igraph package (install if needed) require(igraph) # Data format. The data is in ‘edges’ format meaning that each row records a relationship (edge) between two people (vertices). # Additional attributes can be included. Here is an example: # Supervisor Examiner Grade Spec(ialization) # AA BD 6 X # BD CA 8 Y # AA DE 7 Y # … … … … # In this anonymized example, we have data on co-supervision with additional information about grades and specialization. # It is also possible to have the data in a matrix form (see the igraph documentation for details) # Load the data. The data needs to be loaded as a table first: bsk<-read.table(“http://www.dimiter.eu/Data_files/edgesdata3.txt”, sep=’t’, dec=’,’, header=T)#specify the path, separator(tab, comma, …), decimal point symbol, etc. # Transform the table into the required graph format: bsk.network<-graph.data.frame(bsk, directed=F) #the ‘directed’ attribute specifies whether the edges are directed # or equivelent irrespective of the position (1st vs 2nd column). For directed graphs use ‘directed=T’ # Inspect the data:…

Ethnic job discrimination in the Netherlands

I have more than one reason to care about job discrimination based on ethnicity in the Netherlands. A new study shows that there is plenty to worry about. In short, the researchers sent identical job applications varying only the name – Dutch vs. ethnic (Antillean, Surinamese, Turkish, Moroccan). The ‘Dutch’ applicants had a higher chance of being invited for a job interview. The effect is rather small in size (5-8 percentage points), but is robust and statistically significant. Furthermore, discrimination is greater for ethnic males (20 percentage points), and for the lower-educated. This study investigates ethnic discrimination in the Dutch labor market, using field experiments. Two thousand eighty applications were sent to 1340 job vacancies; one applicant had a Dutch-sounding name, the other a name that signaled immigrant descent. Our aims were (a) to test for the persistence of discrimination in the Dutch labor market; (b) to study the interactions of ethnic background with job characteristics; (c) to study the complexity of discrimination against a background of multiple group membership. Results indicate that discrimination continues to be a problem in selection procedures. Interactions with job characteristics and multiple group membership are discussed. [full text (gated) here] Andriessen, Iris, Eline Nievers, Jaco Dagevos, and Laila Faulk. “Ethnic Discrimination in the Dutch Labor Market: Its Relationship with Job Characteristics and Multiple Group Membership.” Work and Occupations 39, no. 3 (2012): 237-69.  

The hidden structure of (academic) organizations

All organizations have a ‘deep’ hidden structure based on the social interactions among its members which might or might not coincide with the official formal one. University departments are no exception – if anything, the informal alliances, affinities, and allegiances within academic departments are only too visible and salient. Network analysis provides one way of visualizing and exploring the ‘deep’ organizational structure. In order to learn how to visualize small networks with R, I collected data on the social interactions within my own department and plugged the dataset in R (igraph package) to get the plot below. The figure shows the social network of my institute based on the co-supervision of student dissertations (each Master thesis has a supervisor who selects a so-called ‘second’ reader who reviews the draft and the two supervisors examine the student during the defence). So each link between nodes (people) is based on one joint supervision of a student. The total number of links (edges) is 264 which covers (approximately) all dissertations defended over the last year. In this version of the graph, the people are represented only by numbers but in the full version the actual names of people are plotted, the links are directional, and additional info (like the grade of the thesis) can be incorporated. Altogether, the organization appears surprisingly well-integrated. Most ‘outsiders’ and most weakly-connected ‘islands’ are either occasional external readers, or new colleagues being ‘socialized’ into the organization. Obviously, some people are more ‘central’ in the sense of connecting to a more diverse set of people, while others serve as boundary-spanners reaching…