The PRIO Guide to Migration Journals

This deserves more attention that ‘just’ a tweet! The PRIO guide to migration journals is now live: https://migration.prio.org/Journals/

It’s a guide of 29 migration journals you might want to consult once in a while if you consider publishing in migration journals.

What do you get?

The first thing you’ll notice is a list of (currently) 29 migration journals — with a relatively broad understanding of ‘migration’. As is probably necessarily the case, we can quibble about the inclusion of journals in such a list, but in my view the PRIO guide provides a pretty good overview of the publishing options. Having such a list in itself is greatly useful.

For a slightly different list of migration journals, you can consult the excellent list provided by our Documentation Centre: http://www.unine.ch/sfm/home/library/revues-liees-a-la-migration.html

It doesn’t stop here, though, far from it! For each of these 29 journals, you get a detailed portrait that should help you decide whether the journal is a suitable outlet for your research. The headings included are relevant for researchers, and I really like how they managed to provide information about the impact factor without listing it (or other similar measures). (unlike my blunt summary here).

Perhaps the most useful part (but also the most difficult one, thus possibly also the one where we might not always agree) is at the end, where they have picked typical articles. On the one hand, this saves you a trip to the journal website to check recent publications. On the other hand, it doesn’t entirely answer the question of what kind of research do they typically publish? I guess that’s the question we’re asking, but also one which is very difficult to answer when the common factor is the topic (migration) and not the methodology or something like that. In that sense, three articles can never do justice of the diversity of articles in IMR or JEMS, for example.

If open access is a concern for you, the end of the guide nicely summarizes the open access status. This doesn’t include (how could it possibly?) national agreements with publishers.

If Because impact is probably one of your concerns, there’s a nice summary at the end. I really like it how they avoided impact factors of Scimago rankings, yet still provide you with a general idea of ‘impact’ — and with that ‘prestige’.

What don’t you get?

You don’t get journals that publish a lot on migration but are not focused on migration, like some demography journals. The selection of journals is nicely documented, so no quibbles there! You also don’t get journals without peer review — but that’s definitely a good thing!

You don’t get impact factors (that’s probably a good thing), but you also don’t get information about the peer review — that’s a factor many early career researchers (have to) take into consideration. Luckily, we have SciRev for this. While journals have the relevant information about turn-around time or rejection rates, they tend not to publish them in a systematic way — it’s more like advertising: journals often highlight those aspects they do ‘well’. With SciRev, everyone can review the review process, and there are also short comments that can be quite insightful. There are other such guides, like some wiki pages, but SciRev is the only one I know with a systematic procedure, and speaking of migration journals, the only one that spans different disciplines!

One thing that a generic guide like the PRIO guide will struggle to do is capture the prestige of journals in different circles of researchers. This is linked to the question of what kind of research typically gets published in the journals, and can be quite different to impact factors or Scimago rankings… not that a Q4 journal in Scimago will be considered high prestige by some, though. I guess there’s still value in ‘asking around’ a bit.

If you need more information about ‘green’ open access, there’s still https://v2.sherpa.ac.uk/romeo/

How to add text labels to a scatter plot in R?

Adding text labels to a scatter plot in R is easy. The basic function is text(), and here’s a reproducible example how you can use it to create these plots:

Adding text to a scatter plot in R

For the example, I’m creating random data. Since the data are random, your plots will look different. In this fictitious example, I look at the relationship between a policy indicator and performance. It is conventional to put the outcome variable on the Y axis and the predictor on the X axis, but in this example there’s no relationship to reality anyway… The reason I chose min and max values for the random variables here is that I jotted down this code as an explanation for a replication. In this example, we have 25 observations, for 25 units I call “cantons”. The third line here creates a string of characters “A” to “Y”, these are the labels!

policy = runif(25, min=0.4, max=0.7)
perfor = runif(25, min=500, max=570)
canton = sapply(65:89, function(x) rawToChar(as.raw(x)))

For the scatter plot on the left, we use plot(). Then we add the trend line with abline() and lm(). To add the labels, we have text(), the first argument gives the X value of each point, the second argument the Y value (so R knows where to place the text) and the third argument is the corresponding label. The argument pos=1 is there to tell R to draw the label underneath the point; with pos=2 (etc.) we can change that position.

plot(policy ~ perfor, bty="n", ylab="Policy Indicator", xlab="Performance", main="Policy and Performance")
abline(lm(policy ~ perfor), col="red")
text(perfor, policy, canton, pos=1)

The scatter plot on the right is similar, but here we actually plot the labels instead of the dots. There are two differences in the code: First, we add type="n" to create the scatter plot without actually drawing any circles (an empty plot if you will). Second, when we add the text in the third line of the code, we do not have pos=1, because we want to place the labels exactly where the points are.

plot(policy ~ perfor, bty="n", type="n", ylab="Policy Indicator", xlab="Performance", main="Policy and Performance")
abline(lm(policy ~ perfor), col="red")
text(perfor, policy, canton)

Calculating VIF by hand

A widespread measure of multicollinearity is the VIF (short for variance inflation factor). Multicollinearity describes the situation when the predictor variables in a multiple regression model are highly correlated, which is usually not desirable (assuming you haven’t gone Bayesian yet).

In R, the VIF can easily be calculated with a function in library car. It’s actually not difficult to do it by hand — which incidentally helps understand what we measure with the VIF, or why there is no different VIF for logistic regression models, or why the VIF is better than looking at bivariate correlations between predictors.

We start with some random data to run the multiple regression model. Here we create one outcome (y) and three predictor variables (x, z, a), full of random numbers. That’ll do for a demonstration.

x = runif(50) 
y = runif(50)
z = runif(50)
a = runif(50)

Here’s a simple OLS model:


m = lm(y ~ x + z + a)

If you have library car installed, you can easily calculate the VIF:

library(car)
vif(m)

To do it by hand, though, we run a linear regression model (OLS) for each of the predictors. Here’s the code for predictor x. One of the predictors becomes the outcome variable (here x), and the other predictors remain predictors. The variable used as the outcome previously (y) does not appear here.

mx = lm(x ~ z + a)

The VIF is simply: 1/(1-R²) of this model. In R, we can run the following:

1/(1-summary(mx)$r.squared)

Do policies matter? Exploring the Links between Indicators of Integration Policies and Outcomes through MIPEX

Our friends over at MPG are organizing a webinar on the impact of policies. Let’s be frank here, questions of causality will be a challenge, but with the data collected by MIPEX we’ll surely be able to make some headway in this crucial question:

‘Do policies matter? Exploring the Links between Indicators of Integration Policies and Outcomes through MIPEX‘. The webinar addresses what policymakers should know about the impacts of integration policies and what researchers should investigate in the future.

When? 28th May 2021 at 2PM CET

Where? Zoom

Registration and agenda here:

https://zoom.us/meeting/register/tJIod-irpz4jGNDsfx_n2iUgFQCGeKAzvMiF?fbclid=IwAR0h9xg5CMniiIVbqqLyCy9FrVTMc70bIvUFufxRf1PnJGVUJi_TL07wfXw

Agenda

2PM-2.10PM
Welcome and introduction – Giacomo Solano, Head of Research, MPG


2.10PM-2.25PM
Exploring the links between MIPEX and migrant integration outcomes: Lessons learned and new avenues for research – Thomas Huddleston, Research and Strategic Advisor, MPG and Giacomo Solano, Head of Research, MPG


2.25PM-2.45PM The effect of integration policies: which policies do matter and for whom?

Sol Juarez, Associate Professor in Public Health Sciences, Stockholm University

Maarten Vink, Professor of Citizenship Studies, European University Institute

Conrad Ziller, Assistant Professor in Political Sciences, University of Duisburg-Essen