– How to read a log scale: Same distance, same growth rate
– Fitting Bayesian Models using Stan and R
– A visual introduction to machine learning
– If You Say Something Is “Likely,” How Likely Do People Think It Is?
– Tidy Eval Meets ggplot2
– From data to viz
– Analyzing IMDb Data The Intended Way, with R and ggplot2
– A compendium of methods and stats resources for (social) psychologists
– How to read a log scale: Same distance, same growth rate
Har d.d. en kommentar i Berlingske med titlen “Nedskæringer bliver ikke straffet af vælgerne”. I kommentaren konkluderer jeg:
En kløgtig regering, der agerer strategisk i forhold til næste valg, skal dermed ikke holde sig fra nedskæringer, men tværtimod se dem som et politisk redskab til at bevare magten. Med andre ord kan regeringer trygt skære ned i de politikker, som ikke bruges af de vælgere, der vil stemme på dem – og bruge ressourcer på de politikker, som potentielle vælgere favoriserer.
Den kan ligeledes læses online her (kræver abonnement).
Hos Politiken kan man læse, at “Socialdemokratiet går tilbage i ny meningsmåling”. Artiklen bærer titlen “Efter enegang: Socialdemokratiet er gået tilbage siden folketingsvalget, viser ny måling”. Lad os kigge nærmere på den nye måling.
Som altid – når der er en ny måling – er det vigtigt at placere den i den rette kontekst. Ingen måling står sig godt ud alene, hvorfor Figur 1 viser Socialdemokratiets opbakning i målingerne fra 2018, hvor jeg ligeledes har angivet, hvilken der er den nyeste fra Megafon.
Figur 1: Socialdemokratiets opbakning i meningsmålingerne, 2018
Socialdemokratiet fik som bekendt 26,3% af stemmerne ved folketingsvalget i 2015. I omtrent alle målinger foretaget i år ligger Socialdemokratiet på niveauet omkring folketingsvalget eller højere. Der er ingen systematisk evidens for, at Socialdemokratiet er gået tilbage siden valget.
Det eneste sted vi finder denne historie er i en artikel om én måling fra Megafon. For et par år siden var jeg ude og kritisere Megafons målinger (og dækningen af samme) i forhold til Socialdemokraternes opbakning, og intet tyder på, at det er blevet meget bedre. Det kan undre mig, at journalister og politiske kommentatorer hopper i med begge ben.
Endnu mere interessant er det da også, at artiklen forsøger at koble denne tilbagegang på Mette Frederiksens udmelding om ikke at danne regering med Det Radikale Venstre: “Målingen kommer, efter at partiformand Mette Frederiksen annoncerede, at hun vil gå til valg på at danne en regering kun bestående af Socialdemokratiet. Dermed ønsker hun at droppe 25 års parløb med Det Radikale Venstre.”
Hvorfor er dette interessant? Fordi der ikke er nogen evidens for et statistisk signifikant fald i meningsmålingerne fra den forrige Megafon til den seneste fra samme institut. I den forrige måling fra Megafon (fra 31. maj) fik Socialdemokratiet 25,1% af stemmerne. Som altid kan jeg anbefale denne side, hvor du kan indtaste tal fra to målinger og få svar på, om der er en signifikant forskel mellem to målinger. Det er der ikke i nærværende tilfælde.
Artiklen hos Politiken afsluttes blandt andet med ordene: “Politiken har forsøgt at få en kommentar fra Nicolai Wammen, politisk ordfører for Socialdemokratiet. Han er ikke vendt tilbage”. Dette er der absolut intet at sige til, når det vedrører den slags jammerlige målinger fra Megafon.
I en ny meningsmåling foretaget for tænketanken Concito vises det, at klimaet er det vigtigste emne for vælgerne. Dette er dog ikke uden væsentlige forbehold.
Min første bekymring, da jeg hørte om meningsmålingen via en journalist fra Mandag Morgens TjekDet, var, at respondenterne i undersøgelsen nok var stillet andre spørgsmål om klimaet forud for spørgsmålet omkring, hvilket emne de fandt vigtigt. Dette ville føre til, at de ville finde klimaet vigtigere.
Det viste sig også at være korrekt, at respondenterne havde fået stillet spørgsmål omkring klimaet forud for det relevante spørgsmål. Dette gør at undersøgelsen ikke er retvisende, hvilket jeg har udtalt mig om sammen med andre forskere. Artiklen kan findes her.
Udtaler mig hos Altinget omkring, hvordan det går Nye Borgerlige i meningsmålingerne. Det kan findes her. I artiklen, der desværre er bag en betalingsmur, citeres jeg blandt andet for:
“Når det er sagt, skal man altid være skeptisk, når nogle målinger er markant anderledes. I mange målinger ligger Nye Borgerlige omkring 2 procent, og derfor tror jeg, at 4-5 procent til partiet er for højt sat. Kort sagt er der langt mere evidens for, at Nye Borgerlige ligger tættere på spærregrænsen, end at de er langt over den.”
At what age are people being described as being old? I saw the figure below getting a lot of attention on Twitter with the description: ‘As a kid my dad told me “The age you consider ‘old’ is the square-root of your age times 10”. At 9 you think 30 is old, at 16 you think 40, etc. Turns out he was wrong. It’s the square-root of your age times 8.’
That is a great fit, i.e. the overlap between the blue and the orange line, but is it true? A follow-up tweet describes that the figure shows the answers from ~200 people. That is not a lot.
I always tell my students that they should never go out and collect low-quality data if they can download high-quality secondary data for free. Luckily, the European Social Survey (round 4) provides data on this exact question for more than 50,000 respondents. Using this data, I replicated the figure:
In this figure we do not see as great a fit as in the other figure. For 18-year-olds the average answer is 57 years. For 25-year-olds the average answer is 60. This is far from the estimates we get with the square-root of the respondent’s age times 8.
The lesson? Do not overfit your model (especially not to your N≈200 sample).
– ML beyond Curve Fitting: An Intro to Causal Inference and do-Calculus
– Xenographics: Weird but (sometimes) useful charts
– An R package for sensitivity analysis (konfound)
– How to Choose and Design the Perfect Chart
– Digging deeper: online resources for intermediate to advanced R users
– Animated Directional Chord Diagrams
– Making a twitter dashboard with R
– Why not to use two axes, and what to use instead
– Teaching difference-in-differences
– factoextra: Extract and Visualize the Results of Multivariate Data Analyses
– What to consider when choosing colors for data visualization
– Clickbait-Corrected p-Values
Recently, there has been a lot of focus on the implications of using Facebook. One study, “The Facebook Experiment: Quitting Facebook Leads to Higher Levels of Well-Being“, argues that people who leave Facebook feel better with their lives. Matthew Yglesias talks about the study in this clip from Vox:
The study also got some attention back in 2016 when it was published (see e.g. The Guardian). This is not surprising as the study presents experimental evidence that people who are randomly assigned to not using Facebook felt better with their lives on a series of outcomes.
The only problem is that the study is fundamentally flawed.
The study finds that people who did not use Facebook for a week reported significantly higher levels of life satisfaction. The design relied on pre and post test measures from a control and treatment group, where the treatment group did not use Facebook for a week. The problem – and the reason we should not believe the results – is that people who took part in the study were aware of the purpose of the experiment and signed up with the aim of not using Facebook! In short, this will bias the results and thereby have implications for the inferences made in the study. Specifically, we are unable to conclude whether the differences between the treatment and the control group is due to an effect of quitting Facebook or is an artifactual effect.
First, when respondents are aware of the purpose of the study, we face serious challenges with experimenter demand effects. People assigned to the treatment group will know that they are expected to show positive reactions to the treatment. In other words, there might not be a causal effect of not being on Facebook for a week, but simply an effect induced by the design of the study.
An example of the information available to the respondents prior to the experiment can be found in the nation-wide coverage. The article (sorry – it’s in Danish) informs the reader that the researchers expect that using Facebook will have a negative impact on well-being.
Second, when people know what the experiment is about and sign up with the aim of not using Facebook, we should expect a serious attrition bias, i.e. that people who are not assigned to their preferred treatment will drop out of the experiment. In other words, attrition bias arises when the loss of respondents is systematically correlated with experimental conditions. This is also what we find in this case. People who got the information that they should continue to use Facebook dropped out of the study.
Figure 1 shows the number of subjects in each group before and after the randomisation in the Facebook experiment. In short, there was a nontrivial attrition bias, i.e. people assigned to the control group dropped out of the study.
Figure 1: Attrition across conditions
The dashed line indicates the attrition bias. We can see that the control group is substantially smaller than the treatment group.
Third, when people sign up to an experiment with a specific purpose (i.e. not using Facebook), they will be less likely to comply with their assigned treatment status. This is also what we see in the study. Specifically, as is described in the paper: “in the control group, the participants’ Facebook use declined during the experiment from a level of 1 hour daily use before the experiment to a level of 45 minutes of daily Facebook use during the week of the experiment.” (p. 663)
These issues are problematic and I see no reason to believe any of the effects reported in the paper. When people sign up to an experiment with a preference for not being on Facebook, we cannot draw inferences beyond this sample and say anything about whether people will be more or less happy by not using Facebook.
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– Sentiment Analysis of 5 popular romantic comedies
– How to justify your alpha: step by step
– Comprehensive list of color palettes available in R
– Visualizing Outliers
– Generating codebooks in R
– Introducing geofacet
– Causal Inference: The Mixtape
– Prime Hints For Running A Data Project In R
– Introducing Raincloud Plots!
– What to consider when creating choropleth maps
– Statistical vignette of the day as a teaching tool
– Recreate – Sankey flow chart
– Data: Sharing Is Caring
– ggplot2: How Geoms & Aesthetics ≈ Whipped Cream
– Animated population pyramids in R
There is a lot of new and interesting academic research coming out every day. Working papers, book chapters (you can usually ignore these), journal articles, books etc. So, how to stay up to date on all this new research? Here are my personal recommendations.
First and most importantly: Twitter. This is by far the easiest way to keep yourself updated. You don’t need to (re)tweet or in any other way engage in the conversations on Twitter, but you should at least have an account and follow your favourite scholars1.
Luckily, it is impossible not to hear about new research from a person if you follow that person on Twitter. Furthermore, people are usually good at tweeting about interesting research similar to their own interests (which hopefully will overlap with your interests).
That also brings us to the challenge of using Twitter: information overload. The more people you follow on Twitter, the more difficult it is to ensure that you notice the tweets relevant to you. It is very easy to follow new people on Twitter. Good Twitter use is not about following as many researchers as possible but about optimizing the signal-to-noise ratio, i.e. seeing more relevant tweets and less irrelevant tweets.
I can recommend that you do a mental cluster analysis and create (private) lists of people connected within their respective domains. For example, you can create lists with academics within different fields/topics (U.S. political scientists, European political scientists, open science, R, economists, psychologists, sociologists etc.)
While there is an overlap between the different lists, they can structure your Twitter use and make it easier to stay up to date on what is going on compared to one major feed with everybody, especially if you are offline or busy not being on Twitter for multiple days and eventually have to catch up. You can read more about lists on Twitter here.
Second, Google Scholar. An important feature of Google Scholar is that you can follow researchers, articles and key words (so-called email alerts). If you follow a researcher on Google Scholar, this will give you a mail notification when the person has new research. You can also follow citations to that persons, i.e. get mail notifications on the new research that is citing work by the person.
Within any scientific subfield there is usually a review piece or two that everybody cites. It is a good idea to sign up for notifications in relation to those articles so you get a mail when there is new work that cite this work. Last, if you work with specific concepts it is a good idea to follow such key words as well.
Third, journal RSS feeds. This was my main method for years, basically getting notifications about the most recent number of a journal and/or articles available in advance/FirstView. I still follow the journals but it is getting less useful for three reasons. First, there is a heavy delay so you have often seen the work months (if not years) in advance of the actual publication (especially if you use the two methods above). Second, there is an overlap with the above methods, so if anything relevant is coming out, you can be sure that it will reach your Twitter feed. Third, going back to the signal-to-noise ratio, the more generic journals you follow, the more irrelevant research will end up in your feed.
These are just a few of the ways in which you can find new research (again, my recommendations). If you want another example on how you can find new research in line with your interests, see this tweet from John B. Holbein (he usually tweets a lot of interesting political science research).
- If they are not on Twitter you should reconsider whether they are in fact your favourites. [↩]