Alexandros Gotinakos's Avatar

Alexandros Gotinakos

@gotinakos.bsky.social

comparative political behavior, πŸ‡ͺπŸ‡Ί politics, measurement, surveys | Research Fellow Aristotle University of Thessaloniki @datisproject.bsky.social‬, #ActEU Horizon Project

154 Followers  |  143 Following  |  12 Posts  |  Joined: 24.09.2023  |  2.2845

Latest posts by gotinakos.bsky.social on Bluesky

Plot showing perception of voter ideology of candidates overtime. It is separated by House and Senate. Republicans are drifting slightly to be more conservative, Democrats are mostly staying in one place.

Plot showing perception of voter ideology of candidates overtime. It is separated by House and Senate. Republicans are drifting slightly to be more conservative, Democrats are mostly staying in one place.

Plot showing messaging ideology of candidates overtime. It is separated by House and Senate. There is increasing separation between parties over time, with Republicans becoming messaging more moderately during the Trump era and then shifting to the right during Biden.

Plot showing messaging ideology of candidates overtime. It is separated by House and Senate. There is increasing separation between parties over time, with Republicans becoming messaging more moderately during the Trump era and then shifting to the right during Biden.

New paper with @hjghassell.bsky.social and @michaelheseltine.bsky.social out in @bjpols.bsky.social.

We develop measures of voter perceptions of candidate ideology and candidate messaging ideology and find that perception's are related to what candidates say www.cambridge.org/core/journal...

03.10.2025 12:27 β€” πŸ‘ 31    πŸ” 17    πŸ’¬ 1    πŸ“Œ 2
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πŸ€” Do surveys exaggerate democratic support due to social desirability bias (SDB)?

➑️ Using survey-mode variation & list experiments in 24 countries, @pcmagalhaes.bsky.social & @aarslew.bsky.social find no evidence that SDB inflates democratic attitudes www.cambridge.org/core/journal... #FirstView

19.09.2025 06:58 β€” πŸ‘ 54    πŸ” 22    πŸ’¬ 1    πŸ“Œ 0

yes

24.09.2025 18:14 β€” πŸ‘ 32    πŸ” 5    πŸ’¬ 2    πŸ“Œ 0

Americans are most likely to encounter people from different economic classes in gas stations, restaurants and hotels. This mixing is least likely to happen in elementary and secondary schools, while exercising, and in supermarkets. www.sciencedirect.com/science/arti...

17.09.2025 19:38 β€” πŸ‘ 85    πŸ” 25    πŸ’¬ 4    πŸ“Œ 6
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ggplot2 4.0.0 A new major version of ggplot2 has been released on CRAN. Find out what is new here.

I am beyond excited to announce that ggplot2 4.0.0 has just landed on CRAN.

It's not every day we have a new major #ggplot2 release but it is a fitting 18 year birthday present for the package.

Get an overview of the release in this blog post and be on the lookout for more in-depth posts #rstats

11.09.2025 11:20 β€” πŸ‘ 847    πŸ” 282    πŸ’¬ 9    πŸ“Œ 51
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The Weird and Wonderful History of British Election Voter Segmentations This "Stockport Man" outlines his ironic (and unironic) love of the a proud British election tradition

The weird and wonderful history of British voter segmentations: the names, the ideas, the myths!

From how "the man on the Clapham omnibus" changed English common law to how "Gail's Voter" helped the Lib Dems gain 64 seats in 2024 to what works well when doing segmentation (and what doesn't)

09.09.2025 08:30 β€” πŸ‘ 20    πŸ” 12    πŸ’¬ 3    πŸ“Œ 4
In an experiment, Pew Research Center demonstrated that opt-in and probability-based surveys produced very different results about young adults' views of the Holocaust and abortion.

In an experiment, Pew Research Center demonstrated that opt-in and probability-based surveys produced very different results about young adults' views of the Holocaust and abortion.

Remember, if you encounter what seems like an implausible survey finding, ask:
1. Were survey respondents selected randomly or was this an opt-in poll?
2. Could the results, especially for young adults, be driven by bogus respondents?

Keep this post in mind: www.pewresearch.org/short-reads/... πŸ§ͺ

08.09.2025 18:42 β€” πŸ‘ 259    πŸ” 114    πŸ’¬ 5    πŸ“Œ 5

Full (and fun!) days at #ECPR2025 @ecpr.bsky.social last week, presenting and catching great panels with work from our @datisproject.bsky.social s and @eupoplink.bsky.social teams.

05.09.2025 09:56 β€” πŸ‘ 3    πŸ” 1    πŸ’¬ 0    πŸ“Œ 0

Thank you! Absolutely β€” hopefully we’ll cross paths soon =D

05.09.2025 09:26 β€” πŸ‘ 1    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0

Congratulations!!

04.09.2025 16:19 β€” πŸ‘ 1    πŸ” 0    πŸ’¬ 1    πŸ“Œ 0
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Large partisan differences on the effect of tariffs on the economy, though relatively little change over time. >50% of Reps say tariffs help. Those saying hurt the economy up slightly across partisanship; help economy down a bit with independents. @MULawPoll national surveys.

02.09.2025 19:02 β€” πŸ‘ 7    πŸ” 3    πŸ’¬ 0    πŸ“Œ 1

"Black cities were at the epicenter of fraud dialogue... electoral confidence deteriorated most for racially-resentful Whites post-election in 2020.... racially resentful White Americans are especially likely to believe accusations of fraud when...racialized"

link.springer.com/article/10.1...

29.08.2025 18:02 β€” πŸ‘ 59    πŸ” 13    πŸ’¬ 3    πŸ“Œ 4
Models as Prediction Machines: How to Convert Confusing Coefficients into Clear Quantities

Abstract
Psychological researchers usually make sense of regression models by interpreting coefficient estimates directly. This works well enough for simple linear models, but is more challenging for more complex models with, for example, categorical variables, interactions, non-linearities, and hierarchical structures. Here, we introduce an alternative approach to making sense of statistical models. The central idea is to abstract away from the mechanics of estimation, and to treat models as β€œcounterfactual prediction machines,” which are subsequently queried to estimate quantities and conduct tests that matter substantively. This workflow is model-agnostic; it can be applied in a consistent fashion to draw causal or descriptive inference from a wide range of models. We illustrate how to implement this workflow with the marginaleffects package, which supports over 100 different classes of models in R and Python, and present two worked examples. These examples show how the workflow can be applied across designs (e.g., observational study, randomized experiment) to answer different research questions (e.g., associations, causal effects, effect heterogeneity) while facing various challenges (e.g., controlling for confounders in a flexible manner, modelling ordinal outcomes, and interpreting non-linear models).

Models as Prediction Machines: How to Convert Confusing Coefficients into Clear Quantities Abstract Psychological researchers usually make sense of regression models by interpreting coefficient estimates directly. This works well enough for simple linear models, but is more challenging for more complex models with, for example, categorical variables, interactions, non-linearities, and hierarchical structures. Here, we introduce an alternative approach to making sense of statistical models. The central idea is to abstract away from the mechanics of estimation, and to treat models as β€œcounterfactual prediction machines,” which are subsequently queried to estimate quantities and conduct tests that matter substantively. This workflow is model-agnostic; it can be applied in a consistent fashion to draw causal or descriptive inference from a wide range of models. We illustrate how to implement this workflow with the marginaleffects package, which supports over 100 different classes of models in R and Python, and present two worked examples. These examples show how the workflow can be applied across designs (e.g., observational study, randomized experiment) to answer different research questions (e.g., associations, causal effects, effect heterogeneity) while facing various challenges (e.g., controlling for confounders in a flexible manner, modelling ordinal outcomes, and interpreting non-linear models).

Figure illustrating model predictions. On the X-axis the predictor, annual gross income in Euro. On the Y-axis the outcome, predicted life satisfaction. A solid line marks the curve of predictions on which individual data points are marked as model-implied outcomes at incomes of interest. Comparing two such predictions gives us a comparison. We can also fit a tangent to the line of predictions, which illustrates the slope at any given point of the curve.

Figure illustrating model predictions. On the X-axis the predictor, annual gross income in Euro. On the Y-axis the outcome, predicted life satisfaction. A solid line marks the curve of predictions on which individual data points are marked as model-implied outcomes at incomes of interest. Comparing two such predictions gives us a comparison. We can also fit a tangent to the line of predictions, which illustrates the slope at any given point of the curve.

A figure illustrating various ways to include age as a predictor in a model. On the x-axis age (predictor), on the y-axis the outcome (model-implied importance of friends, including confidence intervals).

Illustrated are 
1. age as a categorical predictor, resultings in the predictions bouncing around a lot with wide confidence intervals
2. age as a linear predictor, which forces a straight line through the data points that has a very tight confidence band and
3. age splines, which lies somewhere in between as it smoothly follows the data but has more uncertainty than the straight line.

A figure illustrating various ways to include age as a predictor in a model. On the x-axis age (predictor), on the y-axis the outcome (model-implied importance of friends, including confidence intervals). Illustrated are 1. age as a categorical predictor, resultings in the predictions bouncing around a lot with wide confidence intervals 2. age as a linear predictor, which forces a straight line through the data points that has a very tight confidence band and 3. age splines, which lies somewhere in between as it smoothly follows the data but has more uncertainty than the straight line.

Ever stared at a table of regression coefficients & wondered what you're doing with your life?

Very excited to share this gentle introduction to another way of making sense of statistical models (w @vincentab.bsky.social)
Preprint: doi.org/10.31234/osf...
Website: j-rohrer.github.io/marginal-psy...

25.08.2025 11:49 β€” πŸ‘ 943    πŸ” 283    πŸ’¬ 49    πŸ“Œ 19
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πŸ‡¬πŸ‡· @gotinakos.bsky.social presented Greece’s Country Report on Populism and Euroscepticism.
Focus on party strategies, public attitudes, and the Greek case in comparative perspective.

25.08.2025 16:45 β€” πŸ‘ 3    πŸ” 1    πŸ’¬ 0    πŸ“Œ 0

If you have some discretionary time, a coffee around the marina in Aretsou could be worth it, but you'll most likely need to commute by bus from the city center. Kastra can be lovely too, but ideally not during peak sun hours-also closer to campus.

25.08.2025 12:53 β€” πŸ‘ 0    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0

The problem with 'average over different coding or modelling decisions' is often we will find that different decisions aren't even targeting the same estimand, in ways that may not be clear from the outset

21.08.2025 20:39 β€” πŸ‘ 5    πŸ” 1    πŸ’¬ 1    πŸ“Œ 0

Our project's conference is taking place later this week ⬇️⬇️

19.08.2025 20:24 β€” πŸ‘ 2    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0

Join us on Monday!

19.08.2025 20:22 β€” πŸ‘ 3    πŸ” 0    πŸ’¬ 0    πŸ“Œ 0
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Pleased to share the latest version of my paper with Arthur Spirling and @lexipalmer.bsky.social on replication using LMs

We show:

1. current applications of LMs in political science research *don't* meet basic standards of reproducibility...

17.12.2024 19:50 β€” πŸ‘ 435    πŸ” 167    πŸ’¬ 18    πŸ“Œ 21
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DATIS project, funded by ELIDEK, participated with a presentation by @efteperoglou.bsky.social at EEPE & EKKE event on European Elections 2024, Political and Electoral Analysis, in Athens, on June 14.

28.07.2025 10:15 β€” πŸ‘ 5    πŸ” 1    πŸ’¬ 1    πŸ“Œ 0

The presentation was about the research results of DATIS (Deliverables 4.1, 4.2, 5.1) using @isspsurvey.bsky.social data with the title: β€œSequential Mixed Mode Surveys with Mobile Phone Numbers for Increased Coverage”.

28.07.2025 10:37 β€” πŸ‘ 4    πŸ” 1    πŸ’¬ 0    πŸ“Œ 0
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DATIS project, funded by ELIDEK, participated at the 78th Annual WAPOR Conference 2025, St. Louis, Missouri, USA, May 12-15, 2025, with a presentation by Professor @andreadis.bsky.social

28.07.2025 10:37 β€” πŸ‘ 5    πŸ” 1    πŸ’¬ 1    πŸ“Œ 0
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Ending Political, Electoral and Social Issue Advertising in the EU in Response to Incoming European Regulation From early October 2025, we will no longer allow political, electoral and social issue ads on our platforms in the EU. This is a difficult decision - one

Meta ends political ads on their platforms in the EU by October because they don't want to comply with new transparency and targeting regulations.

The reasons given here are really weak. And if you do *not* allow these ads, you still need to define what they are.

about.fb.com/news/2025/07...

26.07.2025 14:57 β€” πŸ‘ 40    πŸ” 18    πŸ’¬ 1    πŸ“Œ 0
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Interesting article on the (strong) relationship between political ideology and sport preferences in the U.S.

16.07.2025 19:07 β€” πŸ‘ 20    πŸ” 5    πŸ’¬ 0    πŸ“Œ 0
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New article out in World Politcs. We analyze how different groups react to varying programs of social democratic parties. We find less trade-offs than often assumed. Generally, more left-progressive programs increase support among social democratic potentials
muse.jhu.edu/pub/1/articl...

07.07.2025 09:49 β€” πŸ‘ 650    πŸ” 253    πŸ’¬ 14    πŸ“Œ 32
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An Ideology by Any Other Name - Political Behavior The terms β€˜liberal’ and β€˜conservative’ are prominent features of political discourse in the United States, and many citizens choose to identify with one of these ideological labels. Yet, many citizens...

In "An Ideology by Any Other Name," Andrew Trexler & Christopher Johnson find that many Americans identify with ideological labels beyond β€œliberal” or β€œconservative.” They show these alternative labels shape political attitudes and vote choice. Read more:
link.springer.com/article/10.1...

08.07.2025 05:10 β€” πŸ‘ 12    πŸ” 7    πŸ’¬ 0    πŸ“Œ 0
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"It's the economy, stupid"

Voters vote based on the economy.

But there are many economic performance indicators. Which matter the most?

This new paper shows voters react to growth, unemployment, inflation, & stock markets.

But the inflation plays the biggest role.

www.ifau.se/globalassets...

08.07.2025 13:44 β€” πŸ‘ 45    πŸ” 18    πŸ’¬ 2    πŸ“Œ 0
Abstract of the article "Partisan cueing and preferences for fiscal integration in the European Union" by Maurits J. Meijers, BjΓΆrn Bremer, Theresa Kuhn and Francesco Nicoli. Published online first in West European Politics.

Abstract of the article "Partisan cueing and preferences for fiscal integration in the European Union" by Maurits J. Meijers, BjΓΆrn Bremer, Theresa Kuhn and Francesco Nicoli. Published online first in West European Politics.

Table 1, displaying the expected effects of party cues on support for Eurobonds relative to the control group.

Table 1, displaying the expected effects of party cues on support for Eurobonds relative to the control group.

Figure 4, displaying the party cueing effects on support for Eurobonds by country.

Figure 4, displaying the party cueing effects on support for Eurobonds by country.

Figure 5, displaying the party cueing effecs on support for Eurobonds by EU integration priors.

Figure 5, displaying the party cueing effecs on support for Eurobonds by EU integration priors.

Online first: "Partisan cueing and preferences for fiscal integration in the European Union" by @mauritsmeijers.bsky.social @bjoernbremer.bsky.social @theresakuhn.bsky.social & @francesconicoli.bsky.social

doi.org/10.1080/0140...

#Academicsky #Polisky

03.06.2025 19:06 β€” πŸ‘ 11    πŸ” 4    πŸ’¬ 0    πŸ“Œ 2

political scientists democratising their institutions in the age of backsliding, talk about leading by example

27.06.2025 08:07 β€” πŸ‘ 30    πŸ” 9    πŸ’¬ 2    πŸ“Œ 0

@gotinakos is following 20 prominent accounts