Political participation in Poland Latent class analysis Three types of participants: the Disengaged, Activists, and Protesters Region maps I recently came across Jennifer Oser’s 2017 article in Social Indicators Research about “political tool kits”, i.e. profiles (or patterns) of participation in different political activities. Her general argument is that research on citizen participation would benefit from analyses of such participation patterns instead of (or at least in addition to) just looking at determinants of participation in single activities.
Sample correlations Sample correlations by gender Sample correlations by age Sample correlations by education Contrast Conclusion One of the reasons for the harmonization of personal income in addition to household income was to check if the two correlate highly enough to use household income as a substitute for personal income in analyses where economic status is a control variable. This would be great, because household income variables are available in 1177 surveys out of 1721 analyzed in the Survey Data Recycling dataset (SDR) version 1, while personal income only in 453 surveys.
Data Number of response options Item non-response Distributions Harmonized target variables Next steps with Przemek Powałko Individual economic status is a necessary element of almost all sociological analyses, including studies of political attitudes and behavior. To supplement the already harmonized variables in the Survey Data Recycling dataset (SDR) version 1 and for the purposes of my resesarch of the effects of education on political engagement, Przemek and I harmonized two additional variables: personal income and household income1.
Political participation in the ESS Country levels of political participation Inequality of political participation Democracy indicators Economic inequality Matrix scatter plots How to measure political inequality? The Variaties of Democracy project (V-Dem) has a set of political equality indicators that capture the extent to which political power is distributed according to wealth and income, membership in a particular social group, gender or sexual orientation (cf. V-Dem Codebook v.
Cross-national survey projects conduct surveys on representative samples of adult populations. How do the distributions of respondents’ age vary across surveys carried out in the same country in different years and different projects? Like in a couple of previous posts (here, here and here) I use data from the Survey Data Recycling dataset (SDR) version 1, which includes selected harmonized variables from 22 cross-national survey projects. SDR only includes surveys that claim to have samples representative for adult populations.
Data Differences within country-years Differences by groups Gender Age Urban/rural residence Education Sampling scheme The growth in cross-national survey projects in the last decades leads to situations when two or more surveys are carried out in the same country and the same year but in different projects, and contain overlapping sets of survey questions. Assuming that the surveys are based on representative samples - a claim that major cross-national survey projects typically make - it could be expected that estimates from surveys carried out in the same country and year are reasonably close.
Setup tidy TED talks Applause, LOL Sentiment This year I spent two weeks of the summer attending the Summer Institute for Computational Social Science Parter Site (SICSS) in Tvärminne and Helsinki, Finland, organized by Matti Nelimarkka from Aalto University and the University of Helsinki, assisted by two TAs: Juho Pääkkönen and Pihla Toivanen from the University of Helsinki. I highly recommend it to anyone with background in the social sciences and interested in computer and data sciences, or the other way around!
Educational attainment data OECD data SDR data Cleaning and merging SDR and OECD data Results The curious case of ISSP Switzerland Conclusion Appendix with Przemek Powałko General population surveys with representative samples should have a similar education structure as shown by data from administrative sources, especially if survey weights are used. In this post we compare sample aggregates from 15 cross-national survey projects (including the European Social Survey, the World Values Survey and the European Values Study, and others) from the Survey Data Recycling database with educational attainment statistics from the OECD.
Instructions References In the previous post I wrote about downloading and exploring the Survey Data Recycling (SDR), version 1 dataset, which consists of selected harmonized variables from 22 survey projects, 1966-2013. The SDR project will develop a website for browsing, subsetting, downloading, and visualizing data from the SDR project. This website is currently under construction. Meanwhile, I made a Shiny app with basic functionalities of the future on-line browsing and subsetting tool (also serves as its mock-up): https://mkolczynska.
Introduction Downloading the SDR data Exploring SDR: availability of variables by project Exploring SDR: availability of variables with different formulations Identifying surveys containing selected variables Subsetting the Master File Country coverage plot Combining data from different survey projects creates new opportunities for research, alas, at the cost of increased volume (obviously) and complexity of the data. The Survey Data Recycling project created a dataset with data from 22 international survey projects.
Getting data from Twitter Tweets over time Text analysis Tweets by ISA Resesarch Committee The International Sociological Association 19th World Congress of Sociology in Toronto (15-21 July) has received quite some Twitter coverage. Waiting to board the flight back to Warsaw, I wanted to take a look at these Twitter data and apply the newly acquired skills in text analysis (thanks to the Summer Institute for Computational Social Science, SICSS, Partner Site in Tvärminne and Helsinki, Finland).
This blog is going to be mostly about my adventures with R, primarily using survey data, and usually somewhat related to my social science interests; for the fun of it, to share code and hopefully get feedback. How it all started General law of academia: The capacity for generating ideas is greater than the capacity of developing ideas into papers. So why write a blog and not keep a diary or a plain text file sitting safely on your hard drive, kochanie?