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.
Getting and reshaping the data The Dot Plot The August edition of the Storytelling with Data challenge #SWDchallenge stars the dot plot. Here is a simple plot of the gender gap in voting in national elections using the most recent 8th Round of the European Social Survey, ESS. Getting and reshaping the data library(essurvey) # getting European Social Survey data library(tidyverse) # data cleaning and reshaping library(countrycode) # converting country codes to names library(ggplot2) # plots With the essurvey package the ESS data can be downloaded directly to R.
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?