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.
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).
How it all started
Step 1. R
Step 2. On-line resources
Step 3. Done is better than perfect
References
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.
