Marta Kołczyńska

Sociology • Social Science Data & Methods

Toxicity of comments to votes in Request for Adminship on English Wikipedia

This post was written during a research visit at the Department of Computer Science at Aalto University, Finland, supported by the Helsinki Institute for Information Technology. Perspective is an API that uses machine learning models to predict the impact of a comment on the conversation. One of the models predicts the extent to which the comment might be perceived as toxic. A toxic comment is defined as “a rude, disrespectful, or unreasonable comment that is likely to make you leave a discussion.

Codebook from ISSP waves 1985-2017

The International Social Survey Programme offers a wealth of data, with thematic modules repeated around every 10 years, and a solid and relatively stable block of socio-demographics. The data can be downloaded from the GESIS data archive either in separate files per year or with data bundled by topic (e.g., the Social Inequality dataset contains data from rounds 1987, 1992, 1999, and 2009). There is no integrated codebook indicating the availability of variables in different rounds, so someone interested in longitudinal analyses would need to download all files, open them and look for the variables of interest.

Do-It-Yourself Harmonization: Exploring trust items in three European survey projects

Introduction Illustration: Trust in institutions Step 1: Preparation and coding of technical variables Step 2: Selection of source variables for harmonization Step 3: Mapping source values to target values Step 4: Harmonization Results: Availability of trust items Comparability of sample aggregates Appendices: Code examples Appendix 1: Data preparation Appendix 2: Codebook from labelled data in R Appendix 3: Values crosswalk Appendix 4: Harmonization Introduction Ex-post (or retrospective) data harmonization refers to procedures applied to already collected data to improve the comparability and inferential equivalence of measures collected by different studies.

Searchable codebook from labelled data in R

Working with categorical data, such as from surveys, requires a codebook. After spending some time unsuccessfully looking for a function that would create a nice, searchable codebook from labelled data in R, I decided to write my own. What I want to achieve is a simple table with variable names, labels, and frequencies of labelled values like the one below, to search for specific keywords in the value labels and to see distributions of various variables.

So you want to harmonize data?

So You Want to Write a Fugue? Glenn Gould So you want to write a fugue? You’ve got the urge to write a fugue You’ve got the nerve to write a fugue So go ahead and write a fugue that we can sing Pay no heed to what we’ve told you Give no mind to what we’ve told you Just forget all that we’ve told you And the theory that you’ve read

Political trust among electoral winners and losers in Europe

Winner-loser trust gap across countries Winner-loser trust gap in Poland Trust differences across parties in Poland Voting for a party that ends up losing the election is known to be associated with lower satisfaction with democracy and trust in the parliament (cf. Martini and Quaranta 2019). How does Poland compare to other European countries? How has the winner-loser trust gap changed in Poland over time, and how have trust levels among supporters of current and former ruling parties changed in periods when they were not in government?

Downloading country-level indicators on participation and economic inequality 2

Data Packages Varieties of Democracy (V-Dem): Dedicated package Polyarchy: Semicolon delimited CSV file -> rio Freedom House: Excel file with by-year sheets Polity IV: SPSS file -> rio Democracy Barometer: Excel file with header in top rows -> rio The Standardized World Income Inequality Database (SWIID): Plain CSV file -> rio World Bank’s World Development Indicators: Dedicated package Merging all datasets Writing to file Shortly after writing this post on importing datasets in different formats (CSV, XLS, XLSX, SAV) to R, I got the following comment:

Downloading country-level indicators on participation and economic inequality

Data Packages Varieties of Democracy (V-Dem): Dedicated package Polyarchy: Semicolon delimited CSV file Freedom House: Excel file with by-year sheets Polity IV: SPSS file Democracy Barometer: Excel file with header in top rows The Standardized World Income Inequality Database (SWIID): Plain CSV file World Bank’s World Development Indicators: Dedicated package Merging all datasets Country graphs Variable graphs Writing to file with Viktoriia Muliavka Social and political scientists often need to put together datasets of country-level political, economic, and demographic variables with data from different sources.

Voter shifts in parliamentary elections in Poland, 2007-2015

How 2015 voters voted in 2007 and 2011 How 2007 voters voted in 2011 and 2015 About POLPAN Where did the current governing party get their votes from? Did supporters of the previous ruling party switch preferences or did they abstain from voting altogether? Cross-sectional datasets, such as one-off election polls, do not typically provide data to answer these questions. Panel studies, such as the Polish Panel Survey (POLPAN), do.

Measuring meritocracy with survey data

Determining meritocratic allocation Calculating the distance to meritocracy Distance to meritocracy by country Meritocracy is a principle according to which rewards are based on merit, as well as an ideal situation resulting from the operation of this principle. In their 1985 Social Foces paper titled “How Far to Meritocracy? Empirical Tests of a Controversial Thesis”, Tadeusz Krauze and Kazimierz M. Słomczyński proposed an algorithm to construct a theoretical joint distribution of education and income, given their marginal distributions, that would satisfy the conditions of meritocratic allocation.