How to use different forms of reasoning to interpret data.

Excellent data analysis resources for qualitative researchers from Margaret Roller.

Devising new methods? Dr. Helen Kara discusses strategies for creative researchers.

Want to learn about Big Data analysis? Here are some open-access examples.

Welcome a new month's focus on data analysis, with Mentors in Residence Charles Vanover, Paul Mihas, and Johnny Saldana.

Want to learn more about photovoice? Here are some open-access articles!

How can you collect and analyze text you find online?

See two informative video interviews with Trena Paulus and co-authors Jessica Lester and Alyssa Wise.

Big Data = Big Topic. Start with the basics!

In the day-to-day of political communication, politicians constantly decide how to amplify or constrain emotional expression, in service of signalling policy priorities or persuading colleagues and voters. We propose a new method for quantifying emotionality in politics using the transcribed text of politicians’ speeches. This new approach, described in more detail below, uses computational linguistics tools and can be validated against human judgments of emotionality.

Institutions — rules that govern behavior — are among the most important social artifacts of society. So it should come as a great shock that we still understand them so poorly. How are institutions designed? What makes institutions work? Is there a way to systematically compare the language of different institutions? One recent advance is bringing us closer to making these questions quantitatively approachable. The Institutional Grammar (IG) 2.0 is an analytical approach, drawn directly from classic work by Nobel Laureate Elinor Ostrom, that is providing the foundation for computational representations of institutions. IG 2.0 is a formalism for translating between human-language outputs — policies, rules, laws, decisions, and the like. It defines abstract structures precisely enough to be manipulable by computer. Recent work, supported by the National Science Foundation (RCN: Coordinating and Advancing Analytical Approaches for Policy Design & GCR: Collaborative Research: Jumpstarting Successful Open-Source Software Projects With Evidence-Based Rules and Structures ), leveraging recent advances in natural language processing highlighted on this blog, is vastly accelerating the rate and quality of computational translations of written rules.

In the field of artificial intelligence (AI), Transformers have revolutionized language analysis. Never before has a new technology universally improved the benchmarks of nearly all language processing tasks: e.g., general language understanding, question - answering, and Web search. The transformer method itself, which probabilistically models words in their context (i.e. “language modeling”), was introduced in 2017 and the first large-scale pre-trained general purpose transformer, BERT, was released open source from Google in 2018. Since then, BERT has been followed by a wave of new transformer models including GPT, RoBERTa, DistilBERT, XLNet, Transformer-XL, CamemBERT, XLM-RoBERTa, etc. The text package makes all of these language models and many more easily accessible to use for R-users; and includes functions optimized for human-level analyses tailored to social scientists.

This interview was a show-and-tell about analyzing online talk.

There’s a validity problem with automated content analysis. In this post, Dr. Chung-hong Chan introduces a new tool that provides a set of simple and standardized tests for frequently used text analytic tools and gives examples of validity tests you can apply to your research right away.