ACEP is an R package designed specifically for content analysis in Spanish. Learn about it and download the package.

Images contain information absent in text, and this extra information presents opportunities and challenges. It is an opportunity because one image can document variables with which text sources (newspaper articles, speeches or legislative documents) struggle or on datasets too large to feasibly code manually. Learn how to overcome the challenges.

Whether you call it ‘content analysis’, ‘textual data labeling’, ‘hand-coding’, or ‘tagging’, a lot more researchers and data science teams are starting up annotation projects these days. Learn how to avoid potential pitfalls.

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.

For centuries, being a scientist has meant learning to live with limited data. People only share so much on a survey form. Experiments don’t account for all the conditions of real world situations. Field research and interviews can only be generalized so far. Network analyses don’t tell us everything we want to know about the ties among people. And text/content/document analysis methods allow us to dive deep into a small set of documents, or they give us a shallow understanding of a larger archive. Never both. So far, the truly great scientists have had to apply many of these approaches to help us better see the world through their kaleidoscope of imperfect lenses.