Post-Processing Your Data#

You've made good progress encoding your qualitative data. Now what?

Depending on your research methodology, several options are available to you.

  • Write a report or synthesis based on your coded excerpts;
  • Calculate frequencies or other descriptive statistics;
  • Explore your data with more advanced computational methods (visualizations, machine learning, network analysis, etc.);
  • ...

Most of these actions are carried out outside of LibreQDA, using other tools. In fact, we adhere to the principle of modularity.

LibreQDA aims for simplicity. Rather than integrating a growing number of advanced analysis tools directly into the application, the team relies on exporting your data in open formats, which you can then rework with the tools of your choice. This modular approach keeps the interface simple and fast, while leaving people with more advanced needs free to choose the environment that suits them.

Reusing Coded Excerpts in a Text Editor#

A simple way to start writing is to first gather the relevant excerpts, then write around these quotes in your usual word processor.

  1. Locate the excerpts you want to cite using the code exploration tools.
  2. Export this selection of coded excerpts in .docx or .odt format.
  3. Open the exported file in your preferred text editor or word processor, and write your analysis around these quotes.

Exporting your project's .sqlite3 file to query it with a programming language#

The .sqlite3 file exported from the Project tab (see How to Export Material?) contains the structured set of your documents, codes, and coded selections. Since it's a standard SQLite database, it can be opened and queried by a wide variety of tools, independently of LibreQDA.

Languages like R (with packages such as DBI and RSQLite) or Python (with the built-in sqlite3 module) let you connect directly to this file and extract its tables as tabular data. For example, in R:

library(DBI)
con <- dbConnect(RSQLite::SQLite(), "my_project.sqlite3")
dbListTables(con)

Once your data is imported into R or another environment, you can carry out operations that are difficult or impossible to do directly in LibreQDA: producing statistical visualizations, applying machine learning methods, running network analysis, and so on.

Going further#

See Qualitative Coding of Data for a broader discussion of LibreQDA's relative methodological neutrality and the limits of its quantification features.