Computer-aided text analysis is a technique that bridges qualitative and quantitative research, enabling the researcher to conduct quantitative analyses based on the rich narrative content of a text. This technique has been used in a broad range of literatures to measure constructs of interest unobtrusively and with nearly perfect reliability.

CAT Scanner is a free Windows-based application for conducting computer-aided text analyses. CAT Scanner was designed by Aaron F. McKenny, Assistant Professor of Management at the University of Central Florida and Dr. Jeremy C. Short, Professor and Rath Chair in Strategic Management in the Price College of Business at the University of Oklahoma.

CAT Scanner has three main tools:

First, CAT Scanner has a text file cleaning tool. When collecting texts from images or PDFs, garbage characters (e.g.,↑, ¤, or ¬) may be created as a result of the impreciseness of many optical character recognition tools. These garbage characters reduce the validity of computer-aided text analyses by breaking up otherwise coherent words. CAT Scanner can systematically eliminate special characters from text files, resulting in clean narratives, and by extension cleaner data.

Second, CAT Scanner has an inductive word list generation tool. When developing and validating custom dictionaries for computer-aided text analysis it is a best practice to use a combined deductive (theory-driven) and inductive (data-driven) approach (Short et al., 2010). CAT Scanner's inductive word list generation tool identifies all words that are used three or more times in a set of text files, facilitating the inductive aspect of dictionary development.

Finally, CAT Scanner has a dictionary-based computer-aided text analysis tool. CAT Scanner allows the user to use dictionaries developed by others or to develop their own custom dictionaries to measure any construct for which a word list can be developed.

Copyright 2009-2017 Aaron F. McKenny