Short, J. C., Broberg, J. C., Cogliser, C. C., & Brigham, K. H. (2010). Construct validation using computer-aided text analysis (CATA): An illustration using entrepreneurial orientation. Organizational Research Methods, 13, 320-347.

While specifically addressing dictionary validation, this article provides a step-by-step guide to researchers who want to develop, validate, and refine dictionaries for computer-aided text analysis. Researchers follow a two-phase (deductive and inductive) approach to dictionary development that helps maximize content validity of measures.

It was for this procedure that CAT Scanner’s “Inductive Word List Generation” tool was developed.

McKenny, A. F., Aguinis, H., Short, J. C., & Anglin, A. H. (2018). What doesn’t get measured does exist: Improving the accuracy of computer-aided text analysis. Journal of Management, 44, 2909-2933.

This article extends the Short et al. (2010) dictionary validation framework to include explicit consideration of measurement error in dictionary-bases computer-aided text analysis. Whether you are developing a new measure or refining an existing measure, this article provides point-by-point explanations of three major sources of measurement error in dictionary-based analyses, how to estimate their effects, and recommendations regarding how to minimize them in your research.

McKenny, A. F., Short, J. C., & Payne, G. T. (2013). Using CATA to elevate constructs in organizational research: Validating an organizational-level measure of psychological capital. Organizational Research Methods, 16, 152-184.

Whether you are using surveys or content analysis, elevating level of measurement is not as simple as adding the word “organizational” at the beginning of the construct name. This article draws from literature regarding elevating level of measurement using surveys to provide step-by-step guidance to researchers seeking to elevate level of measurement using dictionary-based computer-aided text analysis.