Confirmatory data analysis (CDA) is an approach to data analysis that seeks to test a given set of hypotheses in order to ascertain the validity of a particular set of results. CDA is often used to validate the results of a preliminary analysis and to identify any outliers or other anomalies. CDA is commonly used in research contexts, such as medicine, psychology, and sociology, as well as in business and engineering applications.

In a confirmatory data analysis, the analyst first develops a set of hypotheses about the data. These hypotheses may be based on past studies, existing theories, or the analyst’s own experience. The analyst then uses a variety of techniques to analyze the data and determine whether the hypotheses are supported. These techniques may include regression analysis, factor analysis, structural equation modeling, and other statistical methods.

The purpose of CDA is to establish the validity of the results obtained. Once the analyst has established that the hypotheses are valid, they can begin to draw conclusions about the data. These conclusions may then be used to inform subsequent decisions or actions. CDA is particularly valuable when the data set is large and complex, and when the results of an initial analysis are inconclusive.

The strength of the conclusions drawn from CDA depends on the quality of the analyses performed. The analyst should always ensure that the analyses are appropriate for the data set and the hypotheses being tested. In addition, the analyst should pay careful attention to any potential outliers or other anomalies in the data.

CDA can be a powerful tool for data analysis. By providing a rigorous examination of the data, it can help the analyst draw meaningful conclusions about the data set. However, it is important to remember that CDA is only as good as the assumptions and analyses that are used to support it.


Callegaro, M., & Weisberg, H. (2020). Data analysis: Confirmatory data analysis. In S. Hesse-Biber & P. Leavy (Eds.), Handbook of Qualitative Research (pp. 498-510). Thousand Oaks, CA: Sage.

Kline, R. B. (2015). Principles and practice of structural equation modeling (4th ed.). New York, NY: Guilford Press.

Smith, D. (2018). Statistics and data analysis for the social sciences. London, UK: Routledge.

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