Analysis of Covariance (ANCOVA)
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- Dionisis Philippas 2
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The analysis of covariance (ANCOVA) is a technique that merges the analysis of variance (ANOVA) and the linear regression. The ANCOVA analyzes grouped data having a response (the dependent variable) and two or more predictor variables (called covariates) where at least one of them is continuous (quantitative, scaled) and one of them is categorical (nominal, non-scaled).
The ANCOVA technique allows analysts to model the response of a variable as a linear function of predictor(s), with the coefficients of the line varying among different groups. Briefly, the main idea is the inclusion of additional factors (covariates) as a statistical control to explain variation on the dependent variable, reduce the error variation, and increase the statistical power (sensitivity) of the underlying design. Thus, it differs from the analysis of variance (ANOVA) which is used to determine whether differences among test samples might be caused by random variation.
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European Commission, Joint Research Centre, Unit of Econometrics and Applied Statistics, Ispra, Italy
Dionisis Philippas
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Correspondence to Dionisis Philippas .
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Dipartimento di Scienze Statistiche, Sapienza Università di Roma, Roma, Roma, Italy
Filomena Maggino
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Department of Political Science, University of Naples Federico II, Naples, Italy
Mara Tognetti
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Philippas, D. (2023). Analysis of Covariance (ANCOVA). In: Maggino, F. (eds) Encyclopedia of Quality of Life and Well-Being Research. Springer, Cham. https://doi.org/10.1007/978-3-031-17299-1_82
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DOI : https://doi.org/10.1007/978-3-031-17299-1_82
Published : 11 February 2024
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