Analysis of Covariance (ANCOVA): Optimal Method for Analyzing Pre-post Data with Repeated Measures Two Groups under Experimental Design in Health Science Research

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Pongdech Sarakarn
Pallop Siewchaisakul
Putthikrai Pramual
Nuntiput Putthanachote
Donlagon Jumparway


In health science research, measuring for two groups of pre-post data with repeated measures can be found in an experimental design, including randomized and quasi-experiments. Most of the aim was to evaluate and compare the interesting outcome that was affected differently between the intervention and control groups. Numbers of statistical methods have been adopted for analyzing these kinds of research, but most researchers are still confused about utilizing information to make decisions about choosing the optimal method. This may cause misunderstandings that affect the accuracy and certainty of the results. Therefore, the objective of this article is to introduce and synthesize the six statistical methods for analyzing pre-post data with repeated measures in two groups, including (1) comparing of pre-post data with a t-test, (2) one-way analysis of variance (one-way ANOVA) with post-outcome, (3) one-way ANOVA with change score, (4) one-way ANOVA with percent changes, (5) repeated measures ANOVA, and (6) analysis of covariance (ANCOVA). The results found that ANCOVA is a simple and efficient approach for analyzing pre-post data with repeated measures in two groups compared with other methods, especially in randomized design. In a quasi-experimental design, the cluster or hierarchy distribution of the characteristics is an issue that should be considered before being allocated to the group. However, there it is still necessary to report of the results of each assumption examination and comprehensively reporting of study results consistent with research questions.

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Sarakarn P, Siewchaisakul P, Pramual P, Putthanachote N, Jumparway D. Analysis of Covariance (ANCOVA): Optimal Method for Analyzing Pre-post Data with Repeated Measures Two Groups under Experimental Design in Health Science Research. Health Sci J Thai [Internet]. 2024 Jan. 24 [cited 2024 Apr. 20];6(1):42-51. Available from:
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