What is the Best Way to Analyze Pre–Post Data?

Abstract

Using pre- and posttest scores from two sections of natural science learning on theme 6 and 7 offered during January–February 2020 at 5th-A grade of MI NU Tasywiquth Thullab Salafiyyah (TBS) Kudus, we estimate how the commonly methods used in the PER, DBER, nor STEM-based education research literature i.e. comparing 1) raw change scores; 2) normalized gain scores; 3) normalized change scores; and 4) effect sizes; can lead to erroneous conclusions, and not an effective framework for distinguishing the impact of an instructional intervention from the impact of student characteristics on test score gains. It reveals the fundamental problem of four methods to analyze pre–post data.

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