Where values cannot rise higher (nearly everybody scores near 100% correct on a test). Negatively skewed data may be subject to a "ceiling," Positively skewed data may be subject to a "floor," where values cannot drop lower (nearly everybody scores near 0% correct on a test). The primary attribute for deciding upon a transformation is whether the data is positively skewed (skewed to right, skew > 0) or negatively skewed (skewed to left, skew < 0). You will then want to re-test the normality assumption before considering transformations. If you find outliers that were created by incorrect data entry, correct them. Extreme outliers may be the result of incorrect data entry (or computation). Double-check that these outliers have been coded correctly. These transformations are what you should first use.Ĭheck the data for extreme outliers. Before using any of these transformations, determine which transformations, if any, are commonly used in your field of research. Some transformation options are offered below.
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