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This module explores the fundamentals of score transformations and their impact on statistical measures. Discover why transformations are necessary, including their role in curving grades and adjusting data spread. Learn how adding or subtracting constants affects the mean without altering dispersion, while multiplying or dividing scores influences both. Gain insights into common transformations used in data analysis and how they can better reflect the underlying trends in educational and statistical data. Transform your understanding of these crucial concepts!
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Module 9 Score Transformations and Their Effects • Why transform? • Effects on Central Tendency • Effects on Dispersion • Commonly transformed scores
Why do it? • To curve grades! Higher or lower • When they are less or more spread out than origionaly
Effect of Transformations on MeanCentral Tendency • Transformations – applying the same adjustment to all scores in a data set • Effects on the mean • Adding or subtracting a constant to all scores will increase or decrease the mean by that constant • Multiplying or dividing all scores by a constant will cause the mean to be multiplied or divided by that score
Effect of Transformations on Dispersion • Effects on Dispersion • Adding or subtracting a constant to all scores will not effect measures of dispersion • Multiplying or dividing all scores by a constant will cause measures of dispersion to be multiplied or divided by that score
