Abstract
Recently when asked to help with statistical analyses of the Medicare Provider Analysis and Review (MEDPar) Data, I encountered a common statistical and analytical problem with this national database, which is also common to many national and health databases. This problem was the problem of extremely skewed distributions (g1 and/or g2 typically exceeding 20), which creates numerous difficulties in comparing and interpreting values and results both between and within variables, never mind statistically testing them.
Highlights
This problem was the problem of extremely skewed distributions (g1 and/or g2 typically exceeding 20), which creates numerous difficulties in comparing and interpreting values and results both between and within variables, never mind statistically testing them
Research Note Standard Scores Based on the Median and Inter-quartile Range
A researcher confronted with this problem will convert the raw scores to standard or “z” scores using the mean and standard deviation of each distribution to remove the relativity and make the scores directly comparable and interpretable
Summary
This problem was the problem of extremely skewed distributions (g1 and/or g2 typically exceeding 20), which creates numerous difficulties in comparing and interpreting values and results both between and within variables, never mind statistically testing them. Research Note Standard Scores Based on the Median and Inter-quartile Range
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