Cleaning and Assessing Continuous Data using MEANS, UNIVARIATE, and BOXPLOT

Charles DiMaggio
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Charles DiMaggio: Columbia University, Departments of Anesthesiology and Epidemiology College of Physicians and Surgeons Mailman School of Public Health

Chapter Chapter 10 in SAS for Epidemiologists, 2013, pp 139-157 from Springer

Abstract: Abstract In this chapter, we continue and expand on our brief introduction to continuous data from our consideration of PROC MEANS in Chap. 6. From an epidemiological perspective, the descriptive procedures in this chapter may be all that are needed to give us some summary statistics like means and medians, or simple graphical comparisons. They are often useful for data “cleaning” by providing tools to look for missing data, identifying outlier or erroneous values, and getting an overall sense of the data. These procedures, which in addition to PROC MEANS include PROC UNIVARIATE and PROC BOXPLOT, are also used to evaluate data for the assumptions for analyses such as ANOVA or linear regression. We will be talking (a lot) about statistical significance, variability, etc., and it’s easy to get caught up in the ideal of statistical significance as its own end. But, remember: clinical or epidemiological importance is what we’re really interested in.

Keywords: Sample Standard Deviation; Normal Probability Plot; Proc UNIVARIATE; Default Statistic; Discharge Disposition (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1007/978-1-4614-4854-9_10

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