Show simple item record Park, Hun Myoung 2015-03-25T20:07:27Z 2015-03-25T20:07:27Z
dc.description.abstract Descriptive statistics provide important information about variables to be analyzed. Mean, median, and mode measure central tendency of a variable. Measures of dispersion include variance, standard deviation, range, and interquantile range (IQR). Researchers may draw a histogram, stem-and-leaf plot, or box plot to see how a variable is distributed. Statistical methods are based on various underlying assumptions. One common assumption is that a random variable is normally distributed. In many statistical analyses, normality is often conveniently assumed without any empirical evidence or test. But normality is critical in many statistical methods. When this assumption is violated, interpretation and inference may not be reliable or valid. The t-test and ANOVA (Analysis of Variance) compare group means, assuming a variable of interest follows a normal probability distribution. Otherwise, these methods do not make much sense. Figure 1 illustrates the standard normal probability distribution and a bimodal distribution. How can you compare means of these two random variables? There are two ways of testing normality (Table 1). Graphical methods visualize the distributions of random variables or differences between an empirical distribution and a theoretical distribution (e.g., the standard normal distribution). Numerical methods present summary statistics such as skewness and kurtosis, or conduct statistical tests of normality. Graphical methods are intuitive and easy to interpret, while numerical methods provide objective ways of examining normality. en
dc.rights Copyright 2015 by the Trustees of Indiana University. This content is released under the Creative Commons Attribution 3.0 Unported license ( en
dc.rights.uri en
dc.subject Univariate Analysis, Normality Test, SAS, Stata, and SPSS en
dc.title Univariate Analysis and Normality Test Using SAS, Stata, and SPSS en
dc.altmetrics.display false en

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