SPSS tutorial: interpreting Kolmogorov-Smirnov, Shapiro-Wilk, and Q-Q plots скачать в хорошем качестве

SPSS tutorial: interpreting Kolmogorov-Smirnov, Shapiro-Wilk, and Q-Q plots

Normality analysis is a mandatory assumption checking step before performing parametric tests in econometrics, such as t-tests, analysis of variance, or linear regression models. The core purpose of this procedure is to evaluate whether the sample data is actually distributed according to a symmetrical bell shape. To execute this on the software, users access Analyze, select Descriptive Statistics, and click on Explore. In the appearing dialog box, transfer the quantitative variable to be analyzed into the Dependent List box. Next, click on the Plots button, check Histogram along with the most important option, Normality plots with tests, then press Continue and OK. Users can also run this command by navigating to Analyze, selecting Nonparametric Tests, opening Legacy Dialogs, clicking 1-Sample K-S, and checking Normal. The normality test results are evaluated through the Sig. value of the Kolmogorov-Smirnov or Shapiro-Wilk tests. If p is greater than 0.05, we fail to reject the null hypothesis and conclude that the data is normally distributed. You can also combine observing the Histogram, Q-Q plot, or the Skewness and Kurtosis indices to make the most accurate conclusion.