If it is, the data are obviously non- normal. Here two tests for normality are run. Normality tests based on Skewness and Kurtosis. If the data are normal, use parametric tests. D’Agostino (1990) describes a normality test based on the kurtosis coefficient, b 2. The normal distribution peaks in the middle and is symmetrical about the mean. However, it is almost routinely overlooked that such tests are robust against a violation of this assumption if sample sizes are reasonable, say N ≥ 25. Checking normality for parametric tests in SPSS . 4. You can reach this test by selecting Analyze > Nonparametric Tests > Legacy Dialogs > and clicking 1-sample KS test. This video demonstrates conducting the Shapiro-Wilk normality test in SPSS and interpreting the results. The Kolmogorov-Smirnov and Shapiro-Wilk tests can be used to test the hypothesis that the distribution is normal. 3. The Kolmogorov-Smirnov test is often to test the normality assumption required by many statistical tests such as ANOVA, the t-test and many others. SPSS Statistics Output. This test checks the variable’s distribution against a perfect model of normality and tells you if the two distributions are different. The test statistics are shown in the third table. Just make sure that the box for “Normal” is checked under distribution. If the data are not normal, use non-parametric tests. Hence, a test can be developed to determine if the value of b 2 is significantly different from 3. Recall that for the normal distribution, the theoretical value of b 2 is 3. While Skewness and Kurtosis quantify the amount of departure from normality, one would want to know if the departure is statistically significant. One of the assumptions for most parametric tests to be reliable is that the data is approximately normally distributed. Normal distributions can be divided up into the same proportions by the standard deviations, so 95% of the area under the curve lies within roughly plus or minus two standard deviations of the mean; In this video Jarlath Quinn demonstrates how to use the functions within the explore command in SPSS Statistics to test for normality. If you perform a normality test, do not ignore the results. How to Shapiro Wilk Normality Test Using SPSS Interpretation | The basic principle that we must understand is that the normality test is useful to find out whether a research data is normally distributed or not normal. The hypotheses used in testing data normality are: Ho: The distribution of the data is normal Ha: The distribution of the data is not normal. You will now see that the output has been split into separate sections based on the combination of groups of the two independent variables. 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