Recall that for the normal distribution, the theoretical value of b 2 is 3. Checking normality for parametric tests in SPSS . In parametric statistical analysis the requirements that must be met are data that are normally distributed. If the data are normal, use parametric tests. Just make sure that the box for “Normal” is checked under distribution. SPSS Statistics Output. If it is, the data are obviously non- 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. If you perform a normality test, do not ignore the results. If the data are not normal, use non-parametric tests. 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. Data does not need to be perfectly normally distributed for the tests to be reliable. 4. 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. Hence, a test can be developed to determine if the value of b 2 is significantly different from 3. Normality tests based on Skewness and Kurtosis. The Kolmogorov-Smirnov and Shapiro-Wilk tests can be used to test the hypothesis that the distribution is 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. The normal distribution peaks in the middle and is symmetrical about the mean. Here two tests for normality are run. This video demonstrates conducting the Shapiro-Wilk normality test in SPSS and interpreting the results. 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. This test checks the variable’s distribution against a perfect model of normality and tells you if the two distributions are different. (SPSS recommends these tests only when your sample size is less than 50.) The following two tests let us do just that: The Omnibus K-squared test; The Jarque–Bera test; In both tests, we start with the following hypotheses: 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. D’Agostino (1990) describes a normality test based on the kurtosis coefficient, b 2. 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