I have very simple question: can SPSS indicate whether distribution is normal? I need it for my SPSS research paper and can’t find any distinct answer nowhere.
SPSS research methods have tools to test or determine whether or not your dataset is of normal distribution. In your SPSS Dataview, go to Analyze – Descriptive Statistics – Explore – Plots and click on the Normality plots with test. In the SPSS output window look at the skewness score – it should be close to zero to indicate a normal (symmetric) distribution, or kurtosis – it should also be zero to indicate that the distribution is normal. Lastly, you should look at the Kolmogorov-Smirnov – this is SPSS research method’s test for normality. The p-value (sig value) should be less than or equal to 0.05 to indicate that the distribution is of normal distribution. SPSS research method also has a Shapiro-Wilk test, which also a test for normality specifically designed for databases with less than 50 cases. You can also refer to the sig value of the Shapiro Wilk Test and it should also be less than or equal to 0.05 to conclude normality of the distribution.
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SPSS research methods have tools to test or determine whether or not your dataset is of normal distribution. In your SPSS Dataview, go to Analyze – Descriptive Statistics – Explore – Plots and click on the Normality plots with test. In the SPSS output window look at the skewness score – it should be close to zero to indicate a normal (symmetric) distribution, or kurtosis – it should also be zero to indicate that the distribution is normal. Lastly, you should look at the Kolmogorov-Smirnov – this is SPSS research method’s test for normality. The p-value (sig value) should be less than or equal to 0.05 to indicate that the distribution is of normal distribution. SPSS research method also has a Shapiro-Wilk test, which also a test for normality specifically designed for databases with less than 50 cases. You can also refer to the sig value of the Shapiro Wilk Test and it should also be less than or equal to 0.05 to conclude normality of the distribution.