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What normality tests can be performed in SPSS research?

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What normality tests can be performed in SPSS research? What is the aim of normality tests? When are they used in SPSS research?

1 comment to What normality tests can be performed in SPSS research?

  • admin

    The SPSS researchers performs the following tests of normality:

    Histogram displaying the normal curve (Procedure from SPSS Graphs menu, Histogram, a dialogue of histogram appears where you select the variable under consideration and then check the box on Display normal curve)

    The P-P plot. Procedure from SPSS Graphs menu, P-P… a P-P plot dialogue box appears where you choose the variables to test and also select the distribution to test from the subheading Test Distribution.

    After or before performing normality test one is strongly advised to check if the data set have outliers or not. If outliers exist, this means that violation of the normality or any other distribution exists, giving biased results. A box plot will help a SPSS researcher to identify outliers and eliminate them in your analysis.

    Also one can log transform a data set so that the data is approximately normal. Another way is standardizing the variable (Z-score) under consideration and filtering there after for values out of range of -3 and +3 and the remaining values in between the range are approximately normal.

    The main purpose of normality test is to guide the SPSS researcher on which analysis test or method to use so as not to have misguided or biased results. For instant to compare several means one will use one way ANOVA which have the assumptions of normally distributed data among the others. In this case if your data is not normality distributed then the one of the assumption is violated and the results obtained by this will be false. In such scenario the SPSS researcher can use the non parametric test (k independent samples) instead which does not put into consideration the distribution of the data. Otherwise one can use the above techniques to normalize your data.

    Another example is when carrying out linear regression analysis one can include the P-P plot of the residuals to investigate if the normality assumption is violated. When the plotted points are clouded next to the 45º diagonal line then the data set is normally distributed.

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