DATA NORMALITY TEST
DATA NORMALITY
The normality test is a way to see whether the data in the study are normally distributed. The results
of this test will later influence the next analysis step. The SPSS normality test method usually has 2
options, namely Kolmogorov-Smirnov and Shapiro-Wilk. To determine which one to use, you can
pay attention to the data itself.
Normality Test Provisions
In the normality test, there is an indicator called the significance value. If the data has a significance
value of 0.05, it can be said that the data is normal. Be it for Kolmogorov-Smirnov or for Shapiro
Wilk.
The difference between the use of the two is in the number of samples used. If the sample is less than
50, then Shapiro Wilk is more suitable for use in the normality test. Meanwhile, for large samples of
more than 50, use Kolmogorov-Smirnov so that the results are more accurate.
SPSS Normality Test Steps
Before you can do the analysis, the first thing you need to do is fill in the data in the Variable View
and Data View. Fill carefully in order to get accurate results. If there is a lot of data and it has been
stored in Excel, for example, you can copy and paste it. Then follow these steps:
1. Click the Analyze menu, then enter Descriptive Statistics, then Explore.
2. In the Explore window, there is a Dependent List column, move the variable you want to test into
that column. If the variable is qualitative, move it to the Factor List column.
3. Select Both on Display. Check the Descriptive section, then fill in the Confidence Interval for Mean
with a certain number as needed. Then click Continue.
4. Click Plots, then check the Normality plots with tests. If so, click Continue then click OK.
5. The normality test results can be read for further processing.
Reading the Normality Test Results
The SPSS normality test method has been completed up to the steps above. After that, it's time to read
the results. There are several result columns that will appear. Look at the Tests of Normality table for
normality test results.
Look at the individual numbers in the Kolmogorov-Smirnov and Shapiro Wilk columns. For example,
the number .300 is listed, then the meaning is 0.300. This value indicates that the result is more than
0.05 which is the minimum number of data that can be called normal. So, with a significance value of
0.300 the data is normally distributed.
As already explained, the SPSS normality test is not too complicated. Even so, those who are not
familiar with statistics may still feel confused. But don't worry, Gama Statistika is ready to help to
facilitate the research process. Starting from consultation, data processing, to discussion and
communication during the process.
DATA NORMALITY
The normality test is a way to see whether the data in the study are normally distributed. The results
of this test will later influence the next analysis step. The SPSS normality test method usually has 2
options, namely Kolmogorov-Smirnov and Shapiro-Wilk. To determine which one to use, you can
pay attention to the data itself.
Normality Test Provisions
In the normality test, there is an indicator called the significance value. If the data has a significance
value of 0.05, it can be said that the data is normal. Be it for Kolmogorov-Smirnov or for Shapiro
Wilk.
The difference between the use of the two is in the number of samples used. If the sample is less than
50, then Shapiro Wilk is more suitable for use in the normality test. Meanwhile, for large samples of
more than 50, use Kolmogorov-Smirnov so that the results are more accurate.
SPSS Normality Test Steps
Before you can do the analysis, the first thing you need to do is fill in the data in the Variable View
and Data View. Fill carefully in order to get accurate results. If there is a lot of data and it has been
stored in Excel, for example, you can copy and paste it. Then follow these steps:
1. Click the Analyze menu, then enter Descriptive Statistics, then Explore.
2. In the Explore window, there is a Dependent List column, move the variable you want to test into
that column. If the variable is qualitative, move it to the Factor List column.
3. Select Both on Display. Check the Descriptive section, then fill in the Confidence Interval for Mean
with a certain number as needed. Then click Continue.
4. Click Plots, then check the Normality plots with tests. If so, click Continue then click OK.
5. The normality test results can be read for further processing.
Reading the Normality Test Results
The SPSS normality test method has been completed up to the steps above. After that, it's time to read
the results. There are several result columns that will appear. Look at the Tests of Normality table for
normality test results.
Look at the individual numbers in the Kolmogorov-Smirnov and Shapiro Wilk columns. For example,
the number .300 is listed, then the meaning is 0.300. This value indicates that the result is more than
0.05 which is the minimum number of data that can be called normal. So, with a significance value of
0.300 the data is normally distributed.
As already explained, the SPSS normality test is not too complicated. Even so, those who are not
familiar with statistics may still feel confused. But don't worry, Gama Statistika is ready to help to
facilitate the research process. Starting from consultation, data processing, to discussion and
communication during the process.
