Normality Test of KolmogorovSmirnov Using SPSS
Normality
testing is a common practice before the statistical method. Normality test is
one part of the test data analysis requirements or can be called a classic
assumption. The purpose of the normality test is to find out whether the
distribution of a data follows or approaches a normal distribution, namely the
distribution of data that has a pattern such as a normal distribution.
Probability Test of Normal Distribution
The
basis for decision making can be based on normal data that can be measured by
looking at the probability number (Asymptotic Significance), namely:
1. If
the probability value > 0.05 then the distribution of the population is normal.
2. If
the probability value <0.05, the population is not normally distributed.
Visual
testing can also be done using the Normal Image Probability Plots method in the
SPSS program. Basic decision making:
1. If
the data spreads around the diagonal line and follows the direction of the
diagonal line, it can be concluded that the regression model meets the
assumptions of normality.
2. If
the data spreads far from the diagonal line and does not follow the direction
of the diagonal line, it can be concluded that the regression model does not
meet the assumption of normality.
In
addition, the normality test is used to find out that the data taken comes from
a population with normal distribution. The test used to test normality is the
KolmogorovSmirnov test. Based on this sample the null hypothesis will be
tested that the sample originates from a normally distributed population
against the rival hypothesis that the population is abnormally distributed.
As an example of this test, eguides have a company's income statement data where the variables are Net Profit (Y) and Income (X). Where the data is as follows:
Years

Income (X)

Net Income (Y)

2014

Rp 8.810.164.143

Rp 4.505.095.216

2015

Rp 3.931.105.865

Rp 622.325.559

2016

Rp 12.275.751.456

Rp 8.844.480.929

2017

Rp 12.703.254.096

Rp 8.693.647.027

2018

Rp 5.703.315.375

Rp 1.650.685.099

How to Test KolmogorovSmirnov with SPSS
1. The
first step is to prepare the data to be tested in the form of an excel, docx,
txt or other file to facilitate filling in the data on the SPSS worksheet.
After that, open the SPSS program on your computer, then click Variable View
and fill in the image below:
2. After
that, click "Data View" and enter the data into each variable.
3. The
next step, we will bring up the residual unstandardized value (RES_1) which we
will then test for normality. The trick is to click Analyze menu >> Regressions >> Linear.
4. A dialog box will appear with the name "Linear Regressions", then input the Net Income variable to Dependent and the Income variable to Independent then save.
5. Then the dialog box appears again with the name "Linear Regression: Save", in the "Residual" section, check Unstandardized and ignore the other columns and choices. Next, click Continue and Ok.
6. Ignore the display of output that appears from the SPSS program. pay attention to the "Data View" view, a new variable appears with the name RES_1.
7. Now the final step is to test the Normality of the group using the Residual (RES_1) data that has been obtained previously. Select menu Analyze >> Nonparametric Test >> Legacy Dialogs >> 1Sample KS.
8. A dialog box appears called "OneSample KolmogorovSmirnov Test". Next, enter the Unstandardized Residual (RES_1) variable into the Test Variable List box and in Test Distribution, check or select Normal. Then OK.
9. Now, pay attention to the results in the Output sheet it will look like this.
Interpretation of the KolmogorovSmirnov Normality Test with SPSS
Based on the SPSS output table, it is known that Asiymp.Sig (2tailed) significance value is 0.200 greater than 0.05. So according to the basis of decision making in the KolmogorovSmirnov normality test above, it can be concluded that the data is normally distributed. Thus, the assumptions or normality requirements in the regression model have been fulfilled.
Note: In addition to comparing the Asiymp.Sig (2tailed) value with 0.05, there are other ways to find out whether the regression model is normally distributed or not by comparing the zcalculated value of the SPSS result with the Ztable value.
This is the guideline on how to do the KolmogorovSmirnov normality test with the SPSS program.
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