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1. Normality Test
The normality test is one of the prerequisite tests before entering the linear regression analysis, that is used to find out whether the data is in normal
distribution or not. In this research, the normality test uses the program SPSS 16. The result can be seen from
p
significance on Lilliefors test; with the interpretation if
p
value is greater than 0.05
p
0.05, it tells that the distribution of the data is normal, if
p
0.05, it tells that the distribution of the data is not normal.
The summary of the normality test result of each variable can be seen at Table 6. The computation of SPSS 16 can be seen at Appendix 21.
Table 6. The Summary of Normality Test
Variable P value
Significance Level 5 Decision
Vocabulary Mastery Reading Comprehension
0.343 0.119
0.05 0.05
Normal Normal
Based on the result of normality test using SPSS 16, it can be concluded that the data are in normal distribution because the significance
value of the two variables are greater than 0.05.
2. Linearity Test
Besides normality test, linearity test is also used by the writer for the pre-requirement analysis. Linearity test is aimed to know whether two
variables have significance linear regression or not. The testing by utilizing SPSS 16 uses
Test for Linearity
at the level of significance
p
= 0.05. Two variables are categorized into linear regression if the
p
0.05.
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The summary of the linearity test result of the variables can be seen at Table 7.
Table 7. The Result of Linearity Test
Pre requirenment test
Variable The result of
computation Criteria
Decision H
o
Explanation Linearity test
XY 0.670
0.05 Accepted
The data
is linear
From the computation of linear regression of vocabulary mastery X and reading comprehension Y by using SPSS 16, it shows that the value of F-
obtained is 0.676 and the significance value is 0.670 Appendix 22. Because the significance value is greater than 0.05, the regression of vocabulary
mastery X and reading comprehension Y is linear.
C. The Hypothesis Testing
Since the computation of normality and linearity testing shows that the data are in normal distribution and the regression is linear and significant, the
writer can continue to test the hypotheses of the research stated on the previous chapter. The writer tests the null hypothesis H
o
against the alternative hypothesis H
a
. The statistical hypotheses are as follows. First Hypothesis
H
o
= r
x1y
≤ 0 H
a
= r
x1y
Where: H
o
= Null hypothesis H
a
= Alternative hypothesis r
xy
= the value of r test
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