to the subjects of the study. The tests were held on Saturday, September 30
th
2006 at 10 o’ clock. The students were required to do each test in 50 minutes.
Once the tests were done, they were then scored. The scores of the test were the data required by this study. After the data were gathered, they were then analyzed
and interpreted.
3.9 Data Analysis
In order to answer the research problems, the data that had been gathered was then analyzed and interpreted with regard to the research design. Since the data was in
numerical form, statistical analysis was applied. To find out whether or not there is a relationship between students’
mastery of past tense and their achievement in writing recount, the correlation coefficient showing the degree as well as the direction of the relationship between
the two variables being investigated was computed. As the data were in the form of interval scale and because there was always a possibility that the result of the
study will show no relationship between the variables, the following Pearson Product Moment Correlation was used see Brown, 1988:130:
r
xy
=
∑ ∑
∑ ∑
∑ ∑ ∑
− −
− }
}{ {
2 2
2 2
Y Y
N X
X N
Y X
XY N
In which: rxy
= correlation coefficient
X =
students’ past tense scores Y
= students’ scores in writing recount
N = number of subjects To interpret the relative amount of the variation in students’ achievement
in writing recount that was due to the relationship with the students’ mastery of past tense, determination coefficient was computed. It equals the square of
correlation coefficient and is therefore labeled r
2
. The next step to follow was computing the regression equation. Through
the regression equation, we can find out how the two variables correlate each other. From the regression equation obtained, then, prediction of Y score from X
score can be made. The equation takes the form: Y = a + b X
In which: Y = estimated Y score
a = intercept b = slope
However, before doing any statistical computation and inferences, there were some pre-requisites that should be taken into consideration. Those are the
homogeneity of the data variance, the distribution of the data, and the linear model of the regression equation. These will be discussed in chapter four.
52
CHAPTER IV FINDING AND DISCUSSION
This chapter deals with the data analysis as well as interpretation. It encompasses
the discussion of the correlation analysis aimed at figuring out the correlation coefficient, computation of the index of the determination, the obtained regression
equation, and their interpretations.
4.1 Correlation Analysis
After getting the scores of the students mastery of past tense and their achievement in writing recount as presented in appendix 5, the data were
statistically computed to find the correlation between the two variables. As stated in the previous chapter, the r-value was computed using Pearson Product moment
formula. The following table presents the result of the computation of correlation coefficient.
Correlations
1 .724
. .000
50 50
.724 1
.000 .
50 50
Pearson Correlation Sig. 2-tailed
N Pearson Correlation
Sig. 2-tailed N
X Y
X Y
Correlation is significant at the 0.01 level 2 t il d
.
From the data presented in the table, it is found out that the obtained correlation coefficient is 0.724. For
α = 5 and df = 50-2 = 48, the critical value of r product moment is 0.284. Because r-value is higher than the critical value of r product
