Score Level
Criteria
`Language Use
25-22 EXCELLENT TO VERY GOOD: effective complex
constructions ● few errors of agreement, tense, number,
word orderfunction, articles, pronouns, prepositions 21-18
GOOD TO
AVERAGE: effective
but simple
constructions ● minor problems in complex
constructions ● several errors of agreement, tense, number, word orderfunction, articles, pronouns,
prepositions but meaning seldom obscured
17-11 FAIR TO POOR: major problems in simplecomplex
constructions ●frequent errors of negation, agreement,
tense, number, word orderfunction, articles, pronouns, prepositions and or fragments, runs-ons, deletions,
●meaning confused or obscured 10-5
VERY POOR: virtually no mastery of sentence construction rules
● dominated by errors ● does not communicate ● OR not enough to evaluate
Mechanics 5
EXCELLENT TO VERY GOOD: demonstrates mastery of conventions
● few errors of spelling, punctuation, capitalization, paragraphing
4 GOOD TO AVERAGE: occasional errors of spelling,
punctuation, capitalization, paragraphing but meaning not obscured
3 FAIR TO POOR: frequent errors of spelling,
punctuation, capitalization, paragraphing ● poor
handwriting ● meaning confused or obscured 2
VERY POOR: no mastery of conventions ● dominated
by errors of spelling, punctuation, capitalization, paragraphing ● handwriting illegible ● OR not enough
to evaluate
6. Technique of Data Analysis
After collecting the data needed, the writer started analyzing the data by testing the normality and linearity. In calculating the correlation, a formula called
Product Moment from Pearson was used. These steps are along with Susetyo who said that to study a correlation between two variables a test of normality and
linearity are necessary to be conducted and can use Product Moment from Pearson to calculate the correlation.
2
2
Budi Susetyo, Statistika untuk Data Analisis Penelitian, Bandung: PT Refika Aditama,2010, pp. 170-181.
a. Finding the number of correlation using formula
3
:
N= the number of respondent X= the students’ score in vocabulary
Y= the students’ score in writing ΣX = the sum of vocabulary scores
ΣY = the sum of writing score ΣX
2
= the sum of the squared scores of grammar ΣY
2
= the sum of the squared scores of writing ΣXY = the sum of multiplied score between X and Y
The formula above is used in finding index correlation “r” product
moment between X variable and Y variable r
xy
. To interpret the index scores of “r” correlation, product moment r
xy
the interpretation such as below is used:
4
Table 3.3 Pearson Correlation
The Score of “r” Product Moment r
xy
Interpretation
0.00 - 0.19 There is a relationship between X and
Y, but the correlation is very weak or little so it is ignored or it is considered
no correlation in this rating.
0.20 – 0.39
There is a relationship between X and Y, but it is weak or little.
0.40 – 0.69
There is a relationship between X and Y. The value is medium.
0.70 – 0.89
There is high relationship between X and Y.
3
Ibid.
4
Anas Sudjiono, Pengantar Statistik Pendidikan, Jakarta: Rajawali Press, 2014, p. 193.
The Score of “r” Product Moment r
xy
Interpretation
0.90 – 1.00
There is a very high relationship between X and Y.
7. Statistical Hypotheses
1. If r
o
is the same as or higher than r
t
, the H
a
is accepted which means that there is a relationship between vocabulary mastery and writing
achievement. 2. If r
o
is lower than r
t
, the H
s
is rejected which means that there is no relationship between vocabulary mastery and writing achievement.
28
CHAPTER IV RESEARCH FINDINGS AND DISCUSSION
