CHAPTER III RESEARCH METHOD
H. Technique of Data Analysis
According to previous research, the statistics which used were two types, namely inferential and descriptive statistics. Inferential statistics were used to calculate the comparability between two specific variables that have certain relationships. Whereas, descriptive statistics only describe data without concluding it.
This research was using Pearson product moment / correlation design, the type of statistics that researcher used was inferential statistics which statistics can help researcher in concluding and analyzing data easily.
r = nΣxy – (Σx) (Σy) . √{nΣx² – (Σx)²} {nΣy2 – (Σy)2}
Where :
n = Number of data pairs X and Y.
Σx = Total Amount of Variable X.
Σy = Total Amount of Variable Y.
Σx2 = Square of the Total Amount of Variables X.
Σy2 = Square of the Total Amount of Variables Y.
Σxy = Multiplication Results of the Total Number of Variables X and Y Variables.
This research took independent and dependent variable, for the independent variable the researcher used X symbol to recognize it, while for dependent variable the symbol is Y. For counting the data, the researcher was helped by SPSS application. After collecting data, the researcher would enter
32 each data that has been obtained then analyze it in accordance with the variables that have been determined.
In analyzing data from students’ learning strategy, the researcher was using rate scale in learning strategy. In this case there are three types to generally recognize in analyzes of the learning strategy. The types were cognitive, meta-cognitive, and affective. The researcher used the conversion of percentage range to know what strategy they use.
The researcher used the 5% significant level because field of research was language subject not an exact subject. In the language study, it is better to use 5% significant level. On the other hand, for exact study it is better to use the 1% significant level. The researcher determined the table interpretation of product moment scales, as follow:
Table 3.1 Interpreted to the Criteria by Riduan
Correlation Value (R)
Interpretation 0.800-1.000 Very High Correlation 0.600-0.800 High Correlation 0.400-0.600 Fair Correlation 0.200-0.400 Low Correlation 0.000-0.200 Very Low Correlation
From this formula, it could be gotten the correlation coefficient value (r) of the two variables. And by the interpretation table, the researcher can conclude the significant of the correlation.
33 I. Validity and Reliability
Validity is a measure that shows the level of validity or authenticity of an instrument, a valid instrument that has high quality validity. Conversely, an invalid instrument has low quality validity. Validity was used to assess an instrument prepared whether it meets the requirements for use or not. For the instrument applied by researcher at the time of data collection, there was no need to test validity because the instrument was already well-known and often used in searching learning strategy, it means the researcher does not need to do the validity check.
Reliability is an instrument that can be trusted to be used as a data collection tool because the instrument is already good. These instrument were reliable which have taken several times of the results will remain the same and the instrument must be good enough than it can reveal reliable data. After applying the instrument, the researcher would know the reliability of an instrument. Data obtained will be not doubted because validity and reliability were clear. SILL (Students' Inventory of Language Learning) was the instrument currently used by researcher.
34 CHAPTER IV
RESEARCH FINDING AND DISCUSSION
In this chapter, the researcher presented the data which has been collected in the field of study which consist of research finding and discussion.
In this chapter the researcher will present the data obtained and then calculate it in accordance with the procedures previously written. Each data that has been calculated will provide an answer to every statement that exists.
A. Research Findings
1. The result of Strategy Inventory of Language Learning Score (SILL)
After collecting the Strategy Inventory of Language Learning (SILL), it gave the degree of students’ learning strategy. The following table will present the score of Student Inventory of Language Learning (SILL).
TABLE 4.1 The Result of SILL Scores
NO STUDENT LEARNING
STRATEGIES (X)
X2 CATEGORY
1 E1. 2 4 LOW
2 E2 3 9 MEDIUM
3 E3 2 4 LOW
4 E4 3 9 MEDIUM
5 E5 3 9 MEDIUM
6 E6 1 1 LOW
35
7 E7 2 4 LOW
8 E8 3 9 MEDIUM
9 E9 2 4 LOW
10 E10 3 9 MEDIUM
11 E11 2 4 LOW
12 E12 3 9 MEDIUM
13 E13 2 4 LOW
14 E14. 2 4 LOW
15 E15 3 9 MEDIUM
16 E16 3 9 MEDIUM
17 E17 3 9 MEDIUM
18 E18 3 9 MEDIUM
19 E19 3 9 MEDIUM
20 E20 3 9 MEDIUM
21 E21 3 9 MEDIUM
22 E22 2 4 LOW
SUM 56 133
HIGHEST SCORE
3
LOWEST SCORE
1
MEAN 3
STANDARD DEVIATION
1.143
36 Based on the table above, variable X was found ΣX = 56 and variable ΣX2 =133. Based on the data above was known the highest score was 3 and the lowest score was 1 and the students’ SILL category was at medium category. The classification can be seen in the table below.
TABLE 4.2 PRESENTATION OF STUDENTS’ SILL SCORES
CATEGOR Y
STATEMEN T
AVERAG E SCORE
FREQUENC Y
PERCENTAG E
ALWAYS 4.5 to 5.0 0 0%
HIGH OFTEN 3.5 to 4.4 0 0%
MEDIUM SOMETIMES 2.5 to 3.4 15 68.2%
LOW RARELY 1.5 to 2.4 6 27.3%
NEVER 1.0 to 1.4 1 4.5%
TOTAL 22 100%
Based on the calculation on the table, there were 15 students who acquired in medium score and 7 students who acquired in low score.
After knowing the scores, it can make the groups of category and find the percentage for students learning strategy by using formula :
S= 𝑛
𝑁× 100
Where : S : Students’ Score.
n : the number of students who got score in a level.
N : Total of the student
37 TABLE 4.3 Calculation of Students’ Distribution for Frequency
and Percentage of SILL Scores
NO CATEGORY FREQUENCY PERCENTAGE
1 HIGH 0 0%
2 MEDIUM 15 68.2%
3 LOW 7 31.2%
TOTAL 22 100%
Based on the data above, it can be seen there were 0 student who acquired high score in percentage 0%, 15 students who acquired medium score in percentage 68.52% and 7 students who acquired low score in percentage 31.8%. The chart of Students’ SILL score can be seen below :
The Frequency of SILL Score
0 15 7
38 Figure 4.1
From the chart above can be seen the highest frequency SILL Score in 15 was at medium level than the lowest level was 7. This chart was showed most of students already use the learning strategy especially in language of speaking.
Based on the result of all the data above, students in high level has provide a good score in ability of speaking while low students need to be more active in learning even they did not apply appropriate strategy in learning but they has proved that they can achieve a good score.
2. The Average of The Students’ SILL score
Researcher used formula to find the average of students’ SILL score, the formula as follow:
M=∑𝑋
𝑁
Where : M : Mean.
ΣX : the sum of scores.
N : number of the students.
It was known that : M : 3.
ΣX : 56.
N : 22..
39 As the calculation above, the average Strategy Inventory of Language Learning (SILL) scores of the students was 3. Based on research used in Junior High School of NW Mercapada, the average SILL scores of the students was at medium category. Its mean that most of first year in Junior High School of MTs. NW Mercapada students have used learning strategy.
The speaking strategy was in medium category.
3. Result of Speaking Subject Score
In this study the research was studied about the correlation between students’ learning strategy and their speaking ability at first year students of Junior High School of NW Mercapada. In this part the result of learning speaking was taken from their final exam.
Table 4.4 The Result of Speaking Score
NO STUDENT SPEAKING
SCORE (Y)
CATEGORY
1 E1 87 5
2 E2 88 5
3 E3 89 5
4 E4 84 5
5 E5 95 5
6 E6 79 4
7 E7 79 4
8 E8 89 5
9 E9 85 5
40
10 E10 90 5
11 E11 87 5
12 E12 85 5
13 E13 82 5
14 E14 85 5
15 E15 84 5
16 E16 86 5
17 E17 86 5
18 E18 83 5
19 E19 86 5
20 E20 89 5
21 E21 86 5
22 E22 88 5
Highest Score 95
Lowest Score 79
Average 86
Standard Deviation 3.572
Looking from the result, the researcher found the mean and standard deviation of data. From all students the result shown mean score of students’
final test in speaking (Y) was 86 and standard deviation was 3.572. It means students category of speaking was at high or excellent category.
41 4. The Correlation Between Speaking Learning Strategy and Speaking
Ability.
As the data has shown before, the researcher found every result of each variable. This was the result of correlation between speaking learning strategy and speaking ability. The result has calculated by using Pearson Product Moment which applied on SPSS 24 for testing the hypothesis of this study.
Table 4.5
The Correlation Between Students’ Speaking Strategy and Their Speaking Ability
NO SPEAKING STRATEGY
(X)
SPEAKING ABILITY
(Y)
XY X2 Y2
1 2 5 10 4 25
2 3 5 15 9 25
3 2 5 10 4 25
4 3 5 15 9 25
5 3 5 15 9 25
6 1 4 4 1 16
7 2 4 8 4 16
8 3 5 15 9 25
9 2 5 10 9 25
10 3 5 15 4 25
11 2 5 10 4 25
12 3 5 15 9 25
13 2 5 10 4 25
14 2 5 10 4 25
15 3 5 15 9 25
16 3 5 15 9 25
17 3 5 15 9 25
18 3 5 15 9 25
19 3 5 15 9 25
20 3 5 15 9 25
21 3 5 15 9 25
22 2 5 10 4 25
Total 56 108 277 133 532
42 1. Using Manual Calculating
From the calculation of variable X and Y (table 1.5) above it, it was known that :
∑X = 56
∑Y = 108
∑XY = 277
∑X2 = 133
∑Y2 = 532
Based on the calculation of variable X and variable Y above, it can be known of each variable. Based on product moment formula will be found the result of Rxy, as follows :
𝑅
𝑥𝑦=
𝑁 ∑ 𝑥𝑦−(∑ 𝑥)(∑ 𝑦)√{𝑁 ∑ 𝑥2−(∑ 𝑥)2}{𝑁 ∑ 𝑦2−(∑ 𝑦)2}
𝑅
𝑥𝑦= 22×277−(56)(108)√{22×133−(56)2}{22×532−(108)2}
𝑅
𝑥𝑦= 6.094−6.048√{2.926−1.136}{11.704−11.664}
𝑅
𝑥𝑦= 46{−210}{40}
𝑅
𝑥𝑦= 46√−8400
𝑅
𝑥𝑦= 4691.651
43
𝑅
𝑥𝑦=0,501Based on the manual calculating above, it has found the rvalue
was 0.501 from the table of coefficient correlation (chapter III) was at the level “fair correlation”. The rvalue 0.501 was in interval 0.400- 0.600. So, it means the correlation between students’ learning strategy and speaking ability is in fair correlation. The result of the calculation that was counted by using manual calculating above showed that the index of correlation is 0.501.
2. Using SPSS 24 Program
Table 4.6 Analysis Result of Pearson Product Moment Correlation by SPSS
From the data which has been calculating by using SPSS, the data shown that the index of Product Moment correlation was 0.568 for 0.006 not significant. From the table above, it mean Ho
was accepted because the hypothesis testing that concluded N.Sig
< 5% (0.006 < 0.05).
Correlations
learning_strategi
es speaking_ability
learning_strategies Pearson Correlation 1 .568**
Sig. (2-tailed) .006
N 22 22
speaking_ability Pearson Correlation .568** 1
Sig. (2-tailed) .006
N 22 22
**. Correlation is significant at the 0.01 level (2-tailed).
44 Correlation was unidirectional if the correlation coefficient found to be positive or conversely, if the correlation coefficient was negative, the correlation is called unidirectional. It means correlation coefficient was a statistical measurement of variation or association between two variables. If the correlation coefficient was found to be not equal to zero (0), then there was a dependency between the two variables. If the correlation coefficient was found +1 than the relationship called a perfect correlation or a perfect linear relationship with a positive slope.
5. Hypothesis Testing
This research has done in collecting and calculating all the data and got the result of a correlation. But to answer the research problem, the researcher has counted the hypothesis was rejected or not. The hypothesis will be shown bellow :
Ha : Students’ learning strategy is correlated with speaking ability Ho : Students’ learning strategy is not correlated with speaking ability To find the answer, the researcher used SPSS hypothesis testing based on the N.Sig (number of significance). The result is shown at table 1.5, we got rvalue= 0.568, N.Sig=0.006. For reminding us these the theories in hypothesis based on SPSS 24.
a. Ho accepted if N.Sig ≥ 0.05 (α=5%) b. Ha rejected if N.Sig ≤ 0.05 (α=5%)
45 The result of analyzing the significant 0.006 clarified Ha was rejected.
The hypothesis testing from SPSS concluded N.Sig < 5% (0.006 < 0.05), it means Ho was accepted. It concluded that the speaking learning strategy and speaking ability was not correlated.
B. Discussion
1. The Correlation Between Students’ Learning Strategy and Speaking Ability
From the data collected it has been found the final results and answers for each hypothesis written. N.Sig value <5% (0.054 <0.05), the result of this value indicate that Ha was not accepted and Ho was accepted. The value of correlation coefficient was in the number 0.054 which was in the interval 0.000 and 0.200, it means the correlation value was categorized as very low.
The hypothesis test shows that N.Sig <5% (0.006 <0.05) means that the alternative hypothesis was rejected and the null hypothesis was accepted.
The alternative hypothesis found in this study says "Students 'learning strategy is correlated with speaking ability" has been rejected and the null hypothesis states "Students' learning strategy is not correlated with speaking ability" has been accepted. The value of rvalue is 0.006 which means that the study produced a very low correlation.
However, there were slight differences in the results of the data that have been calculated using manual calculations and the application of the SPSS 24 program. The results obtained using the manual count is 0.501 which was in the category of fair correlation. Meanwhile, for the results of data calculation using SPSS 24 the program produces a number of 0.006 which was in the very low correlation category. However, even though the numbers
46 obtained were different, the results of the category values obtained were same, which was not correlated so that it does not cause a significant difference.
Both results were remained low correlated.
As written by previous researchers that if students use good strategy it would affect the ability of each student. This was stated by some researchers that the more effective students were using the right learning strategy, it would affect their ability including in terms of language.38From the statements that have been given, that proved the importance of using strategy in learning, especially in speaking.
In accordance with the results of all the previous studies that the researcher has written, the results of my research have very different results.
The average results of previous studies that the researcher wrote have a very strong correlation between one variable with another variable. Previous research from Gani, Fajrina, and Hanifa categorized speaking strategies into 6 types, namely social compensation, memory, cognitive, meta-cognitive, and affective. Meanwhile, students are divided into two categories: students with high grades and students who have low grades. The final results of their study stated that students who have high grades pay great attention in the use of learning strategies. As with students who have low grades, they use the strategy unconsciously and the results are not satisfying. This means that the correlation of learning strategy with the ability of students was very significant, seen from the many numbers obtained from high-value students.39
38 Jelisaveta Safranj, Strategies of learning………..p.776
39 Sofyan A. Gani, Dian Fajrina, Rizaldy Hanifa Students’ Learning…….p. 23-27
47 Other studies that have been conducted previously also have the same results, which have significantly correlated because students use strategy thoroughly and continuously. Unlike my research, students use strategy unconsciously and they may not know the benefits and objectives of using strategy in learning. This was also stated by the English teacher at the school,
"Student was learning in different ways every day, they choose to learn according to their comfort at home and class. In this school also students have not taught the benefits and what was the usual strategy used by students to improve their ability. Instead, students choose the way they want to receive and analyze lessons from their teacher."40
40 Zanuddin, interview, Mercapada, Juny, 20, 2020.
48 CHAPTER V
CONCLUSION AND SUGGESTION
A. CONCLUSION
According to the results of the data that have been obtained later calculated above, it can be concluded that the learning strategy does not correlate the ability of students, especially in terms of speaking. Based on the results of the rvalue above which shows the number 0.006 this means that the correlation between student learning strategy and student speaking ability was very low. It was seen from the alternative hypothesis (Ha) was rejected and null hypothesis (Ho) was accepted, because N.Sig < 5% (0.006 < 0.05). In this case, learning strategy does not have any effect on student learning ability seen from the high learning value obtained and the questionnaire results that have been calculated.
B. SUGGESTION
At the end of the research which has done, researcher would give some advices to teachers, students, and some recommendation for other researchers in future.
For teaching staff or teachers must be more active in providing motivation and explanation of the importance of using learning strategy at home and at school to improve learning ability. The teacher need to explain how important, the goals and some effects of uses the learning strategy in learning activities at home and at school.
For students, try to motivate yourself to more often use learning strategy at home or at school. Students who can motivate themselves, the way they would be
49 easier to improve their ability. Awareness in the importance of learning English, especially speaking was very important in this modern era.
For future researchers, the results of this data were calculated according to the data obtained at school. The data used was authentic from the school. Then, after all data have been calculated with statistics and produce data that were not correlated between the learning strategy variable and the student's speaking ability variable. Further researchers were advised to conduct more in-depth research on students' speaking strategy and ability. Other researchers can use various types of instruments that are suitable and considered easy.
Finally, it was recommended that researchers continue to conduct research when students study these subjects, in order to know what responses should indeed be given or answered in the questionnaire distributed. Data collection in this study was conducted when the students had passed the learning period from speaking, so rather than that the researcher assumed that the results of grades were very low correlated because of one factor that the questionnaire distribution was done after the students passed the learning period so that they forgot how they felt when learning and answering questionnaire.
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