CHAPTER III: RESEARCH METHOD
3.6 Technique of Data Analysis
3.5.2.4.9 The researcher gave opportunity to each group to read out the result of the discussion.
3.5.2.4.10 Starting from the comments/ results of the students discussion, the researcher began to explain the material according to the goals to be achieved.
3.5.2.4.11 The researcher asked students whether they understood the material 3.5.2.4.12 The researcher asked students to review the material in their house 3.5.2.4.13 The researcher closed the class by greeting students.
3.5.3 Post-test
After giving a treatment, the researcher gave a post-test to the students as the last test to know improving the students’ writing ability in recount text through example non example method of the second year class VIII.B in SMPNSatap 5 Baraka KabupatenEnrekang.
3.6.1 Analytic scoring rubric of the students’ recount text.
Table 3.2 The analytical Scoring Rubric.
No. Aspect Criteria Score
1.
Content Relevant to topic
Mostly relevant to topic but lacks detail
Inadequate development of topic
Not relevant to topic
4 3 2 1 2.
Organization Ideas clearly stated and supported, well organized ( generic structure) , cohesive
Loosely organized but main ideas stand out, not well organized ( generic structure)
Ideas confused or even no main ideas, bad organized ( generic structure)
Does not communicate, no organized.
4
3
2
1 3.
Language use
Few errors of agreement, tenses, number, word order, article, pronouns of prepositions
Several errors of agreement, tenses, number, word order, article, pronouns of prepositions
frequent errors of agreement, tenses, number, word order, article, pronouns of prepositions
dominated by errors
4
3
2
1 4.
Mechanics few errors of spelling, punctuation, capitalization, and paragraphing.
4
Occasional errors of spelling, punctuation, capitalization, and paragraphing.
frequent errors of spelling, punctuation, capitalization, and paragraphing.
Dominated by errors
3
2
1
Score = Number of correct answer
x100
3.6.2 Classifying the students’ score in to following criteria.
Table 3.3 Classification score
No Scores Classification
1 2 3 4 5
80-100 66-79 56-65 40-55 30-39
Very good Good
Fair Poor Very poor24
3.6.3 Computing the rate percentage of the students’ score by using the following formula:
P = F
x100%
Notation:
P = Percentage
24SuharsimiArikunto, Dasar-dasarEvaluasiPendidikan,( Jakarta: BinaAksara, 1984), p. 236.
F = Frequency
N = Total number of the samples
3.6.4 To find out the mean score of the students writing ability by using the following formula:
X =∑
Where: X = Mean Score
∑ = The sum of all the scores N = The number of students
3.6.5 To calculate standard deviation used the following formula
SD=
( )
( )
Where: SD = Standar Deviation
= The sum of score
( ) = The square of the sum of the score N = The total number of the subject
3.6.6 The test of significant difference between the Pre-test and Post-test
t =
( )
( )
Where: t = test of significant D = the mean score of difference
= the sum of total score difference N = the total number of student 3.6.7 The criteria of testing hypothesis
The statistical hypothesis in this research is a follow;
µ = µ2
µ˃µ2
to test hypothesis the researcher used two tails with 0,05 level of significance.
For independent sample, the formula of freedom (df) is N-1
3.6.7.1 if t-table ˃ t-test, H0is accepted and H1is rejected, it means that using Example non example method cannot improve the students’ writing ability.
3.6.7.2 If t-table ≤ t-test, H1 is accepted and H0 is rejected , it means that using Example non example method can improve the students’ writing ability.
33
CHAPTER IV
FINDINGS AND DISCUSSION
In this chapter presents the findings of the research and its discussion. The findings of the research cover the description of the research and result of the data analysis about the students’ writing ability in pre-test and post-test finding and the discussion about the findings.
4.1 Findings
The findings of this research deal with the classification of the students’ pre- test and post-test. To find out the question in the previous chapter , the researcher gave a test that was given twice. A pre-test was given before treatment to know the students’ writing ability , while post-test was given after treatment through example non example method and the result of the post-test of this research can answer the question of this research that aims to find out through example non example method can be able to improving the students’ writing ability in recount text of junior high school Satap 5Baraka.
4.1.1 The students’ writing ability in recount text through example non example method of junior high school Satap 5 Baraka,.
This parts presents the result of data analysis about the writing ability in recount text through example non example method.
4.1.1.1 The students score in pre-test done before implementation of example non example method. It was conducted on Wednesday, October 17th, 2018. The students assigned to write recount text. The researcher found out the result of the students’
pre-test based on the scoring rubric of writing recount text which are content,
organization, language use and mechanic before giving treatment through example non example method which were analyzed and resulted in the information as shown in the following table.
Table 4.1 the students score in pre-test based on scoring rubric of writing recount text.
No Students
Aspect
Total Content Organization Language
use Mechanics 1. Student 1
1 1 1 2 5
2. Student 2
2 2 2 2 8
3. Student 3
2 1 2 1 6
4. Student 4
2 2 1 2 7
5. Student 5
2 1 1 2 6
6. Student 6
2 2 1 2 7
7. Student 7
2 2 2 1 7
8. Student 8
2 1 1 1 5
9. Student 9
2 1 1 2 6
10. Student 10
1 1 2 1 5
11. Student 11
1 1 2 2 6
12. Student 12
2 1 2 2 7
13. Student 13
2 2 2 1 7
14. Student 14
2 1 1 2 6
15. Student 15 2 2 2 2 8
16. Student16
2 2 1 1 6
17. Student 17
1 2 1 1 5
18. Student 18
1 1 2 1 5
19. Student 19
2 2 2 1 7
20. Student 20
2 1 1 1 5
21. Student 21
1 2 1 2 6
22. Student 22
2 2 1 2 7
23. Student 23
1 2 1 2 6
24. Student 24
2 2 2 2 8
25. Student 25
1 2 2 2 7
26. Student 26
2 1 2 1 6
27. Student 27
1 1 1 1 5
Source : The result of pre-test score
After knowing the students’ score in pre-test based on scoring rubric of writing recount text, the following table are students’ score to find out the mean score.
Table 4.2 the Students’ Score in Pre-test.
No
Students
Pre-test of the students (X1)
Max score Score (X) X2 Classification
1. Student 1
16 31 961 Very poor
2. Student 2
16 50 2500 Poor
3. Student 3
16 38 1444 Very poor
4. Student 4
16 44 1936 Poor
5. Student 5
16 38 1444 Very poor
6. Student 6
16 44 1936 Poor
7. Student 7
16 44 1936 poor
8. Student 8
16 31 961 Very poor
9. Student 9
16 38 1444 Very poor
10. Student 10
16 31 961 Very poor
11. Student 11
16 38 1444 Very poor
12. Student 12
16 44 1936 Poor
13. Student 13
16 44 1936 Poor
14. Student 14
16 38 1444 Very poor
15. Student 15
16 50 2500 Poor
16. Student16
16 38 1444 Very poor
17. Student 17
16 31 961 Very poor
18. Student 18 16 31 961 Very poor
19. Student 19
16 44 1936 Poor
20. Student 20
16 31 961 Very poor
21. Student 21
16 38 1444 Very poor
22. Student 22
16 44 1936 Poor
23. Student 23
16 38 1444 Very poor
24. Student 24
16 50 2500 Poor
25. Student 25
16 44 1936 Poor
26. Student 26
16 38 1444 Very poor
27. Student 27
16 25 625 Very poor
ΣX1= 1055 ΣX12
= 42375 Source: the result of pre-test classification
Based on the table above, the result of students’ writing score before applying example non example method. Total score in pre-test was 1055. The following are the process of calculation to find out the mean score and the standard deviation based on the calculation of students’ score in pre-test of the table 4.2
Firstly, the researcher calculated the mean score of the pre-test.
X
=
∑X
=
= 39
So, the mean score ( X1 ) of pre-test is 39
Secondly, the researcher calculated the standard deviation of the pre-test
SD=
( )
( )
SD=
( )
( )
SD=
SD= √
SD= √ SD= √44 SD = 6.6
So, the result of the standard deviation of pre-test is 6.6
4.1.1.2 The Students’ Score in Post-test.
Meanwhile, the students’ score in post-test is presented in the following table
Table 4.3 the students score in post-test based on scoring rubric of writing recount text.
No Students
Aspect
Total Content Organization Language
use Mechanics 1. Student 1
3 3 1 2 9
2. Student 2
3 2 2 2 9
3. Student 3
3 3 2 2 10
4. Student 4
2 2 2 2 8
5. Student 5
2 2 2 2 8
6. Student 6
3 3 2 2 10
7. Student 7
2 2 1 1 6
8. Student 8
3 2 2 3 10
9. Student 9
2 2 2 2 8
10. Student 10
2 3 2 3 10
11. Student 11
2 2 2 2 8
12. Student 12
2 2 3 2 9
13. Student 13
2 3 2 3 10
14. Student 14
2 2 2 2 8
15. Student 15
2 2 3 3 10
16. Student16
3 2 2 2 9
17. Student 17
2 2 2 2 8
18. Student 18
2 2 3 2 9
19. Student 19
2 3 3 3 11
20. Student 20
2 2 3 1 8
21. Student 21
3 3 2 1 9
22. Student 22
3 2 3 1 8
23. Student 23
3 2 1 2 8
24. Student 24
3 3 2 2 10
25. Student 25
3 3 2 3 11
26. Student 26
3 2 2 1 8
27. Student 27
2 2 1 1 6
Source: the result of post-test score
Table 4.3 is students score in post-test based on scoring rubric of writing recount text. The following table are students’ score to find out the mean score and the standard deviation.
Table 4.4 the Students’ Score in Post-test.
No Students Post -test of the students (X1)
Max score Score (X) X2 Classification
1. Student 1
16 56 3136
Fair 2. Student 2
16 56 3136 Fair
3. Student 3
16 63 3969 Fair
4. Student 4
16 50 2500 Poor
5. Student 5
16 50 2500 Poor
6. Student 6
16 63 3969 Fair
7. Student 7
16 38 1444 Very poor
8. Student 8
16 63 3969 Fair
9. Student 9
16 50 2500 Poor
10. Student 10
16 63 3969 Fair
11. Student 11
16 50 2500 Poor
12. Student 12
16 56 3136 Fair
13. Student 13
16 63 3969 Fair
14. Student 14
16 50 2500 Poor
15. Student 15
16 63 3969 Fair
16. Student16
16 56 3136 Fair
17. Student 17
16 50 2500 Poor
18. Student 18
16 56 3136 Fair
19. Student 19
16 69 4761 Good
20. Student 20
16 50 2500 Poor
21. Student 21
16 56 3136 Fair
22. Student 22
16 50 2500 Poor
23. Student 23
16 50 2500 Poor
24. Student 24
16 63 3969 Fair
25. Student 25
16 69 4761 Good
26. Student 26
16 50 2500 Poor
27. Student 27
16 38 1444 Very poor
ΣX1 = 1491 ΣX12
= 84009 Source: the result of post-test classification
The table above shows that there was an improvement of students’ score after did the treatment. There were two students gained good score, thirteen students gained fair score,ten students gained poor score and two students gained very poor score. The total score in post-test are1491 It proved that there were increasing of the students’ score in post-test.
In this, the researcher analyzed the data of students’ score in post-test to know whether there is or there is no a significant difference of students’ aability before and after learning process in applying example non example method.
The first, to get the mean score of the post-test, used formula;
X =∑
X =1491 27
= 55
So, the mean score ( X1 ) of post-test is 55
Secondly, the researcher calculated the standard deviation of the post-test, used formula:
SD=
( )
( )
SD=
( )
( )
SD=
SD=
SD=√ SD=√64 SD = 8
So, the SD of the post-test are8
4.1.1.3 The result of post-test were presented in the following:
The result of the pre-test and post-test showing in the following table.
Table 4.5 the Mean Score and Standard Deviation of the Pre-test and Post-test.
Test Mean score Standard deviation ( SD)
Pre-test Post- test
39 55
6.6 8
Source: the mean score and standard deviation of pre-test and post-test Based on the table 4.5 above it can be explained that the mean score in pre- test had score 39 with standard deviation was 6.6. The result shows students’ ability before being giving treatment. After being giving treatment the mean score in post- test had score 55 with standard deviation was 8. it means that the result show the ability of students’ after being giving treatment that was using example non example method in learning. It was seen that the students’ ability in pre-test was lower than the students’ ability in post-test. It meant that the students’ writing ability had improvement after doing the learning process that used example non example method.
4.1.1.4 The rate percentage of the frequency of pre-test and post-test
Table 4.6 The Rate Percentage of the Frequency of the Pre-test and post-test
No.
Classification Score Frequency Percentage
Pre-test Post-test Pre-test Post-test
1 Very good 80-100 - - - -
2. Good 66-79 - 2 - 7.4 %
3. Fair 56-65 - 13 - 48.1%
4. Poor 40-55 11 10 40.7 % 37%
5. Very Poor 30-39 16 2 59.2 % 7.4%
Total 27 27 100 % 100 %
Source: the result of pre-test and post-test
The table 4.6 shows the students’ percentage of pre-test was most common in very poor score namely sixteen students and it was the high percentage 59.2 %. There were eleven students gained poor score and the percentage 40.7 %. There is no students gained fair, good and very good score. It means that the students’ writing ability was still low. Especially in writing recount text. Whereas the percentage of post-test indicated that there was increasing percentage of the students in writing because there were two students gained good scores with the percentage 7.4 %.There were thirteen students gained fair score and the percentage 48.1%. There were ten student gained poor score and the percentage 37 %. There were two students gained very poor score and the percentage 7.4 %. It meant that there was an increasing percentage after doing pre-test up to post-test.
4.1.2 Example non example method is able to improve the students’ writing ability in recount text at junior high school Satap 5 Baraka.
This part discusses the result of data analysis about example non Example method able to improve the students’ writing abilityin recount text at the Eight grade students’ of SMPN Satap 5 Baraka.
4.1.2.1 t-test value
The following is the table to find out the difference of the mean score between pre-test and post-test.
Table 4.7 the worksheet of the calculation of the score on pre-test and post- test on the students’ writing ability in recount text.
No. X1 X2 (X1)2 (X2)2 D(X2-X1) D(X2-X1)2
1. 31 56 961 3136 25 625
2. 50 56 2500 3136 6 36
3. 38 63 1444 3969 25 625
4. 44 50 1936 2500 11 121
5. 38 50 1444 2500 12 144
6. 44 63 1936 3969 19 361
7. 44 38 1936 1444 6 36
8. 31 63 961 3969 32 1024
9. 38 50 1444 2500 12 144
10. 31 63 961 3969 32 1024
11. 38 50 1444 2500 12 144
12. 44 56 1936 3136 12 144
13. 44 63 1936 3969 19 361
14. 38 50 1444 2500 12 144
15. 50 63 2500 3969 13 169
16. 38 56 1444 3136 18 324
17. 31 50 961 2500 19 361
18. 31 56 961 3136 25 625
19. 44 69 1936 4761 25 625
20. 31 50 961 2500 19 361
21. 38 56 1444 3136 18 324
22. 44 50 1936 2500 6 36
23. 38 50 1444 2500 12 144
24. 50 63 2500 3969 13 169
25. 44 69 1936 4761 25 625
26. 38 50 1444 2500 12 144
27. 25 38 625 1444 13 169
Total 1055 1491 42375 84009 453 9009
Source: the analysis data
In the other to see the students’ score, the following is t-test was statistically applied:
To find out D used formula as follow:
D
=
D
=
= 16.7
The calculation the t-test value.
t =
( )
( )
t = .
( )( )
( )
t = .
( )
t = .
√ .
t = 16.7
√ .
t =16.7
√
= . .
= 11.92
Thus, the t-test value is 11.92 Table 4.8 The Test of significance
Variable T-test T-table value
Pre-test- post-test 11.92 1.706
Source: the result of t-test of the students pre-test and post-test
The data above shows that the value of t-test was greater than t-table value. It indicated that there was a significant difference between the result students’ pre-test and post-test.
4.1.2.2 Hypothesis Testing
To find out degree of freedom (df) the researcher used the following formula:
Df= N-1 = 27-1 = 26
For the level, significant (p) and df= 26, and the value of the table is 1.706, while the value of t-test is 11.92. It means that the t-test value is greater than t-table (11.92 ≥1.706). Thus, it can be concluded that the students’ writing ability in recount text is significant better after getting treatment. So, the null hypothesis (H0) is refused and the alternative hypothesis (H1) is accepted. It has been found that there is an improvement of example non example method in students writing ability in recount text
4.2 Discussion
4.2.1 The improvement of students’ writing ability in recount text.
By looking at the test finding, from the data provided in classification table in pre-test based on the aspect of writing, clearly to see that there were sixteen( 59.2%) students got very poor score, eleven ( 40.7 %) students got poor score and there is no students got fair, good and very good score . From the data above shows that the students’ ability in writing recount text still low. One of the difficulties that students often experience when writing recount text is they don’t know how to organize their ideas into the text. This is supported by Richard’ stated that writing is the most difficult skill for second language learners to master. The difficulty lies not only in generating and organizing ideas, but also in translating these ideas into readable
text.25But the difficulties experienced by students can be overcome by applying the appropriate method in writing learning, one of them is example non example method.
This method can improve the students writing ability in recount text. This is evidenced from data provided in classification table of post-test based on the aspect of writing, there were two ( 7.4%) students got good score, thirteen (48.1%) students got fair score, ten ( 37%) students got poor score and two(3.7%) student got very poor score. From the result,the researcher concluded the students’ writing ability improve from very poor up to fair classification through example non example method.
There were some example that example non example method is able to improve the students writingabilityin recount text where by looking at the components of writing like George Wishon and Burks said that The components of writing divides into five main areas. They are grammar, mechanics, vocabulary, content and organization.26
Example ; (1) Student 6 which name Elsa Triani in pre-test, she got 7 point and the increased become 9 point at the post-test. According to her exercise in pre-test, her content wasinadequate development of topic. Her language usedominated by errors especially in tenses, sometimes she use simple past and sometimes she use simple present in write recount text while we know that in write recount text we most use simple past tenses. but in post-test her organization mostly complete and her language use especially for the use of tenses it is better than in the pre-test. . (2) student 1, Agus in pre-test she got 5 point whereas his post-test got 9
25Jack C Richard, Willy A.Renadya (ed) , Methodology in Language Teaching: An Anthology of Current Practice, p.303.
26 George Wishonand Burks, Lets Write English, p.128-129.
point . by looking at the organization he got 2 point in pre-test and 3 point in post- test, he had been improvement because his punctuation, capitalization, and paragraphing Still errors in pre-test but in post-test his punctuation, capitalization, and paragraphing still frequent errors. (3) students 5 , Elsa, this student also had improvement, it can be seen from his point in pre-test was 6 point and in post-test increased become 8 point. By looking at the language use she got 1 point in pre-test and 2 point in post test., he had been improvement because her language use dominated by errorsin pre-test but in post-test her language use frequent errors especially in tenses (4) students 13 namely julfianataurisanti, in pre-test she got 7 point whereas in post-test she got 8 point.
Inadequate development but in post-test his topic is relevant . Overall, from the statement above, it can be seen that there was an improvement of students writing, ability especially in writing recount text after the researcher applying example non example method to the students. This finding support Nurhid and Aziz Safa, assumption that The example non example method is a method that uses pictures as a media to encourage students to write. By providing pictures in writing text, it make students easier to understand the rhetorical structure of procedure text and help the students to learn effectively.27
As conclusion, the mean score of post-test ( 55 ) was greater than the mean score in pre-test (39). Even, for the level significant (P) 5% and df= 26, and the value of table is 1.706, while the value of t-test is 11.92. it means that, the t-test value is greater than t-table ( 11.92 ≥1.706). Thus, it can be concluded that the students’
27Nurhid, Aziz Safa, 45 Model PembelajaranSpektakuler , p. 101.
writing abilityin recount text is significant better after getting the treatment. So, the null hypothesis ( H0 ) is refused and the alternative hypothesis (H1) is accepted.
Based on the findings above, the researcher concluded that there is an improvement the students’ writing ability in recount text through Example non example method at SMPN Satap 5 Baraka.
4.2.2 The ways of example non example method in improving the students’ writing abilityin recount text.
To find out how the example non example method is able to improve the students’ writing ability in recount text, the researcher got some pieces of information from the students’ activities in learning process. There were five meetings for doing this research. Two meeting for doing the test and three meeting for doing the treatment to prove that is example non example method can improve the students writing abilityin recount text. Asthe first meeting the researcher just give students a topic about holiday and asked the students to write an essay and be done about 30 minutes by the students. To identify the students’ basic writing in recount text. The second meeting, the researcher started to convey what material that would be learnt by the students and explain the concept of example non example method. She began class presentation. The researcher taught about recount text like definition, the generic structure, language features and example of recount text through example non example method. The third meeting, the researcher prepared the drawing according to the learning objectives about fajar daily activity. Then the researcher put a picture on the blackboard or slides through the slide projector or overhead projector. After that the researcher gave instruction and gave the students a chance to pay attention. Next the researcher asked the students to analyze image. Then, The researcher divided