In this research, the researcher was applied quantitative analysis. This quantitative analysis employed statically calculation to test the hypothesis.
Table 3.2 Scoring formulation for student speaking
Component features Score Criteria
Fluency 9-10
7-8 5-6 3-4 1-2
Directly speaking completely Speaking completely while thinking Directly speaking but not complete Speak while thinking but not complete Speak nothing right
Accuracy 9-10 No mistake
7-8 5-6 3-4 1-2
One inaccurate word Two inaccurate word Three inaccurate word
More than three inaccurate word Vocabulary 9-10
7-9
5-6
3-4
1-2
Speech on all levels is fully accepted by educated native speakers in all its features including breadth of vocabulary and idioms, colloquialisms, and pertinent cultural
references.
Can understand and participate in any conversation within the ranger of his
experience with a high degree of precision of vocabulary.
Able to speak the language with suggicient vocabulary to participate effectively in most formal and informal conversations on practical, social, and professional topics.
Vocabulary is broad enough that he rarely has to grope for a word.
Has speaking vocabulary sufficient to express him self simply with some circumlocutions.
Speaking vocabulary inadequate to express anything but the most elementary needs.
Pronunciation 9-10 7-8
Very good pronunciation Good pronunciation
5-6 3-4 1-2
Fair pronunciation Poor pronunciation Very poor pronunciation28
Some formulas will be applied in this research to process the data follows:
1) Scoring the students correct answer of pre-test and post-test Score = Students score x 100
Max score
2) Classifying the students score in to the following criteria:
Table 3.3 Classification score
Score Classification
81-100 Very good
61-80 Good
41-60 Fair
21-40 Poor
≤20 Very poor29
3) Calculating the frequency and percentage of the students:
28Halifa Pandu, Teaching Speaking Through Rotating Roles Technique of The Second Year of Students at MTS DDI BONTO PERAK PANGKEP (Unpublished Skripsi STAIN Parepare, 2012), P. 47.
29Suharsimi Arikuntu, Dasar-dasar Evaluasi Pendidikan, Edisi Revisi; (Jakarta: Bumi Aksara. 2009), p.245.
Where:
P = Percentage F = frequency
N = Total Number of Sample30
4) Finding out the mean score of the students pre-test and post-test using this formula:
x = ∑ Where:
X = Mean score
∑ = The sum of the all score N = Total number of sample 31
5) Finding out the standard deviation by using the following formula:
SD = √∑ 2 – (∑ 2 N-1 Where:
SD = Standard deviation ∑ 2 = The sum all square
N = The total number of students
∑ 2 = The sum square of the sum of square 32
6) Finding the significant difference between the mean score pre-test and post-test by calculating the value of the test using the following formula :
30L.R. Gay, Educational Research Competencies for Analisys and Application, p. 225.
31L.R. Gay, Educational Research Competencies For Analysis and Application, p. 298.
32L.R. Gay, Educational Research Competencies For Analysis and Application, p. 298.
t =
√∑ ∑
Where: t = Test of significant D = Mean score
∑ = The sum of different score N = The total number of the sample33
33L.R. Gay, Educational Research Competencies For Analysis and Application, p. 331.
35
This chapter deals with the presentation of data analysis as well as the describe on the result of data analysis, if we see the instrument used in this research, the data collected as put forward in the previous chapters, namely objective test consists of pre-test and post-test in describe pictures, the data were analysis in order to describe the students ability in the speaking through describe pictures.
Table : 4.1 The Students Score in Pre-test
No Name Score (X1) X12
1 Lisma 42 1764
2 Linda 25 625
3 Nurul 62 3844
4 Ratna 25 625
5 Asnita 25 625
6 Aisah 30 900
7 Rika 65 4225
8 Amina 35 1225
9 Aziza 25 625
10 Ayu 30 900
11 Citra 45 2025
12 Rizki 61 3721
13 Asraf 45 2025
14 Yusuf 25 625
15 Rasya 42 1764
16 Asim 30 900
17 Adrian 32 1024
18 Imran 61 3721
19 Sainal 30 900
20 Syahrul 35 1225
21 Ali 25 625
Score 33913
Based on the above table, the writer presented a table about the classification, frequency, and percentage of student score in the following 4.2
Table 4.2 The Frequency and Percentage of the Result Pre-test
No Classification Score Frequency Percentage%
1 Very good 81-100
0 0%
2 Good 61-80
4 19.04%
3 Fair 41-60
6 28.57%
4 Poor 21-40
11 52.38%
5 Very poor ≤20
0 0%
21 100%
The table shows that, there were 4 (19.04%) out of 21 students got the classification “Good”, 6 (28.57%) out of 21 students got the classification “fair”, and 11 (52.38%) got the classification “poor”.
Table 4.3 The Result Students Score on Post-test
No Name X2 X22
1 Lisma 65 4225
2 Linda 55 3025
3 Nurul 85 7225
4 Ratna 50 2500
5 Asnita 62 3844
6 Aisah 62 3844
7 Safitri 85 7225
8 Amina 62 3844
9 Aziza 62 3844
10 Ayu 55 3025
11 Citra 70 4900
12 Rizki 85 7225
13 Asraf 82 6724
14 Yusuf 85 7225
15 Rasya 65 4226
16 Asim 81 6561
17 Adrian 62 3844
18 Imran 85 7225
19 Sainal 50 2500
20 Syahrul 62 3844
21 Ali 55 3025
Score 99900 Table 4.4 The Frequency and Percentage of the Result Post-test
No Classification Score Frequency Percentage%
1 Very good 81-100
7 33.33%
2 Good 61-80
9 42.86 %
3 Fair 41-60
5 23.80%
4 Poor 21-40
0 0%
5 Very poor ≤20
0 0%
21 100%
The table shows that, there were 7 (33.33%) out of 21 students got the classification “very good”, 9 (42.86%) out of 21 students got the classification
“good”, and 5 (23.80%) out of 21 students got the classification “fair”.
Based on the table above, its mean that the rate percentage of the students score speaking in post-test is varied. Five students got higher score and there is no students got lowest score.
The mean score of the pre-test and post-test was tabulated as follows:
1) X1 =∑ = = 37.85 2) X2 =∑ = = 67.85
Table 4.5 The Mean Score Pre-test and Post-test
Test Mean score Classification
Pre-test 37.85 Poor
Post-test 67.85 Good
From the result data above shows that the mean score obtained by the students was different. The result of post-test was higher that pre-test. It‟s the proved by the mean score of the post-test 67.85 while the mean score of pre-test 37.85 it means that after gave treatment by using describe pictures, the students score obtained increased and the classification of post-test was good.
The calculating score of the pre-test and post-test
Table 4.6 The Worksheet of the Calculating Score of Pre-test and Post-test
No X1 X2 D D2
1 42 65 23 529
2 25 55 30 900
3 62 85 23 529
4 25 50 25 625
5 25 62 37 1369
6 30 62 32 960
7 65 85 20 400
8 35 62 27 729
9 25 62 37 1369
10 30 55 25 625
11 45 70 25 625
12 61 85 24 576
13 45 82 37 1369
14 25 85 60 3600
15 42 65 23 529
16 30 81 50 2500
17 32 62 30 900
18 61 85 24 576
19 30 50 20 400
20 35 62 27 729
21 25 55 30 900
N 795 1425 629 20739
Table 4.7 Standard Deviation
No Test Standard deviation
1 Pre-test 17.29
2 Post-test 63.08
To find out degree of freedom (df) the researcher used the following formula : Df=N-1
Df=21-1 Df=20
Table 4.8 The Test of Significant
Variable t-test T-table value
Pre-test and post-test 4.51 1.725
The data above means that it can be obtained on t or t-table significance 5%
was 1.725. If it compared with the result of t-test was 4.51, so it can be know that is t-test higher than t-table 4.51≥1.725. Because the result of t-test was higher than t- table, so HO was rejected. It means that there was significance difference in speaking between before and after through reinforcement to motivate the students in learning
speaking. The reinforcement given to motivate students in speaking is reinforcement such as verbal and non verbal reward so students fell happy because the results of their efforts are valued. The award may be applause, praise, smile, and others. The happy feeling of course have a positive influence on students motivation, specially speaking learning.
Based on the result above, it convinces that through reinforcement able to motivate the students speaking.