CHAPTER III: RESEARCH METHODOLOGY
G. Validity and Reliability Test
Validity and reliability test is necessary when a researcher conducts a study. The purpose of validity and reliability test is to see the accuracy of a test instrument as a measuring tool for a research variable. Basically, validity and reliability were related to each other even though they seem to have different concepts. The relationship between validity and reliability showed in the following figure 3.1
Figure 3.1
The relationship between validity and reliability
Validity refers to whether a measuring instrument accurately assesses the behavior or quality that it is designed to measure (Anastasi, 1997). Before the instrument was distributed to the respondents, it was tested for validity and reliability by giving it to the validator. The researcher asked the English teacher from a different school as a validator to provide an assessment of the test instrument. The validator gave instrument sheets the term of three aspects, they are; the face validity, the content validity, and construct validity.
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a. Face validity
Face validity refers to what looks to be measuring something rather than what should be measured. It was a technique of measuring the validity of an instrument only seen from its shape or what appears without seeing its content.
b. Content validity
Content validity is the degree to the items, questions or assignments in a test or instrument that can represent the overall behavior of the sample subject to the test. In this research, the researcher makes the instrument test based on the syllabus at eighth grade students of SMP Negeri 12 Seluma. The students were required to write about descriptive text based on a topic provided by the researcher which was relevant to the syllabus. The content validity was showed in the following table 3.4
Table 3.4 The content validity
Material Competence Indicators Descriptive text Students are able to write
about descriptive text.
Students are able to write the text with a generic structure clearly.
c. Construct validity
The suitability between the results of the measuring instrument and the ability to be measured was referred to as construct validity.
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The students asked a question that was relevant to the syllabus. It was consisted of a single item or essay that was constructed based on the definition of a specific person, place, emotion, symbol, and object in order to evaluate the students' writing ability.
H. Assessment of Writing Test
Assessment needed to evaluate the learning outcomes of students based on what they have learned. O’Malley and Pierce (1996) state that writing assessment should evaluate more aspects of writing than just mechanic and grammar, and should capture some of the processes and complexity involved in writing so that the teacher can know in which aspects of the writing process students are having different.
To find out the final score of assessing writing test, there was the following formula.
Formula: score = C+O+G+V+M x 100 100
Total scores = 100, where minimal scores = 34 and maximum scores = 100.
The results of the test were classified of extremely good, good, fair, low, and extremely low. There was the score classification:
Table 3.5 Score Interpretation
Categories Score
Excellent 80-100
Good 70-79
Average 60-69
Poor 40-59
Very Poor 0-39
(Nurgianto in EenKuswara, 2014)
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To assess the score of writing, there were some aspects that needed such as content, organization, grammar, vocabulary, and mechanics. For more detail, it can be seen in the following table below:
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Table 3.6
Rubric on Assessing Students Writing
Aspect Score Performance Weighting
Content 30%
-Topic -Details
4 The topic is complete and clear and the details are relating to the topic
3x
3 The topic is complete and clear but details are almost relating to the topic.
2 The topic is complete and clear but details are not relating to the topic.
1 The topic is not clear and the details are not relating to the topic.
Organization 20%
-Identification -Description
4 Identification is complete and descriptions are arranged with proper connectives.
2x
3 Identification is almost complete and descriptions are arranged with almost proper connectives.
2 Identification is not complete and descriptions are arranged with few misuse and connectives.
1 Identification is not complete and descriptions are arranged with misuse of connectives.
Grammar 20%
-Use present tense -Agreement
4 Very few grammatical or agreement inaccuracies 2x 3 Few grammatical or agreement inaccuracies but not affect
on meaning.
2 Numerous grammatical or agreement inaccuracies.
1 Frequent grammatical or agreement inaccuracies.
Vocabulary 15%
4 Effective choice of words and word forms. 1.5x
3 Few misuse of vocabularies, word forms, and not understandable.
2 Limited range confusing words and word form.
1 Very poor knowledge of words, word forms, and not understandable.
Mechanics 15%
-Spelling -Punctuation -Capitalization
4 It uses correct spelling, punctuation, and capitalization. 1.5x 3 It has occasional errors of spelling, punctuation, and
capitalization.
2 It has frequent errors of spelling, punctuation, and capitalization.
1 It is dominated by errors of spelling, punctuation, and capitalization.
Source adapted from Brown (2007)
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CHAPTER IV
RESULT AND DISCUSSION
This chapter presented the result of the study which is included data presentation, the result and discussion.
A. Data Presentation
1. The Description Data of Pre-test and Post-Test Score of Experimental Class
In order to examine the students' understanding before and after conducting the treatment in the experimental group, the pre-test and post- test scores are distributed in the following table.
Table 4.1
Frequency Distribution of Students' Pre-test and Post-test Score of the Experimental Class
No. Name Experimental Group
Pre-test score
Predicate Post-test score
Predicate
1. ADS 52 Poor 70 Good
2. ALP 39 Very Poor 56 Poor
3. CL 54 Poor 84 Excellent
4. DPR 47 Poor 76 Good
5. DST 40 Poor 74 Good
6. DMS 39 Very Poor 72 Good
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7. ER 42 Poor 77 Good
8. FAR 48 Poor 76 Good
9. KDR 61 Average 85 Excellent
10. LM 62 Average 81 Excellent
11. MA 78 Good 95 Excellent
12. RA 66 Average 80 Excellent
13. RFS 38 Very Poor 74 Good
14. SNC 57 Poor 80 Excellent
15. YRS 71 Good 92 Excellent
16. YL 38 Very Poor 69 Average
17. ZK 57 Poor 80 Excellent
18. ZS 49 Poor 82 Excellent
19. ZE 65 Average 83 Excellent
Total 1003 1486
Mean 52.7 78.2
Lowest Score 38.00 56.00
Highest Score 78.00 95.00
Range 40.00 39.00
Standard Deviation
12.13 8.61
Standard Error of
Mean
2.783 1.97
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Based on the table 4.1, the post-test score was greater than the pre-test score. It suggests that teaching students to write descriptive texts utilizing the Think-Talk-Write (TTW) Strategy can improve their writing skills and have a substantial impact. Figures 4.1 and 4.2 shows the frequency distribution of pre-test and post-test scores in the experimental group.
Figure 4.1 The Histogram of Frequency Distribution of Students' pre-test score of the Experimental Class
Figure 4.2 The Histogram of Frequency Distribution of Students’
post-test score of Experimental Class
0 1 2 3 4 5 6 7
38-45 46-53 54-61 62-69 70-77 78-85
38-45 46-53 54-61 62-69 70-77 78-85
0 1 2 3 4 5 6 7 8
56-62 63-69 70-76 77-83 84-90 91-97
frekuensi
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Based on the graph above, it can be seen that students’ writing score in post-test is higher than students’ writing score in pre-test. The histogram showed a significant increased before and after applying a treatment to experimental class. It means, applying a treatment TTW Strategy can give significant effect on students’ writing ability in descriptive text.
2. The Description Data of Pre-test and Post-Test Score of Control Class
The pre-test and post-test scores of the students are distributed in the table below in order to analyze the students' knowledge before and after in control group without having a treatment.
Table 4.2
Frequency Distribution of pre-test and Post-test score of Student in the Control Class
No. Name Control Group
Pre-test score
Predicate Post-test score
Predicate
1. AA 45 Poor 55 Poor
2. ADF 41 Poor 42 Poor
3. AW 39 Very poor 62 Average
4. AIS 47 Poor 57 Poor
5. AP 43 Poor 54 Poor
6. BAW 34 Very poor 35 Very poor
7. DRS 46 Poor 42 Poor
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8. EF 56 Poor 70 Good
9. EVE 59 Poor 54 Poor
10. EJP 61 Average 85 Excellent
11. HEP 34 Very poor 65 Average
12. LL 50 Poor 61 Average
13. NT 66 Average 77 Good
14. RPS 55 Poor 59 Poor
15. RP 35 Very poor 42 Poor
16. RS 48 Poor 59 Poor
17. SM 53 Poor 75 Good
18. SRP 45 Poor 48 Poor
19. YRS 59 Poor 83 Excellent
20. YP 57 Poor 71 Good
Total 973 1196
Mean 48.6 59.80
Median 47.5 59
Lowest Score
34.00 35.00
Highest Score
66.00 85.00
Range 32.00 50.00
Standard Deviation
48.65 14.801
Standard Error of
Mean
2.10 3.14
Based on the table 4.5 that the post-test score of the control class was not give significance increase because in this class does not give the treatment Think-Talk-Write (TTW) Strategy.
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The histogram of frequency distribution of pre-test and post-test score in control class can be seen on the following figure 4.3 and 4.4.
Figure 4.3 The Histogram of Frequency Distribution of Students' pre-test score of the Control Class
Figure 4.4 The Histogram Frequency of Students post-test score of control Class
Based on the following figure above, it can be seen that students’ writing score in post-test was no difference than students’
writing score in pre-test. The histogram showed that only a few
0 0,5 1 1,5 2 2,5 3 3,5 4 4,5
34-39 40-45 46-51 52-57 58-63 64-69
frekuensi
0 1 2 3 4 5 6 7 8
35-43 44-52 53-61 62-70 71-79 80-88
frekuensi
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students reach score in excellent category. In this control class not give a treatment TTW Strategy but the researcher only taught the students used conventional method. It means, applying a treatment TTW Strategy can give significant effect on students’ writing ability in descriptive text in experimental class than students writing score in control class who were not taught by using TTW Strategy.
B. Result
This chapter discusses the findings of the study. Based on the data analysis, the results were produced. The data were gained from students’
scores of writing test that consist of pre-test and post-test given to both of experimental and control class. The result showed that the students’ ability in writing descriptive text at eighth grade students’ of SMP Negeri 12 Seluma was improve. The findings of this study were gained from data analysis which was discussed in previous chapter. Before analyze the data should be normal distributed and homogeny. And then, to analyze the data was used t-test formula. The finding were as follows:
1. Normality Test
Before analyzing the data using t-test formula, the data should be measured have normal distributed. To see if the data have normal distribution the Kolmogorov-Smirnov test was used both of group. The used of Kolmogorov-Smirnov because if the sample > 30 and in this research the samples had 39 respondent.
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a. The Result of Normality Data of Pre-test and Post-test score in Experimental Class
The result of normality tests of pre-test and post-test scores of the experimental class showed in the following table 4.3.
Table 4.3 Tests of Normality
Experimental Kolmogorov- Smirnova
Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
Writing Descriptive Text Score
Pre-test .129 19 .200* .937 19 .233 Post-test .110 19 .200* .958 19 .536
Based on the results of the tests of normality used Kolmogorov- Smirnov in the table 4.3. It is showed the significance value (sig.) of the pre-test scores in experimental class was (sig.) = 0.200>0.05 and Shapiro-wilk table (sig.) = 0.233>0.05. It means Ho was accepted because the pre-test scores was higher than 0.05. Meanwhile, in the post-test scores in experimental class the significance value (sig.) was (sig.) = 0.200>0.05 and Shapiro-wilk table (sig.) = 0.536>0.05. It means Ho was accepted because the post-test scores was higher than 0.05. So it can be concluded that pre-test and post-test scores of experimental class were normally distributed. The histogram of the normality data of pre-test and post-test scores of the experimental class showed on the following figure 4.5 and 4.6.
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Figure 4.5 The Histogram of the Students’ Pre-Test in Experimental Group
Figure 4.6 The Histogram of the Students’ Post-Test in Experimental Group
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b. The Result of Normality Data of Pre-test and Post-test score in Control Class
The result of normality tests of pre-test and post-test scores of the control class showed in the following table 4.4.
Table 4.4 Tests of Normality
Control Kolmogorov-Smirnova Shapiro-Wilk Statistic df Sig. Statistic df Sig.
Writing Descriptive Text Score
Pre-test .100 20 .200* .964 20 .621 Post-test .097 20 .200* .971 20 .777
Based on the results of the tests of normality used Kolmogorov- Smirnov in the table 4.4. It was showed the significance value (sig.) of the pre-test scores in experimental class was (sig.) = 0.100>0.05 and Shapiro-wilk table (sig.) = 0.621>0.05. It means Ho was accepted because the pre-test score was higher than significance value (sig.) = 0.05. Meanwhile, in the post-test scores in experimental class the significance value (sig.) was (sig.) = 0.097>0.05 and Shapiro-wilk table (sig.) = 0.777>0.05. It means Ho was accepted because the post- test scores was higher than significance value (sig.) = 0.05. In short pre-test and post-test scores of control class were normally distributed.
The histogram of the normality data of pre-test and post-test scores of the control class showed on the following figure 4.7 and 4.8.
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Figure 4.7 The Histogram of the Students’ Pre-Test in Control Class
Figure 4.8 The Histogram of Frequency of the Students’ Post- Test in Control Class
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2. Homogeneity Test of Variances
Homogeneity test was used Levene statistic of IBM SPSS 20.0 version program. The table below shows the results of testing homogeneity of pre-test and post-test of experimental and control classes.
a. The Homogeneity of Pre-test Score Table 4.5
Test of Homogeneity of Variances
Levene Statistic df1 df2 Sig.
1.587 1 37 .216
Based on the Levene statistics calculating used SPSS 20.0 program, it can be seen the levene statistic was 1.587, the df1 was 1, df2 was 37 and the significance value was 0.216. It means, Ho is accepted because the significance value of pre-test score was higher than α = 5% (0.216>0.05). So it can be concluded that the homogeneity of variances in pre-test score both of group was same data (homogeneous).
b. The Homogeneity of Post-test Score Table 4.6
Test of Homogeneity of Variances
Levene Statistic df1 df2 Sig.
4.341 1 37 .044
Based on the Levene statistics calculating used SPSS 20.0 program, it can be seen the levene statistic was 4.341, the df1 was 1,
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df2 was 37 and the significance value was 0.044. It means, Ho is accepted because the significance value of post-test score was higher than α = 5% (0.044>0.05). So it can be concluded that the homogeneity of variances in post-test score both of group was same data (homogeneous).
3. Testing Hypothesis Using Independent Samples T-test SPSS 20.0 Independent samples t-test was used to see there is whether or not significance effect of students’ writing descriptive text after applied Think-Talk-Write (TTW) Strategy on experimental between control class who not give the treatment. The standard deviation and standard error mean both of group as follows:
Table 4.7
The Group Statistics of Experimental (X) and Control Class (Y)
Kelas N Mean Std. Deviation Std. Error Mean Writing
Descriptive Text Score
Experimental 19 78.21 8.619 1.977
Control 20 59.80 14.081 3.149
In the table 4.8 showed the result of descriptive statistic of experimental class (X) with respondent (N) = 19, mean was 78.21, standard deviation calculation of experimental class (X) was 8.619 and standard error mean was 1.977. Meanwhile, the result of descriptive statistic of control class (Y) with respondent (N) = 20, mean was 59.80, standard deviation (Y) was 14.081 and standard error mean was
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3.149. It can be concluded if there was difference mean of the results of students writing descriptive text scores in experimental class between control class.
Table 4.8
The Descriptive Statistics of Independent Samples T-Test
Class Statistic Std. Error
Class A
Mean 78.2105 1.97733
95% Confidence Interval for Mean
Lower Bound 74.0563 Upper Bound 82.3647
5% Trimmed Mean 78.5117
Median 80.0000
Variance 74.287
Std. Deviation 8.61896
Minimum 56.00
Maximum 95.00
Range 39.00
Interquartile Range 9.00
Skewness -.450 .524
Kurtosis 1.728 1.014
Class B
Mean 59.8000 3.14860
95% Confidence Interval for Mean
Lower Bound 53.2099
Upper Bound 66.3901
5% Trimmed Mean 59.7778
Median 59.0000
Variance 198.274
Std. Deviation 14.08097
Minimum 35.00
Maximum 85.00
Range 50.00
Interquartile Range 21.25
Skewness .112 .512
Kurtosis -.667 .992
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The result of calculating independents samples showed the descriptive statistics of experimental (class A) and control (class B).
Experimental (class A) showed that mean was 78.21, median was 80.00, variance was 74.287. Control (class B) showed mean was 59.8, median was 59, and variance was 198.247.Independent samples t-test was used know whether the significant effect after applying the treatment. So, it was calculating the post-test score both of experimental and control class. It can be seen from the table descriptive statistics that the post-test score of experimental was higher than control class. For more detail to see if there was a significance difference between experimental and control class in the following table below.
Table 4.9
The Calculation of Independent Samples T-Test
Levene’s Test for Equality
of Variances
t-test for Equality of Means
F Sig. t df Sig.
(2- tailed
)
Mean Differenc
e
Std.
Error Differ ence
95% Confidence Interval of the
Difference Lower Upper
Equal variances assumed
4.341 .044 4.893 37 .000 18.411 3.763 10.786 26.035
Equal variances not assumed
4.952 31.732 .000 18.411 3.718 10.835 25.986
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Based on the table the results of independent samples t-test calculation using IBM SPSS 20.0 program, it was showed that the significance t value sig. (2-tailed) 0.000 was lower than t table 0.05. If t value is lower that t table indicates that Ha accepted and Ho rejected. It means, Ha (alternative hypothesis) was accepted and Ho (null hypothesis) was rejected because sig. (2-tailed) 0.000 < 0.05. So it can be concluded that there is significant difference on students results score of the experimental between control class.
C. Discussion
As stated in the research question at the previous chapter, this research aimed to see whether there is significant effect of Think-Talk-Write (TTW) Strategy on students’ writing ability in descriptive text at eighth grade students in SMP Negeri 12 Seluma. The finding of the study interpreted that Ha (alternative hypothesis) was accepted and Ho (null hypothesis) was rejected. It meant the Think-Talk-Write (TTW) strategy is effective toward students writing ability in descriptive text at eighth grade students of SMP Negeri 12 Seluma.
Based on the result finding of the research, the result showed there is Think-Talk-Write (TTW) strategy gives significant difference on students’
writing ability in descriptive text. It is based on the different score showed in pre-test and post-test. The total score of pre-test of the experimental class was 1003 and mean was 52.87. In the total score of post-test was 1486 and mean was 78.2. Meanwhile, the total score of pre-test of the control class was 973
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and mean was 48.6. In the post-test the total score was 1196 and mean was 59.80. Based on the result of data above, it was showed the result of calculating the data by used IBM SPSS 20.0 program. It was found the mean, range, standard deviation, standard error mean from both of group. The mean score of experimental class in pre-test was 52.7, lowest score was 38.00, highest score was 78.00, range was 40.00, standard deviation was 12.13, and standard error mean 2.783. In the post-test of experimental group mean was 78.21, lowest score was 56.00, highest score was 95.00 range was 39.00, standard deviation was 8.619, and standard error mean was 1.977. The mean score of control class was 59.80, standard deviation 14.081, standard error mean 3.149. It means the mean score of students’ frequency distribution in experimental was higher than the mean score of students’ frequency distribution in control class.
The result of the calculating the t-test using IBM SPSS 20.0 version showed that the significance t value sig. (2-tailed) 0.000 was lower than t table 0.05. If t value is lower that t table means Ha accepted and ho rejected.
It means, Ha (alternative hypothesis) was accepted and Ho (null hypothesis) was rejected because sig. (2-tailed) 0.000 < 0.05. The result of the test in the study interpreted that Ha (alternative hypothesis) was accepted because the treatment given was effective toward students’ writing ability in descriptive text and Ho (null hypothesis) was rejected.
Writing most difficult to be mastered and many challenging that should students overcome. It was hoped there was an appropriate strategy to