CHAPTER IV RESEARCH FINDING AND DISCUSSION
C. Data Analysis
In this section the data were taken from two classes, those were the experimental class and the control class. Data analysis was done to find out the comparison between two classes used in this research. The experimental class was taught using Participation Point System as learning method, while the control class was taught by Audio Lingual method. The comparison of the value was seen from the data obtained through the pre-test and post-test. The researcher provided the table list of the score in the pre-test and post-test of experimental class and the control class as follow:
Table 4.5 the Data of 2 Classes Studied
NO Experimental Class Control Class
Pre Test Post Test Pre Test Post Test
1 60 *88 *80 72
2 *76 *96 60 70
3 72 *80 60 *84
4 68 *90 70 *76
5 *80 *84 72 *80
6 56 *76 68 *76
7 *80 *80 *84 *76
8 *80 *92 *92 *80
9 *78 64 *76 *78
10 *88 *96 *88 *88
11 72 *88 56 *76
12 72 *88 64 *80
13 64 *84 68 *84
14 *76 68 60 68
15 72 *80 68 72
16 68 *80 *76 *76
17 55 *84 *80 *80
18 70 *76 52 *92
19 70 *80 68 *84
20 72 *84 56 72
21 *80 *88 52 72
22 72 *82
1. Descriptive Analysis
In making the categorization of student’s speaking ability scores, the researcher used SPSS version 25 to find out the Mean (M) and Standard Deviation (SD). The Mean is the average score of the student’s scores and it can be found by adding all students’ scores and dividing them with number of the students. Meanwhile, the standard deviation is a measurement of how the scores are spread out. In this section, the researcher divided the categorization into four main parts.
They were the category of the experimental group pre-test, category of
experimental group post-test, category of the control group pre-test and the category of the control group post-test.
Table 4.6 Display Data Description Output Descriptive Statistics
N Range
Minimu m
Maximu
m Mean
Std.
Deviation
PreEx 21 33 55 88 71.86 8.284
PostEx 21 32 64 96 83.14 8.064
PreCont 22 40 52 92 69.18 11.257
PostCont 22 24 68 92 78.09 6.031
Valid N (listwise)
21
The data presented above is descriptive statistical analysis result data that includes; minimum score, maximum score, mean score, and standard deviation. By looking at data above, we can find out the mean score or the average score pre-test and post-test in the experimental class and the control class. The post-test of experimental class experienced significant differences from previous pre-test results. This result can be interpreted that there is an effect of Participation Point System to student’s speaking ability at second grade of Excellent Islamic Junior High School Al- Qodiri 1 Jember in academic year 2022/2023.
2. Validity Test
In this research, the researcher uses content validity. Content validity is the degree which items is instruments reflect the content universe which the instrument will be generalized. In general, content validity involves evaluation of new survey instrument in order to
ensure that it conclude all items are essential and eliminates undesirable items to particular construct domain.42
The researcher wrote the specification of the test. Specification for the test includes the topic, speaking used based on topic, and scoring procedures. To make the test valid, the researcher prepares several things to be applied:
1) Designing the test based on curriculum, based on basic competence of speaking participants were targeted to be able to understand the meaning of some words based on the topic, speak accurately, using appropriate grammar structure, and understand how to make the text of descriptive text.
2) Providing clear instruction to participants, such as the participants should presents their descriptive text based as the topic of lesson.
Therefore, to make the instruments valid, it was given to an English teacher who expert speaking as the validator to examine the relation between the instrument and syllabus including basic competence, indicator, and course objective. The instrument was valid if it fit the requirement of curriculum, the instrument for validator was written in appendix. The result of validity test as follow:
42 Taherdoost, “Validity and Reliability of the Research Instrument; How to Test the Validation of a Questionnaire/Survey in a Research.” 5
Day/ Date Activity
Tuesday, 02 August 2022 The researcher sent the expert validation to examine files of test instrument including;
blueprint, test instrument, scoring rubric, and lesson plan.
Wednesday, 03 August 2022 The expert confirmed the test instrument was valid
(See Appendix 4)
Based on the result, it could be concluded that the test instrument was confirmed as valid and ready to be tried out to the students.
3. Reliability Test
Reliability refers to the consistency of the scores obtained, how consistent it is for each individual from one administration of instrument to another and from one set of items. In this research, the researcher used inters scorer reliability. The researcher engaged the English teacher and researcher as the examiners to measure reliability test. So, the researcher was the first rater and the English teacher was the second rater. Then the researcher used VIII D class for the try-out test.
- Here was picture between two examiners on try out test.
The researcher calculated the scores by using the formula of Cohen’s Kappa with SPSS in order to reach the score agreement between two examiners, so that each student only had one score. The instrument was confirmed as reliable if the calculation of test score reached value > 60 which means that the level of agreement was categorized as good or reliable. The detail interpretation of Cohen’s Kappa calculation was as follow:
Table 4.7 The Interpretation of Cohen’s Kappa43
Value of Kappa Level of Agreement % of data that Reliable
0 - .20 None 0 – 4%
.21 - .39 Minimal 4 – 15%
.40 - .59 Weak 15 – 35%
.60 - .79 Moderate 36 – 63%
.80 - .90 Strong 64 – 81%
Above .90 Almost perfect 82 – 100%
The result of reliability test was as follow:
Day/ Date Activity
Thursday, 04 August 2022 The researcher conducted try out to VIII D
Saturday, 05 August 2022 The score of VIII D try out result from two examiners (researcher and English teacher) has been collected.
The scores of try out result from two examiners for 25 students.
Table 4.8 Data of Try- Out Test Class
No. Rater 1 Rater 2 No. Rater 1 Rater 2
1 79 81 14 66 68
2 70 67 15 75 76
3 75 76 16 65 64
43 L.MCHugh, “Interrater Reliability: The Kappa Statistic.” 279
4 80 78 17 58 60
5 66 68 18 66 65
6 58 60 19 75 73
7 75 76 20 81 78
8 56 60 21 78 79
9 78 80 22 57 55
10 66 68 23 65 61
11 78 80 24 80 77
12 84 80 25 72 75
13 76 78
Nilai Try Out
N Valid 25
Missing 0
Mean 71.32
Median 75.00
Std. Deviation 7.983
Minimum 55
Maximum 81
Sum 1783
Percentiles 25 64.50
50 75.00
75 78.00
4.9 Table of Reliability Test Symmetric Measures
Value
Asymptotic Standard
Errora
Approximate Tb
Approximate Significance Measure of
Agreement
Kappa -.043 .017 -1.108 .268
N of Valid Cases 25
a. Not assuming the null hypothesis.
b. Using the asymptotic standard error assuming the null hypothesis.
Based on Cohen’s Kappa decision on probability (p value) if:
P value > alpha (0.05) then H0 is accepted
P value < alpha (0.05) then H0 is rejected.
Then based on data showed above, the result data is 0.268 > 0.05.
Therefore H0 is accepted and Ha is rejected, so it means that there is no agreement between raters on the measured variables.
4. Normality Test
The next step to analyze the data is doing normality test. The Normality test in this research was conducted to quality the absolute prior to statistical analysis. In this phase, the researcher used Shapiro- Wilk as technique of normality test and the result are presented as follow:
Table 4.10 the Normality Data Output Tests of Normality
Kelas
Kolmogorov-Smirnova Shapiro-Wilk Statistic Df Sig. Statistic Df Sig.
Hasil Belajar Speaking
Post- Test Experiment Class
.158 21 .185 .948 21 .317
Post- Test Control Class .136 22 .200* .964 22 .565
*. This is a lower bound of the true significance.
a. Lilliefors Significance Correction
The Normality test above only focused on the significant value of the Shapiro-Wilk. In the Shapiro-Wilk normality test, it can be seen that the data is normally distributed because the significance value is
>0.05. All significance values of Shapiro-Wilk are more than 0.05.
5. Homogeneity Test
Besides testing normal distribution of the data, it’s also necessary to test whether the sample variance was homogeneous or not. The test of the variance homogeneity which is gained from SPSS version 25,
homogeneity test is performed to see whether the post-test data in the experimental class and the control class are homogeneous or heterogeneous, because the homogeneous data is one of the requirements to conduct the independent sample t-test. The result can be seen in table below:
4.11 The Homogeneity Data Output Test of Homogeneity of Variance
Levene
Statistic df1 df2 Sig.
Hasil Belajar Speaking
Based on Mean .968 1 41 .331
Based on Median .733 1 41 .397
Based on Median and with adjusted df
.733 1 36.319 .397
Based on trimmed mean
.888 1 41 .351
According to decision of making for homogeneity test is as follow:
If the significance value is <0.05, it is said that the variants of two or more groups of the data population are not homogeneous.
If the significance value is >0.05, it is said that the variants of two
or more groups of the data population are homogeneous.
Based on the data output above, it is known that the significance (sig.) is 0.331 > 0.05. So it can be concluded that the data on post-test of experimental class and control class are homogenous, it means that the data groups comes from a population with the same variance homogeneous. Therefore, one of the independent sample t-test requirements has been fulfilled.
6. Independent Sample T-test
An independent sample t-test was conducted to determine whether the two sample groups had significant differences on the average or not. Independent sample t-test was conducted by testing the post-test data of the experimental class and the control class. The result shown in table below:
Table 4.12 Output Data of Independent Sample T-test Independent Samples Test
Levene's Test for Equality of Variances
t-test for Equality of Means
F Sig. t
Hasil Belajar Speaking
Equal variances assumed
.968 .331 2.334
Equal variances not assumed
2.318
Independent Samples Test
t-test for Equality of Means
Df Sig. (2-tailed)
Mean Difference Hasil Belajar Speaking Equal variances assumed 41 .025 5.052
Equal variances not assumed
37.010 .026 5.052
t-test for Equality of Means Std. Error
Difference
95% Confidence Interval of the Difference
Lower Upper Hasil Belajar
Speaking
Equal variances assumed
2.165 .680 9.424
Equal variances not assumed
2.179 .636 9.468
The basis for making decision on Independent Sample T-Test is as follows:
If the value is Sig. (2-tailed) < Research Alpha (0.05) then H0 is rejected and Ha is accepted.
If the value is Sig. (2-tailed) > Research Alpha (0.05) then H0 is
accepted and Ha is rejected.
Based on the data above, the significance sig. (2-tailed) is 0.025 <
0.05, then H0 is rejected and Ha is accepted which means that there is a different on the average of data post-test in experimental class and control class.
Group Statistics
Kelas N Mean
Std.
Deviation Hasil Belajar
Speaking
Post- Test Experiment Class
21 83.14 8.064
Post- Test Control Class
22 78.09 6.031
Group Statistics
Kelas Std. Error Mean
Hasil Belajar Speaking Post- Test Experiment Class 1.760
Post- Test Control Class 1.286
Normal Q-Q Plots
Detrended Normal Q-Q Plots