There are several explanations about data analysis technique, there are : 1. Descriptive Analysis
Descriptive analysis is a statistic which the function of it is to describe or to give a description on the observed object by data sample or population without doing any analysis and no conclusion.54. Descriptive analysis is used to describe the variable of this research that is the use of electronic poems to improve students’ pronunciation. The descriptive analysis includes the measurement of central tendency (mean, median, and mode) and the measurement of group variance (range variance and standard deviation). The researcher will take some simple formulas through these following steps:
a) Mean
The mean represents the subject of the study's average score. The mean is calculated by adding up the data from every group member and dividing it by the total number of group members. The means for group data and central tendency actually differ from one another.
b) Median
Median is one of explanation based on the middle value of the data which have been organized from the lowest to the highest or from the highest to the lowest.
c) Mode
Mode is a technique to explain based on the value which is popular or which often being found.
54 Sugiyono, Metode Penelitian Kuantitatif, Kualitatif, dan R&D, (Bandung: Alfabeta, 2012), page 147.
d) Range
Range is a way to talk about the spread of distribution of scores. The range formula is a follows: R = X highest – X lowest
e) Standard Deviation
The standard deviation is the way of showing the spread of the score. It measures the degree to which the group of scores deviates from the mean. In other words, it shows how all the scores are spread out and thus give a fuller description of the test scores than the range, which simply describes the gap between the highest and the lowest marks and ignores the information provided by all the remaining scores. 55
2. Assumption
In this part, the researcher was describing about normality test and homogeneity test of the research :
a. Normality Test
After the test was given to the students, the data were analyzed to meet Assumption Test, those are normality and homogeneity test. It is calculated to know the next step of analyzing the data which is whether using parametric or non- parametric test.
Assumption test analysis conducted as the prerequisite for testing hypothesis. It calls to be done by conducting a normality test.
A normality test is used to find out whether the data are in normal distribution56
55 Sugiyono, Metode Penelitian Kuantitatif, Kualitatif, dan R&D, (Bandung: Alfabeta, 2008) page,56.
56 James Dean Brown, Testing Language Programs: A Comprehensive Guide To English Language Assessment, (New York: Mc Graw Hill, 2005), 27.
In deciding whether the data are in normal distribution or not, the highest value of significant correction is consulted to Kolmogorov Smirnov table. If the value of the statistic is lower than the value of Kolmogorov-Smirnov table for 5% level of significance, it can be conclude that the data are in normal distribution.
On the other hand, if the value of statistic is higher than the Kolmogorov-Smirnov table for 5% level of significance, the data are not in normal distribution. Furthermore, to determine the data, it is determines to the following criteria:
a) If t-value was lower than t-table (t-value < t-table), the data are normally distributed
b) If t-value was higher than t-table (t-value > t-table), the data are not normally distributed.
b. Homogeneity Test
Homogeneity test is used to know the similarity of the populations. Homogeneity test is used to know the similarity of the populations. Homogeneity test is intended to know whether the population has same variance or not.57 It can be used to draw a conclusion about whether two populations have the same distribution.
The researcher uses SPSS 23 program for windows to analyze the homogeneity in the significance of 5% or 0.05. The criteria to determine the homogeneity test are: a. If t-value was lower than α (t- value < α), it means the data are not homogeny b. If t-value was higher than α (t-value > α), it means the data are homogeny.
57 Retno Widyaningrum, Statistika (Yogyakarta: Pustaka felicha, 2014), page 212.
3. Testing Hypothesis
Hypothesis is temporary answer about statement of the problem.58Hypothesis that will be tested is named work alternative hypothesis (Ha) whereas the opposite is null hypothesis (Ho). Hypothesis is a formal statement about an expected relationship between two or more variables which can be tested though on research.59
When the computation result of normality the data are in normal distribution, it can be continued to the next step, testing hypothesis which have been proposed in the previous chapter. In this study, there are two hypothesis that should be tested. To decide whether Ha is rejected or accepted, it is necessary to compute the data, therefore the conclusion for the hypothesis can be drawn based on the result of the computation. The Ha will be accepted if the t-value is higher than t-table meanwhile, the Ho will be accepted if the t-table is lower than t-table.60
58 Sugiyono, Statistik untuk Penelitian. Bandung: Alpabeta, 2012.
59 Arikunto, Suharsimi. Prosedur Penelitian Suatu Pendekatan Praktik. Jakarta: PT Rineka Cipta, 2010.
60 Andhita Dessy Wulansari, Aplikasi Statistika Parametrik dalam Penelitian, (Yogya:
Pustaka Felicha, 2016), Page 151.
40 CHAPTER IV