2 Type column was numeric 3 Width column is filled 8
4 Decimal change this row frow 2 to 0
5 Label column was left blank 6 Value column is none
7 Missing column is none 8 Column is 8
9 Align column is right
10 Measure coulumn is unknown 11 Role coulumn is filled with input
c. Click Data View, in score column, compute “1” as representing
experimental class and “2” representing controlledlededed class. d. In score column, compute score of each class.
e. Click AnalyzeDescriptive StatisticsExplore f. Fill in the Dependents List with score pret-test and then fill in the
Factor List with class. g. Click Plotschecklist Normality Polts with test, Histograms, Power
EstimationContinueO.K If p
– value α, the data is not distributed normal If p
– value ≥ α, the data is distributed normal Note: In SPSS, P
– value is same with Significance Sig.
10
2 Homogenity
Homogenity test is used to wether the data from two classes have same variant in order that the hypothesis can be tastes by t-test. The
procedure are same with normality’s steps. Furthermore, after testing both
normality and homogenity, the writer began test the hyphothesis.
F. Technique of Data Analysis
1. t-test
10
Ibid, p. 40
In addition, the writer also used IBM SPSS statistics version 22 to find the siginificances differences result from the pre-test and post-test data to conduct t-
test. In SPSS, t-test was conducted through Independent-Sample t-test. It is a comparative test that compare the mean score between experimental and
controlled class. the steps are as follows: a. Open the IBM SPSS Statistics 22
b. Go to variable view and fill in the columns as follows. 1 Name
Write “class” in the first row. This is to indicate and differentiate between experimental and controlledlededed class.
write ”score” in the second row. 2 Type column was numeric
3 Width column is filled 8 4
Decimal change this row frow 2 to 0 5 Label column was left blank
6 Value column is none 7 Missing column is none
8 Column is 8 9
Align column is right 10 Measure coulumn is unknown
11 Role coulumn is filled with input c. Click Data View, in score
column, compute “1” as reresenting experimental class and “2” representing controlledlededed class.
d. Click AnalyzeCompare MeansIndependent Sample T-Test e. Fill in Test Variables with score of pre-test and post-test. Then, fill in
the Grouping Variables with class and fill Define Groups with 1 and 2 f. Click Optionsfill Confidennce Interval Percentage with 95
g. Click ContinueO.K If sig. 2 tailed p
– value α, H was rejected and H
a
was accepted If sig. 2 tailed p
– value α, H was accepted and H
a
was rejected
2. Effect Size Formulation
This is the way to know how much the effect size of the treatment that given in experimental class. There are three categorize that stated in Cohen
formulation; small, medium, and large effect size. The formulation is below. d =
Pooled SD
= The result might be interpreted as the criteria follow:
0.2 = small effect size 0.5 = medium effect size
0.8 = large effect size
G. Statistical Hypothesis
Hyphothesisis a statement or assumption about one population or more
11
. In other words, the hyphothesis is a counjecture or a guess at the solution to a
problem or the status of the situation.
12
a. Null Hypothesis H There is no significant difference on students’ reading comprehension of
Descriptive Text by using K-W-L Chart if the result of calculation p α; sig. 2
tailed was greater than alpha, the null hypothesisH are accepted and alternative
hyphotesis H
a
is rejected. It means that the experiment technique is rejected. α
= 0.05 b. Alternative Hypothesis H
a
There is significant differenceson students’ comprehension in descriptive Text by using K-W-L Chart if the result of calculation
p α; sig. 2 tailed was lower than alpha, there is significance differences and the alternative hypothesis
H
a
are accepted and null hyphotesis H is rejected. It means that the
experiment technique is accepted. α = 0.05
11
Ronald E. Walpole, PengantarStatistika, Jakarta: GramediaPustakaUtama, 1992, p. 288
– 289
12
William Wiersma, Stephen G. Jurs, Research Method in Education: Unated States of America,Pearson, 37
32
CHAPTER IV FINDING AND DISCUSSION
In this chapter, the writer would like to present the description of data which consist of preliminary analysis, the score of both pre-test and post-test
between two classes, experimental and controlled class. Furthermore, the description would be followed by the analysis data and discussion.
A. Finding
Based on explanation in the chapter three, this study used quantitative method with quasi-experimental study. Quasi
– experimental study is similar to experimental research in that one or more experimental variable are involved and
conducted by some treatments. In this study, the writer had four meetings to both experimental and controlled classes. In the first meeting, the writer took pre-test
score as the preliminary analysis to know the homogeneity and normality of the classes. Then, after finished the treatment, the writer took the posttest score. Both
of the data would be presented in the following description.
1. Preliminary Analysis
