DATA ANALYSIS TECHNIQUES
F. DATA ANALYSIS TECHNIQUES
In this study, there are two kinds of data analysis used to test the hypothesis. To test the first hypothesis and the use of analysis of variance or ANAVA. Meanwhile, to test the third and fourth hypotheses, analysis of different test used the average of the right side. The fourth hypothesis using different test analysis of the average against the left.
Before hypothesis is implemented, it is done test of the validity of instrument and the requirements analysis of hypothesis test that is for normality test and homogeneity test
Normality test is used to determine whether the sample in this research come from a normally distributed population. Researcher used the normality test is a test of normality using Lilliefors test technique.
Homogeneity test used to determine whether the sample is from
a normally distributed population of homogeneous variance. Researchers used the homogeneity test is Bartlett test. Besides these two tests is to determine whether the results of studying mathematics is really influenced by the treatment, then held Analysis of Variance (ANOVA).
The following is a description of the analysis conducted:
1) Analysis Technic of Research Hypothesis Test The purpose of the research is used to test the difference of the average score with two independent variables, so that the test of research hypothesis that use isAnalisis of Varians (ANOVA) two steps. The steps of ANOVA two-way factorial 2 x 2:
a. Grouping score of student’s speaking skill based on category: - Factor K : The use of the teaching method, K-1 uses teaching method of communicative language teaching method and K-2 use the teaching method of conventional.
- Faktor B: Emotional intelligence, B-1 the high emotional intelligence and B-2 the low emotional intelligence. Grouping score of student's speaking skills by category
Table 3.9
Design ANOVA Two-Way Factorial 2 x 2 Teaching Method
Emotional
B Intelligence
b. Creating a table of descriptive statistics for each group of data table contains descriptive statistics, the prices for each element required in the ANOVA as follows:
Table 3.10 Descriptive Statistics Table for Two-Way
A-1
A-2
B
ܻ B-1
∑Y
∑Y
∑Y
2 2 ∑Y 2 ∑Y ∑Y
ܻ B-2
∑Y
∑Y
∑Y
2 2 ∑Y 2 ∑Y ∑Y
ܻ K
∑Y
∑Y
∑Y
2 2 ∑Y 2 ∑Y ∑Y
Description: NY = Number of subject in the group Y = Average score for each group
ΣY = Number of score in each group ΣY2 = Sum of squares of each score in the group
c. Creating a summary table of two-way ANOVA Based on the descriptive statistical data in the table above, processed to get a summary of the ANOVA table to test the following hypotheses:
Table 3.11
Summary of ANOVA for Hypothesis Test
F t Sumber varians
F t (Ak) (Ak)
Between Column
Db Jk (Ak) Rjk (Ak) F h (Ak)
F t (Ak)
Between Line (Ab)
(Ak)
Jk (Ab)
Rjk
F h (Ab)
F t (Ab)
F t (Ab)
Interaction (I)
Db Jk(I)
Rjk(I)
Db(I)
F t (A) (A)
Between Column
Db Jk(A)
Rjk(A)
In Group
- - (D)
Db Jk(D)
Rjk(D)
(D)
Total in Reduction
- - (TR)
Db Jk(D)
Average/Correction
- - (R)
Db Jk(R)
Rjk(R)
(R)
Total (T)
80 Jk(T)
d. Way to determine Db, Jk, Rjk, F h , dan F t
Determine the degrees of freedom (db), the sum of squares (Jk),
variance (Rjk) and F hitung (F h ) and F tabel (F t ) for filling the shell in the ANOVA summary table above, is obtained as follows:
1) Determine the degrees of freedom
a. db (Ak) = k – 1
b. db (Ab) = b – 1
f. db (TR) = n 00 –1
g. db (R) = 1
h. db (T) = n 00
2) Determine the sum of squares (JK)
c. JK (TR) = JK(T) – JK(R)
∑Y 2 22 ) 2 ( ∑Y 22 ) d. JK(A) =
( ∑Y 22 ) 2 ( ∑Y 22 ) 2 (
+- JK(R) n 22 n 22 n 22 n 22
JK(R)
f. JK(Ab) =
n 22 n 22
g. JK(I) = JK(A) – JK(Ak) – JK(Ab) h. JK (D) = JK (TP) – JK(A)
3) Determine variance (S 2 ) atau RJK:
Jk(Ak)
a. Rjk (Ak) = 2 (Ak) =
db (Ak) JK(Ab)
b. Rjk (Ab) = 2 (Ab) =
db(Ab) JK(I)
a. Rjk(I) = 2 (I) =
db(I)
d. Rjk(A) = S 2 (A) = JK(A)/db(A)
e. Rjk(D) = S 2 (D) = JK(D)/db(D)
2) Determine score F table (F1) = F(a, db1, db2) db1 = db numerator = k – 1 db2 = db denominator = n – 1 k = number of columns / rows / treatment / group n = number of data / sample
e. Hypothesis Testing and Drawing Conclusions
1) For the variance between the columns (Ak) or hypothesis 1 Hypothesis testing criteria:
- Reject H 0 and accept H 1 : if F h >F t
- Accept H 0 and reject H 1 : jika F h <F t
2) For the variance interaction column and row (I) or hypothesis 2 Hypothesis testing criteria:
- Reject H 0 and accept H 1 : if F h >F 1
- Accept H 0 and reject H 1 : if F h <F 1
3) For hypothesis 3, difference of outcome learning of the student’s speaking skill on group of the high emotionsl intelligence. Hypothesis testing criteria:
- Reject H 0 and accept H 1 : if Q h >Q t
- Accept H 0 and reject H 1 : if Q h <Q t
4) For hypothesis 3, difference of outcome learning of the student’s speaking skill on group of the low emotionsl intelligence. Hypothesis testing criteria:
- Reject H 0 and accept H 1 : if Q h >Q t
- Accept H 0 and reject H 1 : if Q h <Q t