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CHAPTER IV ANALYSIS OF THE DATA
In this chapter I analyzed the data obtained from the students: the test result. I gave the test on 7
th
September, 2006. It consisted of 50 multiple choice items. I composed the test items myself, under the consideration that the test items
would be valid. After the data were collected, I organized, analyzed, and interpreted them. Here, I used statistical analysis and non statistical one.
4.1 STATISTICAL ANALYSIS
To process the data, I used a simple computation with the following simple formula:
X =
∑ ∑
T E
X 100 Where:
X = the percentage of incorrect answer ∑E = the total number of various kinds of incorrect answers, and
∑T = the total number of test items. Because the respondents of this study were 26 students, I had 26
percentages of error computations. I divided the errors into words errors, compound words errors, word phrase errors and sentence errors. The result of the
data analysis can be seen in the following table:
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Table 4.1: The percentage of errors Code
∑E ∑T
Percentage of Incorrect answer
S-01 19 50 38
S-02 13 50 26
S-03 23 50 46a
S-04 21 50 42
S-05 19 50 38
S-06 25 50 50
S-07 0 50 S-08 12 50
24 S-09 2 50
4 S-10 25 50
50 S-11 16 50
32 S-12 10 50
20 S-13 9 50
18 S-14 9 50
18 S-15 18 50
36 S-16 11 50
22 S-17 6 50
12 S-18 6 50
12 S-19 16 50
32 S-20 9 50
18 S-21 12 50
24 S-22 6 50
12 S-23 6 50
32 S-24 6 50
12 S-25 8 50
16 S-26 11 50
22
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The first column contains the number of subjects, which are the total number of respondents who participated in the real test 26 Students.
The second column contains the total of various kinds of errors made by respondents out of 50 items.
The last one contains the percentage of the errors which results from the sum of various kinds of errors made by each respondent E divided by the total of
the test items T times 100. The result of the study shows that the lowest percentages of errors made
by students are 0 while the highest is 50. The data are ranked in ascending order from the lowest to the highest as follows:
1 There was 1 student that made 0 errors.
2 There was 1 student that made 4 errors.
3 There was 1 student that made 12 errors.
4 There was 1 student that made 16 errors.
5 There were 3 students that made 18 errors.
6 There was 1 student that made 20 errors.
7 There were 2 students that made 22 errors.
8 There were 2 students that made 24 errors.
9 There was 1 student that made 26 errors.
10 There were 3 students that made 32 errors.
11 There was 1 student that made 36 errors.
12 There was 1 student that made 38 errors.
13 There was 1 student that made 42 errors.
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14 There was 1 student that made 46 errors.
15 There were 2 students that made 50 errors.
Related to items dictated, there were four categories, namely: 1
word a
noun b
verb c
adjective d
adverb 2
compound word a
compound noun b
compound verb 3
word phrase a
noun phrase b
verb phrase c
adjective phrase d
adverb phrase 4
sentence After knowing the percentage of errors, I carried out an error analysis in
order to find out the dominant errors. In this calculation, I used the selected category which is based on Gulo’s formula:
pi = n
fi x 100
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Where: pi = the proportion of error occurrence frequency,
fi = the absolute frequency of category error in a partial type, and n = the total number of category possible errors.
Then I computed the proportion of frequency of occurrence of errors as a whole by using the formula:
PI = N
FI X 100
Where: PI = the proportion of frequency of occurrence of errors as a whole,
FI = the absolute frequency of types of errors of all the categories, and N = the total number of possible errors of all the categories.
The PI was computed as follows: PI =
N FI
X 100
= 1300
328 X 100
= 25.2
The final step was to identify the degree of dominance of the particular error. Any error whose pi-PI is plus + is considered to be dominant. On the
contrary, if the pi-PI is zero 0 or minus -, it is said to be less dominant. After the calculation, in descending order, the most dominant error through the least
dominant one can be seen in the table below:
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Table 4.2: The Most Dominant Errors Number of
type ∑ Items
∑ n fi pi
pi-PI 1a
1b 1c
1d 2a
2b 3a
3b 3c
3d 4
9 6
4 4
7 1
4 3
2 4
6 234
156 104
104 182
26 104
78 52
104 156
70 58
31 27
54 4
19 15
9 12
29 29.9
37.2 29.8
26 29.7
15.4 18.3
28.8 17.3
11.5 18.6
4.7 12
4.6 0.8
4.5 -9.8
-6.9 3.6
-7.9 -13.7
-6.6 Total 50 1300
328 262.5
Where: 1
The first column contains the number of categories of errors. 2
The second column contains the total of item for each predicted errors. 3
The third column ∑ n contains the total number of possible errors of the
category and it is derived from the sum of the item for each category times the total number of students 26.
4 The fourth column fi is the absolute frequency of a partial type of errors on
the category. 5
The fifth column contains pi that derives from the absolute frequency of a partial type of errors of a category divided by the total number of possible
errors of the category times 100.
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6 The last one pi-PI where PI which is derived from absolute frequency of
type of errors of all levels divided by the total number of possible errors of the category times 100.
That is the result of errors of dictation as a testing device of listening made by the fifth grade students of SDN 03 Slawikulon in the academic year 20062007
based on statistical analysis. Then I will discuss those errors based on non- statistical analysis.
4.2 NON STATISTICAL ANALYSIS
Kategori