CHAPTER III METHODOLOGY
E. Data Collecting Technique
Item 8 .444 .478* Valid Item 28 .444 .039 Invalid Item 9 .444 .493* Valid Item 29 .444 .578** Valid Item 10 .444 .329 Invalid Item 30 .444 .520* Valid Item 11 .444 .508* Valid Item 31 .444 .462* Valid Item 12 .444 .198 Invalid Item 32 .444 .520* Valid Item 13 .444 .535* Valid Item 33 .444 .482* Valid Item 14 .444 .520* Valid Item 34 .444 .389 Invalid Item 15 .444 .494* Valid Item 35 .444 .052 Invalid Item 16 .444 .188 Invalid Item 36 .444 .501* Valid Item 17 .444 .482* Valid Item 37 .444 .076 Invalid Item 18 .444 .711** Valid Item 38 .444 .447* Valid Item 19 .444 .689** Valid Item 39 .444 .212 Invalid Item 20 .444 .228 Invalid Item 40 .444 .440 Invalid
Table 3.12
The Reliability of Reading Comprehension Test
The tables 3.11 and 3.12 show the result of piloting test on reading comprehension. From 40 questions administrated to the piloting participants, 28 questions are valid. These valid questions are already met with all indicators of self- regulated learning. Then, reliability value is in 0.898 Cronbach’s Alpha which means it is very high reliable. As a result, 28 questions will be tested to the sample of study.
out the result of students’ self-regulation. At the same time, the tests are given to determine the score of students’ morphological competence and their reading comprehension score. The test will be conducted at various times during the day to collect data. Below are some procedures in collecting the data:
1. Administrating Test
Students will first complete a morphological test in multiple choice. It consists of 19 questions. The question is on Google form that sent via Whatsapp messenger to the participants. A few days later, it is followed by reading test in a multiple-choice with 28 questions. Such morphology test does, the question (in google form) is then sent to the participants via Whatsapp.
2. Distributing Questionnaire
Immediately after a reading test done, students are asked to complete a questionnaire about their ability to self-regulate their reading process. To administer the questionnaire, a link to a google form will be sent.
F. Data Analysing Technique
After After the data is gathered, the analyzing process will be conducted to interpret the data and answer the previously stated research questions. The data will be analyzed in the form of a statistical process because the data is numerical. The pearson correlation coefficient used to determine the relationship between variables.
There will be testing analysis divided into (1) normality test, (2) homogeneity test, (3) linearity test, and (4) hypothesis test will be conducted to analyze the data. The explanation is as follows:
1. Normality Test
This test is implemented to find out the normality distribution on the variable X and Y population. Using Kolmogorov Smirnov formula, the normality will be found. The score of the significance level is α=0,05.
2. Homogeneity Test
The homogeneity used to know the similarity variant of normal sample distribution. Using Levene’s statistics test, data will be analyzed. The homogeneous data will be found if the F-table is lower than F-count at α=0,05 the significant level.
3. Linearity Test
This test is to ensure the dependent and independent variables are linear. The correlation between variables could be estimated through mathematical equations.
Using a model of the statistical equation which Y=a+bX, those variables will be investigated.
4. Hypothesis Test
To test the relationship hypothesis among variables, multiple correlation coefficient will be used to determine the relationship and its degree between variables. The formula to measure various correlation coefficient is below:
The score significance interpretation is as follows: if the significance value <
0,05 indicates a significant relationship between the variables, in contras there is no significant if value > 0,05.
The coefficient determination of R2 is used to find out how strong the differences between one variable and the other variable can be described. R2
coefficient correlation value arranges between 0—1. The interpretation of the score level follows: 0,80—1,00 indicates very strong correlation, 0,60—0,79 shows strong correlation, 0,40—0,59 indicates strong enough, and 0,20—0,39 indicates weak correlation. Below is the formula:
G. Statistical Hypothesis
Through quantitative study, the study's main point is to find out the hypothesis which one is proven. According to the literature review given earlier, the statistical hypothesis will be determined through the test. These hypotheses are expected to display several probabilities that existed between variables. In which:
1. Ho: Rx1 = 0 (There is no relationship between morphological competence and reading comprehension)
Hi: Rx1 > 0 (There is a relationship between morphological competence and reading comprehension)
2. Ho: Rx2 = 0 (There is no relationship between self-regulated learning and reading comprehension)
Hi: Rx2 > 0 (There is a relationship between self-regulated learning and reading comprehension)
5. Ho: Rx12 = 0 (There is no relationship between morphological competence, self-regulated learning, and reading comprehension)
Hi: Rx12 > 0 (There is no relationship between morphological competence, self-regulated learning, and reading comprehension)
Notes:
Ho : null hypotheses Hi : alternative hypotheses
Rx1 : the coefficient of correlation between morphological competence and reading comprehension
Rx2 : the coefficient of correlation between students’ self-regulated learning and their reading comprehension
Rx12 : the multiple coefficient correlation of relationship among relationship between morphological competence, self-regulated learning, and reading comprehension
CHAPTER IV
FINDINGS AND DISCUSSIONS A. DATA DESCRIPTION
Based on the data analysis, three variables are examined in this study: reading comprehension (Y), morphological competence (X1), and self-regulated learning (X2). The following description is provided based on the statistical calculation. The data description presented below are the result of each variable test, the result of requirement test, and the result of hypothesis test. Then, below is the further explanation:
1. The Result of Morphological Competence Test
The results of the calculation performed using SPSS 23 for descriptive frequencies formula, it was revealed that the students got 95 as the maximum score, 32 for the minimum score, 63 for the range score, 70 as the mean score, 16.8 for standard deviation, 63 as the mode, and 68 for the median score. The data of students’
morphological competence is below:
Table 4.1
Statistics Data of Morphological Competence
N Valid 84
Missing 0
Mean 69.60
Median 68.00
Mode 63
Std. Deviation 16.855
Range 63
Minimum 32
Maximum 95
Sum 5846
The table 4.1 above indicates that students of the third semester got 70 as their overall score. It means they have a good understanding in morphological
competence based on the grading system of English education, UIN Syarif Hidayatullah Jakarta (2020). The median of a frequency distribution stands at 68 which implies that students' score has an equal probability of going above or below it. Then, the mode is 63 that most students frequently achieve medium grades. The standard deviation score is 16.8; it is lower than the mean score, which indicates that the mean score adequately represents overall data and is distributed well. The range score is 63, which means the range between the minimum and maximum score is bridged by 63 data. The maximum score is 95, categorized as a high grade; this shows some students have an advanced understanding of morphology. However, the students still got the minimum score at 32 that considered low. It indicates that some students still have lack morphological competence understanding.
Then, the table below is the distribution of morphological test in which the score 0-60 is categorized as low, 61-79 is medium, and 80-100 is high. Therefore, we can imply that 25 (30%) students in low grade, 35 (42%) students in the medium score, and 24 (28%) students in high grade.
Table 4.2
Distribution Frequency Score of Students’ Morphological Competence
Frequency Percent Valid Percent
Cumulative Percent
Valid 32 2 2.4 2.4 2.4
37 3 3.6 3.6 6.0
42 2 2.4 2.4 8.3
47 4 4.8 4.8 13.1
53 6 7.1 7.1 20.2
58 8 9.5 9.5 29.8
63 10 11.9 11.9 41.7
68 8 9.5 9.5 51.2
74 9 10.7 10.7 61.9
79 8 9.5 9.5 71.4
84 9 10.7 10.7 82.1
89 6 7.1 7.1 89.3
95 9 10.7 10.7 100.0
Total 84 100.0 100.0
2. The Result of Self-Regulated Questionnaire
Based on the calculation performed using SPSS 23 for descriptive frequencies formula, it was revealed that the students got 97 as the maximum score, 47 for the minimum score, 50 for the range score, 76 as the mean score, 8.9 for standard deviation, 80 as the mode score, and 76 for the median score. The data of students’ Self-Regulated Learning (X2) as follow:
Table 4.3
Statistic Data of Self-Regulated Learning
N Valid 84
Missing 0
Mean 75.96
Median 76.00
Mode 80
Std. Deviation 8.942
Range 50
Minimum 47
Maximum 97
Sum 6381
The table 4.3 above indicates that students' overall score is 76, which means the students in the third semester of the English education, faculty of educational
sciences at UIN Syarif Hidayatullah Jakarta have a good self-regulated learning skill.
The median of a frequency distribution stands at 76 which implies that students' score has an equal probability of going above or below it. Then, the mode is 80 that most students frequently achieve high grades. The standard deviation score is 8.9; it is lower than the mean score, which indicates that the mean score adequately represents overall data and is distributed well. The range score is 50, which means the range between the minimum and maximum score is bridged by 50 data. The maximum score is 97, categorized as a high grade; this shows some students have an advanced SRL. However, the students still have low SRL which is indicated by the minimum score at 47.
Then, the table below is the distribution of morphological test in which the score 0-60 is categorized as low, 61-79 is medium, and 80-100 is high. Therefore, we can imply that 3 (4%) students in low grade, 52 (62%) students in the medium score, and 29 (34%) students in high grade.
Table 4.4
Distribution Frequency Score of Students’ Self-Regulated Learning
Frequency Percent Valid Percent
Cumulative Percent
Valid 47 1 1.2 1.2 1.2
52 1 1.2 1.2 2.4
57 1 1.2 1.2 3.6
61 1 1.2 1.2 4.8
62 1 1.2 1.2 6.0
63 1 1.2 1.2 7.1
64 1 1.2 1.2 8.3
65 3 3.6 3.6 11.9
67 1 1.2 1.2 13.1
68 2 2.4 2.4 15.5
69 4 4.8 4.8 20.2
70 3 3.6 3.6 23.8
71 4 4.8 4.8 28.6
72 1 1.2 1.2 29.8
73 6 7.1 7.1 36.9
74 5 6.0 6.0 42.9
75 4 4.8 4.8 47.6
76 6 7.1 7.1 54.8
77 1 1.2 1.2 56.0
78 5 6.0 6.0 61.9
79 3 3.6 3.6 65.5
80 7 8.3 8.3 73.8
81 2 2.4 2.4 76.2
82 3 3.6 3.6 79.8
84 2 2.4 2.4 82.1
86 6 7.1 7.1 89.3
87 3 3.6 3.6 92.9
88 1 1.2 1.2 94.0
91 1 1.2 1.2 95.2
92 2 2.4 2.4 97.6
94 1 1.2 1.2 98.8
97 1 1.2 1.2 100.0
Total 84 100.0 100.0
3. The Result of Reading Comprehension Test
Based on the calculation performed using SPSS 23 for descriptive frequencies formula, it was revealed the students got 96 as the maximum score, 32 for the minimum score, 64 for the range score, 63 as the mean score, 18.5 for standard deviation, 61 as the mode, and 61 for the median score. The dissemination data of Reading Comprehension (Y) as follow:
Table 4.5
Statistic Data of Students’ Reading Comprehension
N Valid 84
Missing 0
Mean 63.30
Median 61.00
Mode 61
Std. Deviation 18.567
Range 64
Minimum 32
Maximum 96
Sum 5317
The table 4.5 above indicates that students' overall score is 63, which means the students in the third semester of the English education program, faculty of educational sciences at UIN Syarif Hidayatullah Jakarta have an average grades in reading comprehension ability (Grading System of UIN Jakarta, 2020). The median of a frequency distribution stands at 61 which implies that students' score has an equal probability of going above or below it. Then, the mode is 61 that most students frequently achieve medium grades. The standard deviation score is 18.5; it is lower than the mean score, which indicates that the mean score adequately represents overall data and is distributed well. The range score is 64, which means the range between the minimum and maximum score is bridged by 64 data. The maximum score is 976, categorized as a high grade; this shows some students have an advanced reading comprehension ability. However, the students still have low SRL which is indicated by the minimum score at 32.
Then, the table below is the distribution of morphological test in which the score 0-60 is categorized as low, 61-79 is medium, and 80-100 is high. Therefore, we can imply that 35 (42%) students in low grade, 31 (37%) students in medium score, and 18 (21%) students in high grade.
Table 4.6
Distribution Score of Reading Comprehension
Frequency Percent Valid Percent
Cumulative Percent
Valid 32 3 3.6 3.6 3.6
36 4 4.8 4.8 8.3
39 4 4.8 4.8 13.1
43 5 6.0 6.0 19.0
46 6 7.1 7.1 26.2
50 4 4.8 4.8 31.0
54 5 6.0 6.0 36.9
57 4 4.8 4.8 41.7
61 9 10.7 10.7 52.4
64 4 4.8 4.8 57.1
68 5 6.0 6.0 63.1
71 6 7.1 7.1 70.2
75 3 3.6 3.6 73.8
79 4 4.8 4.8 78.6
82 3 3.6 3.6 82.1
86 3 3.6 3.6 85.7
89 3 3.6 3.6 89.3
93 5 6.0 6.0 95.2
96 4 4.8 4.8 100.0
Total 84 100.0 100.0
4. Requirement Analysis Test
Before conducting regression analysis or testing the hypothesis, it is necessary to conduct a requirement analysis. Variables must have a normal, homogeneous, and linear distribution. A requirement analysis test must be completed to ensure the correlation analysis is done correctly. For this hypothesis test, three requirements analysis tests are employed. First, a normality test is applied to the representative research samples. In order for this test to be a hypothesis test, the sample must be normal. The homogeneity test comes in second; it is to ensure the dependent variable (Y) is categorized according to the independent variable score equation (X1 and X2). The last is linearity, and it is to now the distribution of data in each variable is the same.
a. Normality Test
A normality test is used to determine if the populations of Y and X variables are properly distributed. The Kolmogorov-Smirnov method, with a significance level of 0.05 used to determine whether the normal test should be accepted or rejected. The normality test was calculated in this study using the one-sample Kolmogorov- Smirnov formula on SPSS 23. The results are shown in the following table:
Table 4.7
Normality Test of Students' Morphological Competence, Self-Regulated Learning and Reading Comprehension
One-Sample Kolmogorov-Smirnov Test Morphologic
al Competence
Self- Regulated
Learning
Reading Comprehens
ion
N 84 84 84
Normal Parametersa Mean 69.60 75.96 63.30
Std.
Deviation 16.855 8.942 18.567
Most Extreme Differences
Absolute .093 .073 .086
Positive .069 .064 .086
Negative -.093 -.073 -.068
Test Statistic .093 .073 .086
Asymp. Sig. (2-tailed) .073 .200 .183
a. Test distribution is Normal.
The criterion of normal distribution based on Kolmogorov-Smirnov is if sig
> 0.05. Based on the table above, the research data showed that significant data of X1 is 0.73> 0.05, X2 is 0.200>0.05, and Y is 0.183>0.05, which means the variable dependent and independent data were normally distributed.
b. Homogeneity Test
The homogeneity test is used to determine the degree to which variables X and Y have similar variances. The homogeneity test criterion is that if the significance is less than 0.05, the variance of the data is not equal or homogenous; on the other hand, if the significance is greater than 0.05, the variance of the data is equal or homogeneous. Below are some homogeneity tests of this study.
1) The Test of Variance Homogeneity Y toward X1
The variance homogeneity test was used to determine the relevance of students' reading comprehension toward morphological competency. Because the significance is more than 0.05 (0.511 > 0.05), Ho is accepted. This suggests that the variance of the Y variable is homogenous with respect to X1. The following table summarizes the outcome of the variance homogeneity test of Y on X1:
Table 4.8
Variance Homogeneity Test of Y on X1
Levene Statistic df1 df2 Sig.
.963 18 65 .511
2) The Test of Variance Homogeneity Y toward X2
The variance homogeneity test was used to determine the significance of students' reading comprehension toward self-regulated competence. Because the
significance is more than 0.05 (0.108 > 0.05), Ho is accepted. This suggests that the variance of the Y variable is homogeneous with respect to X2. The following table summarizes the outcome of the variance homogeneity test of Y on X2:
Table 4.9
Variance Homogeneity Test of Y on X2
Levene Statistic df1 df2 Sig.
1.532 18 65 .108
c. Linearity Test
The linearity test is used to determine whether the average of the data sample's three or more groups is on the same straight line. In regression analysis, each group's regression is determined by a dependent variable and will gravitate toward a straight line. Calculating the divergence from linearity was used to determine linearity. 0.05 is considered significant. Comparing the significance level to the significance value yields the significant value in the table significant.
Table 4.10
Linearity of Morphological Competence (X1) with Reading Comprehension (Y)
Sum of Squares df
Mean
Square F Sig.
Reading comprehension
*
Morphological Competence
Between Groups
(Combined) 6213.58
5 12 517.799 1.641 .100 Linearity 4829.16
2 1 4829.16 2
15.30
7 .000 Deviation
from Linearity
1384.42
3 11 125.857 .399 .952
Within Groups 22399.9
75 71 315.493
Total 28613.5
60 83
The result shows that sig = 0.95 > 0.05 means the linearity is also fulfilled for the students' morphological competence.
The result shows that sig = 0.90 > 0.05 means the linearity is also fulfilled for the students' self-regulated learning.
d. Hypothesis Test
The purpose of the hypothesis test in this study is to determine the validity of the research hypothesis. This is used to examine the relationship between students' morphological competence (X1) and their reading comprehension (Y), as well as the connection between students' self-regulated learning (X2) and their reading comprehension (Y).
Table 4.11
Linearity of Self-Regulated Learning (X2) with Reading Comprehension (Y)
Sum of Squares df
Mean
Square F Sig.
Reading comprehension
* Self- Regulated Learning
Between Groups
(Combined) 13072.1
60 31 421.683 1.41
1 .134 Linearity 7346.93
1 1 7346.93 1
24.5
82 .000 Deviation
from Linearity
5725.22
9 30 190.841 .639 .906
Within Groups 15541.4
00 52 298.873
Total 28613.5
60 83
1) The Relationship between Students' Morphological Competence (X1) and Their Reading Comprehension (Y)
The study's first hypothesis is that there was a substantial relationship between variable X1 and variable Y. Correlation coefficients range from 0 to 1. If the value is greater than or equal to 1, the relationship is extremely strong. In the opposite direction, if the value reaches 0, the relationship becomes extremely weak. The following table provides an interpretation of the correlation coefficient adopted from Sugiyono (2009).
Table 4. 12 Coefficient Correlation Interpretation
The correlation coefficient is 0.411 based on the computation of the simple correlation using a formula for significance testing Pearson correlations on SPSS 23.
There is a significant correlation between students' morphological competence and reading comprehension after it reaches 1. The relationship between the variables is strong enough. The calculation yields a significant value of 0.000, which is less than 0.05. If sig is less than 0.05, a significant relationship exists between the variables.
This indicates that the first study hypothesis has been accepted. The following table illustrates the calculation:
Coefficient Interval Coefficient Level
0.80—1.000 Very Strong
0.60—0.799 Strong
0.40—0.599 Strong Enough
0.20—0.399 Weak
0.00—0.199 Very Weak
Table 4.13
The Relationship between Students’ Morphological Competence and their Reading Comprehension
Morphological Competence
Reading Comprehensio
n Morphological
Competence
Pearson Correlation 1 .411**
Sig. (2-tailed) .000
N 84 84
Reading Comprehension Pearson Correlation .411** 1
Sig. (2-tailed) .000
N 84 84
**. Correlation is significant at the 0.01 level (2-tailed).
After finding the relation between the two variables, regression analysis was done. Regression analysis was done to determine how much the independent variable contributes to the dependent variable. The calculation was also done using a formula of significance testing of Pearson correlations on SPSS 23. The measure can be seen in the table below:
Table 4.14
The Simple Regression test of Y on X1
Model R R Square Adjusted R Square
Std. Error of the Estimate
1 .411a .169 .159 17.031
a. Predictors: (Constant), Morphological Competence
From that Table 4.14, it was obtained that R-value 0.411, which means the relation between the variable is strong enough. The R square is transformed into percentage form (0.16 x 100% = 16%), which means the contribution of the
independent variable toward the dependent variable is 16%. And the rest of 84% is contributed from other variables.
2) The Relationship between Students' Self-Regulated Learning (X2) and Their Reading Comprehension (Y)
The second hypothesis is that there was a significant relationship between variables X2 and Y in this study. The correlation coefficient is 0.507, as determined by the calculation of the simple correlation using a procedure for significance testing Pearson correlations on SPSS 23. When it approaches 1, it indicates that there is a strong correlation between students' morphological competency and reading comprehension. And the relationship between the variables is sufficiently classified.
The calculation yields a significant value of 0.000, which is less than 0.05. If sig is less than 0.05, a significant relationship exists between the variables. This indicates that the second hypothesis has been accepted. The following table illustrates the calculation:
Table 4.15
The Relationship between Students’ Self-Regulated Learning and their Reading Comprehension
Self- Regulated
Learning
Reading Comprehensio
n Self-Regulated Learning Pearson Correlation 1 .507**
Sig. (2-tailed) .000
N 84 84
Reading Comprehension Pearson Correlation .507** 1
Sig. (2-tailed) .000
N 84 84
**. Correlation is significant at the 0.01 level (2-tailed).
After finding the relation between the two variables, then regression analysis was done. Regression analysis was done to determine how much the independent
variable contributes to the dependent variable. The calculation was also done using a formula of significance testing of Pearson correlations on SPSS 23. The count can be seen in the table below:
Table 4.16
The Simple Regression test of Y on X2
Model R R Square Adjusted R Square
Std. Error of the Estimate
1 .507a .257 .248 16.104
a. Predictors: (Constant), Self-Regulated Learning
R-value 0.507 was derived from Table 4.16, indicating that the relationship between the variables is sufficiently strong. The R square is converted to percentage form (0.25 x 100% = 25%), indicating that the independent variable contributes 25%
to the dependent variable. And the remaining 75% is accounted for by other variables.
3) The Relationship between Students’ Morphological Competence (X1), their Self-Regulated Learning (X2), and their Reading Comprehension (Y)
The third hypothesis in this study is that there is a statistically significant relationship between students' morphological competence, self-regulated learning, and reading comprehension. The multiple correlation coefficient was calculated using SPSS 23 and the R-value was determined to be 0.549. When it reaches 1, it indicates that there is a relationship between the variables. And the relationship between those factors is strongly enough. The calculated significant value is 0.000, which is less than 0.05. If sig is less than 0.05, a significant association exists between the variables. This indicates that the third hypothesis of this investigation is accepted.
The relationship between students' morphological competence, self-regulated learning, and reading comprehension is significant. As presented in the table below: