CHAPTER IV FINDING AND DISCUSSION
A. Data Description
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:
Table 4.17
The Relationship between Students’ Morphological Competence, Self- Regulated Learning and their Reading Comprehension
Morphologi cal Competenc
e
Self- Regulated
Learning
Reading Comprehen
sion Morphological
Competence
Pearson
Correlation 1 .436** .411**
Sig. (2-tailed) .000 .000
N 84 84 84
Self-Regulated Learning
Pearson
Correlation .436** 1 .507**
Sig. (2-tailed) .000 .000
N 84 84 84
Reading Comprehension
Pearson
Correlation .411** .507** 1
Sig. (2-tailed) .000 .000
N 84 84 84
**. Correlation is significant at the 0.01 level (2-tailed).
Meanwhile, the R square is converted to a percentage (0.30x100% = 30%), indicating that morphological competence and self-regulated learning contribute 30%
to reading comprehension and the remaining 70% is controlled for by other variables.
The calculation is as below:
Table 4.18
The Multiple Regressions between Students’ Morphological Competence, Self- Regulated Learning and their Reading Comprehension
Model R R Square Adjusted R Square
Std. Error of the Estimate
1 .549a .301 .284 15.711
a. Predictors: (Constant), Self-Regulated Learning, Morphological Competence
B. DISCUSSION
Based on the data description, the following discussion are the conclusions of the hypotheses tested in this study. The present study's primary goal is to look at the extent relationship among students' morphological competence, self-regulated learning, and reading comprehension. Below is the interpretation of the data:
1. The Relationship between Morphological Competence (X1) and Reading Comprehension (Y)
Based on the finding of the first hypothesis, H1 was accepted indicating that there was a relationship on both variables. The coefficient correlation (see Table 4.13) showed that morphological competence strongly enough related to reading comprehension. It indicated that morphological competence could predict reading comprehension skills in college students. The R square indicated that morphological competence affects 19% of reading comprehension; the rest of 81% is influenced by the other factors. This result fits well with previous studies that morphological competence is also a significant contributor on reading comprehension (Zhang et al., 2020; De Freitas et al., 2018; Zhang & Koda, 2013; Xie et al., 2019; Levesque et al., 2017; Zhao et al., 2019). However, all the studies above conducted in the beginner level, through this study it is found that morphological competence is also critical contributor for advanced readers.
As stated above, it is suggested that morphological competence contributes to reading comprehension. One rationale is that morphological competence enables students to deconstruct words into their core components. Hence it facilitates comprehension of particular complicated words and contributes to overall text comprehension (De Freitas et al., 2018). Because of this, morphological competence has frequently been identified as a significant factor in word learning and vocabulary knowledge, as it enables students to break down unfamiliar morphological complex words and to implement the role of to extract meanings for unfamiliar vocabulary (Zhang & Koda, 2013). It is also supported by Petrovska (2011); Ku and Anderson (2003) who briefly stated that morphological competence as the ability to distinguish morphemes in words can significantly improve text input. This finding also aligned with the study conducted by Azizah and Fahriany (2017) who found the language
component that students acquire in language learning benefits the reading skill. To sum up, it has been proven morphological competence as one of language components has a contribution to reading comprehension. MC contributes to reading comprehension by allowing students to decode new and unfamiliar words by breaking them down into meaningful chunks.
Nagy (2007) stated that the unique contribution of morphological competence to reading comprehension can be summarized in three ways: To begin, one may attribute the contribution facilitated by the learners' lexical inference abilities. It means that morphological competence allows students to guess the unfamiliar words meaning in the text; this is referred to as 'on-the-spot vocabulary acquisition' . It supports students to bridge the words gaps when reading and results in increased comprehension. Second, students may decode complicated words by utilizing the syntactic clues supplied by suffixes in derived words, which are also important for comprehension. Last, morphological competence can benefit comprehension by its effect on the proficiency of decoding morphologically complicated words. In other words, MC contributes to reading comprehension primarily through particular criteria such as lexical inference, suffix signal analysis, and detection of morphologically complicated words.
2. The Relationship between Self-Regulated Learning (X2) and Reading Comprehension (Y)
According to the findings, H1 was accepted confirmed that there was a relationship between self-regulated learning (SRL) and reading comprehension, was accepted. The correlation analysis indicated that self-regulated learning was strongly enough related to reading comprehension. It was established by regression analysis results (see Table 4.13). The SRL contributes to as much as 25% of reading comprehension, and the rest of 75% may be influenced by other factors. This study indicated that students with a high SRL have a high level of comprehension. These findings were aligned with the previous number of studies by Mohammadi et al.
(2020), Skibbe et al. (2019). However, Kamgar & Jadidi (2016) showed that there is no relationship between SRL and reading beginner and intermediate learners. It
means that SRL significantly contributes to reading comprehension at the upper level or high level of education.
Based on what has been mentioned above, it seems clear that self-regulated learning contributes reading comprehension. One reason for this is that SRL, as a multidimensional activity involving cognitive, metacognitive, and motivational aspects, enables students to set a particular target in reading, activate prior knowledge, and draw inferences in the text (Zimmerman & Schunk, 2011; Woolley, 2011). Furthermore, when students' self-regulation abilities improved, they desired to read and established reading habits. This condition leads, over time, to increased learning strategies, maintaining motivation, and may contribute to their reading comprehension developing at a faster rate (Chu et al., 2020). Students with strong self-regulation skills can improve their English text comprehension by drawing inferences and maintaining focus when reading (Jafarigohar & Morshedian, 2014;
Abbasian & Hartoonian, 2014). That is to say, other than language skill, the psychological element such in self-regulated learning also affects students to facilitate better reading comprehension.
A great number of scholars have elaborated upon the unique contribution of the SRL to reading comprehension. SRL mediating by learning strategy through metacognition, cognitive engagement can facilitate students to use better reading strategy. Thus, the higher self-regulated learning, the more likely the cognitive and metacognitive strategy optimal reading comprehension (Li, 2017). It is supported by Kavani and Amjadiparvar (2018). They argued that a strategic reader might read more effectively and be goal-oriented, which would affect the attribute of success in L2 readers. Furthermore, another aspect of SRL, motivation may influence by SRL’s role to maintain confidence and self-efficacy among the second language learners so that they be more engaging in reading (Mohammadi et al., 2020). Also, Massey in Israel
& Duffy (2009) argued that SRL affects reading comprehension using collaborative strategy instruction such as previewing, monitoring comprehension, understanding the most important ideas in a text, and summarizing strategies. In other ways, SRL contributes to reading comprehension through its self-directed learning. For instance, students with good motivation to read will attribute to their L2 reading achievement
because, in L2 learning, motivation is highly required. Then, as also noticeable in SRL, students activate cognition and metacognition. Their ability to monitor how their reading affects reading comprehension is mediated by the reading strategy.
Furthermore, self-regulated learning strategy has been proposed for effective adult learning or called andragogy learning. An Andragogical learning emphasized at the autonomous learning style in which students can regulate their learning activities.
As mentioned by Youde (2018), there are several core principles of andragogical learning such as: First, students need to know why they learn. Second, adults had to have self-control of their learning. Third, they have to motivate themselves. Fourth, in adult learning, the learning approach used is problem-based. This core concept of andragogical learning fits well with self-regulated learning strategies that highlights on students’ motivation, cognitive, and metacognitive activation in learning.
3. The Relationship among Morphological Competence (X1) Self-Regulated Learning (X2) and Reading Comprehension (Y)
According to the findings, self-regulated learning had a relationship with reading comprehension. Then, the relationship was classified in a strong enough relationship because those variables contributed 30%. The results appeared that morphological competence and self-regulated learning significantly affect reading comprehension. This suggests that learners with good proficiency in morphology and SRL will almost certainly perform higher in reading comprehension. This current results corroborate prior studies which both independent variables (X1) and (X2) have a strong relation with reading comprehension.
Furthermore, to have better reading comprehension, students need to infer the meaning of the text, which involves morphological competence. They also need to recognize the unfamiliar word in the text by manipulating the morphological structure. Therefore, they can get easier on comprehending the text due to many vocabularies they know. By maintaining the motivation to read, which becomes the primary predictor of learning a foreign language (Bonney et al., 2008), students with good SRL will push themselves to be willing to read and do some reading assignments. Then, as also stated by Zimmerman & Schunk (2011) that some phases of self-regulation are important in doing reading strategy such as planning,