regarding language use and its status in the society. The feelings are good, bad and neutral. They can nurture or hinder the learning process effectively” p.1.
Considering this fact, the teachers should always pay a big attention to every child. Their attitude plays a big part in everyday lives and can affect how life may turn
out in the future. If the children have a good attitude, they will be the kind of people who works hard.
2 Students’ Motivation
The second theme in the questionnaire was about students’ motivation on learning English language. The first statement in the questionnaire is
“In general, I found my course is very interesting”. The students agreed with the statement. The
students found the course very interesting. They have several different ways of trying to overcome the problems on learning. They really enjoy to study topics at all the courses.
The next statement is “In general, the lecturers stimulated my interest in my
subject”. When analyzing the data, it can be seen that most students are not very keen on classeslectures, which are in English. The reason why the students do not consider
the classeslectures in English more interesting can be connected with the fact that the methods of teaching content subjects in English is not interesting for them. However,
they build their own m otivation. They don’t find it difficult to get motivated to work on
the course. Though they often find the course boring but they will stick with it. The next statement is
“Overall, I have really enjoyed my studies so far at university level”. The students consider that their studies at university is a fun. The
university is an inclusive place to study. They love their time there. In general, the PLAGIAT MERUPAKAN TINDAKAN TIDAK TERPUJI
students their maximal of work which was required of them since they want to gain high marks at university.
The findings of the present study can therefore provide a substantial contribution to the teaching. Knowing the motivations of students means that teachers and
institutions can act to stimulate the students and maintain motivation levels throughout the program, according to the results found in the present study. The motivated
individual expands effort to learn the language. That is, there is a persistent and consistent attempt to learn the material by doing homework, by seeking out
opportunities to learn more, by doing extra work, etc. First, the motivated individual expands effort to learn the language. That is,
there is a persistent and consistent attempt to learn the material by doing homework, by seeking out opportunities to learn more, by doing extra work, etc. Second, the motivated
individual wants to achieve the goal. Such an individual will express the desire to succeed, and will strive to achieve success. Third, the motivated individual will enjoy
the task of learning the language. Such an individual will say that it is fun, a challenge, and enjoyable, even though at times enthusiasm may be less than at other times
Gardner, 2002. To improve students’ motivation, educators could start just by choosing and
trying possibilities for enriching student motivation. More importantly, educators could watch themselves and their own behaviors to become self-aware of new understandings
about motivation. PLAGIAT MERUPAKAN TINDAKAN TIDAK TERPUJI
3 Student’s Discipline
The last theme in the questionnaire was about students’ discipline on learning English language. The first statement in the questionnaire is
“I obey all of the school’s
rules
”
.
The students agreed with the statement. The students was aware of being discipline. The other statement concluded that the students were aware for being
discipline. As the English educator, it is important to assure the students that discipline is not just about making them suffer like giving such rules or punishment. They should
also understand that discipline is obligatory for a good learning environment and interaction between students.
An educator has the right to recommend the removal of a student who continually disrupts, but only after other discipline options have been tried. The
educators should also enforce rules fairly and consistently. Teaching good discipline is just as important as teaching subject matter.
Considering this fact, the teachers should always pay a big attention to every child. They should pay attention to every child’s activities and every development on
learning they make. Encouraging childrens discipline is hard work. However, it will be dangerous when the teacher ignores the children’s discipline, I may be stunting her
desire to know. The best way for the teachers and parents to encourage childrens curiosity is to stay curious themselves Leslie, 2014.
The findings of this questionnaire can therefore provide a substantial contribution to the teaching. Knowing the students’ attitude, discipline and motivation
means that teachers and institutions can act to stimulate the students throughout several PLAGIAT MERUPAKAN TINDAKAN TIDAK TERPUJI
ways. That is, there is a persistent and consistent attempt to make the students be better for helping them be successful.
By doing so, the students can be motivated to achieve the academic goals. Such an individual will express the desire to succeed, and will strive to achieve success. The
motivated individual will enjoy the task of learning the language. Such an individual will say that it is fun, a challenge, and enjoyable, even though at times enthusiasm may
be less than at other times Gardner, 2002. The questionnaire also gives information about the contribution of attitude,
discipline, and motivation on the learning success gaining. The students could share whether attitude, discipline, and motivation aspect influence their learning success and
are they enough to help them achieve learning success.
Table 4.1. Interpretation of Questionnaire Result Attitude Aspect
No Questions
SA n
5 k
A n
4 k
D n
3 k
DA n
2 k
SDA n
1 k
N X
1. I am quite
confident with my English skill
6
30
10
40
14
42 112
3,73
2. I have a big effort
on achieving my academic goals
8
40
6
24
16
48 112
3,73
3. The improvement
of knowledge, skill, attitude and
responsibility help to achieve a better
life 8
40 14
56 8
24 130
4,33
4. The improvement
of knowledge, skill, attitude and
responsibility help to get a better job
8
40
14
56
8
24 130
4,33
5. I can keep the class
quiet. 8
40 12
48 8
24 4
8 120
4,00
6. I can keep the class
workingtasks 8
40 14
56 10
30 4
8 134
4,47
7. Making mistakes
when learning foreign language is
a common thing 3
15 6
32 10
30 11
22 99
3,30
8. I have strong
beliefs toward my goals that I can
achieve my academic goals:
very agree 12
60 14
56 4
12 128
4,27
9. I avoid getting
punishment 6
30 18
72 6
18 120
4,00
10. I often find my course boring but I
will stick with it 11
55
14
56
5
15 126
4,20
11. Knowledge should be balance with
good skill and attitude
30 15
150 5,00
12 I have strong beliefs toward my
goals 6
30 18 72
6 18
120 4,00
89
Motivation Aspect Discipline Aspect
No Questions
SA n
5 k
A n
4 k
D n
3 k
DA n
2 k
SDA n
1 k
N X
1. I obey all of school’s
rules 14
70 10
40 3
9 3
6 125
4,17
2. I obey all of the
teacher’s instructions
1 5
25 10
2 6
1 2
1 4
117 3,90
3. I do all of my
obligations at school 3
15 24
96 3
6 117
3,90
4. I always come to the
class on time. 1
5 14
56 12
36 2
2 1
4 103
3,43
5. I give my respects to
all of my lecturers 19
95 6
24 5
30 149
4,97
6. I involve in every
activities at class 13
65 8
32 9
27 124
4,13
7. I have the
motivation that comes from self-
conscious 16
80 10
40 2
6 2
4 130
4,33
8. I focus on the
processes to reach the goal by
combining the knowledge, skill,
and attitude 17
85 12
48 1
3 136
4,53
9. I have a big effort on
achieving my goals 13
65 8
32 9
27 124
4,13
10. I often find my course boring but
will stick with it 16
80
10
40
2
6
2
4 130
4,33
11. I have a motivation comes from self-
conscious 16
80 10
40 2
6 2
4 130
4,33
12. I have a big motivation to learn
English language 16
80 10
40 2
6 2
4 130
4,33
No Questions
SA n
5 k
A n
4 k
D n
3 k
DA n
2 k
SDA n
1 k
N X
1. In general, I find my
course is very interesting
12 60 14 56
1 3
1 2
2 8
129 4,33
After the data were calculated and evaluated, it was found that the results of the questionnaires were satisfying. It was found out that the average point of
2. I really enjoy to study
my topics of English materials
11 55
14 56
5 15
126 4,20
3. I don’t find it difficult
to get motivated to work on my course
3 15
24 96
3 6
117 3,90
4. I need to know how
well I am doing in order to feel
motivated to work. 14
70 10
40 3
9 3
6 125
4,17
5. In general, the
lecturers stimulated my interest in
learning 11
55 14
56 5
15 126
4,20
6. I like to learn about
new aspects of my subjects and to
explore new ideas 13
65
8
32
9
27 124
4,13
7. I get satisfaction from
meeting intellectual challenges and
pushing my limits 13
65 8
32 9
27 124
4,13
8. I often find my
course boring but I will stick with it
16 80
10 40
2 6
2 4
130 4,33
9. I feel that I really
belong at university 6
30 18
72 6
18 130
4,33
10. Overall, I have really enjoyed my studies
so far at university level
6
30
18
72 6
18 130
4,33
11. In general, I do my maximal of work
which is required of me
19 95
6 24
5 30
149 4,97
12. I want to gain high marks at university
19 95
6 24
5 30
149 4,97
TOTAL SCORE 4.155
MEAN 4,21
GOOD PLAGIAT MERUPAKAN TINDAKAN TIDAK TERPUJI
the central tendency is 4,21 from the scale 5,0. It shows that the students’ attitude,
discipline, and motivation are quiet good and they consider that those aspects are important to achieve learning success.
The questionnaire results were then analyzed using SPSS Program. The scores of each student’s questionnaire result were computed into SPSS analysis.
Thus, the researcher can infer the relation between attitude, discipline, motivation, and learning success as a way to ensure the validity of the data and the
instruments. The statistic also allowed prediction about the students’ attitude, discipline, and motivation. The value of the statistical analysis presented above
accepts the correlation between attitude, discipline, motivation, and learning
success. B. Assumption of Statistical Test
After the researcher already got the research data and information from the questionnaire, the interview and the document analysis; the researcher used the
data to be analyzed. The assumption of statistical test was done first before the Correlation research and Multiple Regression.
Statistical tests rely upon certain assumptions about the variables used in an analysis Osborne Waters, 2002. Multiple regression examines the
relationship between a single outcome measure and several predictor or independent variables Jaccard et al., 2006. The correct use of the multiple
regression model requires several critical assumptions to apply the model and establish validity Poole O’Farrell, 1971. Inferences and generalizations about
the theory are only valid if the assumptions in an analysis have been tested and PLAGIAT MERUPAKAN TINDAKAN TIDAK TERPUJI
fulfilled. Multiple Regression can be used to test hypothesis of linear associations among variables, to examine associations among pairs of variables, and to test
complex associations among multiple variables Hoyt et al., 2006. The assumptions of Multiple Regression that are identified as primary
concern in the research include collinearity, outliers, normality, linearity, homoscedasticity, and independence of residuals. This section will specifically
define each assumption, review consequences of assumption failure, and address how to test for each assumption, and the interpretation of results.
1 Normality Test
The normality test is tested before the calculation of the hypothesis testing. First of all, the scores are checked through the test of normality. It is aimed at
knowing whether the data distribution is normal or not. If the sig. value is greater than 0.05, the data distribution is normal and vice versa. This test is described
clearly in table 4.1 below. The table shows the normality test of the variables. The requirement for
judging whether the data are in normal distribution is if
p
value or
sig
is greater than α 0.05. A way to check the normality is by using Kolmogorov-Smirnov
test. According to the Kolmogorov-Smirnov test, the significance values show that the data are 0.22. In other words, 0.22 is greater than 0.05 0.22 0.05. It is
concluded that the data are in normal distribution. The table 4.1. below is normal P-Plot of regression standardized residual figure.
Table 4.1. Tests of Normality
The normality test is used to find out whether the data obtained the research variables have a normal distribution of scores or not. In this research, the
test uses the Kolmogoroy-Smirnov test. If
p
value is higher than α 0.05, it means that the scores have normal distribution.
As the scores are normally distributed, as the second stage, the scores are analyzed in paired samples t-test. After computing the scores, there is the
following table obtained as the result. Before going to the final table of computation, this table is carefully seen to check whether the data are already
correct and valid. It can be done by checking the mean and standard deviation for each group and the number of people in each group.
2 Paired Samples Test Table 4.2 Paired Samples Statistics
Paired sample t-test is a statistical technique that is used to compare two population means in the case of two samples that are correlated. After paired
samples test is analyzed, the results are interpreted. The interpretation of the results starts from looking at the value in the column of confidence value and sig.
1-tailed. The confidence value chosen should be 95. If the value in sig. 1- tailed is less than 0.05, it means that H
is rejected. The value in Sig. 2 tailed is 0.00, in which the P-value is 0.05 thus H
is rejected. In other words, it can be concluded that the hypothesis alternative H
1
is accepted. It means there is relationship between attitude, discipline, motivation, and learning success.
3 Linearity Test
Linearity test is used to see the relationship between variables whether or not they have a straight pattern. The pattern will be seen from the increase or
decrease in the quantity of one variable followed by another variable. Linearity test is done using Test for Linearity Test; if the variables meet the probability of p
correlation 0, 05 then the relationship between variables is linear. Based on the results of Linearity Test of this research, it obtains F = 0.00, p correlation 0.05.
Thus, it can be concluded that the relationship between variables is linear.
4 Homogeneity Test Table 4.3 Homogeneity Test
Homogeneity test is a test of whether or not the variances of two or more distributions are same. Homogeneity test is done to find out whether the
data in variables X and Y are homogeneous or not. Based on the table above, it is known that significance value of learning success based on attitude
variable is 0.42 0.05, it means that data of learning success variable based on attitude variable has the same variant.
5 Collinearity Table 4.4 Collinearity Statistic
After the normality of the data are met, the next step to determine whether there is similarity between the variables, it is necessary to do collinearity test.
Collinearity refers to the relationship among the variables. Collinearity occurs when several independent variables correlate at high levels with one another. The
researcher is able to interpret regression coefficients as the effects of the independent variables on the dependent variables when collinearity is low.
The most common summary statistic for evaluating collinearity are tolerance and VIF. The tolerance value for a particular predictor in a particular
model is 1 - R², where the R² is obtained using that predictor as a criterion and all others as predictors. SPSS automatically does a tolerance analysis and will not
enter the regression model any variable with tolerance 0.01 -- thats a variable that shares more than 99.9 of its variance with the rest of the predictor set. If
this value is very small less than .10 it indicates that the multiple correlation with other variables is high, suggesting the possibility of collinearity.
From the table above, it can be seen that “attitude” aspect has a Tolerance of 0.47
. It means that if the researcher ran a multiple regression with “attitude” as the dependent and “discipline” and “motivation” as the independent variables, the
researcher would get an R-square value of 0.47. The other value given is the VIF Variance inflation factor, which
is just the inverse of the Tolerance value 1 divided by Tolerance. The Variance Inflation Factor VIF measures the impact of collinearity among the variables in
a regression model. The Variance Inflation Factor VIF is 1Tolerance, it is always greater than or equal to 1. Values of VIF that exceed 1 are often regarded
as indicating collinearity. In the table of Collinearity above, the VIF of all the variables exceed 1 that indicate collinearity.
6 Outlier
Outliers are data that deviate too far from other data in a data set. The existence of data outliers will make the analysis of a series of data becomes
biased, or does not reflect the actual phenomenon. It is important to identify outliers for several reasons. Some measures of central tendency can be skewed,
such as the mean. Because of this effect, they might represent the most influential values. Sometimes outliers occur because a value is recorded incorrectly. That
value might be excluded from the original data. Outliers can be detected along the PLAGIAT MERUPAKAN TINDAKAN TIDAK TERPUJI
distribution in a scatter plot. Thus, the figure of scatterplot will be presented below along with the explanation of each aspect outlier.
Figure 4.1 Scatterplot
The figure shows that in Scatterplot, there is presence of outlier. The outlier deviates too far from other data in a data set. This existence of data outlier
will make the analysis of a series of data becomes biased. It is important to identify outliers for several reasons. Below, these are the more detailed
information of the outliers in every variable. PLAGIAT MERUPAKAN TINDAKAN TIDAK TERPUJI
Figure 4.2 Graph of Attitude Outlier
On the table of data above, there is only one data outlier. The data looks different and far from others, thus the data can be said outlier data. The existence
of data outliers will make the analysis of a series of data becomes biased, or does not reflect the actual phenomenon.
Figure 4.3 Graph of Discipline Outlier
On the table of data above, there is only one data outlier. The data looks different and far from others, thus the data can be said outlier data. The existence
of data outliers will make the analysis of a series of data becomes biased, or does not reflect the actual phenomenon.
Figure 4.4 Graph of Motivation Outlier
On the table of data above, there is only one data outlier. The data looks different and far from others, thus the data can be said outlier data. The existence
of data outliers will make the analysis of a series of data becomes biased, or does not reflect the actual phenomenon.
Table 4.5 Removing Outliers
To avoid data becomes biased or does not reflect the actual phenomenon,
outliers can either be deleted from the data set or, alternatively, given a score for that variable that is high but not too different from the remaining cluster of scores.
The researcher tried to remove the outliers from the data set using SPSS. After the outliers are removed, all the data are valid.
7 Normal P-P Plot of Regression Standardized Residual
One of the ways that these assumptions can be checked is by inspecting the Normal Probability Plot P-P of the Regression Standardized Residual and
the Scatterplot that are requested as part of the analysis. These are presented at the end of the output. The normal probability plot is a graphical tool for comparing a
data set with the normal distribution. In the Normal P-P Plot, the points will lie in a reasonably straight diagonal line from bottom left to top right. This would
suggest no major deviations from normality. Figure 4.5 below is normal P-P Plot of regression standardized residual chart.
Figure 4.5 Normal P-P Plot of Regression Standardized Residual
Based on the chart of Normal P-Plot Regression above, the points lie in a straight diagonal line from bottom left to top right. This will suggest that there is a
close relationship between variable 1 and variable 2. This will also suggest no major deviations from normality.
8 Scatterplot
Scatter plot is a graph commonly used to see a relationship pattern between 2 variables. As described before, scatterplot can detect all the data
distribution. Thus, the figure of scatterplot will present the distribution of each PLAGIAT MERUPAKAN TINDAKAN TIDAK TERPUJI
data. To be able to use a scatter plot, the scale of the data used should be the interval and ratio scale. If the scatter plot forms a straight line-like pattern, it
indicates that there is a close relationship between variable 1 and variable 2. Figure 4.6 Scatterplot
Based on the graph above, it could be seen that the data are scattered and form a pattern. The points form a line pointing upwards to the right that is,
they start low on the left-hand side and move higher on the right, thus it indicates the positive correlation.
B. Relation between Attitude, Discipline, Motivation, and Learning Success
This section describes research results and the discussion in implementing Correlational Survey technique to answer the first research question. The first
research question is that whether or not there is a relation between attitude, discipline, motivation, and learning success. The data were quantitatively
analyzed to find out the correlation between attitude, discipline, motivation, and PLAGIAT MERUPAKAN TINDAKAN TIDAK TERPUJI
learning success. Both assumption test and hypothesis test were used to analyze the data in this research. The assumption test was done before the Regression
analysis; it was to check linear associations among variables, to examine associations among pairs of variables, and to test complex associations among
multiple variables. The hypothesis testing was used to answer the research questions.
1 Hypothesis Testing
After doing the assumption test of research, it could be seen that the research data is normally distributed and the relationship between variables is
linear. The next step is the hypothesis testing. The hypothesis testing is used to
answer the research questions. To test the hypothesis, the data are processed by using the statistic software of SPSS 16.0. Attitude, discipline, and motivation
aspect are set as the predictor variables. Learning success is set as the predicted variable. An analysis is used to find out how the variables related. The technique
of the analysis is a Multiple Regression. The Multiple Regression is also called as a multiple relation consisting of two or more predictor variables. It is used to
predict one predicted variable Bluman, 2009.
In this research, after analyzing, the quantitative data are presented in some tables and also graphs which including numbers. The table showed the
results of the statistical analysis of the multiple regressions to answer the research question about the relation between attitude, discipline, motivation, and learning
success. PLAGIAT MERUPAKAN TINDAKAN TIDAK TERPUJI
2 Descriptive Statistics
This research focuses on three topics which are as the controllable variablespredictor variables, those are students’ attitude, discipline, and
motivation. There are several variables in this research, those are predictor variable and predicted variable. Predictor variable is the variable that is used to
make the prediction. In this research, the students’ attitude, discipline, and motivation are the predictor variables. Predicted variable criterion variable is the
variable about which the prediction is made. In this research, the predicted variable is learning success.
In the descriptive statistics table above, the researcher tries to find out the mean, range, and standard deviation to summarize and describe the variables. The
mean, range variance, and the standard deviation of the variables will be described below.
Table 4.6 Descriptive Statistic
Based on the finding provided above, the table 4.6 shows an overview of several Regression analyses from the entire thirty students. Variable 2 discipline
aspect came to be the foremost subcategory with the highest average M= 82 PLAGIAT MERUPAKAN TINDAKAN TIDAK TERPUJI
and SD=14. It also indicated that discipline aspect is classified as High in the level of the relation with the learning success. Coming second is variable 3
motivation aspect with the average M= 72 and SD= 3, 5. Coming as the third is variable 2 attitude aspect with the average M= 80 and SD=0.
3 Correlations
Multiple correlation is a technique that predicts values of one variable on the basis of two or more variables. The multiple correlation shows the relationship
between predictor variables and predicted variable. The purpose of multiple regression is to predict a single variable from one or more predictor variables.
Table 4.7 below shows that the relation between three predictor variables which are attitude, discipline, and motivation with the predicted variable which is
learning success
Table 4.7 Multiple Correlations
Table 4.7 shows that the three predictor variables which are attitude, discipline, and motivation are correlated with the predicted variable which is
learning success. From the calculation table above, it can be seen the result of Pearson Correlation, and significance level.
The Pearson Correlation shows that attitude, discipline, motivation, and learning success are all positively correlated with learning success. The coefficient
of correlation has a value between +1 and −1, where 1 is total positive linear
correlation, 0 is no linear correlation, and −1 is total negative linear correlation.
The coefficient of correlation strength of the correlation r between attitude aspect and learning success is
r
1.2
= 0.75. It indicated strong since the value approached 1.
The coefficient of correlation strength of the correlation R between discipline aspect and learning success is
r
1.3
= 0.49. The value of this correlation coefficient is considered medium r= 0.49. Then,
the coefficient of correlation strength of the correlation r between motivation aspect and learning
success is r
1.4
= 0.67. The value of this correlation coefficient is considered strong r = 0.49 since it approached 1. The relationship between attitude and learning
success is stronger than the relationship between discipline, motivation and learning success as shown on the higher co-efficient of correlation.
Later, the significance level, also denoted as alpha or α, is the probability
of rejecting the null hypothesis when it is true. For example, a significance level of 0.05 indicates a 5 risk of concluding that a difference exists when there is no
actual difference. In the table above, all the variables shows the significance level PLAGIAT MERUPAKAN TINDAKAN TIDAK TERPUJI
below 0.05. It means those variables have low risk of rejecting the null hypothesis when it is true.
“Attitude” aspect is listed as the top predictor variable that get significance level at 0.00 1-tailed p correlation .05. Additionaly, in
Pearson Correlation
, the point as approaching in level 0.75 the strongest level is at point +1.
Furthermore, “Motivation” aspect also got the definite relation to learning success with significance level 0.00 p correlation .05 and
Pearson Correlation
level at .67
. Eventually, “Discipline” aspect is also reported gaining positive relation to learning success as well that get significance level at .03 p correlation .05.
As for the prediction of the relation between attitude, discipline, motivation and learning success, the multiple regressions are used with the results
in the following tables. Besides, the multiple regressions are conducted to seek whether there is positive and significant relationship between the variables. SPSS
16 computer program is used to analyze the hypothesis and the data. In hypothesis testing, H
1
is accepted if the value of p correlation 0.05. Therefore, Ho is rejected and H
1
is accepted. The result of the multiple regression is presented in the following table.
The hypothesis testing could be done by using this Multiple Regression Analysis. The alternative hypothesis H1 in this research is “There is a positive
correlation between attitude, discipline, motivation, and learning success. Meanwhile, the null hypothesis Ho is “There is no positive correlation between
attitude, discipline, mot ivation, and learning success”.
4 Model Summary of Multiple Regression Correlation
Table 4.8 Model Summary of Multiple Correlation
Table 4.8 showed how much the variance in the learning success is explained by attitude, discipline, and motivation aspect. It is found that the value
of R square is .60. It could be expressed into percentage multiply by 100 as 60. It means that attitude, discipline, and motivation aspect explained 60 of
the learning success gaini ng students’ GPA.
5 Discussion
The analysis result confirm that there is a positive correlation between attitude, discipline, motivation, and learning success R
1.234
= 0.77, p correlation= 0.00 0.05. This means that the coefficient of correlation between attitude,
discipline, motivation, and learning success is at 0.77 value. The correlation is considered strong and positive since the coefficient of correlation is 0.77 the
value is approaching 1. Attitude and learning success are correlated with r
1.2
= 0.75, this means that the value of correlation strength is big since it approaches 1. Discipline and learning
success are correlated with r
1.3
= 0.49, this means that the value of correlation PLAGIAT MERUPAKAN TINDAKAN TIDAK TERPUJI
strength is medium. Motivation and learning success are correlated with r
1.4
= 0.67, it means that the value of correlation strength is big since it approaches 1.
The coefficient of multiple correlation was relevantly significant in level 0.00 p .05. Hence, the null hypothesis H
can be rejected because of the low significant level 0.05 and the coefficient correlation is greater than 0.00 R =
0.77. It can be concluded also from the research result that there were significant correlation between students’ attitude, discipline, motivation and learning success
in which the coefficient multiple correlation is perfect R = 0.77 0.00 and the significance level is at 0.00 0.05. It can be stated that if the score of the
attitude, discipline, and motivation is higher, so does the score of the GPA.
C. Predictability of Learning Success from Attitude, Discipline, and Motivation
This section describes research results and the discussion in implementing Correlational Survey technique to answer the second research question. The
second research question is that whether or not learning success is predictable from attitude, discipline, and motivation aspect. The data were quantitatively
analyzed to find out whether or not learning success was predictable from attitude, discipline, and motivation and to determine the percentage of the predictor
variables on influencing the predicted variable. Both assumption test and hypothesis test were used to analyze the data in this research. The assumption test
was done before the Regression analysis, it was to check linear associations among variables, to examine associations among pairs of variables, and to test
complex associations among multiple variables. The explanation of the assumption test has been already conveyed by the researcher. The hypothesis
testing was used to answer the research questions.
1 ANOVA Analysis
As noted previously, this study is aimed to investigate the relation between attitude, discipline, motivation, and learning success. Then, the data of correlation
is provided in the table 4.9 below. ANOVA is used to analyze the differences
among group means and their associated procedures.
Table 4.9 ANOVA Analysis
The table Analysis of Variance ANOVA above revealed that coefficient of multiple correlation is relevantly significant in level 0.00 p correlation .05.
F-test is also conducted to test the statistical significance. It is found that F value is 12.9. Afterwards, the F value is compared with the value of F table 2.98. It
means F value is higher than F table 12.9 2.98. Thus, the null hypothesis H0 of hypothesis number one is rejected and indicates that the correlation is
significant. PLAGIAT MERUPAKAN TINDAKAN TIDAK TERPUJI
2 Multiple Regression Coefficient
Multiple regression coefficient is a measure of how well a given variable can be predicted using a linear function of a set of variables. It is the correlation
between the variable’s values and the best predictions that can be computed linearly from the predictive variables.
Table 4.10 Multiple Regression Coefficient
Table 4.10 showed a report of regression coefficients as well as correlation among the predictor variables. By means of 95 confidence intervals of the
analysis, alpha in level .05 is set up. Based on the Beta value above, it could be seen that attitude aspect has largest Beta value, it is 0.56. This means that attitude
aspect has the strongest contribution to explain the predicted variable learning success. Coming second is motivation aspect, it had Beta value of 0.25. The last
one is discipline aspect, it had Beta value of 0.01, indicated that it made less contribution to explain the predicted variable.
To assess the influence of “Attitude” aspect toward learning success, t-test is also conducted. It is found that t value of “Attitude” aspect is higher than the
value of t-table 3.08 2.98. For this reason, the alternative hypothesis H1 of hypothesis number two is accepted.
The next variable is “Discipline” aspect. The study found that t-value of “Discipline” aspect is lower than t-table .056 2.98. It is also found that the p
value of “Discipline” aspect is 0.96 0.05. It means that the null hypothesis of hypothesis number two is accepted.
The last variable is “Motivation” aspect. The study found that t value of “Motivation” aspect is 1.12. This value is lower than t-table 1.12 2.98. It is
also found that p value of “Motivation” aspect is 0.27 or more than alpha level 0.05. Thus, the null hypothesis of hypothesis number two is accepted. It means
that there is relationship between attitude, discipline, motivation, and learning success.
3 Casewise Diagnostics Table 4.11 Casewise Diagnostics
The Casewise Diagnostics table is a list of all cases for which the residual
’s size exceeds 3. These will tell which cases have residuals that are three or more standard deviations away from the mean. These are the cases with
the largest errors and will be outliers. The outliers will have a relatively small impact on the model.
4 Discussion
The analysis result confirm that learning success is predicted from attitude, discipline, and motivation with a multiple regression formula F = 12.94 at p = 0.00
0.05. F test score of 12.94 means that all predictor variables give significant relation to the learning success above 10. F value F table 12.94 2.98 indicates
that the relation is significant and null hypothesis Ho is rejected. Thus, this method can be used for prediction formula since the relation is significant.
Later, the researcher will explain about the proportion of the variance in the predicted variable that is predictable from the predictor variables. R square
value of attitude aspect is 0.56; it means that attitude aspect contributes 56 of the learning success gaining. R square value of discipline aspect is 0.24; it means
that discipline aspect contributes 24 of the learning success gaining. Then, R square value of motivation aspect is 0.44; it means that motivation aspect
contributes 44 of the learning success gaining.
D. ANALYSIS RESULT
The analysis of the quantitative data reveals the answer of the first and second research question. The research result is aimed to answer the two research
questions, they were: 1.
Is there a relation of students’ attitude, discipline, motivation and learning success?
2. Is learning success predictable from students’ attitude, discipline, and
motivation? PLAGIAT MERUPAKAN TINDAKAN TIDAK TERPUJI
The findings of the current study provide support for the first research question and the second research question. The first research question is that about
the relation between students’ attitude, discipline, motivation and students’ learning success. The research result answered the first research question that
there was relation between the students’ attitude, discipline, motivation and students’ learning success. There were several statistically correlation between the
variables. According to the analysis of inferential statistic result using the Multiple Regression, it is found that there is a positive and significant relation
between attitude, discipline, motivation, and learning success R
1.234
= 0.77, p= 0.00. This means that the coefficient of correlation between attitude, discipline,
motivation, and learning success is at 0.77 value. The correlation is considered strong and positive since the coefficient of correlation is 0.77 the value is
approaching 1. The coefficient of multiple correlation was relevantly significant in level
.00 p correlation .05. Hence, the null hypothesis H can be rejected because
of the low significant level 0.05 and the coefficient correlation is greater than 0.00 R =0.77. It can be concluded also that the coefficient multiple correlation is
perfect R = 0.77 0.00 and the significance level is at 0.00 0.05. Attitude and learning success are correlated with r
1.2
= 0.75, this means that the value of correlation strength is big since it approaches 1. Discipline and learning
success are correlated with r
1.3
= 0.49, this means that the value of correlation strength is medium. Motivation and learning success are correlated with r
1.4
= 0.67, it means that the value of correlation strength is big since it approaches 1.
Thus, if the students’ attitude, discipline, and motivation score is high, their learning success GPA will also high. Then, if the students’ attitude,
discipline, and motivation score is low, their learning success GPA will also low. These findings are in agreement with results of other studies like Pintrich
and Degroot 1990, Button et al. 1996, and Brown and Gerhardt 2006. This findings answered the first research question about
the relation between students’ attitude, discipline, motivation and learning success.
Next, the Multiple Regression analysis results showed that the relation between attitude, discipline, motivation and learning success could be predicted.
The score of learning success could be predicted from the score of attitude, discipline, and motivation. The R value was smaller than the significance level
0.00 0.05. The analysis result confirm that learning success is predicted from attitude,
discipline, and motivation with a multiple regression formula F = 12.94 at p = 0.00 0.05. Learning success is predictable from attitude, discipline, and motivation
with a multiple regression formula F = 12.94 at p regression = 0.00 0.05. F value F table 12.94 2.98 indicates that the relation is significant and null hypothesis
Ho is rejected. Thus, this method can be used for prediction formula since the relation is significant. Attitude aspect contributes 56 of the learning success
gaining. Discipline aspect contributes 24 of the learning success gaining. Then, motivation aspect contributes 44 of learning success gaining. These findings
answered the second research question about the prediction score of learning success from the attitude, discipline, and motivation variable. Qualitatively, the
previous discussion on this relationship supports the statistical results of this research. When the hypothesis is accepted, thus the correlation is confirmed. This
means that the prediction formula can be used to predict. Regression models are often constructed based on certain conditions that must be verified for the model
to fit the data well, and to be able to predict accurately. The relation between attitude, discipline, motivation and students’ learning
success seems to suggest that the variables maybe manipulated controllable variables
to improve students’ learning success. Therefore, exploring the relation between attitude, discipline, motivation and learning success of the students will
give important implications for the English learning education. This has been demonstrated in a number of studies.
This finding is in line with the previous studies which reveal that there is a significant positive correlation between attitude and learning success. Those
topics are related to the point the researcher tries to investigate. Those related studies are first by Haibin 2010, second by Purwaningtyas, A.S. 2015, third by
Papaja 2012, fourth by Osman, A. 2010. A research by Purwaningtyas reveals that there is a positive relation
between students’ attitude and learning success. Moreover, this positive relationship is supported by the results of in-depth interview conducted to low-
achiever, middle-achiever, and high-achiever. The students claimed that attitude had contribution to their learning success.
A research by Haibin reveals that there is a positive relation between discipline aspect and learning achievement. Haibin 2010 proposed that for the
students, high learning success and behavioral competence should be regarded as two important indicators for their success. Such findings have prompted that
emotional competencies, such as behavioral regulation, students’ attitude, attentional skills, and the ability to problem solve, are critical to children’s
academic outcomes. A research by Osman, A. 2010 reveals that there is a positive relation
between motivation aspect and learning achievement. Osman, A. 2010 used an achievement test to all the participating students in order to examine the
relationships between the students’ success and motivation levels. The findings show that there is relation among integrative, instrumental, and motivations and
students’ successes in second language learning. Later, the researcher would describe the strength and relationship between
the variables as explained below:
1. Relation between Attitude and Learning Success
The first question of this study was intended to investigate the relation between attitude predictor variable and learning success predicted variable.
Therefore, to determine the relationship, Pearson Analysis – Pearson Product
Moment was conducted. Moreover, this study also directed Multiple-regression analysis by a means to look further the correlation between the variables. The
alpha level of this analysis was set on level .05 intended for all analysis in the study.
According to the results, it was found that there was significant relation between attitude and learning success gains bigger than 0.00 and the coefficient
is 0.00 r
1.2
= 0.75 0.00 and sig = 0.00 0.05. Attitude aspect has a strong linear relation between the learning successes since the correlation score is
approaching +1 0.75. It demonstrates a strong positive linear relation between both variables. It is concluded that the correlation coefficient between attitude
and learning success is significant. R square value of attitude aspect is 0.56; it means that attitude aspect
contributes 56 of the learning success gaining. In other words, attitude aspect has the big contribution in students’ learning success. This finding was in line
with the findings of preceding studies such as Gardner 2012. It also indicates that students with good attitude are reported to have learning success as well.
Additionally, it explicitly states that attitude aspect take a role in achieving the learning success. This finding also supports a research by Jowell, R and Gardner
2015 which shows that attitude gives positive relation to the learning success. This finding is in line with other study which reveals that there is a significant
positive correlation between attitude and learning success. Gardner 2000 states that attitude aspect is one of the predictors of success in learning English
language. Then, to answer the second research question, the researcher employed
Multiple Regression. It is to predict the future of the predictor variables and predicted variable assuming that the identified relationship will persist. Thus, this
Regression analysis result can predict the score of students’ attitude. The analysis used statistical equation of Y= a+ bX. “Y” is the symbol for the predicted
variable learning successstudents’ GPA; “b” is the constant value; and “X” is PLAGIAT MERUPAKAN TINDAKAN TIDAK TERPUJI
the symbol for the variable attitudedisciplinemotivation. Using this statistical formula, the score of students’ GPA Y can be predicted from the score of
attitude, discipline, and motivation aspect score. For the example is that if the students’ GPA is 3.5, thus the calculation of students’ attitude score prediction is:
Y= a + bX 3.5 = -1.37 + 0.04 x X variable
3.5 = -1.37 + 0.04 x X variable 0.04 x X variable = 2.18
X variable attitude score= 2.18 = 65
0.04 Seen from the calculation of students’ attitude score above, it can be
described that the students’ GPA score is 3,5 then the attitude aspect score will be 65. If the students’ GPA score is higher, then the attitude score will be higher too.
If the students’ GPA score is lower, then the attitude score will be lower too. This prove that the attitude aspect has positive relation to the learning success
students’ GPA. Moreover, this positive relation is supported by the results of in- depth interview conducted to low-achiever, middle-achiever, and high achiever.
The students claimed that attitude aspect had contribution to their learning success.
2. Relation between Discipline and Learning Success
In addition, the correlation between discipline aspect and learning success is considered positive which is bigger than 0.00 and the coefficient is 0.00 r
1.3
= 0.49 0.00 and sig = 0.03 0.05. Thus, it is found that there is a positive and
significant correlation between students’ attitude and students’ learning success. It demonstrates a strong positive linear relation between both variables since the
coefficient level is bigger than 0.00 and the significance level is less than 0.05. R square value of discipline aspect is 0.24, it means that attitude aspect
contributes 24 of the learning success gaining. Then, R square value of motivation aspect is 0.44, it means that motivation aspect contributes 44 of the
learning success gaining. This finding was in line with the findings of preceding studies such as Gordon 2005. It also indicates that students with good discipline
are reported to have learning success as well. Additionally, it explicitly states that discipline aspect take a role in achieving the learning success. This finding
supports other related study by Gordon, T. 2012. His research shows that there is correlation between discipline and learning success.
Moreover, this positive relationship is supported by the results of in-depth interview conducted to low-achiever, middle-achiever, and high-achiever. All of
the students agreed that discipline aspect was related to their learning success. If the students’ GPA score is higher, then the discipline score will be higher too. If
the students’ GPA score is lower, then the discipline score will be lower too. This prove that the discipline aspect has positive relation to the learning success
students’ GPA. Moreover, this positive relation is supported by the results of in- depth interview conducted to low-achiever, middle-achiever, and high achiever.
The students claimed that discipline aspect had positive contribution to their learning success.
3. Relation between Motivation and Learning Success
According to the results, it was found that there was significant relation between attitude and learning success gains bigger than 0.00 and the coefficient is
0.00 r
1.4
= 0.67 0.00 and sig = 0.00 0.05. It demonstrates a strong positive linear relation between both variables. Motivation aspect has a strong linear
relation between the learning successes since the correlation score is approaching +1 0.67. Then, R square value of motivation aspect is 0.44, it means that
motivation aspect contributes 44 of the learning success gaining. It also indicates that students with good motivation are reported to have
learning success as well. It means that if the motivation aspect is high, their learning success will be also high. On the contrary, if their motivation aspect is
low, their learning success will be also low. Additionally, it explicitly states that motivation aspect take a role in
achieving the learning success. This finding is also in line with the studies which reveal that there is a strong corre
lation between students’ motivation and their learning success. This study concludes that motivation aspect is contributive in
helping the students to achieve their learning success. If the students’ GPA score is higher, then the motivation score will be
higher too. If the students’ GPA score PLAGIAT MERUPAKAN TINDAKAN TIDAK TERPUJI
