International Journal of Mechanical Engineering and Technology (IJMET)
Volume 10, Issue 03, March 2019, pp. 808–817, Article ID: IJMET_10_03_084
Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=3
ISSN Print: 0976-6340 and ISSN Online: 0976-6359
© IAEME Publication
Scopus Indexed
FACTOR ANALYSIS OF ENGLISH
COMMUNICATION COMPETENCY AMONG
MALAYSIAN TECHNOLOGY
UNDERGRADUATES
Sarala Thulasi Palpanadan, PhD
Centre for Language Studies
Universiti Tun Hussein Onn Malaysia
ORCID # 0000-0001-9140-3937
Iqbal Ahmad, PhD
Faculty of Education,
University of Malakand, Pakistan
Venosha K.Ravana
Faculty of Language and Linguistics
Universiti Malaya
ABSTRACT
This paper aimed to determine factors influencing English communication
competency among Malaysian university undergraduates from technology
departments at Universiti Tun Hussain Onn Malaysia (UTHM). A survey was
administered to a random sample of 102 undergraduates. Factor analysis was applied
to determine the underlying dimensions that influence English competency among the
students. The findings revealed four critical factors: mother tongue interference, lack
of confidence, lack of practice, and home environment. Thus, students need to be
encouraged to communicate in English at the university and home to provide wider
language practice opportunities to master communication skills in English and
perform well in the technology courses.
Key words: English competency, exploratory factor analysis, communication skills,
technology courses.
Cite this Article: Sarala Thulasi Palpanadan, Iqbal Ahmad, Venosha K. Ravana,
Factor Analysis of English Communication Competency among Malaysian
Technology Undergraduates, International Journal of Mechanical Engineering and
Technology 10(3), 2019, pp. 808–817.
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Sarala Thulasi Palpanadan, Iqbal Ahmad, Venosha K. Ravana
1. INTRODUCTION
The two areas of English language and technology are inseparable where they complement
each other very much. Having a good grasp of English language and technology skills
facilitate the learning skills to obtain more knowledge (Ahmadi, 2018). English is becoming
popular day by day all over the world including the technology field. It is used as an
international lingua franca (Ahmad, 2016). English is an important language to have better job
employment opportunities in all fields (Suryasa et al., 2017; Swales & Feak, 2004). Thus,
mastering English in our daily life has become essential (How et al., 2015; McKay, 2002).
Meanwhile, technology also plays a crucial role in bringing about changes in people’s
perception, association and style of lives (Salehan, Kim & Lee, 2018). Technology is a tool
utilized by everyone, especially engineers to uphold the development and improvement of the
world so that everyone can benefit from it. Technological determinism theory (TDT) supports
the idea that the development of a nation based on its societal and cultural values depend on
its progression of technology (Howells, 1997). Thus, it is very important to study the
challenges of English instruction in technology integrated courses among the Malaysian
undergraduates who are pursuing technology and engineering based courses at the
universities.
Malaysia is a multi-race country and Malay is the national language. Malay language is
often used as the language of instruction, administration and employment in government and
non-government sectors (Mahir et al., 2007). Malay is the language that is used most
frequently for communication among people who are not proficient in English in the
Malaysian context. English is usually used for some specific occasions and events at English
Departments in government institutions and some private sectors. As a matter of fact, many
Malaysians still use Malay widely in their daily communication without having to worry
about their incompetency in English as it is easily understood by the majority.
Apparently, the Malaysian education system has promoted bilingualism and
multilingualism school system with three different mediums as instruction, such as Malay,
Tamil and Mandarin language mediums. This is to let Malaysians to have a chance to learn
their own mother tongue according to their own races (Benraghda et al., 2017). Various
languages have benefits for Malaysian students and allow them to get further understanding
about the importance of English language (How et al., 2017). However, as English is still not
widely used in Malaysia, this might affect the mind sets of the people that English language is
not the most important language and therefore, they need not focus in using English in their
daily lives (Heriansyah, 2012; Pandian, 2002). Consequently, people may gradually ignore
the importance of English language in their daily activities. This is a very serious matter that
has to be investigated and discussed as many graduates scored good grades in the examination
but they tend to face difficulties in finding jobs due to the lack of fluency in English language
(Kirkpatrick, 2012; Nunan, 2003). Employers claim that the graduates’ lack of
communication skills was one of the reasons of the increasing unemployment rate in Malaysia
(Shanmugam, 2017). In addition, high unemployment rate among Malaysia graduates in the
private sector is often attributed to lack of English proficiency and communication skills
(Ting et al., 2017).
In order to ensure that people can communicate well in English language, several
continuous processes need to be involved in their daily routine (Rashid & Hashim, 2008;
Thirusanku & Yunus, 2014). Effective communication and understanding are among the
important processes that are involved in peoples’ daily lives which can lead to good grasp of
English. In addition, university students and the surrounding community should consciously
work towards improving the ability to speak in English fluently. Therefore, this research was
carried out at UTHM to determine the reasons that deter effective English communication
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Factor Analysis of English Communication Competency among Malaysian Technology
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skills among the undergraduates of technology courses. It was perceived that the local
students seldom communicated with each other in English due to their own preferences within
the campus compound. Students often used their own mother tongue to communicate with
friends from the same races and use Malay language to communicate with friends from
different races. English language was neither their favourite choice nor a case for stern
learning. Thus, there is a strong reason to study and overcome this phenomena.
2. PROBLEM STATEMENT
Many studies were conducted on how the integration of technology could facilitate the
English language learning (Ince, 2014; Ahmadi, 2018). However, not many studies show how
English language could facilitate the technology based courses at universities. As a matter of
fact, many researchers stated that the local university students in Malaysia are still grappling
with to communicate effectively in English (Musa et al., 2012; Ting et al., 2010). In addition,
many engineering and information technology graduates often remain jobless in the job
market due to the poor command of English language and lack of confidence to converse in
English (Ibrahim & Mahyuddin, 2017).
The low English language proficiency among Malaysian graduates is a serious issue that
needs to be discussed and addressed. The current research was conducted to determine the
factors influencing English communication skills among the undergraduate students from
three technology departments at UTHM Malaysia. The Malaysian universities are offering
various technology courses where most of the lectures are conducted in English. Furthermore,
the notes and resources are mostly available in English Language. Therefore, as much as the
mastery of technology is concerned, the undergraduates’ challenges and ability to process all
the information in English has to be addressed as well. This study is important so that the
problem in communicating in English among the students of the technology courses can be
identified that can help the universities to produce professional and competent graduates in
future.
3. RESEARCH OBJECTIVES
The main objectives of this study are:

To explore factors influencing English communication among the technology students at the
university.

To suggest strategies for the promotion of English language communication skills among the
technology students at universities.
4. METHODS
4.1. Participants
This exploratory study was conducted to explore potential factors influencing English
language communication skills among the students taking the technology courses and
programs at UTHM. The sample consisted of 102 students selected from three departments:
Faculty of Computer Science and Information Technology, Faculty of Technical and
Vocational Education and Faculty of Technology Management and Business. A selfdeveloped 20 items questionnaire was distributed among the participants for data collection.
The first part of the questionnaire consisted of demographic features including races and
gender and second part items concerning English language speaking skills.
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4.2. Reliability and Validity
The questionnaire was tested for internal consistency using Cronbach’s alpha. The content
and face validity was checked by expert review and literature review. The statements of the
questionnaire were refined grammatically based on the feedback from three language experts.
The questionnaire was piloted on 30 respondents. Based on the inter item consistency
analysis, only those items which were above .40 were retained (Hinkin, 1995). The internal
consistency test showed an alpha of .75 for nineteen items which is considered very good as
an alpha value (Hinkin, 1995). The reliability of the questionnaire items was confirmed
through scale statistics and item statistics.
Table 1: Scale Statistics
Mean
Variance
Std. Deviation
N of Items
63.71
94.958
9.745
19
Table 1 shows the scales statistics indicating a total mean 63.71, variance 94.95 and
standard deviation 9.74 for 19 items questionnaire.
Table 2: Item-Total Statistics
No
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Statements
Scale Mean if
Item Deleted
Scale Variance
if Item Deleted
Corrected
Item-Total
Correlation
Cronbach's
Alpha if Item
Deleted
60.31
60.77
60.28
60.43
60.17
60.21
60.32
59.95
60.33
60.43
60.47
60.63
60.36
60.47
61.43
60.47
59.67
60.47
59.67
75.664
94.167
76.539
33.884
93.460
73.762
78.541
87.629
77.336
78.018
99.230
77.529
45.655
80.144
88.488
76.546
88.691
76.546
88.691
.733
.631
.727
.596
.435
.778
.672
.441
.729
.659
.435
.581
.562
.500
.569
.777
.559
.777
.559
.704
.770
.706
.799
.760
.697
.713
.742
.707
.712
.784
.791
.787
.726
.746
.703
.742
.703
.742
Fear of mistakes and criticism
Lack of English speaking platform
Lack of effective learning strategies
Lack of English background
Family background or peer influence
Lack of practice in using English
Lack of using English in daily routine
Mother tongue interference
No interest in English language
Weak grammar usage
Introvert personality
Lack of confidence
Poor academic performance
Job opportunities in future
Unable to give presentation in class
Difficult to communicate with others
Interpersonal relationships
Cannot go overseas for further study
Speech anxiety
Table 2 shows that all items are above .40 meeting the criterion set for retaining items in
the questionnaire.
4.3. Factor Analysis of English Competency among Students
Exploratory factor analysis (EFA) was used to identify factors influencing the English
language competency among the technology students. The EFA is an analytical process and
data reduction that transforms statistical data into linear combination of variables. It is a
useful and meaningful statistical method applied to combine large number of data into small
factors with minimal loss of information (O'Leary-Kelly & Vokurka, 1998). The KaiserMeyer-Olkin (KMO) measure and Bartlett’s Test of Sphericity were used for determining the
sample size.
Table 3: KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
Approx. Chi-Square
Bartlett's Test of Sphericity
df
Sig.
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.961
15785.116
210
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Factor Analysis of English Communication Competency among Malaysian Technology
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Table 3 indicates the KMO and Bartlett’s Test of Sphericity for the current data. The
analysis shows the KMO was .96 with Bartlett’s Test of Sphericity significant at .000. This
indicated the sample adequacy for conducting the factor analysis.
Table 4: Communalities
No
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Statements
Fear of mistakes and criticism
Lack of English speaking platform
Lack of effective learning strategies
Lack of English background
Family background or peer influence
Lack of practice in using English
Lack of using English in daily routine
Mother tongue interference
No interest in English language
Weak grammar usage
Introvert personality
Lack of confidence
Poor academic performance
Job opportunities in future
Unable to give presentation in class
Difficult to communicate with others
Interpersonal relationships
Cannot go overseas for further study
Speech anxiety
Extraction Method: Principal Component Analysis
Initial
Extraction
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
.800
.735
.785
.642
.622
.885
.615
.687
.786
.703
.435
.587
.458
.564
.697
.649
.779
.493
.779
Table 4 shows that the communalities reveal the amount of variance of all the variables
individually. The size of the communality works as an index to assess the amount of variance
in an individual variable that accounts for the factor solution. The commonalties are higher
ranging from 0.435 to 0.800 as shown in Table 4 above.
Component
Total
Table 5: Total Variance Explained
Initial Eigenvalues
Extraction Sums of Squared Loadings
% of Variance Cumulative% Total % of Variance
Cumulative %
1
2
3
4
5
8.241
1.883
1.378
1.097
.923
43.376
9.912
7.253
5.772
4.856
43.376
53.288
60.541
66.313
71.169
6
.860
4.526
75.695
7
.757
3.982
79.677
8
.697
3.667
83.344
9
.610
3.211
86.555
10
.483
2.543
89.098
11
.457
2.403
91.500
12
.380
2.002
93.503
13
.327
1.722
95.225
14
.280
1.475
96.700
15
.231
1.213
97.914
16
.174
.917
98.831
17
.142
.749
99.579
18
.080
1.618
016
.421
8.51
016
100.000
19
8.241
1.883
1.378
1.097
43.376
9.912
7.253
5.772
43.376
53.288
60.541
66.313
100.000
Extraction Method: Principal Component Analysis.
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Sarala Thulasi Palpanadan, Iqbal Ahmad, Venosha K. Ravana
Table 5 indicates that four factors were extracted based on Eigenvalues greater than 1
using principal component analysis. The first component (mother tongue interference)
accounts for 43.37 of the total variance. It is the first influencing factor. The second
component (lack of confidence) accounts for 9.91 percent of the total variance being the
second influencing factor. The third component (lack of practice) accounts for 7.25 percent of
the total variance being the third influencing factor. The fourth component (home
environment) accounts for 5.77 percent of the total variance being the fourth influencing
factor. The entire four components together accounted for 66.31 percent of the total variance
in the scale.
Figure 1
Figure 1 also shows the four factors extracted through principal component method
(PCA). It shows that the curve tailing off after the four factors. Thus, it allows for retaining
four factors as a result of factor analysis.
Table 6 Rotated Component Matrix
No
1
Component
2
3
4
.870
.826
.870
.530
.607
.925
.766
.869
.770
.605
.485
.605
.650
.503
.701
.670
.613
.592
.642
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Table 6 reveals the rotated component matrix showing the factor loadings for each of the
variables on the main components based on varimax rotation. It also shows the correlation of
the components with each other. Values less than .40 were suppressed in the results. Based on
the rotated components, seven variables loaded on factor I which was named ‘Mother Tongue
Interference’ based on the nature of the items. Five variables loaded on factor 2 which was
named ‘Lack of Confidence’ based on the nature of the items. Three variables loaded on
factor 3 which was named ‘Lack of Practice’ based on the nature of the items. Four variables
loaded on factor four which was named ‘Home Environment’ based on the nature of the
items.
Table 7: Factor Loading Results for all Factors
Item
No
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Statements
Factor loadings
Fear of mistakes and criticism
Lack of English speaking platform
Lack of effective learning strategies
Lack of English background
Family background or peer influence
Lack of practice in using English
Lack of using English in daily routine
Mother tongue interference
No interest in English language
Weak grammar usage
Introvert personality
Lack of confidence
Poor academic performance
Job opportunities in future
Unable to give presentation in class
Difficult to communicate with others
Interpersonal relationships
Cannot go overseas for further study
Speech anxiety
.870
.826
.870
.530
.607
.925
.766
.869
.770
.605
.485
.605
.650
.503
.701
.670
.613
.592
.642
Factor
Mother Tongue
Interference
Lack of confidence
Lack of practice
Home environment
5. DISCUSSION
The aim of this study was to explore the factors influencing English communication among
the technology program students at UTHM Malaysia. The factor analysis highlighted four
important influencing factors: Factor 1-mother tongue interference, Factor 2- lack of
confidence, Factor 3-lack of practice, and Factor 4- home environment as explained below in
detail.
Factor 1 (Mother Tongue Interference) is identified as the most powerful influencing
factor that affects students’ communication skills. This factor explains 43.37 percent of the
total variance. Seven variables loaded on this factor as a result of the principal component
analysis with factor loadings ranging from .530 to .870. This factor reveals that mother tongue
interference is one of the biggest factors that affect English communication of engineering
students in UTHM. This finding also supports previous research studies where mother tongue
has been found as a barrier to second and foreign language learning (Gimenez, 2015).
Factor 2 (Lack of Confidence) is the second influencing factor. This factor explains 7.25
percent of the total variance. Three variables loaded on this factor based on principal
component analysis with factor loadings ranging from .503 to .701. This means that lack of
confidence is one of strongest barriers to English language communication among technology
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program students at UTHM. This result is in line with previous research that students have
lower confidence and cannot communicate with others (Heriansyah, 2012; Souriyavongsa et
al., 2013). Consequently, they show poor communication skills and perform low in the
language courses and activities.
Factor 3 (Lack of Practice) is the third strongest influencing factor. This factor accounts
for 9.91 percent of the total variance. Five variables loaded on this factor based on principal
component analysis with factor loadings ranging from .485 to .879. This shows that
engineering students are unable to find opportunity to practice English language which affects
their communication skills in the language. Previous research has also revealed that lack of
practice contributes to poor communication skills (Gan, 2012; Stacey & MacGregor, 1991).
Factor 4 (Home Environment) is the fourth strongest influencing factor. This factor
explains 5.77 percent of the total variance. Four variables loaded on this factor on the basis of
principal component analysis with factor loadings ranging from 592 to .670. This reveals that
home environment is another barrier for effective English communication for technology
program students. Previous studies have also indicated that students who do not find
encouraging environment at home regarding second language communication demonstrated
low language competency. Normally, such students come from non-English speaking
background. Studies have shown that children who do not get encouragement from parents or
other elders at their homes show poor speaking performance (Lessing & Mahabeer, 2007;
Roopnarine et al., 2006)
6. CONCLUSIONS
This study aimed at identifying factors influencing English communication among technology
program students at UTHM. Through application of an exploratory factor analysis, this study
revealed that four major factors affected the English language communication skills of the
technology program students. These factors were mother tongue interference, lack of
confidence, lack of practice, and home environment. Thus, the stakeholders and the respective
lecturers will be able to tailor their lesson and learning activities to cater to needs of the
students based on the four factors identified in this study. Besides, scholars and lecturers also
can suggest to students to discuss with their respective family members to cooperate with
them to provide room for them to improve their communication skills based on their own
capacities. This would pave ways for the students not only to excel in English but also in their
technology courses and programs.
RECOMMENDATIONS
Based on the results this study provides the following recommendations:

It is important that students should get an environment at UTHM where English
communication can be declared mandatory for communication among lecturers and students
in the classroom and within the campus. The university management may notice this issue and
embed this matter it in the curriculum and policy.

The students should be encouraged by lecturers and management to communicate in English
language. Thus, more language enhancement trainings, workshops, seminars, and symposium
could be held from time to time where students could participate actively. This would boost up
their confidence to speak in second language.

Parents should be positive in creating an environment at home where children find ample
opportunity to practice English language. Parents could allow their children to watch English
drama, English language tutorials and other supportive channels and programs. This would
increase their knowledge of English language and also add to their language vocabulary.
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
Further research is needed on this issue in the context of English learning among the
technology students in Malaysian universities. This would provide new knowledge and insight
for language policy development in educational institutions.
ACKNOWLEDGEMENT
The funding for this publication was provided by UTHM Research Fund (E15501).
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