CHAPTER 2 LITERATURE REVIEW 9
3.8 Questionnaire validation
31
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pretesting is widely recognised as a useful strategy for enhancing the validity of qualitative data collection procedures and the interpretation of results.
As claimed by Connelly (2008), extant literature implies that a pilot research sample should be 10% of the sample expected for the bigger parent study. Since the number of international students in UTAR yet to be confirmed, the present study aims to collect data from at 100 respondents, 10% of this will be 10 respondents. This study received 15 respondents to perform the pilot test as shown in Table 3.8 below:
Table 3.8:
Number of Distribution of Questionnaire for Pilot Study
Private University Distribution of Questionnaire UTAR
Male (13.3%)
Female (86.7%)
Total Sample Size
2 13 15
3.10 Reliability Analysis
Reliability are the most important criteria in any survey technique (Hon & Tsz, 2015).
People are always concerned and sceptical about whether a study’s conclusions are accurate or false. According to Yoldas (2011), descriptive statistics are utilised to assess quantitative study data. It will go through a coding procedure in which raw data will be transformed into numerical form using (SPSS), a prominent statistical analysis programme. The numerical data will be presenting in the form of diagrams, bar charts, pie charts and tables, will entering the SPSS programme. Cronbach’s alphas of the items were measured using the Reliability Analysis in SPSS.
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According to Hair et al (2016), the reliability analysis value may be interpreted based on the strength utilising the Rule of Thumb as follows: Poor (α < 0.6), Moderate (α < 0.6 to < 0.7), Good (α < 0.7 to < 0.8), Very Good (α < 0.8 to < 0.9) and Excellent (α > 0.9). The result is as shown in Table 3.8 below:
Table 3.9:
Result of Reliability Analysis
Constructs Items Mean C.A.(α) Strength
Intercultural Challenges (faced by students)
12 2.856 0.947 Excellent
Language Challenges (faced by students)
7 3.305 0.914 Excellent
Learning Sustainability 4 3.567 0.635 Moderate
Intercultural Challenges Scale 7 3.524 0.824 Very Good
Language Challenges Scale 11 2.873 0.783 Good
3.11 Data Analysis Techniques
Quantitative data analysis requires the application of rational and critical thinking to transform raw numbers into relevant facts (Salkind, 2017). The calculation of variable frequencies and differences between variables are examples of quantitative data analysis. A quantitative technique is typically related with locating data to support or refute ideas which the researcher formulated earlier in the research process. Open completion of data collection, the data was coded, screened and cleaned using SPSS version 22, to rectify errors. Then, descriptive analysis was performed, followed by inferential statistics analysis using SPSS. More detailed analysis techniques and results will be discussed further in Chapter Four.
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CHAPTER FOUR
FINDINGS AND ANALYSIS
4.0 Introduction
This chapter covers the results of data analysis acquired from the questionnaire are listed in findings and analysis. The results were obtained from the SPSS.
4.1 Response Rate
The total number of International Students in UTAR Kampar campus is 384 and UTAR Sungai Long campus is 309. The researcher has circulated the questionnaire by sending a broad email to all international students since the institution declined to provide the international students’ email addresses for security reasons. The university’s IT Infrastructure and Support Centre assisted with this. The questionnaire link was provided to a group email account instead of delivering the online survey link to individual overseas students’ email addresses. This might lead prospective recipients to dismiss the email as a routine, uninteresting message. Furthermore, the ongoing hybrid class sessions made data collecting more difficult, since identifying overseas students in a physical environment was nearly impossible. Due to the aforementioned constraints,
35
the current study was only able to acquire 106 replies out of a total sample size of 242, resulting in a response rate of 43.80%.
Based on Roscoe’s (1975), criteria for selecting sample size have been a popular choice. A sample size of higher than 30 but less than 500 is recommended for most behavioural research, whereas a sample size of more than 500 may result in a Type II error. A minimum sample size of 100 is necessary, according to Bullen (2021). The majority of statisticians believe that a sample size of 100 is necessary to draw any meaningful conclusions. If the data set needs to be split down into numerous subgroups such as male/female, rural/urban, local/international for comparative analysis, Roscoe believes that 30 respondents should be regarded the minimum for each category (Roscoe, 1975). The present studies’ respondents are enough to run the SPSS analysis.
4.2 Data Screening and Cleaning Analysis
In this data entry errors, the research responses will be anlayzed for the missing values.
Although, this research was conducted via online survey it is important for the researcher to make sure all the values are present and no missing values. Cleaning procedures will be used if any missing values were found.
Table 4.1:
Data Entry for Demographic Profiles
N
Valid Missing Minimum Maximum
Gender 106 0 1 2
Age 106 0 1 4
Year of Study 106 0 1 5
36 Table 4.2
Data Entry for Section B, C, D, E & F (selected items) N
Selected Items Valid Missing Minimum Maximum
IC3 106 0 2 5
IC6 106 0 1 5
LC1 106 0 1 5
LC4 106 0 1 5
LS3 106 0 2 5
LS5 106 0 3 4
ICS2 106 0 3 5
ICS7 106 0 2 5
LCS7 106 0 1 4
LCS9 106 0 1 4
As shown in the Table 4.1 and Table 4.2 it shows that there are no missing values.
Therefore, the treatment of missing data is no needed for this data.
4.3 Demographic Profiles
The frequency distributions from the data collection in Section A were used to perform descriptive analysis. To further understand the results and conclusions, it was necessary to describe the findings on the respondent’s profile. Below are the pie charts for the demographics.
37 Figure 4.1:
This Illustration of Respondent’s Gender
As shown above in the Figure 4.1, males accounted for 29 (27.4%) of the 106 responses, while females accounted for 77 (72.6%). As a result, females made up the largest proportion of respondents in the gender category.
38 Figure 4.2:
Illustration of Respondent’s Country of Origin
As shown above in theFigure 4.2, 106 (100%) of respondents are from the China.
39 Figure 4.3:
Illustration of Respondent’s Age
The age group of the respondents from 20-22 years old accounted was 99 (93.4%) and below 20 years old was accounted 7 (6.6%) as shown in Figure 4.3.
40 Figure 4.4:
Illustration of Respondent’s Year of Study
As shown in the Figure 4.4, Year 1 respondents accounted 19 (17.9%), Year 2 68 (64.2%) and Year 3 19 (17.9%), in UTAR.
41 Figure 4.5:
Illustration of Respondent’s Faculty of Study
Looking at the respondent’s faculty there are eight faculties involved. The highest number of respondents were from Faculty of Arts and Social Science (FAS) 17 (16%) , Faculty of Science (FSc) (16%), Lee Kong Chain Faculty of Engineering and Science (LKCFES) (16%), followed by Faculty of Accountancy and Management (FAM) 15 (14.2%), Faculty of Creative (FCI), Faculty of Business and Finance (FBF) 11 (10.4%), Faculty of Information and Communication Technology (FICT) 8 (7.5%) and Faculty of Engineering and Green Technology (FEGT) 7 (6.6%)
42 Table 4.3:
Demographic Profile
Demographics Indicators Frequency Percentage (%)
Gender Male 29 72.6
Female 77 27.4
Total 106 100
Country of Origin China 106 100
Total 106 100
Age Below 20 7 6.6
20 – 22 99 93.4
Total 106 100
Current year of study Year 1 19 17.9
Year 2 68 64.2
Year 3 19 17.9
Total 106 100
Faculty FAS 17 16
FSc 17 16
LKCFES 17 16
FAM 15 14.2
FCI 14 13.2
FBF 11 7.5
FICT 8 7.5
FEGT 7 6.6
Total 106 100
4.4 Reliability Analysis
As discussed by the researcher previously in literature review, the reliability analysis value may be interpreted based on the strength utilising the Rule of Thumb as follows: Poor (α < 0.6), Moderate (α < 0.6 to < 0.7), Good (α < 0.7 to < 0.8), Very Good (α < 0.8 to < 0.9) and Excellent (α > 0.9) (Hair et al., 2016). The following were the outcome, as indicated in Table 4.4:
43 4.5 Data Normality
Based on Urbano (2013), the kurtosis and skew measures are used to see if the indicators fulfilled the normalcy assumptions. Descriptive statistics were created to evaluate the normality of the data gathered. Skewness and kurtosis were employed to analyse the normalcy distribution in this study. Table 4.5 shows that there was no violation of the skewness and kurtosis assumptions for a total of 106 respondents, with values falling within the permitted range, skewness values between -3 to +3 are acceptable, whereas kurtosis values between -10 and + 10 are acceptable (Brown, 2006).
Table 4.4:
Reliability Analysis
Constructs Items Mean C.A.(α) Strength
Intercultural Challenges (faced by students)
12 2.8357 0.950 Excellent
Language Challenges (faced by students)
7 3.2857 0.916 Excellent
Learning Sustainability 4 3.0448 0.614 Moderate
Intercultural Challenges Scale
7 3.5539 0.813 Very Good
Language Challenges Scale 11 2.8662 0.788 Good
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N Std. Deviation Skewness Kurtosis
Statistic Statistic Statistic Std. Error
IC1 106 1.0424 -.025 .235
IC2 106 1.3664 .464 .235
IC3 106 .8866 -.538 .235
IC4 106 1.5413 .356 .235
IC5 106 1.3182 .677 .235
IC6 106 1.1708 .048 .235
IC7 106 1.3434 .715 .235
IC8 106 1.1798 -.509 .235
IC9 106 1.2858 .096 .235
IC10 106 1.2121 -.934 .235
IC11 106 1.5286 .738 .235
IC12 106 1.0982 .387 .235
LC1 106 1.1332 -.160 .235
LC2 106 1.0094 .019 .235
LC3 106 .9259 1.036 .235
LC4 106 1.2331 .047 .235
LC5 106 1.0795 -.688 .235
LC6 106 1.1090 -.505 .235
LC7 106 .7367 -1.034 .235
LS1 106 .9076 -.078 .235
LS2 106 .7560 -.385 .235
LS3 106 .7915 -.251 .235
LS4 106 .9155 -.539 .235
LS5 106 .4725 -.733 .235
ICS1 106 .9929 .062 .235
ICS2 106 .6190 .054 .235
ICS3 106 .9019 -.723 .235
ICS4 106 .6859 -.097 .235
ICS5 106 1.2449 -.585 .235
ICS6 106 .9925 -.344 .235
ICS7 106 .9137 .571 .235
LCS1 106 .5016 -.115 .235
LCS2 106 .7377 -.760 .235
LCS3 106 .6635 -.229 .235
LCS4 106 .7705 -.581 .235
LCS5 106 .7377 -.400 .235
LCS6 106 .7204 -.189 .235
LCS7 106 .8725 .529 .235
LCS8 106 .9276 .495 .235
LCS9 106 .7189 -.011 .235
LCS10 106 .7098 -.283 .235
LCS11 106 .8634 .137 .235
45 4.6 Analysis of Intercultural Challenge
The first research objective of the study is to identify the intercultural challenges faced by the international students in Malaysian private university.
RQ1: What are the intercultural challenges faced by international students in Malaysian private university?
Khumsikiew et al. (2015) interpreted mean scores ranging from 1.00 to 1.80, 1.81 to 2.60, 2.61 to 3.40, 3.41 to 4.20, and 4.21 to 5.00 as very low, low, moderate, high, and very high, respectively. Based on this rule of thumb, two items namely IC3 and IC10 are considered to contribute great impact toward intercultural challenges with mean value of 3.934 and 3.453 respectively.
Item IC3 (“I try my best to learn in the class, and sometimes, I need more time to get used to it”.) scored the highest (3.934). This indicated that this is the main problem that faced by the international students of this study. Based on the past study by Yuerong et al. (2017), for international students in the United States, cultural adjustment is seen as a difficulty. While adjusting to life in the host country, international students may face culture shock, as well as other problems such as being separated from their families and a lack of social support. Aung (2019) performed research at the University of New Hampshire on issues faced by international students and their perceived values. From the findings of the study identified that international students at the University of New Hampshire were most concerned about social integration barriers. The
Valid N
(listwise) 106
46
finding of this study is related to the present finding. The respondents in the present study are all international Chinese students, which could also be facing the similar intercultural challenges loses their familiar comforts and own culture which makes them to fit into a new environment.
Meanwhile, IC10 (“I need time and space to learn in my class.”) is the item that scored the second highest mean value which is 3.453. According to the Russell et al. (2010), international students experience isolation and loneliness in various ways while studying in the United States.
In the study showed that 41% of international students in Australia suffer significant levels of stress in a recent survey of 900 international students. In the contrary, the respondents of this study were wanted their own space and time to adapt the environment. Campbell and Li (2007), looked into the cultural adjustment of Asian students in New Zealand, and found that international students face a variety of challenges, ranging from their classroom interaction patterns to a lack of academic norms and conventions to insufficient support inside the classroom. The respondents in this study are all international Chinese students who may be encountering intercultural challenge as they experienced culture shock. Thus, they considered and treated as a foreigner which they need more to time to socialize in their class.
4.7 Analysis of Language Challenge
The second research objective of the study is to identify the language challenges faced by the international students in Malaysia private university.
RQ2: What are the language challenges faced by international students in Malaysian private university?
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Based on this rule of thumb, three items namely LC7, LC6 and LC3 found to be greatly affecting language challenges with mean value of 4.009, 3.613 and 3.462 respectively.
Item LC7 (“Sometimes, I could not understand my local classmates’ slang, or they might have spoken too fast.”) scored the highest mean value of 4.009. This is the main challenge that the respondents faced by Chinese international students. As claimed by Spencer-Oatey and Xiong (2006), that, despite their overall acceptable adaption, Chinese students had difficulty interacting with non-Chinese students. Whereby, Wu et al (2015), claimed that despite having studied English for several years in their native countries, several participants said that American English was difficult for them. Different dialects, speaking rates, and pronunciation issues cause linguistic challenges. Many participants are required to devote more time. One of the respondents of the study mentioned in listening, unable to understand many words, particularly the tempo of speech and pronunciation. Long et al. (2009), pointed out that, despite having good TOEFL and GRE scores, Chinese students found it difficult to interact with their instructors and other students owing to challenges with intonation and speech rate. The international Chinese students who participated in this study were who might have been struggling with the language barrier to participate in social interaction, causing them to miss out on social opportunities.
Meanwhile, LC6 (“When I must communicate with people, I feel nervous to speak up.”) scored the second highest mean value (3.613). Students’ academic learning, participation in various events, and cultural understanding may all be hampered by language problems. Language barriers impede overseas students’ long-term learning by limiting their ability to acquire knowledge and enhance their learning abilities. In terms of English language problems, the outcomes of this study are consistent with earlier research. A Taiwanese student from Wu et al
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(2015), research mentioned that in Taiwan also have English lesson and become frightened when to converse with other people. The Chinese international students those from this study may have mental block and speaking anxiety.
Lastly, LC3 (“I rarely understand the phone or Teams conversation when I call my local (Malaysian) friends/course mates.”) scored the third highest mean value of 3.462. Based on Al- Zubaidi & Richards (2010), the majority of Arab graduates who wish to enroll in Malaysian postgraduate programmes must finish an English language programme requirement in one to one and a half years. This is if they did not satisfy the basic TOFEL or IELTS criterion of 550 points or 6.0. English language barriers had a negative impact on international students’ learning sustainability, which was also strongly confirmed by previous research as described in the literature review (Yassin et al, 2020). The Chinese international students from this study, who have may study English in their native countries, employing it in real-life circumstances may be a challenge for them. International students may face social anxiety and disorientation due to a lack of English competence.
4.8 Hypotheses Testing
4.8.1 Correlation Analysis
According to Bhandari, P. (2022b), the link between variables using a correlation analysis by calculating a correlation coefficient, which is a single number that reflects the degree and direction of the association between variables. Correlation able to quantify the degree of the association between variables using the number. As stated by Wong and Hiew (2007), the
49
correlation coefficient value (r) ranges from r = 0.10 to 0.29 or r = -0.10 to -0.29 for weak correlation, r = 0.30 to 0.49 or r = -0.30 to -0.49 for moderate correlation and r = 0.50 to 1.0 or r = -0.50 to -1.0 for high correlation.
RQ3: Do intercultural challenges relate significantly with learning sustainability among international students in Malaysian private university?
H1: There is a significant relationship between intercultural challenges and learning sustainability among international students in Malaysian private university.
Table 4.6:
Pearson Correlation for Intercultural and Language challenges with Learning Sustainability
Intercultural Challenges
Language Challenges Learning Sustainability
Pearson Correlation
-.733 -.416
Sig. (2 tailed) .000 .000
N 106 106
As shown in the Table 4.6, the Pearson’s Correlation was conducted. The result shows a significant negative relationship between learning sustainability with intercultural challenges with r (106) = -.733, p <.000. Based on the rule of thumb, correlation coefficient is -.733 considered as high. Several studies have looked into intercultural issues and found that they have a detrimental impact on students’ learning and academic results (Jakobsson et al., 2013; Rovio-Johansson, 2016). Intercultural barriers may have a detrimental impact on international students’ learning results, which is linked to achieving long-term learning. Many notable HEIs throughout the world have made it their mission to focus on international students by providing intercultural education.
The ultimate goal is to provide international students with the skills they need to assimilate and
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adapt to the host culture, as well as the knowledge and abilities they need to actively engage in the long-term development of their communities. Hence, the hypothesis is accepted.
RQ4: Do language challenges relate significantly with learning sustainability among international students in Malaysian private university?
H2: There is a significant relationship between language challenges and learning sustainability among international students in Malaysian private university.
The Pearson’s Correlation Coefficients are shown in Table 4.6. The result shows a significant negative relationship between learning sustainability with language challenges with r (106) = -.416 p <.000. Based on the rule of thumb, correlation coefficient is -.416 considered as moderate. Language barriers impede international students’ long-term learning by limiting their ability to acquire knowledge and enhance their learning abilities (Lu et al., 2015). The findings of this study in terms of English language issues are consistent with prior research (Alavi & Mansor, 2011; Robertson et al., 2000)), since language hurdles may limit international students’ capacity to participate actively in classroom learning. Language competency assessments may or may not assist learners in communicating with native speakers in a smooth and successful manner.
Although international students may learn English in their native countries, applying it in real-life settings may still be a challenge for them. Thus, the hypothesis is accepted.
Based on the findings above, both intercultural challenges and language challenges demonstrated significant and negative relationship with learning sustainability. Higher the
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intercultural challenges, lower the learning sustainability. Similarly, higher the language challenges, lower the learning sustainability, and vice-versa. However, it is important to note that intercultural challenges had greater impact on learning sustainability than language challenges.
For Chinese international students in the United States, cultural adjustment is seen as a difficulty (Flannery & Wieman, 1989; Yuerong et al., 2017). While adjusting to life in the host country, international students may face culture shock, as well as other problems such as being separated from their families and a lack of social support. Misunderstanding of international cultural conventions and idioms, in specific host situations is one of international students’ issues. This shows that cultural differences disconnected overseas students and their professors, obstructing their assimilation to the host culture. Previous research by Yassin et., al (2020), have shown that international students improve their English skills before going overseas to study, but they still suffer language hurdles, which has a negative impact on their academic performance. International students face language challenges, particularly since poor English language abilities prevent them from improving their knowledge and skills, which they will need in their future employment.
4.8.2: Determination Correlation Test (R2)
Based on Frost (2022), for linear regression models, R-squared is a goodness-of-fit metric.
R-squared quantifies the strength of the association between the model and the dependent variable by the scale of 0 – 100%. Table 4.7 shows the R values:
52 Table 4.7:
Linear Regression Model
Model R R Square Adjusted R square SE
1 .878a .770 .766 .22883
Hair et al. (2013), stated R2values of 0.75, 0.50, or 0.25 for endogenous latent variables can be classified as considerable, moderate, or weak, respectively. The adjusted R2 value of 0.766 indicates that 76.6% of the variability of the dependent variable (learning sustainability) can be explained by the variability of the independent variable (intercultural and language challenges).
While additional factors not included in the regression model account for the remaining 23.4%.
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CHAPTER FIVE
DISSCUSSIONS
5.0 Introduction
The chapter begins with the discussions of the study findings will be summarised as well as the theoretical and practical contributions. It also makes recommendations for managers and policymakers, examines the study’s limitations, and identifies future research prospects in a related field.
5.1 Discussion
5.1.1 Intercultural challenges face by international students
The first research question in this study is to investigate the intercultural challenges faced by international students in Malaysian private university. Among the 12 items for intercultural challenges there are two items scored high value which are IC3 and IC10. IC3 is about the