This research employed a quantitative method approach for data gathering. A quantitative questionnaire with some items taken from the Student Adaptation to College Questionnaire (SACQ) developed by Baker and Siryk (1989) and other factors found through research will be used to test foreign students’ satisfaction on their performance.
The test used a Likert-type scale. Responses were based on a five-point Likert scale: 1 = very dissatisfying, 2 = dissatisfying, 3 = NA/neutral, 4 = satisfying and 5 = very satisfying.
The respondents were instructed to refer to their experience here in Taiwan as a student and to fill out the questionnaire that has a range of selected items about their experience as a foreign student in Taiwan, and the items are in relation to Geert Hofstede’s theory on power distance, individualism, uncertainty avoidance, and masculinity. The questionnaire is not Hofstede’s but the items are related to the meaning of each dimension as described by Hofstede. The literature review has a more detailed explanation with a table on cultural dimensions in relation to students.
The respondents also supplied their demographic data of gender, age and nationality. They indicated whether their scholarships are ICDF or Non-ICDF. A section rating their grade point average so far in their studies was used with ranges from A to C as indicators of their performance. The respondents also had to indicate whether they were Graduate or Undergraduate students. Finally there was a section to fill in any additional comments they may want to express about their satisfaction and in relation to their academic performance. The nature of the topic and questions in the questionnaire was influenced by previous researches and the conclusion that the researchers have made on what factors affect foreign students.
Population and sample
The population is all international higher education students in the universities and colleges in Taiwan. But from that population the focus were on students who took courses in English. The researcher chooses to use these students since the research will be on higher education in Taiwan. These students had to spend at least one year in Taiwan.
The last enrollment census of higher international students in Taiwan was in 2007 that showed the population of 5259 (N = 5259). Therefore the recommended sample population for the study should be 357 (n = 357) as determined by 95% level of certainty developed by Krejecie and Morgan in 1970 and Raosoft at raosoft.com. The research used a nonrandom or non-probability sampling technique known as snowball sampling.
This sample technique was used because it is hard for the researcher to identify members of the desired sample for the Non-ICDF higher education scholarship students. The researcher hoped through snowballing that the ICDF students could identify at least one Non-ICDF student to reach the desired sample and it resulted in a good sample population.
The ICDF higher education students can be identified from ICDF statistics they have in their database. Of the 132 ICDF students approached 114 responded which is an 86.4% response rate. An additional 142 international students were obtained through various means. As requested the ICDF students identified international classmates and others at their university. The researcher also got some by approaching international students wandering on their campuses; once they fit the criteria, they were asked to do the questionnaire. Of the 350 questionnaires the researcher took out 256 responded which is a rate of 73.1%. The Table 3.1 below provides a view of the sample population and their demographic, status, degree and GPA distributions.
53 Table 3.1. Data of variables by entries and values (n = 256)
Variables Entries Percentage
Gender Male 156 60.9
Female 100 39.1
Nationality by Regions Asia 40 15.6
Europe 20 7.8
North America 22 8.6
Central America 103 40.2
South America 21 8.2
Africa 30 11.7
Australia and Oceania 20 7.8
Degree Pursued Undergraduates 124 48.4
Graduates 132 51.6
Students Status ICDF 114 44.5
Non-ICDF 142 55.5
Grade Point Average Level A = 90 – 100 55 21.5
B = 80 – 89 143 50.9
C = 70 – 79 58 22.7
Instrumentation
The instrument that was used for the data collection of students’ satisfaction effect on their performance was a questionnaire. The items in the questionnaire was gathered from the Student Adaptation to College Questionnaire (SACQ) developed by Baker and Siryk (1989) and formulated from factors observed by the researcher that are problems for international students in various countries. In the first section students identified their demographic data age, gender and their nationality.
The second section had statements based on the variables that were rated from 1- very dissatisfied to 5 - very satisfied. The 35 explanatory variables are interrelated and classified into the four pre-determined factors: Power Distance, Uncertainty Avoidance, Masculinity, and Individualism. Factor I, Power Distance includes Question items Q1, Q4, Q7, Q10, Q13, Q15, and Q18. Factor II – Uncertainty Avoidance contents incorporate Q12, Q14, Q17, Q20, Q21, Q23, Q24, Q26, Q30, Q31, and Q34. Factor III -
Masculinity has Q3, Q6, Q9, Q16, Q19, Q22, Q25, Q28, and Q35. Factor IV - Individualism covers Q2, Q5, Q8, Q11, Q27, Q29, Q32, and Q33.
The third section on Academic Performance was a rating of their academic performance by asking the students to provide as an objective indicator, their school grade point average (GPA) after one year or more of study in Taiwan. The final section asked students to make further comments (if any) on their satisfaction and motivation as international students in Taiwan and in relation to their academic performance.
Validity and reliability
Validity of the instrument was determined by content validity. Content validity is basically the extent to which the measurement device or in this case, the measurement questions in the questionnaire, provides adequate coverage of the investigative questions and hypotheses. The researcher tests content validity of the items in relation to Hofstede’s dimensions by providing definitions of each dimensions used in the study. A table provided in the literature review with descriptions show what meaning the researcher gives to the dimensions. The researcher also reached content validity by discussing the items and the meaning with experts. The experts are teachers who have been in teaching and education for many years. Two in particular have published numerous articles on education. The experts provided an assessment of each item in the questionnaire by determining if they are useful or not.
To test reliability, the researcher conducted a pilot test using 10 samples which indicates a high internal consistency based on the alpha reliability of all items combined .965 (35 items) and for the sections: Power Distance .826 (7 items);
Uncertainty Avoidance .878 (11 items); Masculinity .899 (9 items); Individualism 0.894 (8 items). The final test gave a Cronbach Alpha of .943 for all items combined: Power Distance .790 (7 items); Uncertainty Avoidance .864 (11 items); Masculinity .818 (9 items); Individualism .777 (8 items).
55 Table 3.2. Cronbach alpha value of survey instrument
Tests
Pilot Final
Power Distance .826 .790
Uncertainty Avoidance .878 .864
Masculinity .899 .818
Individualism .894 .777
Complete Test .965 .943
Data analysis
The data for this research was analyzed using the Statistical Package for the Social Sciences (SPSS) PC 12.0 version. But, before analysis the data were coded using number sequences. The 35 questions were coded using a 5-point Likert scale as previously mentioned. The demographic variables, students’ status, students’ degree pursued, and GPA coding can be seen in Table 3.2 below.
Table 3.3. Coding system used in SPSS data analysis (n = 256)
Categories Coding System
Age Age Given
Gender 0 = Male
1 = Female
Nationality 1 = Asia
2 = Europe
3 = North America 4 = Central America 5 = South America 6 = Africa
7 = Australia and Oceana
Education Level 0 = Undergraduate
1 = Graduate
Scholarship Type 0 = ICDF
1 = Non-ICDF Grade Point Average Level 0 = C = 70 – 79
1 = B = 80 – 89 2 = A = 90 - 100
The researcher used from the SPSS software, descriptive and inferential statistics to analyze and interpret the data collected from the sample population. These statistical procedures allowed the researcher to present the relevance and importance of the study.
The descriptive statistics helped the researcher to arrange the data into a more interpretable form by forming the frequency distributions and generating graphical displays and by calculating numerical indexes such as averages, percentile ranks, and measures of spread. Descriptive statistics, e.g., means, frequencies, and a histogram of student responses are often applied to detect the most and the least satisfaction items regarding college programs and services (Damminger, 2001). All this data can be summarized easily or can be examined on their interrelation. The weighting of the data is very essential and important.
The use of inferential statistics helped the researcher to examine relationships, differences and trends, a process also known as hypothesis testing or significance testing.
In effect the researcher is trying to compare the data collected to what was theoretically expected to happen. The inferential statistics provided the researcher with the means to test whether two variables are associated, and to assess the strength between the independent and dependent variable, to predict the value of dependent to independent variable, and compare relative changes in trends.
Different statistical methods used to analyze satisfaction data and students’
performance data. These methods include crosstab and chi-square, and linear regression analysis. Chi-square method is used to identify the significant proportion difference for students’ performance based on their degree. The linear regression method is a useful tool to analyze the relationship between multiple predictor variables and student performance results (Thomas & Galamos, 2002). Linear regression allows the researcher to identify explanatory variables related to academic performance and how it contributes to the overall college satisfaction. This method also permits the researcher to estimate the magnitude of the effect of the predictor variables on the outcome variable. Therefore, regression methods seem to be superior in studying the relationship between the predictor and outcome variables. Specifically, the Multiple Linear Regression is used because the researcher wanted to use a model that would fit the data. Since there are 45 predictor variables, the researcher is interested in a process that can choose a subset of independent
57 variables which best explains the dependent variables. The backward elimination process is used, which examines the p-values for the 45 independent variables, and eliminates the highest insignificant variable in each equation. This means the researcher starts with all the variables in the model, and drops the least "significant", one at a time, until you are left with only "significant" variables. That is, instead of focusing on individual variables, it is important to study the relationship or interaction of these variables designated to be important, because in reality the variables do not exist or exert their influence independently.
59