Analysis of survey data

Cavana et al. (2001) argued that there is a process for effective analysis of quantitative data: 1) arranging data, 2) getting to know the data, 3) testing the goodness of data and 4) testing the

hypotheses. In the first instance irregularities that can be logically corrected should be (Cavana et al. 2001). In this study, in-house editing was carried out to check for errors and omissions in the

questionnaires, subsequently a few minor adjustments were made to ensure the data was more complete, readable and consistent before coding. Only with the graduating students and only in four cases was there a blank response and the computer discounted it. As the number of missing values was less than 15 per cent of the sample, Cavana et al (2001) permit this approach which is more appropriate than the alternatives (assigning the midpoint, mean of respondent‘s responses, mean of all respondents‘ responses, random assignment).

Given internet access and, if internet access was not the issue, then engagement with a web survey, the paper based responses had to be converted to electronic data. To avoid errors and omissions the researcher entered the data manually. Then the standard deviation, range, mean, and variance were used to provide an overview of how effective the items and measures were and how respondents perceived questionnaire items.

Table 21 Summary of measured variables and score ranges that comprise the data file

Survey question number/s & respondents Code in SPSS Variable No. of items

Scoring Choice Respondents

1 – 4 Students A1 – A4 Self and future 4 Open and closed questions Students 1 – 4 Lecturers A1 – A4 Self and career 4 Open and closed questions Lecturers 1 – 6 Employers A1 – A6 Self and business 6 Open and closed questions Employers 5 Students,

Lecturers; 7 for Employers

B1 – B16 Personal characteristics

16 Multiple selection but limited to 6 Students, Lecturers, Employers 6 Students, Lecturers; 8 for Employers C1 – C14 General graduate attributes 14 Ordinal:

Not important, slightly important, quite important, highly important Students, Lecturers, Employers 7 Students, Lecturers; 9 for Employers

D1 – D23 Workplace skills 23 Ordinal:

Not important, slightly important, quite important, highly important Students, Lecturers, Employers 8 Students, Lecturers

E1 – E23 Curriculum focus 23 Ordinal:

Not important, slightly important, quite important, highly important

Students, Lecturers

9 Students, Lecturers

F1 – F23 Teaching focus 23 Ordinal:

Not at all, sometimes, often, always Students, Lecturers 10 Students (Only G1 Semesters studying in

1 Choice of 1 – 6 semesters Only

161 International Chinese students studying and surveyed in Australia) Australia Chinese students studying and surveyed in Australia 10 Employers H1 – H14 Employed graduates skills and attributes 14 Ordinal:

Not important, slightly important, quite important, highly important

Employers

Integrity of data was tested through reliability and validity measures as presented in 6.7. Hypothesis testing was performed to explain any variance in the dependent variables. The results are presented in Chapters Seven and Eight (for the education and business perspectives respectively). Table 28 summarises the measured variables and score ranges that comprise the data file which consists of the scores each graduate, lecturer and employer gave to each item within each variable. The background and demographic responses to Section A were reviewed and collated to establish commonalities within the respondent groups.

Determining the six most frequently selected personal characteristics was a matter of combining all responses and totalling the frequency for each characteristic. Six characteristics were required as an early research design incorporated conjoint analysis - effective when there are four to six

characteristics - to verify the critical characteristics. With lack of access to employers to perform conjoint analysis, a visual scree approach was employed to determine the most important factors (Nasser, Benson & Wisenbaker 2002). Those characteristics that clustered at the top of the frequency table were considered the most important and those characteristics that clustered at the bottom the less important. There was no arbitrary cut off point and no predetermined number of characteristics sought; hence the differing numbers of characteristics in Tables 31, 32 and 45. Both ends of the spectrum were analysed to avoid understatement of factors (Mudrack 2005, p. 819) and to allow the researcher to identify any further consistencies. It should be noted that the reliability of this, as with all approaches that try to generalise or determine patterns, is low when the sample size is low.

For the sets of questions with multiple items rated on a Likert scale, it was possible to assign values of 1 to 4 respectively to the importance rating; the same assigning of values was made to the set of questions which had a frequency rating. The mean for each item within a question set could then be commented on as well as the pattern of distribution. Cross tabulations were carried out and the Mann- Whitney test of significance applied.

The study used the statistical software, SPSS Version 15.0, for analysis of each section of the questionnaire. The frequencies showed the highest and lowest scores for each factor and from them, where applicable, Mann-Whitney t-tests were used to determine difference between samples. This is a non-parametric test for two independent groups on ordinal dependent measures. The quantitative

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research results are presented in tables and discussed in Chapters Seven, Eight and Nine. Data from each group of lecturers and each group of students was analysed separately. As there were few significant differences between the sub-groups, the combined lecturer and graduating student data were compared statistically, where appropriate. The employer dataset consisted of too few

respondents from the various business categories to look internally for statistical differences for the responses to the personal characteristics, attributes and workplace skills. Obtaining a dataset large enough to analyse the data according to management level, experience, size of organisation or business description is the focus for a future study.