**Chapter 3. Methodology**

**3.7. Data Analysis**

The Statistical Program for Social Science (SPSS) version 22 and QSR NVivo 11 software were used to facilitate the quantitative and qualitative data analysis of this research and the descriptive and inferential statistics were employed to report the research outcomes. The quantitative data that comprises the pre-test, post-test, survey’s questionnaire, self-assessment and peer-assessment were analysed using the SPSS and the qualitative data involved during the interviews were analysed using the Nvivo software. The researcher started the data analysis by performing

‘data entry’ into SPSS and by computing univariate descriptive statistics for the demographic variables. Means and standard deviations were computed for all variables with interval level of measurement. Percentages were calculated for the categorical and ordinal variables. The presentation of data analyses followed the order of the Research Questions one to six, also involving qualitative data.

**3.7.1. QUANTITATIVE DATA ANALYSIS **

The quantitative data analysis was started by the researcher by conducting normality tests (Shapiro-Wilk) on the pre-test, post-test, programming test one and programming test two. The tests were carried out to determine whether the data of students’ pre-test, post-test, programming test one, and programming test (semester three & four) were equally normally distributed within groups. If the data was normally distributed, the SPSS’s parametric tests were used, and if the data was not

normally distributed, the SPSS’s non-parametric tests were used. According to Shapiro & Wilk (1965) examining for distributional assumptions in general and for normality, in particular, has been a major area of continuing statistical both theoretically and practically. Razali & Wah (2011) concludes that the Shapiro-Wilk test is amongst the most powerful test for all forms of distribution and sample sizes.

The next stage of the quantitative data analysis addressed Sub Research Question one, two and three, to examine the level of awareness, motivation, perception and the challenges/obstacles of students on Problem-based Learning. The Sub Research Question one, two and three were addressed by conducting descriptive statistical analysis and Independent Samples t-test on the questionnaire items 1 to 25 of students of semester three and four, which calculates the percentage, frequency, mean and standard deviation.

The quantitative data analysis proceeded to address the sub research question four.

The sub research question four investigated whether the students’ prior academic performance has an effect on the learning in the PBL approach of students in semester three and four. The researcher hypothesized (H1) that students of semester three and four with above average CGPA scores have higher scores in both the pre-test and the post-pre-test than those with below-average CGPA scores. The hypothesis was made based on researcher’s observations and experiences of more than 20 years as a lecturer. The analysis first divided the students into two groups with Cumulative Grade Point above Average (CGPA) and CGPA below average. The independent-samples t-test performed to determine the significance between the students’ pre-test and post-test scores of students’ semester three & four and their last semester of CGPA.

The researcher hypothesized (H2) that students of semester four have higher scores on the pre-test than students of semester three. The assumption was made because students of semester four used to attend CNC Milling programming course during their semester three. So, the students of semester four have some prior knowledge of Computer Numerical Control (CNC) that could help them answer the pre-test questions compared to the students of semester three who attend the course for the first time. The independent-samples t-test executed to look at the difference in mean of the pre-test between students of semester three and students of semester four.

The above was followed by quantitative data analysis to test the researcher’s hypothesis (H3) that hypothesized no difference in post-test scores between students of semester three who attending the CNC programming milling and students of semester four who attending the CNC programming lathe. For this hypothesis, the researcher’s opinion was that there should be no difference in post-test scores because both groups have an equal chance of learning the CNC programming.

The quantitative data analysis continued to address the sub research question five and examined whether the CNC simulator benefits students in the PBL approach.

The researcher hypothesized (H4) that students of semester three and four with
above average CGPA scores have higher scores on both the programming test one
and the programming test two than those having below average CGPA scores. Like
the hypothesis H1, the same situation applies to the programming test one and
programming test two. The independent-samples *t-test performed to investigate the *
difference in mean of the students’ programming test one and two scores of
students’ semester three and four.

The data analysis referring to hypothesis five (H5) which stated, that students of semester three and four have higher scores on the programming test two than programming test one. The students (Semester three and four) were expected to have higher scores on the programming test two because they had a CNC simulator to help them in programming, and they could verify their programming work. Whereas programming test one was ‘an ad hoc’ written (on a piece of paper) programming test where they could not verify their programming work. In this analysis, the paired-samples t-test was used to assess whether students of semester three and four have higher scores on the programming test two than programming test one.

The researcher also looked at a connection between scores on programming test one and programming test two of students semester three and four (H6). The researcher hypothesized that there would be a relation between scores on programming test one and programming test two between the students in both semesters three and four.

The rationale was that the students who performed in programming test one should also be able to perform in programming test two and students who were not able to perform in programming test one should have a problem to perform in programming test two even though they had the assistance of a CNC simulator. The Pearson correlation analysis was carried out to examine the relationship between the scores of the students’ programming test one and programming test two (semester three and four).

The last data analysis of this study was to address and further the main research question by focusing on the ways PBL affect the students’ competencies such as learning, technical and social. The quantitative part of data analysis were the tests results from the pre-test, post-test, programming test one and programming test two.

These tests results were analysed to look at the effect of the PBL approach on students’ learning and technical competencies. The descriptive statistical analysis on the self-assessment and peer-assessment instruments were performed in order to have the insight of students’ competencies especially the social competency.

**3.7.2. QUALITATIVE DATA ANALYSIS **

The qualitative data analysis of this study employed an inductive analysis with mixed methods approach. The qualitative data can come from various sources such as documents, video recorders, newspapers, letter, and books (Corbin & Strauss, 2008) and each of these sources can be coded in the same way as interviews or observations (Glaser & Strauss, 1967) and is based on a set of data that systematically related (Corbin & Strauss, 2008). This study follows the three stages of coding introduced by Corbin & Strauss (2008) for data analysis. Corbin & Strauss (2008) classify three different stages of coding namely ‘open’, ‘axial’ and

‘selective’.

The first stage: ‘open coding’ involves the breaking down, comparing, and categorizing the data.

The second stage: ‘axial coding’ involves the placing of the data back together by making connections between the categories identified after the

‘open coding’ process.

The third stage: ‘selective coding’ involves the selection of the main category, linking it to other categories while confirming and explaining these relationships.

According to Thomas (2006), the inductive analysis refers to an approach that

“primarily use the detailed reading of raw data to derive concepts, themes, or a model through interpretations made from the raw data by the evaluator or researcher”. He added that the main purpose of the inductive approach was to allow research findings to appear from the frequent, dominant, or significant themes inherent in raw data. Furthermore, according to Bogden and Biklen (2003), the qualitative data analysis involves systematic searching and arranging of data collected by various means in a study. Creswell (2007), suggests that during the analysis process, the data of interviews and observations should be brought together in order to transform into a meaningful description or into a form of summary, and the summarised data can be saved and organised in a computer and backed up for further analysis as well as descriptive writing. The Nvivo software application can be used to cluster and categorized the data by means of data coding and count codes (Creswell, 2007; Bazeley, 2007), and to see the frequency of similar codes appear in the database, according to Miles and Huberman (1994). The similar codes are considered as saturated if they were frequently emerged in the database (Corbin and Strauss, 2008) and considered as triangulated if the codes appeared from many different sources data (Creswell, 2007).

The qualitative data of this study comprise the data from the group interviews, participant observations (in the classroom and CNC simulation lab) and content analysis. The researcher processed and analysed the qualitative data with the help of QSR NVivo 11 software and Microsoft Excel 2013. The advantages of using NVivo 11 for qualitative data analysis are that it helps the researcher managing chunks of

data appropriately and facilitating the process of analysis. The NVivo 11 has the abilities such as coding generation using auto coding or queries, search themes in the data, link, annotate, create relationships and able to import and process audio video files. The NVivo 11 also has the ability that allows researcher listening, coding, and simultaneously transcribing the audio file. The researcher analysed the data of the group interviews involving ten groups of students. Each group of students was coded to protect their identity and to avoid bias.

Chapter four reports all the findings of this study and presented according to the Research Questions One to Five followed by the Main Research Question.