Quantitative Data Analysis

The quantitative data for this study were generated from 124 completed survey forms that comprised 60 survey forms from practising science teacher participants and 64 from trainee science teacher participants. The initial step for the quantitative data analysis was to organise and code the responses from both sets of the surveys. To do so, a system of “scoring data” (Creswell, 2012, p. 175) was identified. That means, one common numerical score or value was assigned to each similar response or item on both of the surveys. Apart from the slight difference for the background demographic items of the two surveys, the first four Likert scales and the fifth scale were identical. As described in subsection 3.7.3 (the survey instrument), the five-point response scales for the Likert items had preassigned numerical values. That is, 1 = strongly disagree (SD), 2 = disagree (D), 3 = uncertain (UN), 4 = agree (A) and 5 = strongly agree (SA). Other demographic variables were also assigned with codes of different numerical values. For example, the two variables for gender were assigned, 1= male and 2 = female.

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The response data from both sets of the surveys were manually typed and tabulated using Microsoft Excel spreadsheet as a master data set. The response data were first entered as variables for the demographic items and numerical values for the Likert items. As a master data set, the variable and item headings were typed at the top of each columns. Then each participant’s responses were typed across in one row under each appropriate column heading. After the initial entry the variables and values for the items were crossed checked again by the researcher for any items that was not yet typed into the master data set. Then the researcher checked the master data grid in the Microsoft Excel spreadsheet for any missing item as part of cleaning the response data.

The actual assigning of codes and scores for the rest of the variables were done in the master data set. As such, most of the actual responses transferred into the Microsoft Excel spreadsheet were replaced with their codes and scores before the data set was further cleaned. The process of cleaning the data involved the thorough search for missing values (Creswell, 2012). From this point, the researcher manually inspect the values in the master data grid to spot gaps for missing values. Most of the missing values that were found in the Likert items were also missing in the original response in the survey form. As such, the researcher decided to type in the value three (3 = uncertain) in place of the missing values in the Likert items (Rickards, 1998). The data cleaning process for the quantitative data was painstakingly carried out by the researcher.

Later the cleaned master data set in the Microsoft Excel spreadsheet was imported into the Statistical Package for the Social Science (SPSS) software for more statistical and graphical analysis (Pallant, 2013). Initially, the quantitative data were analysed mainly for the frequencies of responses for each of the five scales for the whole sample. This analysis provided information with regards to the number of responses for each item within each of the scales (Creswell, 2012). Then, Cronbach’s Alpha reliabilities were also calculated for the first four scales to identify the internal consistencies of the responses to each scale. This analysis was significant to determine the general trends of responses with respect to the scale themes. Accordingly, higher values of Cronbach’s Alpha reliability indicates that the majority of items were related in construct and concept as well as, it can be inferred that the majority of respondents responded in a similar way for many of the items in one scale (Cohen et al., 2007).

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Basically, Cronbach’s Alpha reliability values greater than 0.90 are very highly reliable while values between 0.80 and 0.90 are highly reliable, values between 0.70 and 0.79 are reliable, and values between 0.60 and 0.69 are marginally reliable and are acceptable for an exploratory study as this present study (Cohen et al., 2007; Tavakol & Dennick, 2011).

Subsequently, Corrected Item-Total Correlation and Cronbach’s Alpha If Item Deleted were also calculated for each item within each of the first four scales. The Corrected Item-Total Correlation analysis was conducted to identify items that were highly or least correlated with other items in each of the first four scales. A value closer to one indicated how close one item is correlated to other items in the same scale while a value close to zero indicated the opposite (Streiner, 2003). The ‘Cronbach’s Alpha If Item Deleted’ indicated how one item would affect the scales Cronbach’s Alpha reliability if it was deleted from the scale. Hence, if an item with low correlation was deleted, the Cronbach’s Alpha reliability for the scale would increase. The opposite would happen if an item with high correlation value was deleted. This was significant to assess the correlation of responses to the items in scale one as well as, to make inferences on how participants might have responded to each individual item. It was determined that Item – Total Correlation from 0.40 to 1 was significant (Cohen et al., 2007). The results for these additional quantitative analysis were tabulated and are attached in Appendix H. Besides, factor analysis for the first four scales as well as mean score for each of the five scales were tabulated and are attached in Appendix I.

According to research design of this study, the quantitative data were collected and analysed separately from the qualitative data. Hence, the quantitative findings for this study are reported in chapter four of this thesis, separate from the qualitative findings.