Criminales plebeyos

The SPSS software version 20 was used for statistical analysis. The quantitative data were used to answer the research questions 1 and 2. Hence, it is used to test out the proposed SDLR notion and working hypotheses by looking into the correlation between the SDLR and learning styles, and SDLeR and teaching styles. Additionally, the quantitative data were also used to profile the SDLR and SDLeR. In this section the analyses methods are discussed in detail.

4.8.1 The correlation between SDLR and Learning styles, and SDLeR and teaching styles

The data collected from the SDLRSbio and the PLSbio were analysed to determine the correlation between SDLR and learning styles. Meanwhile, the data collected from the SDLeRSbio and the TSS were used to determine the correlation between SDLeR and teaching styles.

Non-Parametric statistics refers to statistics that do not assume that the data or population have any characteristic structure or parameters. It is normally used to identify the correlation between a discrete data and a nominal data (Creswell, 2012). In this research, there were two types of quantitative data collected. Firstly, the readiness data which was continuous data. Secondly, the teaching styles and learning styles data which were discrete data. In order to study the correlation of both types of data it was suggested

113

to use the non-parametric statistics (Balnaves & Caputi, 2001; Creswell, 2012; Muijs, 2012). Hence, Spearman’s rho correlation was used in the study. Table 4.30 shows the types of analysis according to the types of data collected.

The correlation generated from the data was used to test out the proposed SDLR notion and working hypotheses which postulated very minimum variation in the correlation between SDLR and learning styles, and SDLeR and teaching styles.

Table 4.30 Bivariate Statistics

Nominal Ordinal Continuous

Nominal Cross-tabulation + Chi square + Phi Cross-tabulation + Chi square + Phi Two nominal groups: t-test Ordinal Cross-tabulation + Chi square + Phi Cross-tabulation + Chi square + phi or Spearman’s rho Spearman’s rho

Continuous T-test (two groups)

+ Cohen’s d Spearman’s rho Pearson’s r

Sources: Muijs (2012)

4.8.2 The profile of SDLR and SDLeR

The SDLR and SDLeR profiling was done in 3 steps. The first step involved the generation of two continua, one for SDLRS and another for SDLeR. The second step further refined the two continua into four categories of readiness. The third step went in- depth into the readiness of domains within the SDLR and SDLeR. Each will now be discussed in turn. Besides displaying the data into the continua and categories of readiness, the quantitative data were also used for the analysis of the correlation between the variables.

114

4.8.2.1 Step 1: The general continuum

Figure 4.10 below shows an example of locating the individual scores of A, B, or C on a continuum based on the total marks scored from the SDLRSbio and SDLeRSbio respectively. This continuum displays the distribution of readiness according to the scores the individual scored from the readiness scales. For better understanding of the readiness, the continuum was further categorized into 4 categories of readiness. This is discussed in the following section.

Figure 4.10 Profiling of Readiness on the Self-Directed Learning Readiness Continuum

4.8.2.2 Step 2: The categories of readiness

The data were categorized to 4 categories of readiness according to quartiles. This was based on Balnaves and Caputi's (2001) approach for better discussion and interpretation of large amounts of data. Thus, 4 categories have been put forward and verified by the Delphi panel. Hence, each continuum was divided into 4 quartiles based on the total score of the scales. For SDLRSbio, the total score of the scale was 230 and the lowest score of the scale was 46. The quartiles were based on the score in the range of 25% (score range from 46 – 92), 50% (score range from 93 – 138), 75% (score range from 139 – 184), and 100% (score range from 185 – 230). Similarly, the SDLeRSbio was divided into the 4 quartiles. The score range of each quartile is shown in Table 4.31. The frequency was counted for each quartile to determine the distribution of readiness categories for SDLR and SDLeR. The quartiles were verified by the same Delphi panel.

More ready for SDL

Less ready for SDL

Learners/teachers are moving along the readiness continuum of SDL

A

C B

115

Table 4.31 Readiness Categories for SDLR and SDLeR

Readiness

scales Categories of readiness (Score range for SDLRSbio and SDLeRSbio) Low Readiness Average

Readiness Above Average Readiness High Readiness

SDLRSbio 46 – 92 93 – 138 139 – 184 185 – 230

SDLeRSbio 53 – 106 107 – 159 160 – 212 213 – 265

4.8.2.3 Step 3: The readiness of domains

After the general categorisation was determined for SDLR among students and SDLeR among teachers, the next level of analysis was carried out. For the SDLR there were six (6) domains. Data were analysed for each domain to portray the distribution of domains within each of the categories of readiness. The analysis involved the calculation of the median of each domain in the SDLR. The median was used as it reflected the middle category of the distribution (Muijs, 2012) which represents the respondents choices more accurately for each domain of readiness compared to the mean.

The SDLeR had seven (7) domains. The same analysis approach was used to portray the distribution of the SDLeR scores within each of the four categories of readiness. Figure 4.11 shows a sample layout of the level of readiness of domains. A sample distribution of the readiness of domains is shown in Figure 4.12.

Figure 4.11 Readiness of Domains Distribution Layout

0 1 2 3 4 5 C LS E LabS ES DAS IS _{Readiness of Domains} General Skills Readiness C = Cognition

LS = Learning Skills E = Emotional

Specific Biology Skills Readiness LabS = Laboratory Skills

ES = Experimental Skills DAS = Data Analysis Skills IS = Interacting skills

116

Figure 4.12 Sample of Readiness of Domains Distribution

Note: Median values are plotted