Multiple regression

of variables is able to predict a particular outcome” (Pallant, 2016, p. 149). The researcher planned to adopt path analysis to examine causal relationship between variables: (1) ECTs’ intentions to engage with inclusive practice; (2) ECTs’ future career intentions; (3) attitudes towards inclusive practice, subjective norms; (4) perceived behavioural control; (5) past experience with educating students with SENs in a regular classroom; (6) a unit of study with regard to special and inclusive education undertaken at a pre- service level; (7) school support; and (8) number of years of teaching experience. Path analysis refers to a “statistical method used to examine hypothesized relationships between two or more variables” (Lleras, 2005, p. 25). Within the path analysis, it is possible to examine both indirect and direct relationships among variables within a “hypothesized model” (Lleras, 2005, p. 29). The current study was based on a Theory of Planned Behaviour as a theoretical framework. Thus, it was expected to examine the

relationships among variables within a Theory of Planned Behaviour model using a path analysis. However, a path analysis requires a minimum sample size of 100 to 150 to obtain generalisability (Wang & Wang, 2012). The number of responses in the current study was 79. Thus, it was judged inappropriate to adopt a path analysis as an analysis method for the current study.

For the generalisability purpose, the sample size of multiple regression needs to be drawn based on a formula: N > 50 +8m where ‘m’ refers to the number of independent variables (Pallant, 2016). When examining to what extent attitudes towards inclusion, subjective norms, and perceived behavioural control influence ECTs’ intention to engage with inclusive practice, there were three independent variables (i.e., attitudes towards inclusion, subjective norms, and perceived behavioural control). Thus, the required minimum sample size was 74 based on the formula above.

As mentioned in Chapter Three, multiple regression analysis is used when more than two independent linear variables predict one dependent variable (Field, 2013). MacFarlane and Woolfson (2013) adopted multiple regression analysis to examine the impact of attitudes towards inclusion, subjective norms and self-efficacy on 111 primary school teachers’ intentions to engage with inclusive practice. Multiple regression analysis was adopted because of two main reasons: (1) five independent variables were employed in their study and (2) the sample size of 111 was above the required minimum sample size when there were five independent variables. Kuyini and Desai (2007) also applied multiple regression analysis to examine to what extent three independent variables - attitudes towards inclusion, perceived behavioural control, and subjective norms – had an effect on teachers’ engagement with inclusive practice (n = 128). This is because more than one independent variable was implemented to examine the impact of independent variables on teachers’ engagement with inclusive practice.

Standard multiple regression was used to examine to what extent: (1) the attitudes towards inclusion, subjective norms, and perceived behavioural control predict ECTs’ intention to engage with inclusive practice; and (2) past experience with educating students with SENs, school support, and perceived behavioural control explained ECTs’ intention to stay in the teaching profession. The data was split based on the theoretical model in the current study. Standard multiple regression is used if the

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researcher wants to know “how much variance in a dependent variable they were able to explain as a group or block” and “how much unique variance in the dependent variable each of the independent variables explained” (Pallant, 2016, p. 150).

When more than one independent variable is included in a model, the strength of correlation between independent variables needs to be considered. If there is strong correlation between independent variables, multicollinearity occurs. Multicollinearity refers to “the relationship among the independent variables” (Pallant, 2016, p. 152). If multicollinearity between independent variables occurs, the regression coefficient is impossible to be estimated accurately. There are three ways of identifying the correlation between independent variables: examining the correlation coefficient, tolerance, or variance inflation factor (VIF). If the correlation coefficient is above 0.9 or 0.8, tolerance is 0.1 and VIF is above 10, then there is multicollinearity between independent variables. If VIF is around 1, there is no

multicollinearity between independent variables (Field, 2013). As shown in Table 4.3 and Table 4.4, VIF of each independent variable was around 1. Thus, it was demonstrated that the independent variables were not correlated.

4.2.2.1 Intention to engage with inclusive practice. The results showed that attitudes towards inclusion, subjective norms and perceived behavioural control explained 20% of ECTs’ intention to engage with inclusive practice (R2 _{= .20, F(3, 75) = 6.13, p < .01). As shown in Table 4.3, VIFs among}

independent variables were from 1.036 to 1.249. It indicates that there was no correlation among independent variables.

Among attitudes towards inclusion, subjective norms and perceived behavioural control, attitudes towards inclusion and perceived behavioural control were found to be significant predictors of ECTs’ intention to engage with inclusive practice. Attitudes towards inclusion made the strongest contribution to explaining ECTs’ intention to engage with inclusive practice (β = .281, p = .016). Perceived behavioural control was the second strongest contribution of ECTs’ intention to engage with inclusive practice (β = .246, p = .036). Subjective norms were not a significant predictor of ECTs’ intention to engage with inclusive practice (β = .013, p = .900). It appears that attitudes towards inclusion and perceived behavioural control are the highly related to ECTs’ intention to engage with inclusive practice.

Table 4.3

Results of Multiple Regression on Intention to Engage with Inclusive Practice

Independent Variables B Beta t Sig. VIF

Attitudes towards inclusive practice* .171 .281 2.469 .016 1.214

Subjective norms* .006 .013 .126 .900 1.036

Perceived behavioural control* .186 .246 2.134 .036 1.249 *Dependent variable: Intention to engage with inclusive practice

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4.2.1.2 ECTs’ future career intention. To examine to what extent past experience with educating students with SENs, school support and perceived behavioural control explained ECTs’ intention to stay in the teaching profession, a standard multiple regression was employed. These independent variables were chosen based on the literature review. Past experience, school support and perceived behavioural control explained 16% of ECTs’ intention to stay in the teaching profession (𝑅2 ₌

0.164). Results are shown in Table 4.4.

Table 4.4

Results of Multiple Regression on ECTs’ Future Career Intention

Independent Variables B Beta t Sig. VIF

Past experience* - .265 - .102 - .957 .341 1.015

School support* .248 .300 2.825 .006 1.015

Perceived behavioural control* .394 .225 2.113 .038 1.020 *Dependent variable: Intention to stay in the teaching profession

It was found that both school support with regard to inclusive practice and perceived behavioural control were the significant predictors of ECTs’ intentions to stay in the teaching profession, F(3,75) = 4.89, p < .006. School support was the strongest signifier (β = .30, p < .006) while perceived behavioural control made the second strongest contribution (β = .225, p < .038). The results showed that past

experience of educating student with SENs had a negative contribution to explaining ECTs’ intention to stay in the teaching profession. That is, if an ECT had experience with educating students with SENs, the ECT was less likely to stay in the teaching profession. However, it appeared that past experience with educating students with SENs had no statistically significant correlation with ECTs’ intention to stay (β = - .102, p > .05).