Reliability Analysis Of Cronbach's Alpha
4.3 Reliability Analysis
According to Saunders et al. (2009), reliability test is an indicator of measure for internal consistency and to ensure the quality of the questionnaire. In order to achieve a more accurate questionnaire, this study used the Cronbach’s Alpha which is the most popular model to determine the correlation among the items and to assess the internal consistency of a questionnaire (or survey) that is made up of multiple Likert-type scales and items.
According to Sekaran and Bougie (2010), the range of Cronbach’s Alpha less than 0.60 is consider poor and the range above 0.60 and 0.70 indicate at the level of acceptable. If the Cronbach’s alpha coefficient is closer to 1.0, the consistency of the items would be higher. As …show more content…
All the Cronbach’s Alpha values for all the items are more than 0.6 therefore all the items are well-established with acceptable level of reliability.
4.4 Multiple Regression Analysis
In order to predict and project the effect of psychological factors (perception, motivation, learning and attitude) towards online purchase intention, a multiple linear regression analysis was employed. A multiple regression was run to predict buying decision from perception, motivation, learning and attitude. The result of multi regression analysis was presented in each of the tables below and detail discussion will be explained.
Table 4.7: Model Summary for Independent Variables toward Online Purchase Intention.
Model Summary
Model R R Square Adjusted R Square Std. Error of the …show more content…
In this case, it showed that R value represent a simple correlation which is 0.560 which indicate acceptable degree of correlation.
According to the table, it indicate that 31.3% variance of purchase intention can be explaining through the variance of perception, motivation, learning and attitude. There are another 68.7% unexplained variance by the independent variables (perception, motivation, learning and attitude) which can be explored for future study.
Table 4.8: ANOVA for Relationship between Independent Variables and Online Purchase Intention.
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 144.022 4 36.006 11.400 .000b Residual 315.825 100 3.158 Total 459.848 104
a. Dependent Variable: online purchase intention
b. Predictors: (Constant), attitude, motivation, learning, perception
The F-ratio in the ANOVA table is a result whether the overall regression model is a good fit for the data. According to the results of ANOVA in Table 4.8, it can interpreted as the research model stated that F(4, 100) = 11.400, is significant at level of less than 0.05 (p=0.000 <0.05). Meanwhile, it showed that there is at least one independent variable is able to predict purchase intention of online shopping. The regression model is a good fit of the data for this
According to Saunders et al. (2009), reliability test is an indicator of measure for internal consistency and to ensure the quality of the questionnaire. In order to achieve a more accurate questionnaire, this study used the Cronbach’s Alpha which is the most popular model to determine the correlation among the items and to assess the internal consistency of a questionnaire (or survey) that is made up of multiple Likert-type scales and items.
According to Sekaran and Bougie (2010), the range of Cronbach’s Alpha less than 0.60 is consider poor and the range above 0.60 and 0.70 indicate at the level of acceptable. If the Cronbach’s alpha coefficient is closer to 1.0, the consistency of the items would be higher. As …show more content…
All the Cronbach’s Alpha values for all the items are more than 0.6 therefore all the items are well-established with acceptable level of reliability.
4.4 Multiple Regression Analysis
In order to predict and project the effect of psychological factors (perception, motivation, learning and attitude) towards online purchase intention, a multiple linear regression analysis was employed. A multiple regression was run to predict buying decision from perception, motivation, learning and attitude. The result of multi regression analysis was presented in each of the tables below and detail discussion will be explained.
Table 4.7: Model Summary for Independent Variables toward Online Purchase Intention.
Model Summary
Model R R Square Adjusted R Square Std. Error of the …show more content…
In this case, it showed that R value represent a simple correlation which is 0.560 which indicate acceptable degree of correlation.
According to the table, it indicate that 31.3% variance of purchase intention can be explaining through the variance of perception, motivation, learning and attitude. There are another 68.7% unexplained variance by the independent variables (perception, motivation, learning and attitude) which can be explored for future study.
Table 4.8: ANOVA for Relationship between Independent Variables and Online Purchase Intention.
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 144.022 4 36.006 11.400 .000b Residual 315.825 100 3.158 Total 459.848 104
a. Dependent Variable: online purchase intention
b. Predictors: (Constant), attitude, motivation, learning, perception
The F-ratio in the ANOVA table is a result whether the overall regression model is a good fit for the data. According to the results of ANOVA in Table 4.8, it can interpreted as the research model stated that F(4, 100) = 11.400, is significant at level of less than 0.05 (p=0.000 <0.05). Meanwhile, it showed that there is at least one independent variable is able to predict purchase intention of online shopping. The regression model is a good fit of the data for this