The importance of gender has been pointed out in sections 1.5 and 3.2.2.9. The research model will be examined from two prospective, the measurement and structural. In the measurement model, the research model will examined for the differences between genders in term of the measured variables. In the structural model, the research model will be examined for the differences between genders in term of the hypotheses. The multi group analysis in AMOS categorise the data based on the grouping value (i.e. gender), and the group analyse will be performed simultaneously between genders (Byrne, 2010).
Moreover, the difference in chi-square ∆ will be used to examine if there significant different between genders on the measurement and structural models level. Chi-square is “statistical
measure of difference used to compare and estimated covariance matrices” (Hair et al., 2010).
The chi-square will be calculated for the measurement model via the confirmatory factor analysis, and for the structural model via the structural equation modelling. The difference in chi- square ∆ can be computed by calculating the chi-square for the targeted model twice; first without weight constrains and second with weight constrains (Byrne, 2010). If the difference in chi-square ∆ is significant then the model is not equivalent over genders.
The measurement model test: The chi-square for the measurement model was calculated before
and after applying the weight constrains to the measured variables. The results showed that there is no significant different (chi-square ∆ 24.378 and ∆df 19, which means that the perception of males and females towards the measured variables is the same (see Table 5-14). The difference in chi-square ∆ result significance can be decided using Chi-Square Distribution Table which is commonly used in statistics.
Table 5-14: The chi-square ∆ for the measurement model
Measurement Model df (degree of freedom)
Unconstrained Model 1527.750 664
Constrained Model 1552.128 683
The difference in chi-square ∆ 24.378 19
The structural model test: The chi-square for the structural model was calculated before and
after applying the weight constrains to the research hypotheses. The results showed that there is a significant difference between males and females in between the research hypotheses (see Table 5-15). After proving that there is a significant difference between males and females towards the research hypotheses; the next step will be to identify the hypotheses that are causing these
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differences. The identification of these hypotheses will be by repeating the weight constrains method on each hypothesis individually and calculate the difference in chi-square ∆2 again. There research model contains of 18 hypotheses thus, the difference in chi-square ∆2 was calculated 18 times for each hypothesis.
The analysis showed that there are five hypotheses that are significant different between genders (see Table 5-16). Among the variables that were hypothesised to affect perceived ease of use, learning goal orientation was the only variable that males and females had different perception toward it. Where hypothesis H7a (learning goal orientation) was accepted for males and rejected for females. Moreover, the hypotheses for information quality (H1a) and functionality (H2a) toward perceived usefulness were significantly different between genders. Finally, the differences between genders toward behavioural intention were between two variables computer playfulness (H5b) and enjoyment (H6b). Furthermore, the structural model test showed that the explained variance (i.e. the variance in the dependent variable that was accounted for by the independent variables) towards the dependent variables, perceived usefulness, perceived ease of use and behavioural intention, was different between the two genders (see Table 5-17).
Table 5-15: The chi-square ∆ for the structural model
Structural Model df (degree of freedom) Unconstrained Model 1619.886 676
Constrained Model 1662.095 694 The difference in chi-square ∆ 42.209 18
Table 5-16: The significantly different hypotheses over genders Hypothesis Paths Male Female Standardised coefficient Probability value 0.05 Standardised coefficient Probability value 0.05 H7a LGO → PEOU 0.137 *** 0.067 0.203
H1a IQ → PU 0.366 *** 0.120 0.181
H2a FL → PU 0.100 0.141 0.602 ***
H5b CP → BI 0.208 *** 0.412 0.145
H6b E → BI 0.112 0.134 0.702 **
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Table 5-17: The explained variance for the dependent variables between genders
Gender Perceived Usefulness Perceived Ease of Use Behavioural Intention
Male 61% 52% 63%
Female 66% 48% 47%
5.5 Summary
This chapter presented the data analysis for the developed model and research hypotheses. This chapter can be divided into four main sections based on the statistical analysis, data screening, confirmatory factor analysis, multiple regression analysis and multiple group analysis. In data screening the collected data was examined for missing data, normality, linearity, outliers and multicollinearty. In confirmatory factor analysis the developed model was examined for unidimensionality, goodness of fit and constructs validity. First, unidimensionality to ensure the measured variables are loading into one underlying variable. All of the measured variables have achieved the unidimensionality condition by having factor loading above (0.50), except for one measured variable (LGO4). Second, the developed model goodness of fit was measured using five measures, GFI, AGFI, RMSEA, SRMR, CFI and TLI. The developed model has passed the goodness of fit measure among the five measures. Moreover, the model goodness of fit was further improved by removing five measured variables that have not achieved the squared multiple correlations and factor loadings conditions.
Third, the multiple regression analysis was used to examine the developed model and research hypothesis. The results confirmed the model ability to explain better variance among the dependent variables. Moreover, the research hypothesis supported the importance of six variables to predict students' acceptance of LMSs. Finally, the multiple group analysis examined the gender differences effect on the developed model. The results showed that both genders have different perception towards the model variables. The next chapter will discuss the data results and findings in details. The chapter will discuss the results in term of the model variables. Moreover, the variable will be also discussed based on their dimension, intrinsic or extrinsic. Furthermore, the chapter will evaluate the developed model performance by comparing its results with the existing models results. Finally, the chapter will discuss the gender differences effect towards the developed model.
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Chapter Six: Discussion
This chapter will discuss the data analysis results in term of the research model variables, hypotheses, and performance. The significance of each variable will be explained individually and as a group depending whether they belong to the extrinsic or intrinsic group. The research model performance will be measured by the explain variance in the dependent variables which are perceived usefulness, perceived ease of use, and behavioural intention. The research model performance results will be compared against similar models used in the e-learning context. Finally, the moderating effect of gender will be discussed by pointing out the differences in males and females perception.