Testing the Difference Between Respondent Demographic GroupsAs mentioned in Chapter 4: Table 4-3, there was a very large difference between the number of respondents within some demographic groups, for example gender groups included 185 males and 25 females and similarly, academic background groups included 3 high school graduates, 185 graduates and 85 postgraduates. Therefore, generalizing the results of testing the difference between the means of these groups may turn out to be invalid and is meaningless. However, it could give general indicators on the opinion of each group regarding the achievement of SECI and the innovation processes within Egyptian banks. In this term, there are two methods to test the difference in means between groups depending on the measurement scale of the variables tested. and the normality distribution of the data. Parametric test (e.g. t-test and one-way ANOVA) is suitable for ratio or interval scale and normally distributed data. Contractually, nonparametric tests (e.g. the Mann-Whitny and Kruskal-Wallis tests) are suitable for nominal and ordinal scales and for data that are normally distributed or not (Field, 2009; Kleinbaum, et al. 2008). ). As mentioned previously, the normality distribution of the research data was studied by skewness and kurtosis tests (see Table 5-1) and the study used the factor scores (which are not ordinal scales or nominal) produced by the factor analysis for the following. statistical analysis. Therefore, parametric tests could be suitable techniques to test the difference between demographic groups of respondents regarding SECI and innovation activities in Egyptian banks. In this term, there are two parametric statistical techniques...... middle of paper ...... male and female staff in terms of performing these processes in Egyptian banks. In contract, the Levene test on the outsourcing and innovation processes shows that the F ratios were significant (p < .01 and p