Analysis is about the search for explanation and understanding of the collected data. The collected data in this thesis are quantitative, which can be analysed descriptively and by multivariate analysis techniques (Blaxter et al., 2010). Descriptive methods of analysis were used to explore the demographic variables and to understand the characteristics of the acquired data and sample. In addition, various statistical analyses were used (see Chapter 4, Section 4.2). These include factor analysis, fitness of the measurement model, and validity of latent and correlation.
5.11. 1 Descriptive Analysis
Descriptive analysis is mainly used to describe the phenomena of interest (Sekaran & Bougie, 2010). In such analysis, descriptive information is analysed statistically in terms of how frequently certain phenomena occur (frequency), the average score or central tendency (mean), and the extent of variability (standard deviation). In this study, descriptive analysis was conducted in all sections of the research instrument. Other statistical analyses were also conducted in the current study. These are described
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5.11. 2 Correlation Analysis
Understanding the relations between variables is usually is interesting subject for empirical researchers, managers, and also decision makers. Sekaran (2003) proposes that to measure the relationship between two variables, researchers need to compute the correlation coefficients of observed variables. Therefore, this study employed the Pearson Correlation method to determine the numeric linear relationship among variables and sub-variables of the study (see Figure 1.2). In this respect, Spearman’s correlation is not suitable for measurement of the relationship between different variables, as it is applied to the measurement of non-parametric ranges and monotonic relationships (Fujita et al., 2009; Hanke and Kossowski, 2011). The magnitude of coefficients shows the strength of a relationship and the value closer to +1 or -1 indicates the strength. The direction of relationship is determined by the sign of correlation coefficients. Results of paired correlation among the latent constructs of the study are presented in Table 4.18 in the next chapter.
Correlation analysis is primarily designed for measuring the association between two variables. In other words, correlation analysis measures how a variable relates to another variable (Hair et al., 2007; Sekaran & Bougie, 2010). Correlation analyses in this study consist of Pearson's correlation and canonical correlation.
Pearson’s correlation is used to assess the linear association between variables of a continuous data, and as participants’ responses are actually the averages of Likert scores across a set of statements (detailed in the case study in chapter 7 and in chapter 8) and are not the raw Likert scores, Pearson’s correlation was used. In contrast to this, canonical correlation was used in the above reported reliability analysis as the analysis was conducted directly on the raw ordinal data (Murray, 2011 & Norman, 2010).
The number representing the Pearson correlation is referred to as a correlation coefficient. By using Pearson's correlation analysis, the researcher was able to understand the nature, direction, and significance of the bivariate relationship of the variables used in the study (Sekaran & Bougie, 2010). In addition, canonical correlation was also employed to examine the relationship between two sets of variables (Hair et al., 2010). In this study, canonical correlation analysis was employed to predict the relationships between the set of TQM elements and the set of KM elements, between
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the set of TQM elements and the set of EP perspectives, and between the set of KM elements and the set of EP perspectives
5.11. 3 Multiple Regression Analysis
There is always a deficiency in the result of the correlation coefficient as it only gives the degree of relationships between the variables under test without necessarily giving an idea of how much the variance in the dependent variables or criterion variable will be explained when several independent variables are theorized to simultaneously influence it (Sekaran & Bougie, 2010). The correlation may exist not only in the relationship between independent variable s and dependent variables but also among themselves or inter-correlations. Thus, multiple regression analysis was used to measure the concurrent effects of several independent variables on a dependent variable (Cavana et al., 2001; Sekaran & Bougie, 2010).
Adjusted R2 is the statistic that can be used to measure how well the dependent variables can be predicted by the independent variables. Sample size has a direct impact on the statistical power of multiple regression. It is suggested that the minimum ratio is (5 to 1), meaning that there must be five observations for each independent variable (Hair, et al., 2010). Four assumptions that must be met under regression analysis are linearity, heteroscedasticity, normality and no serious multicollinearity problem (Coakes & Steed, 2007; Hair et al., 2010).
In the present study, regression analysis was applied to measure the significance of the relationship between TQM elements and KM elements, between TQM elements and EP perspectives, and between KM elements and EP perspectives. This analysis also provided information regarding the linear relationship between TQM elements with both KM elements and EP, and the linear relationship between KM elements with EP perspectives.
To investigate the linear relationship between TQM core elements with KM elements and EP, separate regression models were developed for each dependent variable so that two general models were posited. The first model was aimed to measure the linear relationship between TQM elements and KM elements, while the second model was developed to find out the linear relationship between TQM elements and EP perspectives.
5.11. 4Structural Equation Modeling
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social, and educational scientists (Raykov & Marcoulides, 2006; Byrne, 2010). SPSS is also a family of statistical models and multivariate techniques, with mixing characteristics of factor analysis and multiple regressions that enables the researcher to test simultaneously a series of interrelated dependence relationships among the measured variables and latent constructs (Hair et al., 2010). In this study, the Statistical Package for the Social Sciences SPSS was applied for assessing the role of the relationship between KM and TQM.
Many researchers and statisticians (e.g., Bollen, 1989; Hair et al., 2010; Iacobucci, Saldanha, & Deng, 2007; James, Mulaik, & Brett, 2006; Kline, 2011) have revealed that SPSS performed better than regression while assessing the mediating role of a research variable. Hence, suggesting that SPSS was a superior statistical technique over the regression. According to Hair et al. (2010), the standard errors in the SPSS model are minimized due to the simultaneous estimation of all parameters in the SPSS model.
5. 12 Summary
This chapter focused on the methodology that was adopted in the current research. It highlighted the main issues related to the philosophical assumption of the research design, sample population, and setting up the quantitative approach for collecting the necessary data. The next three chapters will deal with the statistical analyses of the quantitative data that were obtained by the questionnaire.
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CHAPTER SIX
DATA ANALYSES AND RESPONDENT CHARACTERISTICS
6. 1 Introduction
The previous chapter focused on the concept of methodology that was adopted in the current research. The outcomes of the previous chapter are discussed and analysed here and in the next chapters. The current chapter deals with analysing the characteristics of the academic staff or demographic data of the responses to the online questionnaire. Generally, the main characteristics of respondents were included in the questionnaire. These included gender, age group, degree, academic ranking, time worked in the field, current position, and university affiliation.
The Statistical Package for the Social Sciences (SPSS) was used to analyse data that were obtained from the questionnaire. The total number of respondents was 351 or 70.2%. These respondents belonged to two groups: academic staff in Jordanian public universities (200, 66.7% out of 300 academic staff) and academic staff in Jordanian private universities (151, 75.5% out of 260).
The demographic data that were obtained from the questionnaire were analysed statistically by SPSS. These included mainly descriptive statistics, which summarise and describe data related to the demography of respondents, the mean, and standard deviation. This chapter includes the characteristics of respondents.