According to Schumacker and Lomax (1996), there are various statistical methods used in the field of statistical analysis, which include relational, descriptive and inferential statistics.
5.3.1 Relational Statistics
In this category of statistics, calculations are grouped into bivariate, univariate and multivariate analysis (Babbie, 2007). Bivariate analysis is studying the interconnection or relationships between two variables. Univariate analysis is the testing of one variable and its characteristics. Multivariate analysis is concerned with the analysis of more than three variables under study (Babbie, 2007). Such techniques can analyse variables that are in single or multiple relationships. As the fourth objective of this research is to examine the relationships and relevancy among the factors influencing the adoption of ERP systems by HEIs based on the TAM model (see Chapter One), it is most appropriate to utilise multivariate analysis techniques. CFA and SEM are the techniques of multivariate analysis that will be applied in this research.
5.3.2 Descriptive Statistics
This type of statistics aids the researcher in producing and presenting the relevant findings of any research process. Much of the focus is directed on acquiring the mean of any presented data, which is one of the main ingredients used in the measure of central tendency. However, it does not ignore other measures of central tendency such as variance and standard deviation. To acquire an average in the score of the feedback from any test, the mean is applied. This is done by adding up all the numbers and then dividing the sum by the number of available results (Forzano, 2008). For instance, the means identify the average frequency distribution number.
160 On the other hand, standard deviation shows the exact measurements of the result either below or above the mark (Fink, 2008). In cases where the results gathered indicate lower numbers than the standard deviation, the results are similar to minor variations. However, large values of difference indicate that the results acquired lack consistency (Forzano, 2008). Descriptive analysis of the sample was applied in this research – such as standard deviation and the mean – in the process of data analysis.
5.3.3 Inferential Statistics
Inferential statistics have been labelled the easiest method to apply when conducting research since the technique largely depends on assumptions. Sampling is recognised as the champion approach to adopt in inferential statistics; this is because researchers are able to carry out minimal experiments with a given population or geographical location and make justifiable assumptions about the population at large by basing the conclusion on the sample result (Diamantopoulos and Schlegelmilch, 2000).
The results derived from a study population are presented empirically as p-values (Forzano, 2008). The p-values are rated in an ascending order, whereby results with higher p-values are said to have a reduced likelihood of correlation between the variables under review (Fink, 2008; McClave et al., 2008). For example, a p-value of 0.05 picked out randomly shows that the population mean score lies within the numbers indicated in the range with a high likelihood of 95% (McClave et al., 2008).
Inferential statistics are usually utilised in order to examine the differences within groups as well as to test relationships (cause and effect) among a group of variables. According to Cooper and Schindler (1998), inferential statistics encompass different types of tests, such as: Pearson’s correlation coefficient, the chi-square test, the t-test and one-way analysis of variance.
Inferential statistics are categorised as parametric statistics and non-parametric statistics. Parametric statistics refer to testing statistical hypotheses while non-parametric statistics
161 are known as population estimations of values (Steinberg, 2008). Non-parametric statistics (e.g., chi-square tests) are frequently used when there are no systematic orders and rules in the way things operate, such as in a case of examining nominal data. Alternatively, in cases where the data under review has some priority connected with them – such as Likert-scale responses – the t-test or analysis of variances (ANOVA) – that are examples of parametric statistics – could be applied.
This research applied both methods to analyse the statistics. Pearson’s correlation coefficients and chi-square tests were used in the CFA stage to guide the formation of conclusions. Then, the t-test and the ANOVA analysis approach were employed to identify anomalies among the different groups in the sample. In this research, different steps and techniques were applied to analyse the quantitative data – as shown in Figure 5.5.
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Figure 5.5: The Main Steps and Statistical Techniques Used in the Analysis.
The first validity of measures will be performed with the help of CFA on the proposed measurement model. The examination done on the CFA comprised the estimation of the covariance matrix followed by sequenced assessment and practice to show the degree of fit about the covariance matrix. SEM CFA will be applied to examine the validity of the hypothesised measurement model. Both discriminant validity and convergent validity will be evaluated. By testing the research model through SEM, the advocated proposed structural model will be revisited and examined. Finally, the ANOVA technique will be carried out to determine demographic differences that arise over the factors of the study.
Step 1: CFA: Examining Validity of Measure
Step 2: CFA: Finding the Best-fitting Proposed Models
Step 3: SEM: Revising and Examining the Proposed Models
Step 4: SEM: Testing the Hypotheses
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