Data Analysis

This study was conducted using quantitative research methodologies. The study instruments include student questionnaires with closed-ended questions. The closed-ended questions were analysed using the Statistical Package for the Social Sciences (SPSS). The SPSS programme is known to be one of the most reliable statistical programme available for obtaining accurate answers, as many researchers have indicated (e.g. Pallant, 2005).

Questionnaires’ analysis went through different stages, which started with creating a data file, then defining the variables. This was followed by entering data into the system, modifying the data, enhancing the quality of data by cleaning up erroneous data, and then selecting the appropriate statistical tests, which were thought to be appropriate to answer the research questions.

After data screening, Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA) and Structure Equation Modeling (SEM) procedures were performed for three different tests related to pedagogy. Details of each test and data analysis are included chapters 5 through 7. Below are generic steps and justifications for using these procedures.

Exploratory Factor Analysis: Exploratory factor analysis helps us screen out the problematic items of the questionnaire. The measures to check the EFA are:

53

 First the value of Kaiser Meyer Olkin Measure (KMO) of sampling adequacy and Bartlett’s Test for Sphericity.

 The second measure is Communalities, which are initially at 1 for every factor in consideration. If the extracted value of the communality for a certain variable is high (i.e. communality value is closer to 1), this implies that the extracted factors account for a large proportion of the variable’s variance.

 The third measure is the cumulative variance explained, this means that the current extracted factors are explaining how much variance in the data. The closer the value is to 100%, the better the variance explained by the model.

 The fourth measure is the rotated component matrix/pattern matrix. This measure checks the loading and correlation of the items with each other.

Structure Equation Modeling: Structure equation modeling modelling (SEM) is a very popular method in the information sciences and it is used to confirm the theorised concepts. SEM is also known as the path analysis with latent variables, covariance analysis (Gefen, Straub, & Boudreau, 2000). SEM is defined “as a multivariate technique, which combines features of multiple regression and factor analysis in order to estimate a multiple of networking relationships simultaneously” (Hair, Black, Babin, & Anderson, 2010). SEM also checks whether data fits according to the hypothesis model.

The current study uses the SEM for the following reasons:

 SEM is very important to confirm the constructs of the model (CFA), which helps the researcher determine the construct validity and readability for both variable and item levels.

 Confirmatory factor analysis should be performed on the constructs extracted through exploratory factor analysis, otherwise, it cannot be used in the further analysis.

 The relation of independent and dependent variable is quite reliable in SEM technique.

3.10 Summary

The details of the research methodology adopted for this research is described in this chapter. The chapter began with an overview of what has been achieved in past chapters, and how this chapter adds to the sequence of steps required to achieve the objectives of this research.

After this, the purpose of the research was discussed, i.e. to describe the explanatory research investigating the impact of pedagogy on the effectiveness of e-learning. This was followed by

54

a discussion of the research philosophy. Four different kinds of philosophical standpoints were discussed along with the key differences and their applicability.

Although different aspects of pedagogy in e-learning have been widely discussed, there has been no research that empirically tests the relationship of different aspects of pedagogy and its effect on the perception of quality, leading to the effectiveness of e-learning. Technology is dynamic and hence underlying problems must be understood independent of technological barriers. Consequently, pragmatic philosophical standpoint is used as it allows the use of multiple methods, which are useful in order to investigate the problem from diverse perspectives. Current lack of research into pedagogy and effectiveness of e-learning requires a pluralist approach as supported by a pragmatic philosophy.

Following this, the choice of quantitative strategy is discussed. In the past e-learning, researchers have focused mainly on quantitative methodologies, due to its benefits such as generalisability, validity, reliability etc.

The data collection tools used for this research was then described. The reason for selection of questionnaire surveys is justified and details of their application in the data collection process are provided along with their limitations and benefits. The sampling strategy adopted is discussed, and the data analysis approach is discussed.

55

Chapter 4

Development of a Validation Model

4.1 Chapter Introduction

After identifying the most critical issues, i.e., Pedagogical issues, which are hindering the success of e-learning, we needed to find a theoretical model which we could use to base our experiments on. For this purpose, we looked at different technology acceptance models and system quality models. Since e-Learning is a phenomenon that uses computer and internet technology at its core, we looked at the technology acceptance and information system success models. Similarly, education is a service, therefore, we looked at service quality models as well. All these models are suitable for testing a particular dimension but none of these models could test all three dimensions, i.e. ‘service’, ‘information’ and ‘system’ quality. We needed a model or a system which could test the quality of all three aforementioned dimensions at the same time. Therefore, we developed a model, e-Learning Quality model (ELQ), which covers all these dimensions. The development of this model is important since this model will provide a structure that will allow as to consistently test the impact of factors on quality perception. Secondly, this model will be a contribution in this field, since no such model currently exists.