5.6 Data Analysis
5.6.2 Qualitative Data Analysis
Unlike quantitative data, qualitative data is rarely subjected to statistical testing, and instead, the analysis process mainly involves making sense out of text (the words of the participants) (Robson, 1993; Creswell, 2003; Neuman: 2006).
The approaches commonly used in qualitative data analysis (e.g., coding, memoing, content analysis and grounded theory) are more diverse and less standardised compared to those associated with quantitative data analysis.
As the purpose of the interviews in this study is interpretive, the researcher decided to apply coding as this provides clear and simple procedures to manage, analyse and generate the results from qualitative data (Omar, 2007). Coding can be used as an index and as a basis for storing and retrieving qualitative data (Punch, 1998). Rubin and Rubin (1995) state that it is the process of
grouping interviewees’ responses into categories that bring together similar ideas, concepts or themes.
Coding can be done using software programs such as Nvivo or manually. In this study the researcher decided to use a manual method as the number of interviews and the interview material were manageable. Also, as qualitative data analysis depends mainly on deep understanding of data by the researcher and as software programs do not analyse qualitative data in depth, it was decided that the use of software was inappropriate.
In analysing the interview data, the researcher followed the undermentioned steps suggested by Collis and Hussey (2003), as being necessary for the analysis of qualitiative data:
1. All interviews were transcribed into English after ascertaining the validity of the English translation by a linguistic specialist.The researcher then added her thoughts and reflections in a separate field (column) to help with the tentative analysis.
2. The material collected from interviews was properly referenced to indicate who was involved, the date, and the time of the interview.
3. A code was allocated to each identified theme. This helped in storing, retrieving and reorganising the data in a variety of ways.
4. The codes were reviewed and grouped into smaller categories according to emergent patterns or themes.
5. The findings were summarised and recorded, and defeciences were highlighted and remedied.
6. The researcher used her summaries to construct generalisations with which to confront existing theories and interpret the findings of the quantitative analysis.
Furthermore, in order to give the reader “some of the flavour of the replies” (Oppenheim, 1992:112 quoted in Ghazali, 2004:114), most of the responses are reported in full to support the interpretation of the findings of quantitative analyses as will be seen in Chapter Seven.
5.7
Summary
This chapter has indicated the research methodology employed to achieve the study objectives, having considered the usefulness of both quantitative and qualitative research approaches and methods. The method of measurement used in respect of the levels of compliance with IFRSs disclosure requirements by companies listed on the EGX and ASE has been presented, and the method of examination of the causal relationship between corporate governance test variables
(board independence, board leadership, board size, government ownership, management ownership, private ownership, and public ownership) and the dependent variable (overall level of compliance with mandatory IFRSs disclosure requirements) has also been shown. In this respect, a discussion of the quantitative research method (disclosure index) as the main research tool, and semi-structured interviews (qualiatative research method) to support the interpretation of the findings of the quantitative analysis, was provided.
The chapter identified the research hypotheses and discussed the construction of the unweighted disclosure index used to measure the level of compliance with IFRSs. Having identified the research population and provided details of the eventual sample, the chapter then demonstrated the procedures followed in conducting the empirical analysis, explaining the use of descriptive, univariate and multivariate analyses.
With respect to the qualitative aspect of the study (semi-structured interviews), details of the interview sample, ethical considerations, and the general analytical procedures were presented. The next chapter will report the results of the quantitative data analysis.
CHAPTER SIX
Extent of Compliance and Corporate Governance Structure -
Quantitative Analysis
6.1
Introduction
The main purpose of this chapter is to answer the research questions presented in Chapter One. In respect of the first research question,comprehensive descriptive statistics are obtained which allow for the evaluation of the extent of compliance with IFRSs disclosure requirements by companies listed on the EGX and ASE. To answer the second research question that seeks to identify corporate governance factors significantly associated with compliance levels, the statistical techniques of univariate and multiple regression are used. Initially, the effect of each individual variable on the degree of compliance with IFRSs disclosure requirements is determined (univariate analysis), subsequent to which regression analysis is performed to establish the joint influence of corporate governance structures on the levels of compliance among companies lised on the scrutinised stock exchanges.
Consequently, the remaining part of this chapter is organised as follows. Section 6.2 provides the descriptive analysis of the extent of compliance with IFRSs disclosure requirements by companies listed on the EGX and ASE. Section 6.3 provides the descriptive analysis of test and control variables in the investigated companies. Section 6.4 reports the results of the test for statistical significant differences between the Egyptian and the Jordanian contexts (H1). Section 6.5 presents the results of the univariate analysis investigating the relationship between the overall level of compliance with mandatory IFRSs disclosure requirements and each corporate governance-related variable. Section 6.6 reports the outcome of the multivariate analysis and answers the last research question by analysing and interpreting the statistical output and its theoretical implications. Finally, section 6.7 summarises and concludes the chapter.