Ensuring Credibility, Reliability and Validity

Issues of credibility, reliability and validity must be adequately addressed in qualitative research because of the nature of the data, which is fluid and emergent with no linear and fixed lines between data collection and data analysis. Examples of qualitative data or data sources include written texts, transcripts, images, audio and video files. The process of data collection and analysis can occur concurrently as was the case in this study involving the use of GT method. As such, issues of credibility, reliability, conformability and transferability need to be adequately addressed in the research process.

In the case of this study, the use of purposive sampling, snowball sampling, stratified sampling, and triangulation (of sources of data) provided multiple sources of evidence that ensured credibility, as suggested by Yin (1989) and Creswell (1998). The use of three case study sites and the selection of interview participants (clan leaders) from all four forest blocks of the three timber projects complement each other, providing triangulation of data, and can be viewed as promoting validity.

The safekeeping of interview memos, digital recording, and their corresponding written scripts for all participants from the three case studies, together with the adequate descriptions of the research method, ensures dependability. The ability of the researcher, in particular his skills, competence and rigour in the designing and conducting field studies without any problems, contributes to the credibility of the study. The findings from this study will help in better understanding issues affecting forestry-based LOCs and how best they can be addressed. That encapsulates the importance and usefulness of this study. The issues of transferability in

qualitative studies is not so much of an issues as the findings can only be generalised to the theory and cannot be extended to another situation, setting or population.

This chapter ends with the chapter summary.

3.10 Chapter Summary

The primary purpose of this chapter is to describe the study approach for Phase 1 of the study. That has been done in great detail (above) and a summary is contained in this section. The research method for Phase 1 of the study was largely qualitative in nature and it was guided by the constructivist-interpretive paradigm. According to Easter-Smith (1991) and Schwandt (1994) (1994), the constructivist-interpretive paradigm is based on the assumption that knowledge is created in the interaction between the researcher and the respondents, and the interaction helps explain reality from the point of view of the people who live in it.

The methodological approach aligns well with the ontological and epistemological assumptions. The ontology of the study is the complex systems involving landowner related businesses that is linked with the development of the forest resources in PNG, while the epistemology is concerned with how the landowners and their businesses are to be studied to gain some in-depth knowledge and understanding about their performance through the interactions between the researcher and the study objects (landowners). Indeed, the role of the researcher is very important in this methodical approach.

The methodological approach comprises a case study (CS) research strategy and the grounded theory (GT) method. This study adopted CS research strategy protocols to define its cases and the protocols to access the cases to generate data of interest. Case studies can be single or multiple case studies, and the cases can be in the form of a person, situation, event or place. In this study, the cases, at the general or broad level, are the three case study LOCs associated with each of their respective timber projects. At the specific level, the cases of interest are the clan leaders and business executives for each of the three LOCs. Clan leaders were the preferred cases over ordinary landowners because of their leadership status, and they were the first point of contact when dealing with landowners and are representative of the different clans. Clan leaders and LOC executives were the basis for the generation of data of interest for Phase 1 of this study.

The application of the GT method is in data generation and data analysis. An overview of GT is contained in Section 3.5; this study adopted a combination of the two approaches of GT. For

the general approach or conduct of the research and data generation during the interviews, the Glaserian approach of GT is adopted, while for data analysis, the Straussian approach is adopted. Coding is the primiary tool for data analysis and theory development.

Stratified, purposive, snowballing and theoretical sampling techniques provide for the selection of cases for interviews, as discussed in Section 3.7. Stratified sampling provided for the stratification of cases according to their different forest blocks and their locality within each of the forest blocks in reference to physical distances to existing infrastructure or from coast to inland. Purposive and snowballing sampling techniques are used in recruiting the cases for interview and are guided by theoretical sampling with the idea that the selection of each new cases should be guided by the search for new and invaluable information that will contribute towards theory development. Interviews for the purpose of data generations were supported by the in-depth interview techniques and probing techniques. To ensure that every aspect of information relevant to the study was collected, a framework containing leading questions and some specific subjects areas for probing was developed as shown in Table 3.2.

The data analysis method was thorughly described in Section 3.9. Coding was the primary tool for data analysis. Coding often involves three phases – open coding, axial coding and selective coding. Coding requires the input of the researchers in sorting, organising and categorising relevant information, giving it meaning and hence promoting theme or theory development. In this study, the codes reflected issues relating to LOC businesses. The whole approach of data analysis conformed to the ‘Straussian approach’ of GT.

While coding can be done manually, this study found it convenient to used NVivo to do the coding. Contained in Section 3.9.1 is an explanation of what it is and what it does. In this study, NVivo was used to create free nodes; and these nodes represent a code, a theme or an idea. Free nodes are nodes that stand on their own, while tree nodes are nodes that are connected to each other to form the shape of a tree with its trunk/stem, the main branches and the smaller ones, leading to the leaves. It is from these free or tree nodes that comparions are made, relationships determined and themes developed. While NVivo can be used to develop nodes, it does not do the analysis such as deciding what constitutues a theme. To complete that requires the input of the researcher in grouping nodes of similarity, from which relationships and themes are developed.

In summary, the research method for Phase 1 of the study is comprehensive, addresses the issues of credibility, reliability and validity, and aligns itself with the Phase 1 of the study’s research objective.