3.5.1 Rationale for qualitative enquiry
The quantitative methodology on its own is not efficient to proffer interpretation of the social realities considering the different contexts and complexities, and hence qualitative inquiries are often needed to complement these. Body image and obesity are social paradigms and they require qualitative research to deal with for better interpretation of the realities (230,231). The belief is that a holistic research assumption of ‘no quantification without qualification’ is critically important. As measurements of social facts hinge on categorising the social world, social activities, therefore, need to be distinguished before any frequency or percentage can be attributed to any distinction (231). In addition, qualitative enquiry enhances interpretations with context. Although quantitative research reaches its conclusions quasi-automatically, qualitative research has strengths in interpretations of realities quantified.
3.5.2 Sampling procedure
Obese and non-obese men and women in the cohort and other members of the community were purposively selected for the qualitative interview (focus group interviews). The criteria for the selection were: participant should be an existing PURE study partivipant, or a member of the PURE study Township or rural community; and should be aged 30-70
59
community leaders and members in each of the two communities. Some of the urban participants were approached for participation in the FGD during physical measurement sessions, usually conducted in groups at designated centres. In the rural communities, cohort participants in adjoining streets were approached to participate in a focus group in a household that accepts to host the group.
3.5.3 Data collection Focus group discussions
Information on body-image perceptions and attitudes, obesity risk perception and weight control decisions were collected during the follow-up survey in 2014/2015 to address objective 2 (Phase 2). FGD were undertaken with separate groups of women and men based on weight category to explore perceptions about body image, obesity risk perceptions, and weight control decisions in the obese and non-obese groups. A total of 13 FGD were conducted with purposively selected separate obese and non-obese men and women groups. Eight FGD were undertaken in the urban township and five in the rural community. Body image perceptions were further explored using Stunkard figures during the discussions. Group discussions were facilitated in the local language (isiXhosa) and audio-recoded. Detailed procedures of the focus group discussion facilitation, validity measures undertaken and data coding and analyses are discussed in Chapter 5, section 5.2.
The FGD guide is attached in Appendix 3. Because of the similarity in the preliminary findings of the FGD data in the urban and rural locations, findings from only the urban community FGD are reported in this thesis.
Prior to the group discussions, participants’ weight and height were measured using calibrated scales and a height meter. For these measurements, they were wearing light clothing, standing erect and without shoes. Each participant’s BMI was calculated in kg/m2 and their weight categories determined using standard cut-offs (188). Participants
were then classified into normal (or optimal) weight (BMI 18-25 kg/m2), overweight (BMI 25-30 kg/m2) and obese (BMI ≥30 kg/m2) groups based on their BMI.
Rigour
Some steps were taken to ensure validity and reliability of the study. Detailed steps that were taken to ensure rigour in this qualitative study are presented in Chapter 5, sub-section 5.2. The goal of the qualitative study was to obtain sex-specific and weight-based information regarding body image, risk perception and weight control intentions. In order to achieve this goal, the focus groups were organised based on gender and weight status to facilitate comparison of the views of the groups. This is of importance since the findings are expected to support the provision of targeted interventions.
Two trained indigenous research assistants with experience in community-based qualitative research facilitated the group sessions in the local Xhosa language, and developed the transcripts. Data were validated during coding, analyses and interpretation.
Furthermore, the triangulation method (232) was used to enhance results of the qualitative research. In that way, findings from the FGD were compared and contrasted with the quantitative data to provide a more credible explanation of the concepts under investigation.
3.5.4 Data coding and analysis
Atlas.ti software was used to facilitate coding and organisation of the themes for analysis.
Because of time constraints and resources, qualitative data from the urban location were analysed. Data were analysed using the inductive thematic analysis approach (233).
Transcripts were first hand-coded, and an initial coding framework was developed based on the identified categories and sub-themes that emerged from the data. The PWM
61
attitude (i.e. perceived vulnerability and threat), their subjective norms and prototypes, and examined how these components impact on their behaviour, intention and willingness. The coding was guided by the PWM and the themes were obtained inductively (234). Codes with similar themes were grouped to form sub-themes. To obtain greater abstraction, sub-themes addressing similar concepts were further grouped to form the final themes. The themes and data analyses procedures adopted are discussed in detail in Chapter 5, sub-section 5.2.
ATLAS.ti is a powerful workbench for the qualitative analysis of large bodies of textual, graphical, audio, and video data offering a variety of tools for accomplishing the tasks associated with any systematic approach to unstructured data. In the course of the qualitative analysis, ATLAS.ti helped in exploring the complex phenomena hidden in the data. For coping with the inherent complexity of the tasks and the data, ATLAS.ti offers a powerful and intuitive environment that keeps one focused on the analysed materials (235). It offers tools to manage, extract, compare, explore, and reassemble meaningful pieces from large amounts of data in creative, flexible, yet systematic ways (236).