Research Methods

Survey Methods

I developed the survey as a tool to generate data on all the key research themes in the thesis, which are explored in more depth using qualitative research tools. The survey questionnaire included five modules: livelihoods (how families derive a living, including food, shelter, water, medicines), human-animal interaction (who, where, when human- animal interaction occurs), framings of risk for zoonotic disease (what is risky and what is not, how different people perceive risks of animal illness, and how risk is managed locally), experiences with febrile illness (including lay interpretation of symptoms and labelling for febrile illness), and healthcare-seeking behaviour (where people seek treatment and how decisions are arrived at). Each of these was further broken down into specific questions that allowed for brief responses (both qualitative and quantitative), following Sudman and Bradburn’s (1982) guidelines on designing a research questionnaire. These include analysing the concepts involved in the research questions, formulating specific questions, and measuring these against the concepts in order to check for ambiguities and vagueness. As Sudman and Bradburn (1982: 12) explain, “the more clearly formulated and precise the research question, the more easily the actual questions can be written, and the questionnaire designed”.

However, as Bernard (2006) points out, surveys need not necessarily be about quantifying research, but can also provide a basis upon which in-depth interviews and content analysis can be enhanced. In my case, I used the survey to collect information about various aspects of my study, and then selected a sub-sample of respondents for in-depth qualitative study that followed on from the survey, throughout the ten months that I spent in the village.

Survey responses were recorded on a tablet using the Open Data Kit software,25 co- designed by myself and a local assistant. The questionnaire included 39 semi-structured questions based on the five modules described above and was administered to a total of 200 households (these were multiple sub-households within an olmarei) that were

25 Open Data Kit (ODK) is an increasingly adopted software for designing and implementing surveys, particularly useful where large sample sizes are involved. Due to the nature of my field sites and the fact that we did not have any means of transport other than walking for long durations between households, I decided to engage ODK to make the survey efficient and timely. The responses were immediately useable as they were all in electronic format.

purposively selected; 17 olmareis in each sub-village participated in this research. The time taken to respond to the entire questionnaire varied from person to person, but on average it took one and a half hours to complete.

I used the purposive sampling technique that Bernard (2006) advises, because in the case of Naiti village, the population is highly homogenous, and this approach helped to attain a sample that best represented the features that would answer my research questions. These were for example, those households that closest to a health facility and those that were furthest, or households with members involved in local administration, Christian religion, local healing etc. In many of the participant households, both the husband and wife or wives were respondents at different times, while in others only one spouse responded to the questionnaire (as in the case of widows and widowers).

I entered the survey responses into Microsoft Excel and scrutinised them for converging and diverging themes. These were then clustered into core thematic areas, which included a livelihood activity profile of each participant for a typical day, patterns of human-animal interaction, and their framing of zoonoses and risk. I also asked participants to rank febrile illnesses from the most to the least commonly experienced in their households. I probed them for corresponding symptoms, perceived causality, commonly used treatments and evaluation of these treatments. I then clustered the lists per household – where more than one adult member of the household responded to the questionnaire – and compared household-level responses against variables such as gender roles, age, wealth status, distance to health centre, education, and access to information technology such as mobile phone and radio. I used the results to generate frequency tables, which I used to identify recurrent themes that helped in exploring further during interviews, focus group discussions and participant observation.

Interviews

Most qualitative researchers endorse the use of interviews as data collection tools (Ritchie, 2003; Rubin and Rubin, 2005; Bernard, 2006; Walsh, 2001; Gillham, 2005; Denzin and Lincoln, 1994; Denzin, 1970; Pope and Mays, 2006; Gubrium and Holstein, 2004; 2012). However, few have explained what an interview is and why it is preferred. Silverman (2014: 166) invites us to think of an interview as a “keynote”, “in which the interviewer allows the interviewee freedom to talk and ascribe meanings while bearing in mind the broader aims of the project”. So, whereas in my survey design the questions

were pre-meditated and structured, interviews were collaboratively produced with respondents based on broader themes that emerged from the survey, rather than a list of individual questions. Participants in my interviews were active knowledge producers. As Gubrium and Holstein (2004: 150) put it, interviewees should never be passive “vessels waiting to be tapped”. Also, Arksey and Knight (1999: 15) note that interviews help clarify human beliefs and meanings, since “what people claim to think, feel or do does not necessarily align well with their actions”.

I interviewed a total of 73 participants that were purposively selected from the sample of 379 surveyed. Silverman (2014: 60–61) recommends purposive sampling because it

Allows us to choose a case because it illustrates some features or processes in which we are interested… it demands that we think critically about the parameters of the population we are interested in and choose our sample case carefully on this basis.

I conducted 46 in-depth interviews with 23 women and 24 men, and 26 key informant interviews with 17 men and nine women. Although I aspired to recruit an equal number of men and women to participate in the study, women often referred me to their husbands when the subject of enquiry was to do with livestock, as they believed livestock management was a man’s responsibility. A few women however did agree to talk to me, although they deferred to their husbands in some questions about animal handling and health. One woman advised me that:

If you want answers about animal sickness, the best person to ask is my husband because he oversees all animals in the olmarei, whereas I only look after the milking cow and goats and sheep.26

Although I heeded the women’s advice, I also sought out men and women who were referred to me (by their family and friends) as key informants on specific subjects. I found these people enormously knowledgeable and I spent most of my time in the field with them. Tremblay (1957: 1) describes key informants as a select category of people who are experts in “kinship and family organization, economic systems, political structure, and religious beliefs and practices”. Marshall (1996) defines a key informant simply as an expert source of information. Tremblay (1957: 7) further highlights the characteristics of an “ideal” key informant, including the informant’s role in the community, which can

be either formal or informal, and that exposes the informant to the kind of information being sought by the researcher. As Ole-Miaron (2003) indicates, a key informant should be well respected by members of his or her community as a knowledge reference point. For me, 26 people emerged as key informants on the various themes from the survey that I needed further details on (see Table 3.2 below). These were: five lay healers, seven community elders, 13 local leaders and one clinician. Lay healers provided me with information on local therapies and emic interpretations of febrile illness and healing. I interviewed these people several times over the duration of my fieldwork.

In addition, I also interviewed various people who were selected based on their lay knowledge and experience with livestock, human health and lay healing. They included 12 febrile patients (four were ill at different periods during the study, while eight had been treated and recovered), 14 carers, two local meat inspectors (both male), 15 herders, two women group leaders, and one Christian religious leader.

I also conducted 20 focus group discussions involving 50 men and 50 women, with an average group size of five members per session. The Focus group discussions were constituted as disaggregated by sex and by gender, as was the analysis of the FGDs. As I pointed out earlier, local leaders and elders (both men and women) and ordinary people were involved in the initial recruitment of interviewees based on their familiarity with these people’s skills. They knew the customs and interactions within the village and were crucial in helping me understand the dynamics of village life. For example, they helped me understand how seasons and weather changes affected the availability of participants and thus the planning of my survey. During the rainy season (March to June 2017), more men were available to participate in the research as they tended livestock closer to home, while in the dry season these activities took them further away. Women’s availability was determined by farming and trading responsibilities, which meant that they were less likely to be available during the crop planting, cultivation and harvesting season (April to June 2017). Table 3.2 below shows the interview schedules and participant samples.

Table 3.2: The Schedule and Protocol for Data Collected During Fieldwork.

All interviews were semi-structured and open-ended. Semi-structured interviews were used so as to achieve what Holstein and Gubrium (2003: 74; see also Gillham, 2005) describe as enabling “access to people’s ideas, thoughts and memories, in their own words, rather than in the words of the researcher”. Semi-structured interviews with open- ended questions are also described by Walsh (2001: 65) as enabling the “interview [process] to develop as a guided conversation, according to the interests and wishes of the interviewee”. This method of data collection was suitable for my research, which focused on people’s behaviours and actions. Furthermore, open-ended interviews were preferred to “avoid too much pre-judgement where the questions were predetermined… [and to] obtain interviewees’ real views and beliefs” (Walsh, 2001: 66). I also wanted to achieve what Seidman (2006: 15) describes as enabling a participant to “reconstruct his or her experience within the topic under study”, in this case avoiding my presumptions and focusing on what Seidman terms the “subjective experience of the participant” (ibid.: 85). The benefit of these interviewing techniques was that respondents were able to direct the interview, and this brought out information about their views, outlooks and prejudices, some of which I had not realised or considered in advance of the interviews.

The skill of interviewing involves establishing rapport with the interviewee(s) and actively listening to them. It also involves improvising where common objects are used as analogies for concepts such as illness. As an example, a local healer described his 200 Households (HHs): 2 respondents from 179 HHs, 1 respondent in each of remaining HHs

Total = 379

participants Male Female Total Timeline

HH survey

(all participants) 168 211 379 Oct. 2016 – Jan. 2017

In-depth

interviews 24 23 47 March 2017

Key informant

interviews 17 9 26 May 2017

Focus group

discussions 50 50 100 May – June 2017

Observation of

techniques as a “cloud”, which represented the different stages of healing in his patients. He explained that:

healing is like clouds in the sky. You see them start to form, but there is no rain yet. Do not be impatient because the clouds need time to solidify into rain, just like the therapy takes time to rain life to the patient.27

I used probing to encourage interviewees to give details about a phenomenon, for example short responses were followed by “please tell me more, that is interesting, and then what happened…?” In the above example, I probed for details about how the healer related his skill to a cloud and where this analogy came from, as well as other situations in which it might be applied.

Interviews were conducted in either Maa or Swahili. As I could only speak Swahili, I recruited two research assistants, one male, Lazaro Arangare, and one female, Mati Chenga, who were both proficient in Maa and Swahili, and who therefore helped me navigate the language difficulties. Lazaro was particularly instrumental since he was a Maasai and was familiar with the local WaArusha culture. Lazaro commuted from Makuyuni market where he lived and worked with me all day, every day, while Mati commuted twice a month from Arusha, and stayed in Makuyuni for the days that she assisted with the fieldwork.

A clear majority of respondents were familiar with both Swahili and Maa, and therefore it was easy for me to follow up with participants and clarify things with them in Swahili. There is no Maa or Kiswahili translation for zoonoses, so a description was used instead, i.e. illness that participants could get from livestock. I asked respondents specifically about their experiences of febrile illness or homa (in Swahili). Interviewees were specifically asked about what they thought caused febrile illness, how people got sick and how they responded to episodes of febrile illness in their households.

Figure 3.2: Key Informant Interview with Female Lay Healer and Her Assistant, Facilitated by Lazaro (left) and the Author.

Focus Groups

A focus group is a way of collecting qualitative data which typically involves recruiting a small group of people (between four and eight) who share particular characteristics and encouraging an informal group discussion focussed around a particular subject (Silverman, 2014). In my research, it was necessary to disaggregate focus groups by sex, age and gender to respect local culture and expectations as to who might sit together for a discussion.

I conducted 20 focus groups involving 100 participants (50 men and 50 women). The average group size was five although sometimes I had four or six people per group. Focus group participants were also identified from the surveyed sample. Like interviews, discussions focussed on themes derived from analysis of the survey data and were built around concepts in my research questions. Where it was necessary and possible, visual objects were used to stimulate the discussions. For example, participants referred to “colourful maize” (a local maize variety, see Figure 3.3) to describe various stages of febrile illness, from common colds developing into malaria and into serious medical conditions. Colourful maize is a multicoloured corn variety that is traditionally popular

in Africa and is used in conversations to symbolise complexity. Participants in Naiti, for example, used it to describe the continuum of severity for febrile illness, from “normal” illness (like maize that is mainly white with only a little bit of colour) to serious illness (very colourful maize). I suspect that multicoloured corn was used in descriptions because, as farmers, this variety of corn was commonplace in participant’s homes. Figure 3.3: Sample of “Colourful Maize” in Naiti, Used in the Analogy of Febrile Illness.

Rather than ask questions to each participant in the group, we encouraged group members to interact with each other, and I took notes on converging and diverging themes during the discussions. Effectively managing focus groups required facilitation skills, flexibility and an ability to stand back from the discussion to let group dynamics emerge (see Silverman, 2014: 166).

I used focus groups “to clarify the extent or quality [of] findings produced by other methods” (Silverman, 2014: 208), presenting participants with summarised findings from the survey, translated into the local language by Lazaro, and presented on flip charts. These group discussions also provided me with an opportunity to identify issues that participants converged around or that resulted in divergent views or opinions, and to explore these further with key informants for greater clarity. My field diaries, as described above, helped provide context for the discussions.

Upon returning to IDS in September 2017 after completing my fieldwork, I undertook further data analysis, building on earlier preliminary analysis that I had undertaken while in the field. I adopted two analytical methods, the “ideal cases” method and the “method of difference” (Sarantakos, 2013: 370). In the former, I identified cases from the data that I found to be “ideal cases”, i.e. ones where there was greater convergence in responses, and then used these cases to compare and contrast with observation of actual events and cases. For example, where a majority of respondents answered “yes” to the question “Do you consume boiled milk?” I compared this with my observation of the respondent in real-life scenarios, for example by attending meal times at their homes. This method enabled specific and unique elements to emerge that I would otherwise not have considered. For example, I realised that “boiling milk” meant something different to me and to my respondents. To some participants “boiled milk” implied “warm” milk straight from the cow, and not necessarily boiled using heat. However, this is not unprecedented, as there are similarities between boiled milk and milk straight from the cow, in that they are both warm and frothy. A significant number of respondents told me they preferred drinking “warm” milk as soon as it was obtained from the cow. They largely perceived such milk to be the “safest”, as it would not have been “contaminated” from human handling and processing. This world view may challenge the very idea of “safe milk”, in which experts promote boiled milk as safe, because the terminology, if not used carefully, may get interpreted as milk that is freshly obtained from the cow (I will return to this later).

Consequently, I adapted the question to: “Do you cook milk before consuming?”, which carried specific meanings that implied heating on a fire, and in a cooking pot. With this clarity, I began getting more insights into the complexities of consuming raw or uncooked/undercooked animal products. Some of these were that milk, meat and blood consumed as medicine are not cooked because of a belief that cooking compromises the medicinal value of these products. However, these products are cooked to “enhance their taste”28 when consumed as food.

In the method of difference, I looked for similarities and differences in opinion on the same issue, and at cases that were similar in some ways and different in others. For instance, I compared responses about sources of treatment for febrile illness, from women

in polygamous marriages and those in monogamous marriages, to understand how a wife’s position in marriage influences her health-seeking behaviour.

For further analysis, I used the NVivo software to search for more themes and to establish common trends and patterns in the data. NVivo enabled me to identify various analytical concepts relating to my research questions and to class these into codes. Through constant comparison of the codes and the analytical concepts, and by eliminating unrelated categories (Strauss and Corbin, 1998), I constructed the empirical material presented in chapters Four, Five and Six.

In Section 3.5 below, I discuss my positionality in the field, before reflecting on the ethics and the research process in the final two sections of this chapter.