Quantitative data collection

The quantitative approach entails representation of an observation in a numerical form, with an aim of explaining and describing the phenomenon represented by those

observation (Casebeer & Verhoef, 1997:2). Hancock (2002) and Hamilton (2003) note that quantitative research allows the collection of numerical data, making of observations and measurements of the phenomena of interest. They further add that such numerical data, as collected through quantitative means, can be subjected to statistical analysis and can be replicated under similar conditions. This means that quantitative research follows specified scientific procedures which, when repeated under similar conditions, can yield similar results and lead to same conclusions. These scientific procedures, on which the methodology is dovetailed, allow for statistical tests and analysis which qualify the results to be generalized.

Different scholars such as Levin et al. (1997) and Nicholls (2011) have observed that quantitative researchers have the ability to reduce voluminous data to numbers and present it in numerical form which, according to them, is a unique feature of a quantitative research. They further observe that this is the major strong point of the quantitative approach to research. As if emphasizing the same point, Marshall (1996) and Castro et al. (2010) assert that the reduction of voluminous data into numbers is seen as the main source of objectivity and reliability of the quantitative research findings. Furthermore, this approach to research subscribes to the tenets of positivism – the view that “social research should adopt scientific methods which consist of rigorous analysis of numerical data that takes the form of quantitative measurements” (Atkinson & Hammersley, 1994:251). Greenhalgh and Taylor (1997) claim that research findings are more likely to be accepted if they are quantified.

Quantitative approach to data collection and analysis is the most commonly used research approach in food security studies (See: Crush et al., 2010; Tawodzera, 2011; Tevera et al., 2012; Mulenga, 2013; Tevera & Simelane, 2014; Raimundo et al., 2014; Leduka et al., 2015). This is because it allows for collection and rigorous analysis of food security data, most of which is in numerical form, to uncover patterns and further allow for the quantification of the problem and ultimately the generalization of findings (Atkinson & Hammersley, 1994). Furthermore, quantitative data allows for cross tabulations to be performed to help uncover patterns and allow deeper understanding of food security issues - a complex and multi-dimensional phenomenon.

For the purpose of quantitative data gathering, the researcher used a standardized household questionnaire which was administered to 145 sampled households in

kaKhoza, which were selected from a total of 456 households9 _{(CSO, 2007). Cohen and}

Manion (1994) argue that the sample size for a quantitative survey cannot be determined in one way, but several ways can be used to determine an appropriate sample size. Israel (1992), therefore, suggests three methods that can be used to determine a sample size for a research study. According to Israel, a researcher can determine a sample by imitating a sample size of similar studies, can use published tables, and can apply formulas to calculate a sample size. In this study, Yamane's (1967) widely used formula for determining sample size was used (Equation 1). The same formula has also been used by scholars such as Israel (1992).

Where N is the size of the population, e is the level of precision and n is the sample size.

Equation 1: Sample size determination Source: Yamane (1967)

There are three things a researcher needs to decide on before determining a sample size that will be appropriate for his or her study and these things include: the level of precision (sampling error)10_{, the level of confidence or risk}11_{, and the degree of}

variability12 _{in the attributes being measured (George & Michener, 1976; Monette et al.,}

2002). In this study, a Confidence Level of 95 percent was decided on and the researcher was willing to allow a sampling error of 7 percent. Since the variability in the population was not known, a maximum variability (p=5) was assumed. George and Michener (1976) and Israel (1992) concur that there should not be rigidity in terms of what one considers an appropriate sample size since in a quantitative inquiry, this, they

9 _{A household is a unit where “a person or group of persons who may be related (family) or unrelated or}

both who live together and share meals (eat from the same pot)” (CSO, 2017:17).

10 _{Level of precision or sampling error refers to the range in which the true value of the population is}

estimated to be (Israel, 1992).

11 _{Level of confidence – a term that denotes that when a population is repeatedly sampled, the average}

value of the attribute obtained by those samples is equal to the true population value (Israel, 1992).

note, depends largely on the study objectives, the analysis type the study will utilize and the error margin that the researcher is willing to allow for the results. Scholars, however, agree on the issue of representativeness and concur that the sample must be representative of the total population from which it has been selected. Tawodzera (2010:81) further adds and cautions that the sample should comprise of sufficient sub- groups to afford the research study a foundation for making generalizations and comparisons.

Considering the objectives of the study, the research design used and analysis employed, the researcher found this sample size sufficient to yield the results that will address the study objectives. In fact, Israel (1992:2) allows an even smaller sample if the population is highly homogeneous, he notes “the more heterogeneous a population, the larger the sample size required to obtain a given level of precision. The less variable (more homogeneous) a population, the smaller the sample size”. The same view is encored by Adams and Schvaneveldt (1991) who also argue that homogenous populations do not necessarily need large samples and maintain that a smaller sample size is adequate to accurately reflect the characteristics of that population. This means that more diverse populations require larger sample size than their counterparts, to accurately reflect the total population from which they have been selected. In this study, efforts have been made to ensure representativeness. Although the acceptable sample size was 141 households (Equation 1), the researcher decided to increase the sample size to 145 households to increase the precision level.

To adhere to the scientific procedures followed when conducting scientific research, systematic sampling technique was employed to select the sample for this study. This technique allows for selection of subjects at regular intervals and is more appropriate when the target population is highly homogenous. This approach to data collection was found to be more appropriate in the selection of respondents in kaKhoza. The population of kaKhoza displays a high level of homogeneity when considering their socio-economic conditions and hence is likely to experience more or less similar food security condition and exposure to drought impacts. Very few people are permanently employed in the study area. Majority of the residents are either unemployed or

attendants, salesman and other similar jobs. Likewise, their food sources and food sourcing strategies are more or less similar, hence are likely to share almost similar food security challenges and more likely to be affected similarly by drought. As such, it is expected that their adaptive responses to drought induced food shortages would not vary much. The advantage of systematic sampling as a technique is that it allows for regular coverage along the full length of study and helps to eliminate human bias.

Again, the researcher made use of kaKhoza’s Master list (2014), which was used as a sampling frame. Rubin and Babbie (1997) advise that to select a representative sample, it is vital that the sampling frame includes all (or nearly all) members of the population. The Master list used in this study, therefore, contained nearly all households in kaKhoza since it was developed for the purpose of the upgrading programme and each household was captured. Each household was assigned a number from 1 to 456 which were then arranged in ascending order. A die was thrown to determine the starting point, following which, subjects were picked or selected at a regular and predetermined interval until the predetermined sample of 145 was reached. The GPS coordinates for the 145 selected households were loaded into a GPS which, again helped in identifying the geographic location for each selected household.

5.3.2.1 The standardized household questionnaire

The designed structured questionnaire (Appendix B) was administered to the sampled households in kaKhoza. This standardized questionnaire, which contained mainly

closed ended questions, was administered to the heads of households13 _{in the sampled}

households. The household heads, as the units of analysis, were assumed to have extensive knowledge about their household’s food security situation, effects of drought on access to food and strategies employed by household to cope with effects of drought to ensure adequate access to food.

13 _{Head of household - is a person who is a usual resident (has lived there continuously for most of the last}

12 months) who is responsible for that household and makes most of the decisions (whether young or old). He may not necessarily be the eldest, but is the one who is responsible for what the household will eat and the number of meals on a daily basis (CSO, 2017:17).

The development of the questionnaire was preceded by an initial review of literature on drought and urban food security. The review, which was carried out by the researcher, helped him to familiarize himself with critical issues on the connection between drought and urban food security, more especially those that relate to the effects of drought on access to food in the urban environment. Strauss (1987) notes that reviewing literature is important since it sensitizes the researcher to existing literature that can be of great help in trying to explain the research problem. True to Strauss’ observation, this review did not only help the researcher to have a general understanding of urban food security and the dynamics involved in general, but also sensitized the researcher on current debates and key variables that the researcher needed to understand in order to frame questions that would be broad enough to sufficiently capture the important issues for the inquiry, hence help fulfill the study aim and objectives.

Since data collection was undertaken parallel to data analysis, this analysis yielded very important information on how urban households’ access to food was restricted by drought and to uncover the strategies they employ to remain resilient in the drought situation. Some of these insights were used to add and restructure the questionnaire to ensure that it captured, almost perfectly, the effects of drought on access to food and the strategies employed by the residents to counter the effects of drought, and how prevalent these effects are, as well as coping strategies that go with them in kaKhoza. Through the in-depth interviews, it also transpired that urban households in kaKhoza maintain links with their rural relatives from which they get food and sometimes send same. Such information gathered from the qualitative survey was crucial and needed to be quantified since it has a direct influence on the food security of the sending (or receiving) households, hence it was important to know the number of households sending and/or receiving food from rural relatives.

The questionnaire (Appendix B), which was used to collect quantitative data, was crafted in such a way that it could capture data that would make it possible to measure the validity and strength of the patterns or trends observed during the qualitative research process. Through the questionnaire, the researcher was able to capture information ranging from demographic characteristics of the household to food security

questionnaire survey included, but was not limited to, household income, household expenditure on food, food consumption patterns, households’ food sources, impacts of drought on access to food, urban agriculture and copying strategies employed by households to secure food.

The administration of each questionnaire took approximately an hour, although in the first three days, the time could go beyond an hour. However, as the researcher got familiar with the questionnaire, the time to administer questionnaire grew lesser and lesser, which made the data collection exercise flow and become much enjoyable and exciting. The researcher was able to fill an average of 10 questionnaires per day. The researcher gained vast experience during the field work as he interacted with different kinds of people, some of which were very friendly, making the whole exercise interesting and enjoyable. There were also those who were ‘difficult’ to deal with. Sometimes the researcher had to negotiate for extra time when the interview took longer than an hour. In general, majority of the participants were very cooperative and keen to participate and did so happily and without duress.

The household food situation in kaKhoza was too bad in most households such that when some unsampled households heads saw the researcher passing by their households, they could sometimes call him with the idea that he was working for the NDMA which commonly distributes food aid. The researcher had to keep on explaining that the purpose of the survey was purely academic and had nothing to do with food aid distribution to affected households. In spite of their disappointment, most households happily participated in the survey, mentioning that it is only after researchers have been conducted and report written about their sad situation that the policy makers and government can know about their predicament, and probably intervene. This was of great interest to the researcher and he was actually happy for such unexpected level of understanding the importance of research.

5.3.2.2 Observation matrix

During home visits in the process of quantitative data collection, the researcher was also observing some observable phenomenon in and around each household. This strategy was very much helpful in validating the data that was gathered in every

household. The researcher used an observation matrix (Appendix G) on which he was able to indicate certain important observable phenomenon in the study site. Among the variables which were observed included signs of the practice of urban agriculture (backyard gardens), crops or vegetables grown, animals kept, condition of crops grown (to see if there is any sign of dehydration on crops), land size available for cultivation and irrigation system used. It was also highly possible that respondents could inflate responses on frequency of taking meals, hence observation helped to look for any sign that shows that a meal might have been taken recently. Anytime the researcher was allowed to have the interview in the kitchen, he would look at the pots, plates, and other kitchen utensils to see the possible time of the last meal in every household visited.