Quantitative data analysis

The process of quantitative data analysis was a very tedious process which involved a couple of stages before the actual analysis began. To aid in the analysis, the Statistical Packages for Social Sciences (SPSS V20) was used in which the data (for the entire 145 questionnaires) was entered for analysis. The data cleaning process started after all questionnaires where entered in the software and responses checked for consistency, gaps and omissions which were then rectified before running queries for analysis.

The computer-aided quantitative analysis generated, among other things, cross tabulations for identification of relationships among variables, frequency tables and facilitated testing of level of significance of gathered findings using statistical tools (e.g. chi-square). Microsoft excel was used to calculate percentages and to generate graphs. Among the variables that needed quantification include food sources used in order to identify the frequency of patronage of each source. This became helpful in identifying the most commonly utilized food sources and how they were impacted by drought. Other variables that were quantified include effects of drought as identified by

respondents in the study area, and coping mechanisms employed by respondents, among other things.

In terms of food security measurement in the study area, the study used three food insecurity indicators which included Household Food Insecurity Access Prevalence indicator (HFIAP), Months of Adequate Household Food Provisioning (MAHFP), and Household Dietary Diversity Score (HDDS). These indicators and food insecurity measures are commonly used in most food security studies nationally and regionally (See: Battersby, 2011; Crush, 2012; Pendleton et al., 2012; Tevera et al., 2012; Mvula & Chiweza, 2013; Acquah et al., 2013; Crush & Caesar, 2014; Alexander et al., 2014; Raimundo et al., 2014; Leduka et al., 2015; Leroy et al., 2015). These food insecurity indicators were designed for measuring household food insecurity by the Food and Nutrition Technical Assistance (FANTA) and are used globally.

Household Food Insecurity Access Prevalence Indicator (HFIAP)

This HFIAP is a useful measure that captures the degree of food insecurity in relation to access to food in the month preceding the survey (Coates et al., 2007). It is founded mainly on the notion that households or rather people’s experiences of limited access to food result to certain reactions that may be foreseeable and that such responses can be quantified and encapsulated in a scale. Respondents were subjected to a nine-question brief survey based on their reactions and behavior in times of food shortages where they are mostly vulnerable. The results from these nine questions were then analyzed. This helped to categorize households into four food insecurity levels such that any given household could either be severely food insecure, moderately food insecure, mildly food insecure or food secure.

5.7.2.1 The Household Dietary Diversity Score (HDDS)

The Household Dietary Diversity Score (HDDS) measures the access component of food at household level, particularly the quantity and quality of the food consumed by households (Swindale & Bilinsky, 2006; Leroy et al., 2015). It is a simple count that uses diverse food groups that households consume over a given reference period to compute a proxy measure for household food insecurity. The logic behind the computation of the

household food insecurity levels, more so because food insecure households, as most scholars have observed, tend to be over-reliant on starchy staples with a complete exclusion of proteins and other nutrients from their diet. Households with low dietary diversity (eating less varied meals) were therefore considered food insecure.

5.7.2.2 The Months of Adequate Household Food Provisioning (MAHFP)

The Months of Adequate Household Food Provisioning (MAHFP) indicator captures changes in household’s ability to ensure that food is available above a minimum level throughout the year (Bilinsky & Swindale, 2007). Specifically, the MAHFP enumerates the months in which households have access to adequate food. It was used in this research to capture household’s ability to address vulnerability by ensuring food availability above a minimum level all year round. The higher the number of months that a household did not have adequate food provisioning, the more likely that the household was food insecure and therefore the less resilient it would be to food shocks. This measure was, therefore, handy in indicating how food insecurity (at the household level) fluctuated throughout the year within the researched households.

It was also important to consider issues of validity and reliability during the data collection phase, analysis, and data presentation stages of the research process. For the purpose of quantitative data collection, therefore, as already noted, a standardized instrument (questionnaire) was used. The use of standardized instruments in research is considered by scholars as one criterion to ensure credibility since standardized instruments ensure objectivity and hence increase validity and reliability of research findings. The use of standardized instrument in this research inquiry was accompanied by a clear and detailed procedure outlining the research process followed in this study.

According to Hancock (2002) and Hamilton (2003), this enables that the study can be repeated and replicated under similar conditions, either by the same researcher or another. This also emphasizes the issue of reliability and validity in this study. In addition, data collected using the standardized pre-corded questionnaire was converted into numerical data and subjected to statistical analysis to enable running of tests. This was not done just to increase acceptability of the study findings, but because

quantifying findings is regarded as the main source of objectivity and reliability (Marshall, 1996; Castro et al., 2010).

5.7.2.3 Rainfall, yield and food price correlation

Secondary data was also gathered and analyzed to detect patterns and trends in important variables that were crucial in the understanding of the effects of drought on urban food security in Swaziland. Data on maize yield over a period of 10 years was gathered from the Ministry of Agriculture and was correlated with maize price for the same period to establish if there was a correlation between the two and determine the strength of the relationship. The same was done for maize yield, which was correlated with rainfall amount (weather data was gathered in the Department of Meteorology) to establish the existence of any relationship between the two, and establish the strength and direction of the observed relationship.

Finally, rainfall data was correlated with food prices, where the Consumer Price Index (CPI) (CPI data gathered from Central Statistics Office) for the different food items considered under the HDDS. The Product Moment Correlation was used and the sample product moment coefficient was calculated to determine if there was an existence of any relationship between the cross-tabulated variables, determine its direction and its strength. Since a smaller sample population was used due to data limitations, the results were further tested for significance using chi-square to determine the extent to which they reflected the total population from which the sample was drawn.

The results of these tests were very important for this study. The study is anchored on drought and its effects on food security (urban food security, to be specific) and also investigates food dynamics of food flows between rural and urban areas, to determine if there is any connection with drought. It was important, therefore, to first draw evidence from weather data if, indeed, the country experienced drought conditions and the years drought has occurred to determine if drought affects crop productivity (Maize yield) in Swaziland. The existence of a relationship between rainfall and maize yield meant something to this study (as well as lack of such relationship) and was crucial for every conclusion drawn in this study.

The same was true also with food price and rainfall. When food production declines, food demand is met through food purchase where food price is key. Besides, the urban population relies mostly on purchased food. It was important, therefore, to establish again if rainfall influences food prices. The important question here was: what happens to food price when rainfall declines causing a decline in crop yield? Do food prices respond to changes in rainfall pattern? Again, the existence of a relationship (as well as the lack of such relationship) between rainfall (drought) and food prices was important for this study to draw accurate conclusions.