Data management involves organization data into a form that is suitable for analysis. In data analyses raw data are evaluated and interpreted into meaningful and significant conclusions that other researchers and the public can understand and use. Data analysis involved both
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qualitative and quantitative methods. The adoption of a multi-methodology strategy in data analysis in the research facilitated the interpretation and understanding of the research issues. Quantitative data was used to put figures on what existed and what was representative and provided a context for cases. Qualitative data analysis provided clarifications to the differences portrayed in the quantitative data thus producing a richer analysis.
3.5.1. Management of quantitative data
Questionnaires were checked by the researcher at the end of each day for unanswered questions or incomplete responses. Corrections were done on the spot or where necessary households were returned to, to effect corrections or to complete responses. A pre-coding system was designed for closed-ended questions and responses to open-ended questions were coded upon completion of the survey. The questionnaires were edited and re-checked by the researcher prior to data entry.
The quantitative data collected on home garden species were analyzed and the species diversity in home gardens estimated using the Shannon–Wiener index (H'). The Shannon– Wiener index (H') = -Σ (ρi log ρi), where ρi is the proportion of occurrence of the ith species in a home garden in a study population expressed as a proportion of total species occurrence (N) (Kent and Coker, 1992). From this, the evenness (E) of species will be calculated as E = H'/ H' max with H' max = log N to estimate the homogeneous distribution of crop species in home gardens of the two groups of households. For further details see Chapters 4-6.
A dietary diversity score (DDS) was estimated for each household as the sum of the number of different food items consumed over the previous 24 h preceding the interview with reference to the 16 food groups recommended for dietary studies (FAO, 2007). Foods consumed at breakfast, lunch and dinner were considered in the study and fruits which were eaten between meals were also included. Foods consumed on multiple occasions during the previous 24 h were counted only once. Dietary diversity score was calculated with food items from the home garden (DDS(+HG)) and without food items from the home garden (DDS(–HG))
home garden produce.
A separate coping strategies index (CSI) was estimated for the post-harvest period and for the lean season for each participating household from the multiple round survey data. The researcher initially gave severity weight to each of the coping behaviours adopted by the household based on the rankings assigned by the focus groups at the initial qualitative phase of the research. The mean frequency (number of days/week) of the individual coping behaviours adopted by the household over the 30 day was determined using the method of
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Hoddinott (1999), where 4 = "often" (5 or more times/week); 3 = "sometimes" (4-2 times/week); 2 = "rarely" (once/week); and 0 = "never" (zero time/week). The numerical value representing the category of frequency was multiplied by the weighting factor assigned to the individual coping behaviours to generate a simple numeric score the coping strategies index (CSI) (Maxwell and Caldwell, 2008).
3.5.2. Analysis of quantitative data
Quantitative data were subjected to descriptive statistics, non-parametric statistics and correlation analysis to determine the empirical relationships between different variables. Percentages were used to determine and explain proportions, while means and medians were mainly used to determine differences in household socio-demographic characteristics, field and home garden characteristics and labour input in home garden cultivation practices and dietary diversity scores when distribution of the variable was normally distributed and medians for non-normally distributed data. The Superior Performance Statistical Software (SPSS 14.1 version) was used in quantitative data analyses. The Student t-test was used to compare mean values of variables concerning basic demographic and socio-economic characteristics; home garden species composition, domestic livestock and poultry reared; labour input in home garden cultural practices; domestic livestock and poultry rearing; and dietary diversity between HIV-positive and HIV-negative households. This was supplemented by Fisher's exact test for pair-wise comparisons. Pearson correlation analysis was used to quantify the association between home garden species diversity and household dietary diversity. Two-way-ANOVA tests were used to compare mean values of demographic and socio-economic characteristics; home garden species composition, domestic livestock and poultry reared; labour input in home garden cultural practices; domestic livestock and poultry rearing and dietary diversity across multiple groups of HIV-positive and HIV- negative female-headed and dual-headed rural households when the distribution of these individual variables was normal. This was supplemented with Fisher's protected LSD-tests used for pair-wise comparisons across the four groups. Comparisons across multiple groups of HIV-positive and HIV-negative rural households with subsistence-oriented and commerce- oriented home gardens were made using Kruskal-Wallis tests to compare median values of variables that described demographic and socio-economic characteristics; field and home garden characteristics; domestic livestock and poultry reared; labour input in home garden cultural practices; domestic livestock and poultry rearing and dietary diversity when distribution of the individual variable was symmetric, when the variable had non-normal
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distribution or small sample sizes. Pair-wise comparisons were made using the Dunn’s multiple comparison tests. The Chi-square test was used to evaluate the farming characteristics and consumption of a staple crop from the home garden among HIV-positive and HIV-negative rural households with subsistence-oriented and commerce-oriented home gardens (Fukuda and Ohashi, 1997; Motulsky, 1995; Olsen, 2003). Due to the small sample sizes descriptive statistics such as median values and frequencies were used to compare the demographic and socio-economic characteristics; CSI scores; proportions of HIV-positive and HIV-negative farm households that often adopted the individual food related coping behaviours; engaged in the individual agricultural and non-agricultural income activities; owned, sold domestic livestock; owned, sold poultry; harvested, sold staples; harvested, sold vegetable crops from home gardens; and fields of HIV-positive and HIV-negative farm households during the post-harvest period and in the lean season. The details are presented in Tables 3.1, 3.2, 3.3.1, 3.3.2 and 3.4.
3.5.3. Management and analysis of qualitative data
Interviews with key informants and observations were captured as extensive field notes and re-written as reports (Welman et al., 2005). Focus group discussions and in-depth interviews were recorded on audio tape and manually transcribed by the researcher. Information gathered from key informant interviews, focus group discussions, in-depth interviews and field notes was subjected to a process of careful and systematic analysis using content analysis. The qualitative data was comprehensively described for detailed text-based content analysis manually. The analysis of qualitative data was dialectic. The data was dissembled into elements, themes or components: these materials were examined for patterns and relationships, in connection to ideas derived from literature, existing theories or insights that emerged from field work. The data was reassembled to look for hidden new meanings or other explanations to existence or absence of certain themes. The resulting evidence was analyzed, evaluated and critically examined; it was accepted, or rejected entirely or with modifications. The process was repeated to test further the emergent theoretical conception and to expand its generality or otherwise its usefulness (Bernard, 2002). The data gathered from different categories of households in the case studies were treated independently of each other after which a comparative analysis of all categories of households was done. Flower and Hayes (1984) indicated that a case study is much more convincing and accurate when it is based on different sources of information and that using multiple sources of evidence increases reliability and validity of data.
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