ANALISIS COMPARATIVO, EVOLUCION, TENDENCIA Y PERSPECTIVAS

The significance of data analysis is that the raw data collected will be transferred to information. Mouton (1996) describes data analysis as the stage where the researcher analyses the data by relating the individual findings to a hypothesis that would best describe the data. Terre Blanche, et al. (2011) note that the data analysis procedure can be divided into quantitative and qualitative techniques. Quantitative techniques employ a statistical analysis to make sense of the data whereas qualitative techniques identify themes in the data and the relationships between these themes (Terre Blanche, et al., 2011:52). It is important that the correct technique be used to answer the research questions. Details of my process to analyse the data is described below. I first analysed the quantitative data and thereafter the qualitative data.

3.7.1 Quantitative Data Analysis

Babbie (2010) describes quantitative data analysis as the numerical presentation of data collected with the intention to analyse and describe the phenomenon of the study. The data collected must be coded and imported to a statistical software package for easy analysis. In this study, the data was quantified to make it easier for assessment. The quantitative data collected via the questionnaire was pre-coded and transferred from the questionnaires to a codebook (Excel spreadsheet). Neuman (2000: 314) describes the data coding process as the “systematical reorganizing of the raw data into a format that is machine readable”. After the data was coded, it was checked for accuracy and the data was “cleaned”. The accuracy of the data is of utmost importance as it will influence the validity of the measures resulting in distorted results (Neuman, 2000). Data from the Excel spreadsheet was imported to the econometrics computer package, STATA 12.1, for analysis. STATA has been recognized as the most powerful statistical software to generate descriptive and inferential statistics (Anon, 1996). With descriptive statistics, the socio-demographic and economic trends of the households were investigated by the scores of each variable to determine if there were any relationships between the variables. Descriptive data analysis has given me the opportunity to

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gain an impression of the data collected (Neuman, 2000). With the inferential statistics, conclusions were drawn from the sample data about the population.

The following paragraphs identify the statistical test that was conducted to analyze each research question outlined in Table 3.1 in this chapter of the thesis.

Research objective 1: Descriptive statistics were used in answering the first research

question. The analysis describes the level of food security, source of food as well as the food included in the daily diet of the household, with summary statistics and frequency distribution tables. The graphs describe the shape, variability and central tendency of the distribution. The analysis determines if the participants experience high, marginal, low or very low levels of food security as defined by Labadarios, et al. (2009). Simultaneously, bivariate relationships between food security as a constant (dependent) variable and education, income and the number of people in the household as an independent variable were described (Neuman, 2000: 200).

Research objective 2: To answer this research question, both descriptive and inferential

statistics were used. Descriptive analysis identifies with graphs and summary statistics the household’s benefits from the urban food gardens project. Inferential statistics were used to test the association between the variable “food security” and the variable “benefits from the urban food gardens project”. The test also indicates the other livelihood strategies outcomes and benefits from the urban food gardens. For this purpose, the chi- Square test (χ²) goodness of fit was used.

Research objective 3: For the anwer in this research question, the study analysed the

livelihood strategies adopted. The study determined what (1) capabilities, (2) what social resources and (3) what economic resources the participants employed to be more food secure. Descriptive statistics were used to identify the livelihood strategies adopted with graphs and summary statistics.

Research objective 4: In response to this research question, an analysis of the requirements

as given by the participants to extend or to start-up an urban food garden was identified by descriptive statistics with graphs.

42 3.7.2 Qualitative Data Analysis

Babbie (2010: 394) describes qualitative data analysis as analysing non-numerical data (e.g. interviews or focus group discussions as to discover new relationships or meaning. The qualitative analysis brings more depth to the research topic. In the semi-structured interviews, the style was conversational and flexible and the in-depth discussions shed more light on the issues, solutions and experiences (May, 2002: 225). With the focus group discussions, it was important to hear the opinions of each participant. The data was recorded and transcribed.

Scholars recommend that the qualitative data collected, be coded in themes and in that way patterns can be identified (Babbie, 2010). The themes below have been identified as key to answering the research objectives and the data will be analysed accordingly.

Theme 1: Participant’s definition of food security. The participant’s perception of food

security relates to the first research objective. The opinions of both the beneficiaries as well as the community members were included in the data analysis.

Theme 2: Urban food gardens as a livelihood strategy. This theme has been analysed in

response to the second research question. It was imperative to understand the reasons for the participation of the community members in the urban food garden projects as well as the livelihood outcomes.

Theme 3: Livelihood strategies adopted to be food secure. In this theme, the coping

mechanisms and the livelihood strategies of the households will be analysed and discussed. This theme sheds light on the third research objective.

Theme 4: Sustainability and lessons learnt from the urban food garden projects. This theme

will shed more light on the fourth research question. The response to this theme is of utmost importance for recommendations and future research possibilities.

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Table 3.2: Research techniques, data collection paradigms and participant

Research Techniques

Data Collected Research Participants

Quantitative Closed-ended Questionnaires

83 Participants which included community members and beneficiaries

Qualitative  Focus Group Discussions

 Semi-structured Personal Interviews

 30 Participants - Community members (17) and Beneficiaries (13)

 Two Interviews: Key official at the Department of Social Development (1); Key official at the Department of Agriculture (1)

Source: Author’s field data 2014.

The preliminary interview was done in August 2014 with the Chairperson of the URDCBP to determine the possibility of conducting the research. The research started in November 2014 and concluded in December 2014.