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which are conducted face-to-face with respondents. The structured interviews help in determining the frequency of answers as well as in establishing how answers relate to different questions. One advantage of structured interviews is in overcoming both misunderstanding and misinterpretations of responses or questions (Bless, Higson-Smith, & Kagee (2011). The disadvantage of these type of interviews is that they allow little flexibility in how questions are asked or answered (Fontana & Frey, No date in Denzin & Lincoln, 2005: 702).
Structured interviews were administered to supplement the unstructured interviews with the view of determining patterns and trends on the subject matter.
6.7.4.2 UNSTRUCTURED INTERVIEWS
According to Bless et al, (2011) the unstructured interview help in clarifying concepts and problems. One is able to develop possible answers or solutions.
Unstructured interviews emphasise the ideas of the interviewees, who get an opportunity to express their ideas once the interviewer has introduced the theme or topic. In this study, unstructured interviews were administered to departmental and municipal council officials. The purpose was to establish their perspective on the research subject
6.7.4.2 OBSERVATION METHOD (IZIMBIZO)
The researcher attended some imbizos (traditional/cultural gatherings) organised by the Department of Rural Development and Land Reform where land reform projects were discussed. This enriched the researcher’s understanding of the implementation of the land reform programme. It further assisted the researcher to obtain first-hand information from the perspectives of both the public and the beneficiaries regarding the impact of land redistribution. However, due to the fact that the proceedings of the imbizos do not follow a structured approach, the responses are not included in the data.
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studies a population from which the problem can be better understood and solutions proposed. Even within a larger group, a certain number or group, referred to as a sample, is normally studied. This section discusses the research population and the process followed to identify the sample. It further explains how the data collected from the sample was analysed.
6.6.1 Population
In research, population is defined as the set of objects or people which constitute the focus of research and about which the researcher wants to determine some characteristics (Bless, Higson-Smith and Kagee, 2006:98;
Brynard and Hanekom, 1997: 134). This means that, for any research, the researcher would identify a set or group of people to study. In this study, the research population were beneficiaries of the land reform projects within the Greater Letaba Municipality, municipal councillors, government officials and traditional leaders. Owing to the size of the population, it was not possible to study all the units, and a fraction of the beneficiaries was therefore selected for the research. This subset of the whole population which was selected and studied as representative of the whole population is referred to as the sample.
6.6.2 Sampling frame
According to Alreck and Settle (1985: 63) sampling refers to the selection of a portion of the population to represent the whole population. This sample has to be properly chosen otherwise it will not be representative and it must be large enough to meet reliability requirements. Sampling is a process of selecting a number of individual cases from a larger population (Adler & Clark, 2011: 100).
Sampling is used because time and cost considerations may make it impossible to study every object or member of the group. To determine the sample that would fairly represent the whole, a sampling frame is used.
Berends and Zotolla (in Lapan & Quatrolli, 2009) explain that a sampling frame is used to identify the group which is selected for participation in the survey.
The sampling frame can be determined in several ways:
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by identification of units of analysis from a list of individuals or objects in the population
by selection of a sample from a group participating in a specific activity
by sampling in multiple stages, where individuals are selected from a list
beginning with a large cluster and at each stage reducing the number of participants Berends and Zotolla (in Lapan & Quatrolli, 2009: 88)
The sampling frame in this research was from the geographical area of the study, which is GLM. The sample for the land reform beneficiaries was drawn from a list provided by the project officer at the Department of Rural Development and Land Reform of participants in the four land redistribution projects within the GLM. The sample for the Traditional Authorities was drawn from all 10 Traditional Authorities that fall within the geographic area of the research. The sample for officials of the Department of Agriculture’s extension service was drawn from those working in the GLM. The sample for the Department of Rural Development and Land Reform focused mainly on officials working in the wider Mopani District, which encompasses the research area. The sample for the municipal councils was drawn from 10 councils that comprise the Land and Infrastructure Portfolio Committee within the Greater Letaba Local Municipal Council.
6.6.3 Sampling technique and sample size
Two sampling methods are used in research, namely: probability sampling and non-probability sampling. According to Babbie (1998: 194), while probability sampling technique involves the selection of a “random” sample from a list containing the names of everyone in the population, non-probability sampling is often used in situations where the researcher is unable to select the kinds of samples used in large-scale social surveys. The random sampling method, which is one of the types of probability sampling, was used to determine the units of analysis in this study. This method was selected because it offers each element the same chance of being selected for the sample (Brynard &
Hanekom, 1997: 45).
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The sample was divided into various categories, and a sample of 30% was selected from each key category. This percentage was considered large enough to provide a fair representation of the characteristics of the entire research population. All the categories came from the GLM, which is the geographic research area, and were categorised as follows:
a) Municipal Council:
A sample of three members was determined from the Land and Infrastructure Portfolio Committee which consists of 10 members.. The councillors were selected in terms of their availability to participate in the research.
b) Traditional Authorities:
Ten Traditional Authorities fall within the GLM. A 30% sample amounted to three Traditional Authorities. The Traditional Authorities’ offices were approached to secure an appointment with the presiding head. The three who were readily available for an interview were selected.
c) Land Reform beneficiaries:
Two hundred and seventy people participated in the land redistribution programme. They comprised members of four projects: the Makhamotse Communal Property Association; the Majakaname Communal Property Association; the Lehlareng Communal Property Association; and the Makhabeni Communal Property Association in the GLM. The sample amounted to 81 members. The sample was selected using systematic random sampling, where each name was allocated a number that ranged from 001 to 270. A random starting point was determined and every third name was selected until the total of 81 was reached. This method was used because it was simple, gave each name a chance of being selected, and was cost-effective and time- saving.
d) Government officials:
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These were extension officers in the Department of Agriculture working in the GLM, Mopani District Municipality, and officials in the Department of Rural Development and Land Reform working in the Mopani District.
They were targeted because they dealt directly with land reform beneficiaries. There were 15 such officials and the 30% sample amounted to five units. The Department of Rural Development and Land Reform recommended one official who worked in the Mopani District that covered the study area. In the case of the Department of Agriculture, four extension officers based in the local office in the GLM were available to respond to both the questionnaire and the interviews.
These officials were selected because their prime responsibility was to assist the beneficiaries and other local farmers with extension services.
Their duties are related to the implementation of the extension service policy. They plan agricultural activities around extension services and ensure that resources are made available to needy farmers.
6.6.4 Data Analysis
According to Birley and Moreland (1998: 61), researchers can draw conclusions from quantitative data in two ways. First, data can be presented in the form of pie charts, tables or graphs in order to describe a situation. This method of analysis is referred to as descriptive statistics. Second, inferential statistics can be used, whereby a researcher infers something about the population based on the sample findings.
During the analysis, the descriptive statistics method was mainly used and data was presented in the form of tables, charts and graphs. The collected data was analysed and interpreted using both descriptive and inferential statistics. The data was analysed manually and Microsoft Excel was used to develop different graphs, charts and tables.