DISPOSICIONES TRANSITORIAS CAPITULO I

Sampling in research basically means the selection of a suitable sample for study. Fraenkel et al. (2012: 91) stress that “One of the most important steps in the research process is the selection of the sample of individuals who will participate (be observed or questioned). Sampling refers to the process of selecting these individuals.”

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To select an accurate sample for a study, in order to enhance the validity and reliability of the research, certain steps need to be put in place. To do that, the researcher considered the 1585 high schools scattered in 12 districts in KZN (EMIS, 2012) and used the lottery system to select four districts out of the 12 which is seen as representative enough to represent the other districts since the teachers in the selected districts share the same or similar characteristics with their counterparts in the other districts not selected through the lottery system. The allocation of the schools is represented in Table 5.1 below:

Table 5.1: Number of Schools per District in KZN

District Number of Schools

Amajuba 53 Empangeni 194 Ilembe 117 Obonjeni 155 Othukela 118 Pinetown 140 Sisonke 77 Ugu 143 Umlazi 154 Umzinyathi 109 Ungmgundlovu 139 Vryheid 186 Total 1585 Source: EMIS- KZN (2012)

In order to obtain a sample representative of the entire population of teachers in the district, the multi-stage sampling technique was used. Sarantakos (1998) holds the view that in a multi-stage sampling method, a sequence of samples is drawn from samples already selected but only the last sample of subjects is studied. Sarantakos further

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opines that “the main advantage of this sampling procedure is that it allows the establishment of a sample that is directly related to the research project. With every additional drawing, the sample becomes more specific and more relevant to the research question, and the results are expected to become more relevant and more representative”. Similarly, Sharma postulates that multistage sampling is carried out in various stages where the population is regarded as made up of a number of primary units, each of which is further composed of a number of secondary stage units and so on, till the desired sampling unit in which lies the researcher’s interest is ultimately reached. The multi-stage sampling technique was selected because it enabled the researcher to have a more concise and less scattered sample for the study.

The procedure for applying the multi-stage sampling technique was first to identify all the districts that fall under KZNDBE. Information gathered from the KZNDBE (EMIS, 2012) indicated that there are 12 districts in the province, so the researcher used the proportional stratified sampling procedure to select 50 percent of teachers from the four districts that were earlier selected through the lottery system. The researcher applied the technique by putting the numbers 1 to 10 in a box and drew one out at random. The number that was drawn was used to represent the nth term, which means that every nth school in the selected districts was selected until the required number of schools that has been allocated is selected to make up the total of each district was attained. After this the next process was to send the total number of 262 questionnaires in the cross- sectional survey and that meant sending a questionnaire each to the schools selected for the study but the researcher sent two questionnaires to each school in order to ensure high rate of return. In other words, when the saturation point was reached questionnaires left were not used for the research.

Though, the multi-stage sampling technique has the weakness of periodicity, especially where the sampling frame is already listed in a special order. That notwithstanding, the researcher ensured that there is no bias by starting at random and also made sure that the list was randomly organized. It must be emphasized that the main strength for

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choosing the systematic sampling with a random start over the simple random start is the huge number of schools represented when time and energy to be expended is taken into consideration.

Also, the stratified sampling was deemed appropriate for this study because the schools are categorized into various districts. To justify the use of the stratified sampling technique further, Badu-Nyarko (2009:108) opines that: “Stratified sampling is regarded as more efficient than simple random sampling because of the small number of subjects used for each stratum. Breaking the population into subgroups allows the researcher to compare subgroups results.”

The table 5.2 below indicates the sample size of the number of schools selected for the study.

Table 5.2: Proportional Allocation of Schools

District Schools per district Sent questionnaires

Ilembe 117 59 Sisonke 77 39 Ugu 143 71 Vryheid 186 93 Total 523 262 5.4.4 The Questionnaire

A questionnaire refers to any structured research instrument used to collect social research data in a face-to-face interview, self-completion survey, telephone interview or web survey. Generally, questionnaires use standard questions to gather data from a normally wide range of respondents in order to permit comparison of the replies that may be received. Results from questionnaires are mostly aggregated and summarized and often, but not invariably, quantitative analysis of the results are done using multi- variate methods.

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The rationale for using a questionnaire lies in the fact that it is widely used and it is a useful instrument for collecting survey information and providing structured, often numerical data. The fact that it can be administered without the presence of the researcher, and because it is often straightforward to analyse (Van Wyk, 2007) makes it a good and useful instrument to use in collecting data. The questionnaire as a data collection tool also allows respondents in a study to respond to the same set of questions on a particular topic in a predertemined way (Fraenkel et.al., 2012), that is, if it is highly structured or strictly closed-ended. In the case of semi-structured or open- ended questionnaires, respondents are able to air their views almost limitlessly, thereby making the later suitable for both quantitative and qualitative designs.

Fraenkel et.al (2012: 13) conclude that responses are then tabulated and reported, usually in the form of frequencies or percentages of those who answer in a particular way to each of the questions. Their conclusion suggests that questionnaires are easier to analyse compared to other data collecting tools.

5.4.4.1 Design of the Structured Questionnaire

The questionnaire was the instrument chosen for the cross-sectional survey (quantitative phase) of this study for reasons which have been outlined above. The questionnaire was made up of six sections with closed-ended questions which made use of various Likert scales. The design of the questionnaire is summarised in the table that follows:

140 Table 5.3: Design of the Structured Questionnaire

Section Item Number of Questions

Total

A Biographic data 08 08

B Teaching and learning 14 14

C Cooperative learning 12 12 D STAD 21 21 E English (FAL) 05 05 F Challenges in using STAD 06 06 Total 66 66

Closed-ended questions were used because such questions are normally associated with quantitative designs, which is the phase of this research currently. Another reason is its ability in enabling respondents to select answers from a number of options, thereby saving their time and making the whole process easier compared to administering open-ended or semi-structured questionnaires. As indicated by Fraenkel et. al. (2012), most surveys rely on closed-ended questions to measure opinions, attitudes and knowledge level of people for easy quantitative analysis. Closed-ended questions are easy to use, score and code for analysis on a computer.