Population is defined as the entire group of persons or set of objects and events the researcher wants to study (Magwa & Magwa, 2015). Wandera (2011) defines population as well defined or set of people, services, elements, and events, group of things or households that are being investigated in order to gather information required. From the cited definitions, it is clear that a research population is supposed to a designated set of criteria. A research population is composed of two groups; the target population and accessible study population (Magwa & Magwa, 2015). The two authors distinguish between target population and accessible population. A target population includes all the cases about which the researcher would like to make generalisations. This type of population is not always accessible to the researcher, hence, only a part of it that is available can be studied. In the context of the current study, the target population comprises all Zimbabwean government secondary school teachers and Ordinary level learners. The accessible/study population, on the other hand, is often a non-random subset of the target population available for a particular study (Bowen, 2010). It comprises a group of individuals to which researchers have access and can legitimately apply their conclusions. The study population may be limited to a region,
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district, city or institutions. In this current study, the accessible population comprised all the 362 teachers, including school heads, and the entire “O” level learners (650 boys & 780 girls) in the seven government secondary schools in Mutare urban district. In their line of duty, all these teachers are responsible for the welfare of learners and therefore, have a role to play in the promotion of CFS environments. As for the learners, they are the recipients and beneficiaries of this educational initiative.
3.5.1 Sampling procedures
Sampling is that part of statistical practice concerned with the selection of a sub-set of individuals from within a population to yield some knowledge about the whole, especially for the purposes of making predictions based on statistical inference (Bamberger, 2012). Basically, this definition seems to be quantitatively biased. From a qualitative perspective, sampling is a process of selecting participants to take part in a research investigation on the ground that they provide information considered relevant to the research problem (Yin, 2013). It is a sub-set of the population selected for a given research enquiry which helps to inform the quality of inferences made by the researcher that stem from underlying findings (Tashakkori & Teddlie, 2010).
Sampling in quantitative research typically follows random sampling procedures, whereas qualitative sampling is less direct (Guetterman, 2015). It is believed that qualitative sampling is not a single planning decision, but an iterative series of decision throughout the process of research (Emmel, 2013; Oppong, 2013). However, sampling in mixed methods research involves the selection of units or cases for a research, using both probability/random sampling (to increase validity) and purposive sampling strategies (to increase transferability) (Jeanty & Hibel, 2011;Teddlie & Yu, 2007). However, the utilisation of some form of purposeful sampling is more prevalent in MMR (Onwuegbuzie & Collins, 2007), and the stratified purposive sampling technique is basic (Tashakkori & Teddlie, 2010). The purposive aspect is inherent in both probability and non-probability sampling techniques.
Patton (2015) explains that purposive sampling involves selecting information-rich cases. The identification of sub-groups in a population, and selection of participants
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from each sub-group entails stratified purposive sampling. In light of these insights on what sampling entails, the current study employed a concurrent mixed methods sampling procedure in which a stratified purposive sampling was used to generate data for both quantitative and qualitative components. This meant that the study had two sets of samples produced by a parallel relationship sampling design. The design specifies that the samples for the qualitative and quantitative components of a study would be different but are drawn from the same population of interest (Tashakkori & Teddlie, 2010).
The first sample sets of 170 teachers and 145 learner respondents were selected through a stratified random sampling procedure to cater for the quantitative component. Three strata were considered for teacher respondents (graduate and non-graduate, and trainee teachers) representing both males and females. Learner respondents were drawn from “O” level classes (boys and girls) in the seven government secondary schools.
Stratified random sampling is a procedure where the examined population is divided into distinct subgroups, called strata, and a pre-defined number (or percentage) of individuals is selected from each stratum (Thompson, 2012). It is believed that stratified random sampling can significantly reduce the sample size without reducing the representativeness of the sample. The sample of 170 teachers came from a total of 385 teachers in the seven government secondary schools in Mutare urban district. The teacher sample, which constitutes 44.1% of the population, participated in answering the semi-structured questionnaire. This sample size was deemed large enough to assure validity of the findings. The 170 teachers were selected from the following teachers’ stratum: graduate teachers (56 male and 89 female), non- graduate teachers (78 male and 112 female), and trainee teachers (15 male and 35 female) as illustrated in Table 3.4. The learner sample of 145 was drawn from 650 “O” level boys and 780 girls respectively.
The proportionate stratification sampling procedure was used for the quantitative component where the sample size of each stratum was proportionate to the population size of the stratum. Strata sample sizes were determined by the following equation:
123 nh = (Nh/N)*n.
nh is the sample size for stratum h
Nh is the population size for stratum h
N is total population size
n is total sample size
The teacher and learner populations were divided into subgroups (strata) based on common descriptorssuch as, qualifications and gender. For the teacher sample, there were three strata (qualifications) and two strata (gender), and two for learner sample considering gender.
Table 3-3: Proportionate sample size from the seven government secondary schools in Mutare urban district
Teacher qualifications Stratum population Size (Nh) Stratum sample size (nh) Sub-stratum sample size (male) Sub-stratum sample size (female) Graduate 145 64 25 39 Non - graduate 190 84 34 50 Trainee teachers 50 22 6 16 TOTAL 385 (N) 170 (n) 65 105 Learner Level “O” Boys 650 66 “O” Girls 780 79 TOTAL 1430 (N) 145 (n)
The actual number of participants from both teachers and learners was calculated using simple proportion based on the actual numbers of each of the teachers’ or learners’ stratum from each school. The total samples from each of the seven government secondary schools based on the number of teachers or learners in each school stratum was calculated to determine the sample size of teachers and learners to cater for the quantitative component as illustrated in Table 3.4
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Table 3-4: Teacher and learner samples distribution per stratum per school Teacher Category/de scriptor Sample size per stratum School A M F School B M F School C M F School D M F School E M F School F M F School G M F Graduate 64 2 1 5 4 5 3 3 9 5 10 2 6 2 6 Non- graduate 84 2 8 8 11 5 5 6 8 8 7 4 3 1 7 Trainee 22 1 2 1 1 1 1 2 1 2 1 3 1 2 1 5 Total 170(S1) 16 30 21 29 34 18 22 Boys 66 3 10 24 9 11 9 0 Girls 79 6 14 0 16 14 12 17 Total 145(S2) 9 24 24 25 25 21 17 Key: Sch = School A, B, C, D, E, F, G = Schools M = Male F = Female
S1 = Teacher sample size
S2 = Learner sample size
The calculated representative samples for both teachers and learners who responded to the semi-structured questionnaires and it is assumed that the data to be collected were allegeable for generalizations.
For the qualitative component of this study, individuals were selected to participate in the research based on their first-hand experience of the phenomenon of interest. In order to select relevant participants, purposive sampling procedure was followed. Purposive sampling involves selecting information rich cases (Patton, 2015). Participants in this sample were assumed to be directly involved and responsible for the welfare of learners in their respective schools, and therefore, likely to be knowledgeable
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about the CFS concept and willing to share their perceptions and experiences with regard to the role of teachers in the promotion of CFS environments.
The sample set in this study comprised the seven (7) Guidance and Counselling focal persons and seven (7) school heads for the respective schools involved in the study. No calculations were done to establish the sample. Instead, the researcher deliberately handpicked these participants based on their administrative positions in the schools. The qualitative sample set was individually interviewed with the intention of capturing their views, feelings and attitudes on teachers’ role in the promotion of CFS environments.