4.5 Sampling technique
4.5.2 Snowball Sampling
Snowballing is a specialised type of sampling which uses personal contacts to build a sample to be studied (Remler & Van Ryzin, 2011). According to Check and Schutt (2012), researchers use this method to identify appropriate participants who are difficult to locate.
This prescribes that snowball sampling is not based on random sampling but recruitment of participants is done using other participants. This is referred to by Kurant, Markopoulou and Thiran (2011) as a recruitment method that uses a referral system where the first identified participant can refer to further nominations of others who have the same characteristics as herself/himself.
In most cases snowball sampling is often used because one may be dealing with a sensitive topic or because the people under study are hard to find due to low numbers of such people (Merriam, 2009; Drăgan & Maniu, 2012). Usually, snowball sampling technique is often used in areas that are remote, where researchers find it difficult to identify necessary candidates (Kurant et al., 2011; Yin, 2014). As has been discussed earlier in chapter two, remote rural people are a hidden population, inarticulate and invisible at the same time marginalised socially, politically and economically (Madu, 2010). There are many schools in each district but few of these schools lie in the remote rural areas, I knew I might be tempted to do my research with schools in the urban fringe, the commuter belt or accessible rural areas, so I chose to use this sampling procedure which removes sampling decisions from me. I was too determined because I wanted to get the most credible data from what actually happens in the remote rural ECD schools. Hence, to be able to get this sample right I had to use snowball sampling to get the most knowledgeable individuals for my study.
People with specific characteristics that are needed for the research are easily found through recommendations that are given by the first nominated participant (Merriam, 2009; Kurant, et al., 2011; Remler & Van Ryzin, 2011). Snowball sampling focuses on sampling techniques that are based on the judgement (as proposed by the purposive sampling) of the researcher (Krista & Handcock, 2011). Upon my judgement and or required characteristics in mind, I requested the District Education Officers from both districts, Chiredzi and Zaka, each to recommend one information-rich school that is in the remote rural areas of their districts. The DEOs supervise all schools in their districts, thus I considered them to be well informed about these schools. Chiredzi District Education Officer recommended Vukosi primary school and the Zaka District Education Officer recommended Muzorori primary school.
When I got the names of these two schools, I went out to the schools and personally introduced myself to the school heads to expand my sample. Referrals should continue so that the researcher gets other potential participants to make up the study sample (Merriam, 2009;
Sigurðardóttir, 2010; Remler & Van Ryzin, 2011; Yin, 2014). Thus, I visited the first schools in each district, the school heads further nominated other schools with the characteristics I required. This referral system continued until I got a reasonable number of schools. This facilitated the identification of hard-to-find cases (Merriam, 2009; Remler & Van Ryzin, 2011). As a result, I managed to reach those hidden schools far away in the remote rural areas of these districts.
The process was very cheap, simple, and cost-effective and it needed little planning compared to other sampling techniques (Salganik & Heckathorn, 2004; Lohr, 2009).
However, like any other procedure snowballing has some disadvantages attached to it. I was aware that I had little control over the sampling method; since the participants nominated schools they know well (Drăgan & Maniu, 2012) however, this was a „blessing in disguise‟.
It helped me to reduce researcher bias. I followed the nominations and reached furthest schools in remote rural areas. Aptly, to eliminate all errors/biases associated with this design, I also referred to previous researchers, for example, Schumacher (2010) who successfully made a combination of purposive and snowball sampling in their study of cultural beliefs and practices in the rural areas of the Dominican Republic. Likewise, in a research done in the faculty of health, Leeds Metropolitan University, Woodall (2013) used a combination of snowball and theoretical probability sampling. This literature informed the sampling procedure that I followed. Research can influence other research, and I knew I was not going to make an error by combining purposive sampling with snowball sampling methods in order
to reach the hard-to-reach participants who can give rich data to answer my research questions. Purposive sampling is not just an agenda of getting rightful participants (but „how‟
to get the rightful participants) thus I employed snowballing to get those unknown and rightful participants.
Purposive sampling intertwined with snowball sampling is undertaken with deliberate aims in mind (Denzin & Lincoln, 2011; Creswell, 2013). I managed to obtain the required sample with required characteristics in eight remote rural ECD schools from the two districts. All of the school heads and deputy heads were substantive in their posts and had served for more than ten years in these schools. However, when they completed the biography tables they referred to experience as „substantive‟ in the posts, but in actual fact, they were working as acting heads or acting deputy heads for more than ten years because of the „freezing of posts‟
in the country due to economic hardships in the past ten years. All the participants had a homogeneous background in their respective categories. According to Check and Schutt (2012), homogeneity in background rather than in attitude is the objective in selecting participants for observing and interviewing. The homogeneity was however, slightly limited by the range of the ages of the participants. While the oldest in their fifties the youngest was in his thirties. With a total number of twenty-four participants, I coincidentally managed to be gender sensitive; fourteen men out of the total number formed my sample. Table 4.1 gives the biographic data of the research participants and schools‟ ECD enrolments.
Table 4.1 Biographic data of research participants
1.Goko School School head Deputy head TIC
Location Level RA ECD enrolment
Age Sex Qualifications Experience Age Sex Qualifications Experience Age Sex Qualifications Experience
Remote rural
Pr&ECD RDC 140 +51 M Bed 12yrs +41 M Bed 1yr +51 F CE 4yrs
2.Hlolwa School School head Deputy head TIC (ECD)
Location Level RA ECD enrolment
Age Sex Qualification Experience Age Sex Qualifications Experience Age Sex Qualifications Experience
Remote rural
Pr&ECD RDC 139 +51 M Bed 3yrs +51 M Bed 18yrs +41 F DE 1yr
3.Mande School School head Deputy head TIC
Location Level RA ECD enrolment
Age Sex Qualifications Experience Age Sex Qualifications Experience Age Sex Qualifications Experience
Remote rural
Pr&ECD RDC 62 +51 M Bed 16yrs +51 F Bed 1yr +41 F CE 10yrs
4.Vukosi School School head Deputy head TIC (ECD)
Location Level RA ECD enrolment
Age Sex Qualifications Experience Age Sex Qualifications Experience Age Sex Qualifications Experience
Remote rural
Pr&ECD RDC 141 +51 M BA 14yrs +41 M Bed 1yr +41 F Bed 21yrs
5.Mashi School School head Deputy head TIC (ECD)
Location Level RA ECD enrolment
Age Sex Qualifications Experience Age Sex Qualifications Experience Age Sex Qualifications Experience
Remote rural
Pr&ECD RDC 97 +51 M Bed 15yrs +31 F Bed 2yrs +41 M Bed (ECD) 2yrs
6.Dambara School School head Deputy head TIC Location Level RA ECD
enrolment
Age Sex Qualifications Experience Age Sex Qualifications Experience Age Sex Qualifications Experience
Remote rural
Pr&ECD RDC 159 +51 F Bed 25yrs +51 M CE 5yrs +51 M DE 12yrs
7.Mungwezi School School head Deputy head TIC
Location Level RA ECD Enrolment
Age Sex Qualifications Experience Age Sex Qualifications Experience Age Sex Qualifications Experience
Remote rural
Pr&ECD RDC 130 +51 M Bed 15 yrs. +41 F Bed 1yr +41 F DE 4yrs
8.Muzorori School School head Deputy head TIC
Location Level RA ECD enrolment
Age Sex Qualifications Experience Age Sex Qualifications Experience Age Sex Qualifications Experience
Remote rural
Pr&ECD RDC 164 +41 M BSc.
Psychology
12yrs +51 M Bed 5yrs +41 F BSc Geo 3yrs
For issues related to the protection of the schools identity as well as the sensitivity of the research, the eight school heads identified pseudo names which are being used in this study.
These are Goko, Hlolwa, Mande and Vukosi for Chiredzi district and Mash, Dambara, Mungwezi and Muzorori for Zaka district. I must point out that (Table 4.1) six schools out of eight had an enrolment of over hundred ECD children, which clearly designated viability of the ECD programme in these schools.