Sampling Strategy

‘Population’ involves the people who fall into the category of concern, while ‘sampling’ refers to a smaller group within the population. The key point is the relationship between the sample and its population. It is better to keep in mind that the main reason of using sampling is saving time and money as well, but

sampling may create an opportunity to generalise the findings to the researched population. Therefore, a high-quality sample is a vital precondition for high-quality research. How one might choose a relatively small number of cases to find out about a much bigger number depends on the sample’s accuracy (Oppenheim, 1992). The first step is defining the population a researcher will work within. In this study, the population was RC teachers in two adjacent LAs in the Midlands of England. Reaching every single RC teacher in the certain area was difficult, yet it might be also costly. This study employed case study design with a number of data collection techniques which required different sampling strategies.

4.3.1. Sampling for Élite Interviews and School Observations

For élite Interviews and school observations purposeful sampling with the maximum variation technique was used. This sampling strategy owes much to Patton (1990) and according to him; purposeful sampling is the dominant strategy in qualitative research, as it investigates information-rich cases or instances that can be studied in depth. Maximum variation sampling is a type of purposeful sampling which describes qualitative research methods and is typically used when focusing on a varied yet limited number of informants, who are selected strategically so that their in-depth information will give insight into an issue about which little is known. In other words, if a researcher wants to obtain as complete as possible insight in a certain issue in all its variations, maximum variation sampling will be used.

Patton (1990) stated that for small samples a great deal of heterogeneity can be a problem because individual cases are so different from each other.

According to Patton (1990) the maximum variation sampling strategy turns that apparent weakness into strength by applying the following logic: any common patterns that emerge from great variation are of particular interest and value in capturing the core experiences and central, shared aspects or impacts of a program (Patton, 1990:172).

Therefore, for the élite interviews originally six informants who had some understanding and access to the policy decision-making process were identified, though two in practice were unavailable. Also, for observations originally five primary schools’ RC were chosen in a LA as maximum variation, including urban with social and ethnic mix, rural with advantaged social intake, and rural with social mix. Yet, two teachers withdrew at an early point of the study.

In the schools target children were chosen according to their gender, age within the year group and the ethnicity of the children. In the study, while sampling target children in order to reduce the risk of a bias, the researcher used systematic sampling in order to choose the sample (Sira-Blatchford and Siraj-Blatchford (2001). Thus, the age record of the children was used to chose the second oldest and the second youngest children in the class. The evidence from baseline studies (Sammons and Smees, 1996; Tymms et al., 1997; Strand, 1999) showed that gender, birth-month within the year group and ethnicity was important variables which had considerable effect on children’s success. In two rural schools there was no need to consider ethnicity as all the intake was white British; meanwhile in the third urban school ethnicity was a factor to be considered as it was a multicultural school.

Yet, in the urban School two white British children were chosen for consistency.

4.3.2. Sampling for Survey

In the above it was already stated that the population of the study was RC teachers in a LA in the Midlands. For the survey an effective as well as accurate sampling strategy needed to be drawn. The ideal way of doing this is randomising or probability sampling (Oppenheim, 1992: Gorard, 2001 and Robson, 2002). According to Oppenheim (1992) most populations are structured in some way, or could be divided into sub-sections because of their certain characteristics; that is, they can be clustered. Sampling RC teachers in a LA means some important factors are needed to be put into account, such as their size, socio-economic, socio-cultural status, as well as the area of the school (i.e. urban, rural or semi-rural). For the questionnaire survey total 161 primary schools with RC were identified by the help of the primary schools partnership office of the local university and a questionnaire was sent out to each school regardless of how many RCs there were in each. This could not be regarded as a representative sample of schools/RCs but at least it provided a maximum variation sample, including a variety of school types and structures, with varied social intake. Table 4.2 presents the sample for the three main methods: survey questionnaire, the case studies and élite interviews.

2 Figure 4.2. Sample of the study

Sample of the Study

Survey Questionnaire

161 RC teachers in West Midlands

School Observations

-In 3 schools with 3 Reception Class Teachers

-6 Target Children

(2 observations a term, 6 in total)

Interview 4 Lead Educators/Academics