In social sciences research, there are two major sampling methods:
probability and non-probability sampling. In the current study, both probability and non-probability samplings were used for the questionnaire and interview phases, respectively. In probability sampling, also known as random sampling, participants are drawn from a larger population in such a way that each member of the population has an equal chance of being included in the sample (McMillan & Schumacher, 2010; Teddlie & Yu, 2007). Probability sampling techniques include simple random sampling, systematic random sampling, cluster random sampling, multistage random sampling, and stratified random sampling (McMillan & Schumacher, 2010). These types of sampling are considered to be the most precise sampling methods in
quantitative research as the samples will likely represent the population from which they were drawn, thus researchers can make generalizations to the population (Creswell, 2012).
In non-probability sampling, also known as non-random sampling,
researchers choose participants who might represent certain types of characteristics or be accessible, hence, no random selection is involved in these methods (McMillan & Schumacher, 2010). These types of sampling techniques consist of convenience sampling, purposive sampling, and quota sampling (Creswell, 2012; McMillan & Schumacher, 2010). Non-probability sampling methods are more appropriate for qualitative research where researchers are not seeking for generalizations, rather, describing a particular context in depth (Gay et al., 2009).
In order to draw a representative sample for the questionnaire phase, two random sampling methods were adopted: cluster random sampling and systematic random sampling techniques. In cluster random sampling, groups, for example, classrooms or schools, not individuals, are randomly selected (Gay et al., 2009; McMillan & Schumacher, 2010). The cluster random sampling technique is more convenient when the population is spread over a wide geographic area (Gay et al., 2009). As discussed earlier, the current study was conducted in the Madinah administrative area, which is one of the largest regions in Saudi Arabia. Thus, the cluster random sampling technique seemed to be appropriate for this study. Despite that, this method may sometimes produce samples that are not representative of the population if researchers choose too few clusters. Hence, it is important for
researchers who intend to use the cluster random sampling technique to select a large number of clusters to increase the probability that the selected clusters represent the population adequately (Gay et al., 2009). With regard to the systematic random
sampling technique, a researcher, in using this method, selects every nth case in the population until he or she reaches the desired sample size (McMillan & Schumacher, 2010). This method is useful because the population members do not have to be numbered such as in the case with simple random sampling (Creswell, 2012). The systematic random sampling technique was used in the questionnaire phase when selecting both the required numbers of schools and the required numbers of teachers from each school.
To illustrate in detail the sampling techniques used for the questionnaire phase of this study, the following steps were applied. First, the population included all the 1779 mainstream primary school teachers in the Madinah administrative area, and the desired sample size was 230. The researcher had a list of all 63 schools in the Madinah administrative area including both male and female schools. To increase the likelihood that the selected schools represented all schools in the Madinah
administrative area adequately, eight teachers from each school were selected. Thus, the number of clusters, that was, schools, to be selected was 29 (the desired sample size divided by the number of teachers needed from each school). Therefore, 29 out of 63 schools were selected using a systematic random sampling technique. This involved determining n by dividing the number of all schools in the Madinah administrative area (63) by the number of clusters needed (29), which gives n ≈2. The researcher started at a random point in the list of all schools and then took every second school in the list until the desired number of schools (29) was achieved. After selecting 29 schools, the systematic random sampling strategies to select eight teachers from each school was repeated, by considering the calculation of n for each school depending on the actual number of teachers in that school. This resulted in a final sample of 202 teachers (28 teachers did not return the questionnaire) for
conducting questionnaire phase of this study. All of these teachers who took part in the study were volunteers and were provided with enough information about the study to make an informed decision about whether or not to participate.
For the interview phase, a purposive sample of eight teachers was selected from participants who provided their consent to participate in follow-up interviews. As mentioned earlier, purposive sampling method is a non-probability sampling technique. Purposive sampling method is commonly used in qualitative research and may be defined as selecting participants based on specific purposes related to
answering the questions of a research study (Teddlie & Yu, 2007). There are different sampling strategies in purposive sampling (Ary et al., 2010; Collins et al., 2007; Creswell, 2012; McMillan & Schumacher, 2010). Each strategy has different intent, depending on the research purposes and the questions a researcher would like to answer in his or her study (Creswell, 2012).
Because the interview phase of the current study mainly aims to understand ‘‘how’’ Saudi mainstream teachers perceive the inclusion of students with AD/HD- related behaviours, stratified and random purposeful sampling techniques were adopted in this phase of the study. The sampling frame for this part was participants who provided their consent to participate in follow-up interviews. First, by using stratified purposeful sampling method, the participants from the mentioned sampling frame were divided into four strata to obtain relatively homogeneous subgroups. The strata were established after the analysis of the quantitative part, and they were based on the participants’ attitude towards the inclusion. The four strata were male teachers with positive attitude, male teachers with negative attitude, female teachers with positive attitude, and female teachers with negative attitude. After identifying the
homogeneous subgroups, a random purposeful sampling method was applied in order to randomly select a number of two teachers from each stratum.