Speaker 1: Hi, I'm Aisha, you are watching Educational Hub. In order to answer the research questions, it is doubtful that researcher should be able to collect data from all cases. Thus, there is a need to select a sample. Okay, first we need to understand, what is sampling? Sampling is the selection of a subset of the population of interest in a research study. Today our topic is, how to choose sampling techniques for research? Okay, now, illustrates the stages that are likely to go through when conducting sampling. Stage 1. Clearly define target population. The first stage in the sampling process is to clearly define target population. Population is commonly related to the number of people living in a particular country. Stage 2. Select sampling frame. A sampling frame is a list of the actual cases from which sample will be drawn. The sampling frame must be representative of the population. Stage 3. Choose sampling technique. Prior to examining the various types of sampling method, it is worth noting what is meant by sampling, along with reasons why researchers are likely to select a sample. Sampling can be used to make inference about a population or to make generalization in relation to existing theory. In essence, this depends on choice of sampling technique. In general, sampling techniques can be divided into two types. 1. Probability or random sampling. 2. Non-probability or non-random sampling. Before choosing specific type of sampling technique, it is needed to decide broad sampling technique. I will shows the various types of sampling techniques. 1. Probability sampling. Probability or random sampling has the greatest freedom from bias, but may represent the most costly sample in terms of time and energy for a given level of sampling error. Simple random sampling. The simple random sample means that every case of the population has an equal probability of inclusion in sample. Disadvantages associated with simple random sampling include. 1. A complete frame, a list of all units in the whole population, is needed. 2. In some studies, such as surveys by personal interviews, the costs of obtaining the sample can be high if the units are geographically widely scattered. 3. The standard errors of estimators can be high. 2. Systematic sampling. Systematic sampling is where every nth case after a random start is selected. For example, if surveying a sample of consumers, every fifth consumer may be selected from your sample. The advantage of this sampling technique is its simplicity. 3. Stratified random sampling. Stratified sampling is where the population is divided into strata, or subgroups, and a random sample is taken from each subgroup. A subgroup is a natural set of items. Subgroups might be based on company size, gender or occupation. 4. Cluster sampling. Cluster sampling is where the whole population is divided into clusters or groups. Subsequently, cluster sampling is advantageous for those researchers whose subjects are fragmented over large geographical areas as it saves time and money. The stages to cluster sampling can be summarized as follows. 1. Choose cluster grouping for sampling frame, such as type of company or geographical region. 2. Number each of the clusters. 3. Select sample using random sampling. 5. Multi-stage sampling. Multi-stage sampling is a process of moving from a broad to a narrow sample, using a step-by-step process. If, for example, a American publisher of an automobile magazine were to conduct a survey, it could simply take a random sample of automobile owners within the entire American population. 2. Non-probability sampling. Non-probability sampling is often associated with case study research design and qualitative research. With regards to the latter, case studies tend to focus on small samples and are intended to examine a real-life phenomenon, not to make statistical inferences in relation to the wider population. 1. Quota sampling. Quota sampling is a non-random sampling technique in which participants are, chosen on the basis of predetermined characteristic, so that the total sample will have the same distribution of characteristics as the wider population. 2. Snowball sampling. Snowball sampling is a non-random sampling method that uses a few cases to help encourage other cases to take part in the study, thereby increasing sample size. This approach is most applicable in small populations that are difficult to access due to their closed nature. 3. Convenience sampling. Convenience sampling often helps to overcome many of the limitations associated with research, for example, using friends or family as part of sample is easier than targeting unknown individuals. 4. Purposive or judgmental sampling. Purposive or judgmental sampling is a strategy in which particular settings, persons or events are selected deliberately. It is where the researcher includes cases or participants in the sample because they believe that they warrant inclusion. Stage 4. Determine sample size. In order to generalize from a random sample and avoid sampling errors or biases, a random sample needs to be of adequate size. What is adequate depends on several issues which often confuse people doing surveys for the first time. This is because what is important here is not the proportion of the research population. There are numerous approaches, incorporating a number of different formulas, for calculating the sample size and the number of samples. For calculating the sample size for categorical data, for example, n equals p, 100 p, z2, e2, n is the required sample size, p is the percentage occurrence of a state or condition, e is the percentage maximum error required, z is the value corresponding to level of confidence required. There are two key factors to this formula. First, there are considerations relating to the estimation of the levels of precision and risk that the researcher is willing to accept. The second key component of a sample size formula concerns the estimation of the variance or heterogeneity of the population. Stage 5. Collect data. Once target population, sampling frame, sampling technique and sample size have been established, the next step is to collect data. Stage 6. Assess response rate. Response rate is the number of cases agreeing to take part in the study. These cases are taken from original sample. In reality, most researchers never achieve a 100% response rate. Reasons for this might include refusal to respond. Thank you for watching. Like share and subscribe my channel for more informative videos about research.