Sampling and Population PDF

Sampling 7.1 SAMPLING AND POPULATION 7.2 RANDOM AND NON-RANDOM SAMPLING 7.1 Sampling and population A sample is a subgroup of the population which is the focus of your research enquiry and is selected in such a way that it represents the study population. A sample is composed of a few individuals...

Sampling 7.1 SAMPLING AND POPULATION 7.2 RANDOM AND NON-RANDOM SAMPLING 7.1 Sampling and population A sample is a subgroup of the population which is the focus of your research enquiry and is selected in such a way that it represents the study population. A sample is composed of a few individuals from who you collect the required information. It is done to save time, money and other resources. The differences between sampling in quantitative and qualitative research In qualitative research a number of considerations may influence the selection of a sample, such as the ease in accessing the potential respondents, your judgement that the person has extensive knowledge about an episode event or situation of interest; how typical the case is of a category of individuals, or simply that is totally different from others. You make every effort to select either a case that is similar to the rest of the group or one which is totally different. This consideration is not acceptable in quantitative research In quantitative research you are guided by a predetermined sample that is based upon a number of other considerations in addition to the resources available. However, in qualitative research you do not have a predetermined sample size but during the data collection phase you wait to reach a point of data saturation. When you are not getting new information or it is negligible, it assumed you have reached a data saturation point and you stop collecting additional information from other respondents. In quantitative research, considerable importance is placed on the sample size in quantitative research, depending upon the type of study and the possible use of the findings. Studies which are designed to formulate policies, to test associations or relationships or to establish impact assessments place a considerable emphasis on large sample size as this will ensure the inclusion of people with diverse backgrounds. In qualitative research, sample size does not play any significant role as the purpose is to study only one or few cases in order to identify the spread of diversity and not its magnitude. In such situations the data saturation stage during data collection determines the sample size. In quantitative research , randomization is used to ensure that a sample is selected in such a way that it represents the study population and to avoid bias. In qualitative research no such attempt is made in selecting a sample. You purposely select ‘information-rich’ respondents who will provide you with the information you need. In quantitative research this is called biased sample. 7.2 Random and non-random samplings For random sampling, it is imperative that each element in the study population has an equal chance and independent chance of selection in the sample. The concept of equality implies that the probability of selection of each element in the population is the same;that is, the choice of an element in the sample is not influenced by other considerations such as personal preference. For example, suppose there are 80 students in a class. Assume 20 of these refuse to participate in your study. You want the entire population of 80 students in your study but as 20 refuse to participate, you can only use a sample of 60 students. The 20 students who refuse to participate could have strong feelings about the issues you wish to explore but your findings will not reflect their opinions. Their exclusion from your study means that each of the 8o students does not have an equal chance of selection. Therefore, your sample does not represent the total class. Methods of drawing a random sample 1. The fishbowl draw- if your total population is small, an easy procedure is to number each element using separate slips of paper for each element, put all the slips into a bowl and then pick them out one by one without looking, until the number of slips selected equals the sample size you decided upon. This method is used in some lotteries. 2. A computer program –there are a number of programs that can help you to select a random sample. Example: Random.org (https://www.random.org/ Research Randomizer (https://www.randomizer.org/)). 3. A table of randomly generated numbers- a table of randomly generated numbers in their appendices. Non-random/non-probability sampling Non-probability sampling design do not follow the theory of probability in the choice of elements from the sampling population. These designs are used when either the number of elements in a population is unknown or the elements cannot be individually identified. In such situations the selection of elements is dependent upon other considerations. Methods of drawing non-random probability sampling designs 1. Quota sampling- the main consideration behind quota sampling is the researcher’s ease of access to the sample population. In addition to convenience, you are guided by some visible characteristic such as gender or race, of the study population that is of interest to you. You select the sample from a location convenient to you as a researcher, whenever you see a person with this relevant characteristic, you ask that person to participate in the study. The process continues until you have been able to contact the required number of respondents. 2. Accidental sampling- Accidental sampling is also based upon convenience in accessing the sampling population. Whereas quota sampling attempts to include people possessing an obvious/visible characteristic, accidental sampling makes no such attempts. You stop collecting data when you reach the required number of respondents you decided to have in your sample. You are not guided by any obvious characteristics, some people contacted may not have the required information 3. Convenience sampling Accidental and convenience sampling designs are extremely similar. Convenience sampling is primarily guided by the convenience to the researcher, whatever this might be-easy accessibility, geographical proximity, known contacts, ready approval for undertaking the study, or being a part of the group. Accidental sampling is primarily selecting a place you are likely to find your potential respondents, which place may or may not be most convenient to you and if a person of interest comes along, you collect the required information till either you have collected the information from a specific number of respondents or have reached the saturation. 3. Judgemental sampling or purposive sampling The primary consideration in judgemental sampling or purposive sampling is your judgement as to who can provide the best information to achieve the objectives of your study. You only go to those people who in your opinion are likely to have the required information and be willing to share it with you. This type of sampling is extremely useful when you want to construct a historical reality, describe a phenomenon or develop something about which only a little is known. This sampling strategy is more common in qualitative research but when you use it in quantitative research you select a predetermined number of people who in you judgement are best positioned to provide you with the information needed for your study. 4. Snowball sampling Snowball sampling is the process if selecting a sampling using networks. To start with, a few individuals in a group or organisations are slected and the required information is collected from them. They are then asked to identify other people in the group or organization and the people in the group or organization and the people selected by them become a part of the sample. Information is collected from them and then these people are asked to identify other members of the group and in turn, those identified become the basis for further data collection. The process is continued until the required number or a saturation point has been reached in term of the information being sought. What is saturation point? The concept of saturation point refers to the stage in data collection where you as a researcher are discovering no or very little information from your respondents. In qualitative research this is considered as an indication of adequacy of the sample size.