Sampling methods for research
- 1. SAMPLING METHODS DR HARDEV SINGH s/o HARPAL SINGH
- 2. INTRODUCTION • Sampling is a method or process of selecting respondents or people to answer questions meant to yield data for field study (Baraceros, 2016) • Sampling is a method of selecting a subset or individual members of the population to make statistical inferences from them and estimate characteristics of the whole population. • The sample selected should be representative of the population to ensure that we can generalize the findings from the research sample to the population as a whole.
- 3. TYPE OF SAMPLING METHODS • Type of sampling methods can be subdivided into two groups: probability sampling and non-probability sampling. • Probability Sampling : It is a sampling technique in which sample from larger population are chosen using a method based upon theory of probability . For a participants to be considered as probability sample, he or she must be selected using random selection (Bhat,2019). • It start with a complete sampling frame of all eligible individuals from which has been selected for the sample. • Non Probability Sampling: It is a sampling technique where samples are gathered in process that does not give all individuals equal change of being selected. • It does not start with a complete sampling frame, so some individuals have no chance of being selected.
- 4. Type of Probability Sampling 1) Simple Random Sampling It is a subset of statistical population in which each member of the subset has an equal probability of being chosen. Example: Write name in papers and fold then randomly mix and select the names. This method allows the sampling error to be calculated and reduces selection bias but this simple random sampling method may not select enough individuals with interest of certain characteristic , especially if that characteristic is uncommon. 2)Systematic Sampling It is a method which sample members from larger population are selected according to a random starting point and a fixed periodic interval. Example: Population of 1000 , sample size 100, every 100th person in the list is selected. Systematic Sampling is easier to administer but may also lead to bias, for example if there are underlying patterns in the order of the individuals in the sampling frame, such that the sampling technique coincides with the periodicity of the underlying pattern. For example , choosing every 5th road user for road hazard in a college would result bias in sample of all males or all females.
- 5. 3) Stratified Sampling • It is a type of sampling method in which total populations is divided into a smaller group or strata to complete the sampling process. • Example: Population size 1000, Sample size 100, group population by age then get the sample by age. • Samples within should be randomly selected for example, in a study of the health outcomes of nursing staff in Malaysia, if to select from three hospitals, each with different numbers of nursing staff (hospital A has 500 nurses, hospital B has 1000 and hospital C has 2000), then it would be appropriate to choose the sample numbers from each hospital proportionally as 10 nurses from hospital A, 20 nurses from hospital B and 40 nurses from hospital C. • This ensures a more realistic and accurate estimation of the health outcomes of nurses across the county by reducing sampling bias but it requires knowledge of the appropriate characteristics of the sampling frame and it can be difficult to decide which characteristic to stratify.
- 6. 4) Cluster Sampling • It is a sampling method where multiple clusters of people are created from a populations , rather than individuals where they are indicative of homogeneous characteristics and have an equal chance of being a part of the sample known as clusters • Example, Population 10,000, sample size 1000, group the population by age then get the samples of ages. • This method can be more efficient that simple random sampling, especially when the population is large or when it involves subjects residing in large geographic area but if the chosen clusters are not representative of the population, resulting in an increased sampling error. This would increase the risk of bias.
- 7. Types of Non-Probability Sampling Methods 1. Convenience sampling • Also known as availability, grab, opportunity or accidental sampling and can be considered as easier method of sampling because participants are selected based on availability and willingness to take part but the results are prone to significant bias, because those who volunteer to take part may be different from those who choose not to participate. It creates volunteer bias and the sample may not be representative of other characteristics, such as age or sex. Example, status of mental disorder among students, only certain males was willing to participate 2. Quota Sampling. • It is non probability sampling technique wherein the assembled sample has the same proportions of individuals as the entire populations. Often used by market researchers as interviewers are given a quota of subjects of a specified type to attempt to recruit. • Example, an interviewer might be told to go out and select 20 adult men, 20 adult women, 10 teenage girls and 10 teenage boys so that they could interview them about their television viewing. Ideally the quotas chosen would proportionally represent the characteristics of the underlying population. Advantage of being relatively straightforward and potentially representative but the chosen sample may not be representative of other characteristics that weren’t considered.
- 8. 3. Voluntary Sampling Sampling method where people are voluntary to participate in a survey. For example a game show in the television request the viewers to visit the relevant website and respond to the online poll. The people who watched the show and understand the game show will be oversample from the people who don’t understand the game show. This create respond bias. 4.Purposive Sampling Also known as Judgement sampling, selective, or subjective sampling as this sampling method relies on the judgement of the researcher when choosing who to ask to participate. It is selected based on characteristics of a populations and the purpose of the study. This approach is often used by the media when canvassing the public for opinions and in qualitative research. The advantage of Purposive Sampling are being time-and cost-effective to perform but volunteer bias is present and it is also prone to errors of judgement by the researcher and the findings, whilst being potentially broad, will not necessarily be representative.
- 9. 5.Snowball Sampling Where research participants recruits other participants for a test or research. Example, when carrying out a survey of risk behaviors amongst intravenous drug users, participants may be asked to nominate other users to be interviewed. Advantages of snowballing is effective when a sampling frame is difficult to identify but selecting friends of subjects already investigated by choosing a large number of people with similar characteristics or views will create a risk of selection bias.
- 10. CONCLUSION • Sampling is very common phenomenon in decision making process. Before delving deeply into sampling process, one must be aware of several basic constructs involved with sampling namely; population, target population, elements, sampling units and sampling frame. Determining the final sample size for the research involves various qualitative and quantitative considerations. • Selecting a suitable sampling methods not only able to reduce cost and time but also produce a valid and reliable information if the sample size with appropriate method and bias is taken considerations.
- 11. Thank You