Case Study On Selecting A Relevant Sample
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Discussion Question 1: Selecting a Relevant SampleThe research question or purpose of the study guides decisions about the research sampling. How might researchers ensure that they select a sample relevant to the research without introducing a bias?
Selection of a relevant sample without bias is the basis for research validity and reliability. Selection bias seems inevitable in the healthcare research, but there are ways in which scientists can work out to avoid it (Domecq et al., 2014). Firstly, researchers should ensure that the target group ends up used as the respondents despite the costs and time. Accepting the cost of finding the target population will help in getting the data required. Some researchers use readily available respondents for conveniences who might not be having the required information. Glidewell et al. (2012) stated that using the respondents from the target population reduces chances for selection bias leading to the use of relevant samples. For instance, the use of online panels ease the choice and access to respondents, but the majority of individuals in target group might end up left. Researchers should thus stick to using individuals from the target population for relevant samples.
Researchers might also work out to reduce selection bias in ensuring the use of relevant samples by avoiding judgmental sampling. Sampling with assumptions or experiences from the using past knowledge renders the study vulnerable to irrelevant sample use (Glidewell et al., 2012). Selecting of samples with representation based on prior knowledge does not cater for changes in the target population like age and health condition. The research might end up offering invalid results for application in healthcare setting. Additionally, specification of target population provides room for relevant sample groups. In cases where the researcher does not specify the target population subgroups, data might leave out specific sect of the target population. Domecq et al., (2014) discussed that every subgroup of the population requires specification to allow their inclusion. All relevant sample groups’ inclusion during data collection enables the participation of all individuals in the target population without bias.
Discussion Question 2: Evaluating a Sample
When critically analyzed, it is evident that the two articles differ in terms of sampling in guide of the questions below.
What sampling design is used?
Article 1 used a judgmental sample design. The researchers began by getting the information about the population before carrying out the main study. Additionally, the researcher had a well-defined research purpose requiring a description. A judgmental sampling design as used in article 1 uses respondents that fit a particular description involving patients from Minnesota medical center describing their choice of a health provider (Bondesson and Traat, 2013). Comparatively, article 2 used a probability sampling design. The research allowed the participation of all employees at the healthcare organization found in the South-Eastern United States.
b. Is the sample size adequate?
The sample sizes of the two articles vary in their adequacy. The sample size for article 1 is much more adequate than that of article 2. The target population for article 1 was 699 that offered a sample size of 467 patients in Minnesota University medical centre. An adequate sample size provide a good representation of respondents during the study (Marshall, Cardon, Poddar and Fontenot, 2013) . On the other hand, the sample size used in the article 2 study is inadequate. Comparing the total number of employees in relation to the sample used shows a low representation of respondents. Out of 5,000 employees at the healthcare organization, only 2572 ended up used.
c. How does the sample affect the validity of the conclusions of the study?
The sample size affects the validity of the study conclusions because of the target group representation. The conclusion of the article 1 study seem more valid that that of article 2. The article 1 had an adequate sample size that offered the required data without the researchers’ presumptions. Bondesson and Traat (2013) argued that an adequate sample size lowers the chances for the researchers’ assumptions. Relatively, the conclusions for article 2 study are invalid because the sample size used was inadequate. Article 2 conclusions are likely the researchers’ views based on assumptions.
Bondesson, L., & Traat, I. (2013). On sampling designs with ordered conditional inclusion probabilities. Scandinavian Journal of Statistics, 40(4), 724-733. Retrieved from: http://search.proquest.com/business/docview/1461287343/F9A12E47A1D94604PQ/9?ac countid=45049
Domecq, J. P., Prutsky, G., Elraiyah, T., Wang, Z., Nabhan, M., Shippee, N., Murad, M. H. (2014). Patient engagement in research: A systematic review. BMC Health Services Research, 14, 89. Retrieved from: http://search.proquest.com/business/docview/1503671002/AD93200740F04F83PQ/6?acc ountid=45049
Glidewell, L., Thomas, R., MacLennan, G., Bonetti, D., Johnston, M., Eccles, M. P., . . . Grimshaw, J. M. (2012). Do incentives, reminders or reduced burden improve healthcare professional response rates in postal questionnaires? two randomised controlled trials. BMC Health Services Research, 12, 250. Retrieved from: http://search.proquest.com/business/docview/1220444131/59AA67A2626A4119PQ/3?ac countid=45049
Marshall, B., Cardon, P., Poddar, A., & Fontenot, R. (2013). Does Sample Size Matter In Qualitative Research?: A Review Of Qualitative Interviews in Research. The Journal of Computer Information Systems, 54(1), 11-22. Retrieved from: http://search.proquest.com/business/docview/1471047612/D3B8834B9DC4494FPQ/5?ac
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