There are several factors that need to be considered before deciding on a sampling strategy, and a sample size. A sampling strategy is mainly dependent on the amount of time and money available, the availability of the members of the population and the level of accuracy that is required by the research. On the other hand, the sample size of a qualitative analysis research is mostly dependent on factors such as the level of accuracy required, representativeness, and the variety of the data to be collected (Kerr, Breen, Delaney, Kelly and Miller 2011). This paper intends to determine the best sampling strategy and the sample size required for a qualitative research plan that attempts to asses the scope of stress among different individuals and obligations of the human resource departments in organizations in addressing stress borne by the workforce.
The most important aspect of statistics is the population. Without the population, no data can be collected. In this case, employees from ten companies may be used as the population. The bigger the number of companies, the better the quality of data that shall be obtained. This is because it is intended to investigate the obligations of the human resource departments in organizations in addressing stress borne by their workforce (Kerr et al 2011). A bigger number of HR departments will result in more representativeness of these departments hence giving more unbiased results.
A sampling method is mainly dependent on the kind of data that needs to be collected. A sample should be as representative as possible of the entire population. There are several methods of selecting a sample. A simple random sample gives each member of a population the same chance of being selected. This may be done by allocating a number to each member of the population. The numbers are then picked at random and the members whose numbers are selected are added to the sample. However, many populations don’t have a convenient frame available. In this case, to select a list of members for this sample, a k-in-1 systematic sample may be selected. This research intends to sample a population of employed personnel who are employed by different employers. To use simple sampling, the researcher would write a list of all members of the population obtained from different employers, and then pick every kth member of the population. Systematic sampling however is disadvantaged by the possibility of a periodic failure (Bansal and Corley 2012). A good example, every kth member of the population may come from the same company. If it so happens, the data obtained ceases to be representative of the entire population. Alternatively, a stratified random sample may be obtained.
In a stratified sample, the population is split into different subjects known as strata. The researcher then draws randomly from these strata (without replacement) to obtain the sample. In this research, the researcher may opt to randomly draw a given number of employees from each company. Each employer then becomes a stratum. Both simple sampling methods and stratified sampling methods are random sampling methods. Random sampling methods are preferred to any other method since they are more representative and are unbiased by the person collecting the data.
The best method to sample the population in question is through stratified sampling. It has an advantage over simple random sampling by reducing the standard error. It also results in more significant statistics in a smaller sample. A stratified random sample often requires a smaller sample since it is more accurate hence it is more cost-friendly (Bansal and Corley 2012).
We also have an option of either using proportionate or disproportionate stratification. In proportionate stratification, the sample size of each stratum is proportional to the size of its population. The sampling fraction of each stratum is therefore equal. In disproportionate stratification, the sampling fraction in each stratum may vary. This may result from selecting the same number of members from each employer in this care. However, stratified sampling is more accurate.
We also require determining the number of sampling points. These are locations at which data will be collected for each member of the population. The time available for the research, the resources available and the availability of each of the members of the different strata are some of the factors that may be considered when deciding on the number of sampling points to be used. Asking all the members selected to come to the researcher will require more funding from the researcher but it will be more time efficient for the researcher. It may also lead to a low turnout hence biasing the results. It would be more convenient for the researcher to visit each of the companies during working hours and question each of the employees. It would be even more convenient if the researcher visited the employees at department level. The time factor would be required more but the savings on the other factors would cover up for the time used. The number of sampling points will therefore be equivalent to the total number of departments in the companies.
Sample size is the other factor that needs to be determined. As stated earlier, the sample size should be as representative of the population as possible. In other words, it should be big enough to be accurate and small enough to be affordable. It should also be made sure that a sample is big enough to give all, (or most of) the answers (reasons, opinions, or perceptions). The larger the sample size, the more likely we are going to get all the possible answers. Failure to uncover any of the answers may lead to some negative results and the research may even be said to have failed. First, a problem may not be discovered and therefore not corrected. Second, a problem that is beginning would not be discovered until it has erupted. The size of a statistical sample is also determined based on the cost of data collection, time available and level of accuracy required. Larger sample sizes lead to more accuracy but are also more expensive to sample and would require more time. The level of accuracy is a very important aspect of qualitative research work which should result to values that are as a dose to those of the population as possible. However, available resources may limit the level of accuracy in a research project (Bansal and Corley 2012).
A perfect sample size should be enough to reach saturation, which is reached when no information can be added to the data already obtained. (Marshall, Cardon, Poddar and Fontenot 2013).The sample size needed to reach saturation is dependent on how the sampling is done, researcher experience and he number of interviews intended for each participant. However, limitations of resources sometimes does not allow for saturation to be reached. In that case, it’s only practical to strive to reach that point when all possible answers have been obtained. According to different sources 20-30 interviews are most likely to produce good data that is complete. In this case, a sample size of 30 will be taken (Marshall et al 2013). Every interviewee will have two interviews. This will ensure that he/she gets enough airtime to communicate their views. This will result in rich, deep analysis of the issue in question. Also going far beyond saturation will misuse limited resources of time and money. Every interview will last one hour. The entire data collection process will take place over the period of one month.
conclusion, the qualitative research which intends to investigate the scope of stress among
different individuals and obligations of the human resource departments in
organizations in addressing stress borne by their workforce, a group of all
employees from ten companies will form the population. From among these, a
sample of 30 interviewees will be selected proportionately using random
stratified sampling. Interviews will be conducted at department levels and
each interviewee will attend two interviews one hour long. By so doing, well
representative data will be obtained that will contain all information
required. The cost of data collection will also be affordable and the amount of
time used will be lowered. This therefore implies that this sampling strategy
is perfect for this research.
Marshall, B., Carldon, P., Poddar, A., & Fontenot, R. (2013). Does Sample Size Matter in Qualitative Research?: A review of Qualitative Interviews in Research. Journal of Computer Information Systems 54(1) 11-22
Bansal, P. and Corley, K. (2012, June) Publishing in AMJ – Part 7: What’s Different about Qualitative Research? Academy of management Journal. 509-513
Kerr, R. A., Breen, J., Delaney, M., Kelly, C. & Miller, K. (2011) A Qualitative Study of Work Place Stress and Coping in Secondary Teachers in Ireland. Irish Journal of Applied Social Studies 11(1) 27-38
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