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Before discussing a sampling plan, there has to be clear and unambiguous definitions of what a sample and sampling are. Despite diversity in the definition of a sample, the best meaning is that a sample could be considered as a subset of a population, with which a researcher would like to use as participants in a given research study (Landreneau & Creek, 2012). According to Deming (1990), sapling is a science, which specifically guides quantitative studies, materials, behavior and the different causes of difference. In other aspects of research such as the qualitative research, sampling could be considered as the art of selecting a part of a population, in a given research area that is a representation of the entire population.
Both the qualitative and quantitative researchers approach their sampling differently. For the quantitative researchers, samples which are selected are those that will give the researcher easy time when trying to achieve statistical conclusions that are valid and also generalize their final results. Because of this reason, they have to come up with a sapling plan that is specific on matters of participant selection. For the qualitative researchers, they focus on sample plans that will develop rich and holistic understandings the phenomenon of interest (Polit & Beck, 2007).
Factors to be considered when developing a sample plan
When developing a sampling plan, one of the most fundamental objectives is to come up with a favorable sampling frame. Basically, a sampling frame is the list that entails the population that the researcher intends to take selections from. A sampling frame has to be complete and current list of the population a researcher intends to research on. The best example would be the use of surveys in data collection, where the sampling frames are developed before the registration of the population. The best examples of a sampling frame would be telephone directories, attendance lists for schools and workplaces and voting registers (Denscombe, 2007).
Developing a sampling frame
When a researcher wants to device an efficient sampling frame, it is of great importance to actually distinguish between study populations and the target population. In many circumstances, the sampling frame is gotten from the study population, where there is an assumption that the study population reflects characteristics of a target population (Gerrish & Lacey, 2010). Especially for the researchers who are conducting surveys, they are obliged to search for the most suitable sampling frame. The researcher should, therefore, go to greater lengths of getting labels for purposes of posting, purchasing addresses from specific computer databases and many other sources (Denscombe, 2007).
Biasness in sampling frames
Researchers have to be aware, and ensure that there is no danger or possibility for biasness. In the first place, the researcher has to ensure that the sampling frame is not by any chance out of date. For instance, a private address research is not supposed to include those who are in the streets. Any electoral register is not supposed to be inclusive of those below the age of 18 years and are not ready to vote. If by any chance the sampling frame excludes items that have to be included, or includes things that are not supposed to be there, then the frame being developed will inevitably succumb to biasness. It is relevant that the researcher checks on the completeness of the sampling frame, to ensure it is up-to-date (Denscombe, 2007).
Elements of a good sampling frame
For a sampling frame to be considered efficient and effective, there are elements that have to be presented in the frame. These include; relevance, meaning its contents should be linked directly to the topic under research. It should also be complete in that all the relevant items needed in it have to be considered. Preciseness is the other factor requiring attention. The sampling plan has to exclude any items of irrelevance. Lastly, the sampling frame needs to be up-to-date. This means that any recent changes and additions have to be incorporated. On the other hand, redundant items have to be completely excluded from the frame unless they are necessarily required (Denscombe, 2007). The frame will be essential when identifying and selecting the best sample in a certain probability sample design, and also establishing contact with the selected elements (Sarndal, Swensson & Wretman, 2003).
According to Arturo and Gomez (1984), a sampling unit is the unit on which actual measurement is made. In case a given plot is the population of an area, then the sampling unit has to be smaller than the plot itself. An appropriate sampling unit has to be made and selected in order to attain the conditions of the research experiment. A sampling unit can otherwise be defined as an element, or many elements which are eligible for selection during the sampling process. For instance, a given airline could decide to choose every 20th passenger that is supposed to fly in a given date. Considering the example, the sampling unit will definitely be same as the element. On the other hand, the airline could decide to choose specific flights which would be the sampling units, then further choose passengers from the flights, who would be the elements of the sample (Zikmund & Babin, 2012).
Features of an appropriate sampling unit
An effective sampling unit has to consider some specified elements for appropriateness. One of the factors is the ease to measurement. When choosing a sample unit, the researcher has to consider that the unit will be measured easily to avoid future inconveniences. The unit also has to be of high precision and of low cost on the other hand. Precision could easily be measured using reciprocal of variance of a given sample unit. Cost, however, can be measured and based on the spending of time when measuring samples. If the sampling measurement time is short, then the costs are likely to be reduced. In order to keep the costs down and retain a reputable precision, then the researcher has to ensure the variability of the sampling unit (within a given plot) has to be small in size (Gomez & Arturo, 1984). For reference purposes, it is necessary to define what a primary sampling unit (PSU) is. When sampling surveys, PSU's arise when population elements are categorized and the different categories then become the units in the sampling process (Lepkowski, 2010).
In most cases, the sample size is the most fundamental feature that affects any empirical study. Practically, the sample study is based on the expense of collecting data and also the required statistical power. When selecting a sample size, different methods could be applied in determining the sample size. These include; expedience, use of target variance and using a target with the statistical test. When determining the sample size, the precise approaches that need to be adopted include confidence intervals, computer intensive methods and power analysis (Dattalo, 2008).
Factors of consideration when determining sample size
Variables of measurement
The researcher has the task to decide on the appropriate variable to incorporate when calculating the sample size using formulas. For instance, if one chooses the seven- point scale when taking measurements of a continuous variable such as job satisfaction, and wants to determine whether the study respondents are different by specific categorical variables such as gender and educational levels, the best variable to use. If not, the primary variable (gender) will most likely result to a larger sample size than expected if seven-point scale was to be used (Bartlett, Kotrlik & Higgins, 2012).
One of the critical components of formulas of sample size is when estimation of variance is a variable of study interest. Though the researchers have no control over the variance of the study, they have to integrate the variance estimates into the research design. There are different ways of estimating population and sample sizes. One of the ways to estimate is to divide the sample into two different steps, and then use the results acquired in the first step to determine the number of additional responses to consider in the next step. The second estimate is use of pilot study results. The researchers could also take advantage of studies that were taken earlier in circumstances where they studies are same or the population is similar. The last resort would be to guess or estimate a structure of a population, as long as there is evidenced logically by mathematical results (Bartlett, Kotrlik & Higgins).
Target population and accessible population
A target population is the collection of individuals, cases, items and objects that a researcher is interested in, and expects to take a sample from. For instance, in the case of a healthcare case, the target population could be patients between the age of 17 and 50 years with pains for at least duration of a year. Accessible population could be defined as the portion of a target population. The portion is accessible to the researcher for the reasons of adopting a specific study. A sample can only…[continue]
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