research and sampling help

Selecting a relevant sample

  It is impossible to study the whole population of interest that is available when conducting a research. Therefore, it is advisable to select the samples that are relevant to the research from the population. In selecting the sample different techniques can be applied to sieve out the best samples for the study. These techniques are sometimes referred to as the sampling designs.

          Bias can occur in the planning, data collection, analysis, and publication phases of research. “Quantitative approaches tend to be more standardized, less subjective, and hence less subject to bias” (Shi, 2007, pg. 118). This study is successfully used by combining summaries of research results on the chosen area so that the examiner can review all appropriate findings objectively. Researchers may also recruit outside experts who have no vested interest to help with selecting a sample relevant to the research (Shi, 2007, pg. 124).

2) Please expand on the statement, " It must be registered that any study cannot be conducted on all the entire population."

*For more information read the whole response below re: Adequate sample size

With the achieved results it is conclusively convenient to say that the sample sizes were adequate. The purposes of each study were achieved; this is due to the data collected from the samples that were surveyed. The analysis of the data was concrete as it yielded to results. It must be registered that any study cannot be conducted on all the entire population. For economical reasons, a sample is pecked from the population. This sample will represent the whole population in the study or the research.

Adequate Sample Size

There are different types of sampling designs. The use of a particular design is relevant to the study or research in question. Selecting a particular sample design depends on a number of factors especially the situation or the scenario presented by the research. In the first study about factors that influence the patient’s decision making, the scenario is not an emergency case (Lohr, 2010). The study was done on patients that were under surveillance for a certain period of time. This implies that the sampling design that was employed in the case is simple random sampling. 467 patients were selected randomly and surveyed for a period of time. The results of the study were determined from the recorded data. 

The second article is about minimizing deviant behavior in healthcare organization and the effects of supportive leadership and job design. It was conducted to find ways of reducing the deviant behaviors of employees that were threatening to the effective leadership of the organization. The study was conducted on over 1900 employees of the American healthcare who were selected randomly. The determining factor was that an employee had to be of the American healthcare. This implies that the sampling design that was used was simple random sampling. Just like in the above scenario it was not a case of emergency. Simple random sampling is the most commonly used sampling. The reason for this is that simple sampling is accommodative for it gives time for the study therefore it is suitable for any study.

1.     Adequate sample size

            With the achieved results it is conclusively convenient to say that the sample sizes were adequate. The purposes of each study were achieved; this is due to the data collected from the samples that were surveyed. The analysis of the data was concrete as it yielded to results. It must be registered that any study cannot be conducted on all the entire population. For economical reasons, a sample is pecked from the population. This sample will represent the whole population in the study or the research.

2.     The validity of the conclusion

            The conclusion of each study is considered valid if the same conclusion would be confirmed by another study of the same or different sample sizes. Similar conditions must be assumed for the two studies. It is advisable to choose a sample size that is three-quarters of the entire population (Seber & Salehi, 2012). This will improve the validity of the conclusions. It is also more effective in minimizing errors and in studies that seek to determine trends. It must also be noted that choosing the right sample depends on the purpose of the study. For example patients were chosen as the sample in the first study about the factors that influence the decision-making by patients. The conclusion of this study would not be valid if the study was conducted on sample size different from patients. It is therefore convenient to conclude that the sample determines the validity of the conclusion of the research or study.

Reference

Lohr, S. L. (2010). Sampling: Design and analysis. Boston, Mass: Brooks/Cole.

Seber, G. A. F., & Salehi, M. M. (2012). Adaptive Sampling Designs: Inference for Sparse and

            Clustered Populations. Dordrecht: Springer.