Sampling Methods and its Impact on Business Decision Making

This assignment explores various sampling methods, including random, systematic, convenience, cluster, and stratified sampling. It delves into the concept of sampling bias, specifically focusing on cluster sampling bias (CSB) and its implications. The assignment explains that CSB arises when certain clusters are disproportionately sampled, potentially skewing research results and affecting the validity and generalizability of data analysis. It highlights the potential for CSB to underestimate intervention effects and influence business decision-making. The assignment also discusses the importance of addressing sampling bias and suggests strategies to mitigate its impact, emphasizing the need for representative samples to ensure accurate and reliable research outcomes. References to relevant research papers are also provided.

Initial Post
Performing a research study requires collection and evaluation of primary and
secondary data. Sampling is one useful method for collection of data. There are various
sampling methods that are used in collection of data used for a research. There are mainly
five types of sampling method that are used in a research and these sampling methods include
random sampling, systematic sampling, convenience sampling, cluster sampling and stratified
sampling (Sarstedt & Mooi, 2014). While discussing the sampling methods, the concept of
sampling bias comes into play. Sampling bias indicate that the samples of a stochastic
variable that are considered to determining the distribution of the sample are incorrectly
selected and it fails to represent the true distribution.
In this context, cluster sampling and its related bias can be discussed. This type of
sampling is generally used when a homogeneous group who are internally heterogeneous, are
evident in a statistical population (Singh & Masuku, 2014). In this sampling method, the
researcher mainly divides the population into separate groups which are called clusters and a
simple random sample of clusters are mainly selected from the chosen population. After data
collection, in this sampling method, the researcher generally conducts his analysis from the
sampled clusters. The method of cluster sampling is linked with a sampling bias which is
known as cluster sampling bias (Patten & Newhart, 2017). This type of bias mainly occurs
when a particular cluster is given a particular territory that are likely to be sampled in
comparison to the others. Prevalence of this type of bias can significantly impact the validity
and the generalizability of the data analysis results.
The presence or prevalence of Cluster sampling Bias (CSB) can have a significant
impact on the validity and generalizability of data analysis results. This bias can significantly
result in underestimating the effect of intervention and leading to generation of results, which
are overestimated (Etikan & Bala, 2017). However, in many cases, the conclusions drawn
from the corresponding evaluation may not be biased and the effect of the bias can be site
specific. Thus, the sampling method can significantly affect the business decision making as
the results obtained from the research will alter the business strategies as well. Sampling bias
is quite common in applied research and therefore, effective measures are needed to be taken
to address the concern and the issue related to sampling bias. CSB can have a significant
effect of business decision making particularly because it is not just a statistical property but

also a human behaviour. For example, in a clinical trial, the choice of the participating centres
can have a large impact on the generalizability of the results of the trial. Therefore, the CSB
can be controlled and its impact on the business decision making can be controlled as well.
The simplest way to manage this problem is to select a non-representative sample which may
not be received by the stakeholders of the study as favourable.

References
Etikan, I., & Bala, K. (2017). Sampling and sampling methods. Biometrics & Biostatistics
International Journal, 5(6), 00149.
Patten, M. L., & Newhart, M. (2017). Understanding research methods: An overview of the
essentials. Routledge.
Sarstedt, M., & Mooi, E. (2014). A concise guide to market research. The Process, Data,
and, 12.
Singh, A. S., & Masuku, M. B. (2014). Sampling techniques & determination of sample size
in applied statistics research: An overview. International Journal of Economics,
Commerce and Management, 2(11), 1-22.