Business Research Method Research Proposal 2022

Business Research Method; Research Proposal

Business Research Method; Research Proposal
Question One
The sample size refers to the total number of observations that make up the sample
selected from the population for a study (Biggs & August, 2013). Once the target population
has been selected, the collection of sample observations follows. The determination of the
sample size to be used in a study is a very important aspect of the study; this is because the
sample size has to be representative of the population size of the target population.
The ideal sample size for any study would be a situation of complete enumeration.
Complete enumeration refers to a study property in which the entire target population is
considered and used (O'Neil, 2011). That is, the sample size for the case of complete
enumeration equals the population size of the target population. Complete enumeration, such as
in the case of the census, is advantageous since it is completely representative of the population
size. Hence the findings from a study that has applied complete enumeration can be described as
conclusive and highly accurate. However, conducting complete enumeration is an expensive
exercise and therefore most studies prefer using sample observations instead of using the entire
population (Lenca & Ferretti, 2018).
This then implies that in the selection of the sample size for a study, two general criteria
must be met simultaneously. The sample size must be large enough to be considered as
representative of the population size but still small enough to be within the budget for the study.
Other criteria considered for the determination of the sample size are; desired confidence level,
desired precision level and degree of attribute variability.
In order to meet these criteria, there are two main methods used for determining the
sample size of studies; Cochran Sample Determination Formula and Simplified Formula. Below
is the Cochran Sample Determination Formula (Cochran, 1977):
na= Z2 pq
e2
Z represents the corresponding values in the Z tables, a is the attribute considered for
proportionality, p is the proportion of the attribute considered, q = p – 1 and e represents the
desired margin of error. However since this research did not consider any attribute in the
sampling process, the (Cochran, 1977) formula will not apply.
The formula for the simplified formula is as given below (Boden, 2011):
Sn= Ps
1+ Ps (e2 )
Sn represents the sample size, Ps represents the population size and e represents the desired
margin of error. Since this formula does not consider any proportions of the attributes it would
apply for this research case. Considering a 95% margin of error and the population of all the
employees in the 63 banks in Belgium, 69000, then the sample would be;
Sn= 69000
1+69000(0.052)=397.69 ≅398 employees
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Business Research Method; Research Proposal
Therefore, we can say that the 15000 sample size was not necessary since it is too large
considering that a sample size of 398 would be sufficient for the study. The sample size of 15000
also made the study costly, whereas a smaller budget would have been enough if a sample size of
398 was considered.
Question Two
The research uses the random sampling technique for the collection of data for the
intended sample size of 15000. The random sampling technique is a sampling technique that first
identifies the sample size (either by considering the desired size or using sample determination
formulas) then selects the sample observations in such a manner that every observation in the
target population has an equal likely chance of being selected (Marshall & Rossman, 2011).
The random sampling method has the following advantages:
1. The random sampling technique is a fairly easy technique of sampling. Very little
background mathematical knowledge is required in conducting random sampling. This
allows the technique to be applied in researches across many disciplines that do not
necessarily have mathematical foundations (Babbie, 2010).
2. The random sampling technique reduces the chance of error and bias in the findings made
by a study. This is mainly due to the randomness of the selection of the sample
observations without the consideration of any present conditions and preferences
(Himmelfarb Health Sciences Library, 2011).
3. The findings from a study in which the random sampling technique has been used for the
data collection is more representative of the reality in the population. This is based on the
equal likely chance of selection of any observation in the target population into the
sample (Cao, Cox, & Eslick, 2016). This implies that every member of the population
had a chance to be selected for the data collection process.
The random sampling method has the following disadvantages:
1. The random sampling technique represents an uninformed mode of sampling. This
technique does not put into consideration other attributes in the population that might
improve the level of representativeness of the sample (Himmelfarb Health Sciences
Library, 2011). These aspects include gender ratio, age ratio and race ratio.
2. The effectiveness of random sampling technique is highly reliant on the size of the
sample. The results from studies that use random sampling increase in accuracy as the
sample size increases. A large sample size improves the chances of random sampling
factoring in the different attributes in the population (Cao, Cox, & Eslick, 2016).
3. The large sample size necessary for reliable results in studies that use the random
sampling technique means that the cost of conducting the study increases as well. This in
a sense implies that using random sampling techniques is a costly approach of sampling
in research studies (Babbie, 2010).
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