Determining Sample Size and Sampling Method in Research

This article discusses the determination of sample size and sampling method in research, highlighting the tradeoffs involved and the importance of accuracy and heterogeneity. It also explores the advantages and disadvantages of simple random sampling and the role of control variables. Additionally, it examines the reliability and validity of measures used in research and the correlational research design and its pros and cons.

RESEARCH AND STATISTICAL METHOD
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Question 1
The researchers with regards to their research face an issue in relation to determination of
sample size which should be considered for the research. The key requirement of the
sampling process is for the selected sample to represent the population of interest in a faithful
and accurate manner. It is noteworthy that in relation of determining appropriate sampling
size, there is an inherent tradeoff involved which must be elaborated. In case of selection of a
large sample size, the chances of the sample being representative of the population tends to
increase but the downside is the additional costs involved in selection of this large sample
and the associated cost of data collection. On the other hand, with a lower sample size, it
may so happen that the accuracy is compromised owing to the sample not being
representative (Eriksson & Kovalainen, 2015).
Owing to the inherent trade off, it is essential to seek the determinants which enable the
researcher in deciding the requisite sample size that would be necessary. One of these is
obviously the accuracy which the researcher desires. In case of higher accuracy being
desired, a higher sample size would be more desirable as compared to a situation where the
accuracy requirements are low. Another factor is the heterogeneous nature of the population
which would tend to determine the appropriate sample size. The mathematical expression for
minimum sample size is also broadly based on these elements as highlighted below (Hair et.
al., 2015).
Thus, in line with the above formula, it is apparent that higher the heterogeneity in the
population of interest, higher would be the minimum sample requirement. Further, higher the
MOE or Margin of Error that is acceptable to the researcher, lower would be the sample size
requirement (Flick, 2015).
The current case needs analysis in the wake of above theoretical discussion. The population
of interest for the given study consists of 69,000 employees belonging to Belgian banks and
the selected sample size is 15,000 comprising about 21% of the population. The sample size
seems appropriate taking into consideration the heterogeneous nature of population as there
are employees from as much as 63 banks. Further, there are additional attributes which tend
to be divided these employees such as levels, gender along with educational status. Any

sample which is lower may lead to sampling errors and hence the chosen sample may not be
representative of the population, thus compromising the results of the study (Hillier, 2016). In
wake of the above discussion, it is fair that a large sample has been selected for the study
under consideration.
Question 2
The sampling method that has used to select the 21000 employees from the population of
63000 employees is simple random sampling. This is referred to a sampling method where all
the elements comprising the population have an equal chance of getting selected. For the
given research study, this may be carried out by labelling the employees with a unique
integer. Then using computer program, 21000 numbers can be randomly selected from the
pool of numbers allocated to employees. The employees corresponding to the numbers
selected would form the sample for the given research. The advantages and disadvantages
associated with the given sampling technique are highlighted below.
Advantages (simple random sampling)
1) The primary advantage of this sampling method is the convenience of use and limited
knowledge requirement. This is quite necessary especially when the sample size chosen is
significantly large (Flick, 2015).
2) Since in this sampling method, there is no need of any classification, hence any errors
which may be incurred on that count are absent in this sampling method (Hair et. al.,
2015).
3) It is an extensively used method which tends to result in representative sample selection
especially if the size of the sample is sufficiently large considering the population
characteristics (Eriksson & Kovalainen, 2015).
Disadvantages (simple random sampling)
1) The key issue with this sampling method is the biased sample which may be possible
when the sample needs to accurate represent certain key attributes. This aspect can be
substantiated with the example of the research study at hand. For the sample data, there are
a lot of critical attributes like gender, employee level, bank name which have to be fairly
represented. This tends to become difficult in case of random sampling since it can

potentially happen that there is overrepresentation of a particular attribute and
underrepresentation of another (Hillier, 2016). For instance, it if possible that a large
proportion of male employees are selected while the proportion of female selected is
comparatively small. Especially another concern is that the population data consists of
employees of as many as 63 banks. As a result, there would be certain banks with limited
employee strength. In simple random sampling, it is possible that such low representation
to population may not be captured in the sample selected. Hence, it makes sense to first
classify the population in accordance with the key attributes and then proceed with simple
random sampling in each of these classifications.
2) Considering higher biased, possibility potentially the standard error would be higher
which would have an adverse impact on the accuracy and applicability of the results
obtained (Flick, 2015).
Question 3
For the given measures used in the research study, a relevant discussion in relation with the
reliability and validity has been carried out as follows.
 Quantitative job insecurity – The measurement of reliability of this measure can be
adjudged the use of cronbach’s alpha. The general rule is that for quantitative
variables or measures, it should be above 0.8 for the reliability to be considered
satisfactory. For the given measure, this parameter is in excess of 0.8 implying that
reliability of measure does not pose any challenge. Validity concerns also should not
arise since in a similar context the measure has been used in a previous study. Hence,
on both counts i.e. reliability and validity, there does not seem to be any concerns
with the use of the given measure (Hair et. al., 2015).
 Qualitative job insecurity - The measurement of reliability of this measure can be
adjudged the use of cronbach’s alpha. The general rule is that for qualitatative
variables or measures, it should be above 0.7 for the reliability to be considered
satisfactory. For the given measure, this parameter is in excess of 0.8 implying that
reliability of measure does not pose any challenge. Additionally, concerns on account
of validity could potentially arise as only 10 out of the 17 measures have been
considered and hence it may so happen that the researcher may have left out the

crucial ones which potentially could undermine the validity of the results (Hastie,
Tibshirani & Friedman, 2011).
 Psychological distress – Like the above measures, reliability for this measure seems
fine considering a cronbach alpha in excess of 0.8. However, on account of validity,
there could be issues considering the fact that the questionnaire version used seems
quite old and hence may not be valid in the current scenario. Over the last five
decades, numerous studies have been conducted on psychological distress
measurement and it would be preferred that a more recent measure in this regard is
considered so as to pacify validity related concerns (Hillier, 2016).
On account of the above discussion relating to the key measures of various factors, it may be
concluded that reliability does not seem to be an issue but the same cannot be concluded
about validity which can be further improved.
Question 4
The key objective of the given research study is to illustrate the level of association in the
quantitative and qualitative measures related to job security and well-being. One of the
interesting observations is that besides the above measures which are imperative for the given
research, incremental variables such as education, age and gender have also been introduced
in the research. These variables have been included as control variables as any modification
in these inputs can lead to change in the dependent variable even though the independent
variable may remain constant. Thus, changes in these variables can thereby adverse impact
the result validity (Hastie, Tibshirani & Friedman, 2011) Even though control variables are
important, but it is noteworthy that these do not form the main concern of any researcher
since the focus is on measuring values of dependent variable by altering independent
variable. In order to let the researcher focus on the primary objective, the control variables
are identified so that they can be kept constant for the study so that no effect on the dependent
variable is on account of these control variables (Flick, 2015).
Through the example of the given research, the role of control variable can be explained.
Take for instance the control variable age. It is likely that employees falling in higher age
bracket would be more stressed and dissatisfied on account of quantitative measures
considering that they are concerned at the prospects of searching a new job. Also, over the
years, these employees would be expected to adapt to the quantitative issues which would

cease to be that important. This is in sharp contrast with the employees that are younger in
age. These tend to be ambitious and hence more demanding than their elder counterparts.
Also, the quantitative measures of dissatisfaction would be less significant for these
considering they are more adapting and hence can look for job elsewhere. For these
employees, the qualitative factors are expected to be more critical (Hillier, 2016).
Also, similar to age, the other two control variables in the form of education level and gender
would be critical too. For those employees with higher education level, finding an alternative
job may not be difficult owing to which the quantitative measures of dissatisfaction may not
be too relevant. However, the qualitative measures are critical for these owing to the higher
expectations that these education employees would have from their employer. In case of
employers who are not very educated, retaining the job is the primary concern and the
conditions are job are not critical. Hence, for these employees, the quantitative measures of
dissatisfaction would be more significant and representative in comparison with qualitative
measures. Similarly, owing to the gender roles, the prominent factors of dissatisfaction for
both the genders would differ (Hair et. al., 2015). Considering the above, it makes sense to
take these variables as control variable so that these cannot impact the dependent variable
under study.
Question 5
The relevant research design to be deployed for the given research is known as correlational
research design. The associated positive and negatives of this research design are highlighted
as follows.
Advantages (correlational research design)
1) A key advantage is that in this research design, the amount of data collected is much
higher in comparison with experimental studies. This may be related to the objective
of the correlation design which is not to specify the precise relationship but to identify
a trend between the variables of interest (Hastie, Tibshirani & Friedman, 2011)
2) Further this research design also serves as starting point for many other researches
which tend to take the initial cue from such research studies and then use a descriptive
research design in order to develop a causal relation which tend to show an
association relationship (Hillier, 2016).

3) Also, the correlation research design and the related results provide useful data and
association relationship for other studies which can take these into consideration for
the formulation of various hypotheses which can be further validated using
experimental or descriptive design (Flick, 2015).
Disadvantages (correlational research design)
1) A key limitation or disadvantage of using this research design is that it only focuses
on commenting the strength and direction of association but not focus on
understanding the underlying reason and the other factors which may be responsible
for this association. Further, no causal relations can be derived from such research
designs as these would require further validation (Hastie, Tibshirani & Friedman,
2011)
2) The use of this research design is appropriate when only two variables are involved as
it cannot simultaneously account for more than two variables (Hair et. al., 2015).

References
Eriksson, P. & Kovalainen, A. (2015) Quantitative methods in business research (3rd ed.).
London: Sage Publications.
Flick, U. (2015) Introducing research methodology: A beginner's guide to doing a research
project (4th ed.). New York: Sage Publications.
Hair, J. F., Wolfinbarger, M., Money, A. H., Samouel, P., & Page, M. J. (2015) Essentials of
business research methods (2nd ed.). New York: Routledge.
Hastie, T., Tibshirani, R. & Friedman, J. (2011) The Elements of Statistical Learning (4th
ed.). New York: Springer Publications.
Hillier, F. (2016) Introduction to Operations Research (6th ed.). New York: McGraw Hill
Publications.