Factors to consider for sample size determination
Discuss about the Quantitative And Statistical Research Method.
A relevant issue that researcher faces in relation to conducting research is to decide on the relevant sample size that is requisite for conducting the research. The key condition which a sample should meet is that it should be representative of the underlying population so that any conclusions that may be drawn from the same are reliable. Also, in the process of determining sample size, a trade-off is present which needs to be detailed. On one hand, the representativeness of the sample can be improved by choosing a large sample size but it has a potential cost and time factor attached which needs to be taken into cognizance. The alternative is to go for a smaller sample size which tends to save on costs and data collection time but may compromise on the accuracy of the sample which may be biased and hence would have an adverse impact on the results (Eriksson & Kovalainen, 2015).
In wake of this trade off, the researcher needs to consider the priority according to each of the factors and also ascertain the level of accuracy that would be required. For instance, if the subject of the study is such that a higher accuracy level is desired, then it makes sense to choose a higher sample size. Yet another relevant parameter is the level of heterogeneity present in the population. A largely homogeneous data can be well captured by a small sample size but for a heterogeneous population, a larger sample size is recommended so that the vagaries and deviations can be reproduced in the sample. These, factors are indicated in the formula for minimum sample size which is captured below (Hair et. al., 2015).
population having higher standard deviation would require a greater minimum sample size. The accuracy factor is represented by MOE (Margin of Error) which represents the acceptable tolerance level and needs to be decided by the researcher (Flick, 2015).
In the wake of the theoretical framework presented above, the relevant details of the current study need to be critically analysed. For the given study, the relevant population has 69,000 employees which are spread across more than 60 banks. The sample size selected for the study is 15000 which form 21% of the given population. A key aspect which justifies the rather large sample is that the population data would be quite heterogeneous considering that the data corresponds to 63 banks which are divided into different levels along with differing demographic parameters. In this case, a selection of a lower sample size could lead to a non-representative sample selection and thus would lead to issues regarding validity and reliability (Hillier, 2016). Hence, the given sample size seems to be fair in wake of the heterogeneous nature of the underlying population along with the accuracy desired by the researcher.
Sample size selection for the study
The sampling method which has been used for the given study is simple random sampling since a random sample of 15,000 has been selected from the given population with no classification has been done for various demographic parameters. A characteristic of this sampling method is that every element represent in the population has an equal chance of getting selected. This has been facilitated in the given case through the use of addresses since randomly 21% employees have been selected by each bank and personalised envelops have been mailed by the employer to collect the data.
- A key advantage of this method of sampling is the underlying convenience and requirement of minimal knowledge. This is pivotal considering the scope of the given study which involves selection of 15,000 employees (Flick, 2015).
- For each of the 63 banks, there has been no classification based on the key attributes and hence classification errors would not be witnessed which could be an issue in stratified random sampling (Hair et. al., 2015).
- This method is quite commonly used and tends to lead to representative sample selection owing to samples being randomly selected. This is especially true when the sample size is large enough (Eriksson & Kovalainen, 2015).
- One of the key issues in relation with simple random sampling is that in case of heterogeneous population, the sample may not be representative. This is prevalent for the given study also since for neither of the banks there has been any quota for key attributes such as employment level, gender and education level. In such a situation, it is possible that a particular gender or employment level may have a representation in the sample which exceeds the actual representation level in the population. Further, over-representation of one attribute automatically implies under representation of the other (Hillier, 2016). For example, it is likely that a particular gender could have an overwhelmingly high representation in the sample data and hence the data obtained from such a sample would not be representative of the population as a whole. Ann additional concern in this sampling could be regarding representation of those attributes which has limited presence. For instance, there may be only a few employees at the top levels and hence there is a fair chance of these level of employees being underrepresented leading to questioning of the validity and reliability of the study. .
- Owing to the issues in relation to the sample being representative, it is possible that standard error for such a sample would be higher and hence accuracy would be comprised to some extent (Flick, 2015).
It is pivotal that the given measure should be reliable which implies that similar data can be obtained if the same study is replicated with different sample data. The validity on the other hand for measurement variable implies that it should accurately capture the key parameters under study. In the context of the given research study, the discussion regarding the variables of measurement and their underlying reliability & validity is carried out below.
- Quantitative job insecurity– The reliability of measure can be captured using cronbach’s alpha. Considering that the given parameter is quantitative thus, value higher than 0.8 would be required to ensure satisfactory performance in terms of reliability. This condition has been fulfilled in the case of given measure. Owing to the usage of the concerned measure in a previous study based in a similar context, the validity concerns also do not arise for the given measuring variable (Hair et. al., 2015).
- Qualitative job insecurity – The reliability of measure can be captured using cronbach’s alpha. Considering that the given parameter is qualitative thus, value higher than 0.7 would be required to ensure satisfactory performance in terms of reliability. This condition has been fulfilled in the case of given measure. Validity concerns could potentially arise since the researcher has decided to go ahead with 10 selective measures and this selective choice can undermine the validity of the measurement variable (Hastie, Tibshirani & Friedman, 2011).
- Psychological distress – The reliability of measure can be captured using cronbach’s alpha. Considering that the given parameter is qualitative thus, value higher than 0.7 would be required to ensure satisfactory performance in terms of reliability. This condition has been fulfilled in the case of given measure. But, there are validity concerns in this case taking into consideration the fact that the questionnaire used is about five decades old and hence may lack validity under the current environment. Thus, it would be better that any other measurement for psychological distress should be selected based on some recent study as it would help in alleviation of validity concerns (Hillier, 2016).
The broad conclusion that can be derived from the discussion carried out above is that for the measurement variables used, reliability concerns are absent but validity concerns are present to some extent.
The given research study aims to highlight the association level between the qualitative and quantitative measures in relation to well-being and job security. However, it is noteworthy that the given research does not focus only on the above variables but considers additional demographic variables related to age, gender & education in the capacity of control variables. This is essential as potential changes in the control variables could alter the dependent variable and thus undermine the validity of the relationship between the independent and dependent variable. This is because a change in any of the control variables can produce a corresponding change in the dependent variable but it would not be on the basis of the independent variable (Hastie, Tibshirani & Friedman, 2011). The researchers intend to measure the changes observed in dependent variable only on account of the change made in the independent variables and hence identify the various control variables. By keeping the control variables identified constant, it becomes possible for the researcher to attribute that any change in the dependent variable is on account of changes in the independent variables (Flick, 2015).
The relationship between the various control variables and the measurement variables can be highlighted. One of the control variables is age. This is because there is difference in job expectations of the different age groups. For the employees who are aged and have been in the company since long are more concerned with quantitative measures since they want stability in the career and would not prefer to switch to another organisation. Also, the qualitative issues for these employees would cease to be that important considering the adaptability with organisational processes and culture over time. On the contrary, the younger employees would be more concerned about qualitative measures since they would be more ambitious and would not be hesitant to shift jobs for a better work environment. In comparison, the quantitative factors would be of lesser importance in comparison with their elder counterparts (Hillier, 2016).
The education level and gender also tend to play a significant role in influencing the priorities of the employees. It is expected that employees which have higher educational qualifications would value the qualitative measures more considering they would have high expectations regarding the work environment and nature of work. Additionally, owing to their better educational qualifications, they would not worry about quantitative measures as they would be able to secure job elsewhere. However, for the employees lacking in any higher education qualification, quantitative factors would be more crucial in comparison to qualitative factors. This is because these employees would face difficulty in finding an alternate job and hence they are willing to compromise on the attributes of work (Hair et. al., 2015). Further, gender is also a crucial variable impacting the preferences of the employees and thus need to be controlled for this research study.
For the purposes of the given research, the design used would be categorised as correlational design considering the emphasis is on using a particular sample data for understanding association between qualitative and quantitative measures. This research design has various advantages and limitations that have been outlined below.
- The given research design leads to higher collection of data as compared to studies based on experimental design, One potential explanation of this could be the aim of the correlation design to track the broad trend rather to find precise values and test given hypotheses (Hastie, Tibshirani & Friedman, 2011)
- This research design tends to provide leads for a plethora of other researches. Since the correlational research design is concerned with understanding association and not underlying causes, hence further descriptive and experimental research design can be deployed Hillier, 2016).
- Besides, correlation research design plays a vital role in theory building by providing initial direction of potential association between two or more variables which then can be carried forward with additional researches for building theory (Flick, 2015).
- One of the key limitations of this research design is that only association between the variables is indicated and hence a cause and effect relation cannot be confirmed. Additionally, even if association relationship is formed , the underlying causes for the same cannot be explored using the given research design and it would require assistance from other research designs (Hastie, Tibshirani & Friedman, 2011)
- The number of variables that can be represented in this research design is rather limited since at a given time association relationship between only two variables can be captured (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.
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