Determination of the sample size is normally the first stage of carrying out any research study. In preparation of any research paper, there are ethics and methods that should be considered. Where there are two studies conducted using similar research methods, attaining same findings but only distinct in terms of size of the sample could lead the researcher to make distinct decisions. Hence, in an ideal situation, the sample size should not be very small neither should it be very large contrary to popular belief (Faber & Fonseca, 2014).Sampling is the criteria of selecting a part of the entire population. Each stage of the research process requires some form of sampling as it is never possible to determine all the variables required that might be relevant, to interview each person who could provide essential information, or to include in the final report (Peil, 1995). Therefore, sampling is done after identifying the aim of the study, the location, the allowable sampling error, and after identifying the target population including the data to be collected.
According to Miaoulis and Michener (1976), as cited by Israel (1992), apart from the aim of the research and size of the population, three criterions will normally need to be stipulated to establish the adequate sample size. This will include level of precision, confidence level and the variability degree in the characteristics being calculated.
The Level of Precision: the level of precision also referred to as sampling error in the gap within which the true population parameters estimates lie. This gap is normally given in percentage for instance ± 5 per cent. Hence if a researcher determines that 70 percent of employees support corporate social responsibility with a precision rate of ± 5%, then he can infer that 65 to 75 per cent of the population support corporate social responsibility initiatives
The Confidence Level: the confidence or risk level bases its explanation from the central limit theorem that were the population is sampled several times; the mean value of the feature achieved from the samples is equivalent to the true population parameter. In addition, the values derived from the samples are normally distributed. In a normally distributed data, an estimated ninety-five percent of the sampled data lies within 2 standard deviations of the true population parameter for instance the mean.
Degree of Variability: this refers to the distribution of the sample values in the population. Where the population is more varied, means the larger a sample size required to achieve a particular precision level. Likewise, the less diverse a population, the smaller sample size required. It is important to note that a proportion of fifty percent shows higher variability than 30% or 70 percent since 30% and 70% do or do not, respectively have or not have a feature/characteristic of interest. That is, they are swayed less by a slight change in circumstances than a 50% proportion. Since an alpha level of .5 shows the maximum variability in the populace, it is normally utilized in establishing a more stringent sample size. This means that the size of the sample could be bigger than when the true variance of the populace characteristic was utilized.
The question that arises therefore that arises is does a large sample size guarantee better results than a smaller sample size? A small sample size has the advantages of cost effectiveness in collecting, coding analyzing, and interpreting results. In addition, it is less time consuming and convenient for a study. Population definition is critical at the early stage of planning any research as generalizations of the population are hazardous since we do not have a proof of how the population may respond because if we had this information, then research would be irrelevant.
Kaplan et al. (2014) notes that despite a large sample having a number of merits, researcher can add little or no value when the large sample size does not represent the population for which the findings will be targeting or does not have information from certain sub-groups particularly when the sampling was non-random.
Inasmuch as a large sample size will endeavor to represent the population parameters, care should be taken not to use a very large sample size. To illustrate how a large sample size can be detrimental, Kaplan et al (2014) gives the example of the public opinion poll of the 1936 presidential election in the United States. In the opinion poll conducted by Literacy Digest over two million Americans were interviewed on their preference of either voting for Governor Alfred Landon of Kansas or the serving president Franklin D. Roosevelt. The poll results were unequivocal that Landon would take office by a large margin. However, after the election was conducted, Roosevelt won 46 of the then 48 states proving the large sample size results wrong. The Literacy Digest magazine had surveyed its readership that was largely skewed to groups who were supporters of Landon. Great care should therefore be taken in selecting the sampling frame to include all aspects of the target population as biased sampling produces skewed data that is unreliable and quite misleading in terms of generalizing the population (Freedman, Pisani, & Purves, 2014)
The current method of sampling used to select individual respondents from the banks was simple random sampling. The main advantage of form of sampling technique is that since it is probability sampling, each individual has an equal chance of selection and in this way; the element of biasness in sample selection is eliminated. However, based on the current research topic, simple random sampling could fail to capture adequately the entire population aspects as no form of stratification or sub-division of the population was done hence affecting reliability of the data. The other disadvantage is that simple random sampling can be very expensive especially when data has to be collected in different geographical regions as in this case.
The current data collection procedure, though efficient when dealing with small sample sizes would benefit from some improvement. At the outset, it is important to identify clearly the research objective or aim. Secondly, as research is done to solve a problem, describe or explain a situation, or concerning more information to predict future occurrence, the sample size needs to be adequate to effectively capture and make inference of the population. The research design is also important. The cross-sectional design chosen by the researchers was appropriate to answer the research question since it covered one particular point in time and since it was geared towards answering the research questions from different sections or subsets of the population. The next step is to identify the target population for which the data is to be collected. These aspects require careful planning. The sampling techniques applied by the researcher hence determines the level of reliance on the outcome of the study since an inappropriate sampling technique will give results yes but may fail to capture the general view of the population.
In this regard therefore, it is suggested that the researcher employ multi-stage sampling. According to Kaplan (2013), multi-stage sampling technique involves organizing the sampling process in manageable stages. This is whereby large sections or clusters of the population are split further into small manageable groups of the target population. Therefore, to improve on the sampling technique, the following sampling stages are suggested:
Stratified Proportionate sampling. This involves dividing the target population into strata or sections based on Each department population should be established then apportioned proportionately according to the desired total target population. The reason for proportionate sampling is because an assumption is made that each department has a different number of employees.
After stratifying, simple random sampling should be done in an endeavor to capture all the aspects of the population based on social economic status and demographic data such as age, level of education and marital status.
For any research study to be successful, the data collection methods have to be chosen carefully. A researcher may have to choose the most appropriate method of collecting data that is geared towards answering the research questions. Questions normally seek to determine the demographic information of the target population such as age, marital status and education level. In addition, questions are formulated that are relevant to answering the research questions. Questions may be asked personally by means of an interview or impersonally using a questionnaire. In this study, whereas a self-completed questionnaire has its benefits such as simplicity and low cost, it has a number of disadvantages. For instance, the researchers suffered a number of obstacles while collecting data including low response rates. The logistical challenges aside, there was the question of language. Some french speaking respondents received Flemish (Dutch) questionnaires and vice versa. The researchers made an erroneous assumption that respondents who lived in Flanders (predominantly Flemish speakers) were all Flemish speakers while the French speaking side of Belgium were all Flemish speakers. This terrible oversight culminated to the angry phone calls received from respondents.The Belgian banks utilized a decentralized way of operations. This provided a logistical nightmare in addition to higher costs of collecting the required data. As a general rule of the thumb, the right to knowledge must be balanced by the right to personal privacy and honesty. A survey should be organized in such a way that it is both consensual and voluntary. For instance, initiatives employed by some banks to issue letters violated one of the ethical concerns in research, informed consent. The respondents have the basic right of withholding or give their consent. The unions’ also guided the respondents how to answer certain questions. These initiatives had the potential effect of getting biased responses and low response rates hence the outcome or findings of the research would be misleading and would not give the true reflection of the situation in the population.
To address the problems faced in the study, since Brussels is mainly bi-lingual where the main language spoken is English, future study should consider including a questionnaire in English or better still use a standardized English version. Inasmuch as the researchers chose to distribute the questionnaires in the preferred language of the respondents, care should have been taken to ensure that a respondent does not receive a questionnaire in a language other than his or her native language.
The secondary dataset that can be used to verify the reliability of sampled data is data can be derived from for instance, Belgian Bankers’ and Stock broking Firms’ Association (ABB-BVB). This is an association that lists all the Belgian Banks including stock brokers. It contains among other things CSR activities of the banks in Belgium (see https://www.febelfin.be/en). Data obtained from previous research studies on the job satisfaction in Belgian banks could also be useful to check the reliability of the data of the current study.
References and Bibliography
Faber, J., & Fonseca, L. M. (2014). How sample size influences research outcomes. Dental Press Journal of Orthodontics,19(4), 27-29. doi:10.1590/2176-9451.19.4.027-029.ebo
Freedman, D., Pisani, R., & Purves, R. (2014). Statistics. New York: Norton.
Israel, G. (1992). Determining Sample Size. Florida Cooperative Extension Service, 1-5.
Kaplan, D., Lacetera, N., & Kaplan, C. (2008). Sample Size and Precision in NIH Peer Review. PLoS ONE,3(7). doi:10.1371/journal.pone.0002761
Kaplan, R. M., Chambers, D. A., & Glasgow, R. E. (2014). Big Data and Large Sample Size: A Cautionary Note on the Potential for Bias. Clinical and Translational Science,7(4), 342-346. doi:10.1111/cts.12178
Miaoulis, G., & Michener, R. D.(1976). An Introduction to Sampling. Dubuque, Iowa: Kendall/Hunt Publishing Company.
Peil, M. (1995). Social science research methods: a handbook for Africa. Nairobi: East African Educational.
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