THEORETICAL FRAMEWORK AND CONCEPTUAL DEVELOPMENT
4.6 Sampling Design
Apart from its development banking activities, the bank also provides corporate and commercial banking facilities involving both domestic and foreign transactions at very competitive rates and on flexible terms. These includes: retail banking, trade finance, development finance product, treasury services, funds management, international banking, SME loans, N.I.B micro save, western union money transfer, boafo yena savings, daakye nkosuo savings, among others. To ensure that the bank’s customers receive consistent and efficient services, the bank has resorted to the use of highly-trained personnel to in order to provide standard banking needs promptly to their clients. By relying on proper HR practices, the bank is able to deliver quality services tailored to meet the requirements of customers.
unit or section (Boudreau and Ramstad, 2005, 2007; Becker and Huselid, 2006;
Becker, Huselid and Beatty, 2009; Collings and Mellahi, 2009).
4.6.2 Population
The population is the group of people that the researcher is interested to learn more about and is the group about whom we want to draw conclusions (Babbie, 2013). It is the collective elements, sharing some common set of characteristics, which make up the universe for the purpose of the research problem (Creswell, 2014).
The most important thing in defining the target population is the precise specification of who should and who should not be included in the sample. The target population for this study consists of talented employees from three public commercial banks in Ghana. Therefore, to be considered a part of the target population of this study, all of the respondents had to meet the pre-set criterion of being labelled talented employees by their employer (the selected banks). Previous studies (Bethke-Langenegger et al., 2011; Sadeli, 2012; Björkman et al., 2013; Chami-Malaeb and Garavan, 2013; Gelens et al., 2015; Luna-Arocas and Morley, 2015) of TM have all concentrated on individuals designated by their organisations as talents.
4.6.3 Sample Frame
A sampling frame is the listing of the elements of the target population from which the sample will be drawn (Babbie, 2013; Creswell, 2014). The sampling frame for this study was based on talented employees from three public commercial banks in Ghana. The sampling units or list of talented employees were obtained from the HR managers of these three public commercial banks. The list was made up of seven hundred and seventy-four (724) talented employees. This was made up of three hundred and forty-eight (348), two hundred and twenty-five (225) and one hundred and fifty-one (151) from public bank A, B, and C respectively.
4.6.4 Sampling Procedure/Technique
The sampling technique makes possible the drawing of valid inferences or generalizations on the basis of careful observation of variables within a relatively small proportion of the population (Best and Kahn, 1998). The assumption behind a
sampling technique is that a small number of elements from a larger defined group can be selected and information gathered from this group will allow judgements to be made about the larger group (Hair, Black, Babin, and Anderson, 2010). The ultimate purpose of sampling is to select a set of elements from a population in such a way that descriptions of those elements accurately portray the total population from which the elements are selected (Babbie, 2013). Therefore, sampling techniques provide a variety of methods that enable the researcher to reduce the amount of data that need to be collected by considering data from the subgroup rather than all the possible cases and allowing gneralisations to be made of the entire population.
Two main sampling techniques were adopted for this study: non-probability and probability techniques. First, purposive sampling technique was used to select the banks. The non-probability sampling technique does not involve random selection.
Even though this technique may produce biased samples of banks in Ghana (Thomas, 2004) I was interested in studying only public banks. Thus, this sampling technique is mostly used when the researcher possesses sufficient knowledge that it may be possible to select one or a few units because they have characteristics relevant to the objectives of the study (Thomas, 2004; Babbie, 2013; Creswell, 2014). The sample of banks for this study consisted of three licensed public/government banks in the Bank of Ghana (BoG) database as of 31st December, 2014. The database has the list of all the banks in Ghana.
The second sampling method is probability sample. This method is where all of the population units have an equal chance of selection. In this case samples are drawn at random from a list of all the population units known as a sample frame (Thomas, 2004; Babbie, 2013; Creswell, 2014). Therefore, after the public banks were selected contact was made with them to seek their consent to participate in the study.
The list of talented employees was then obtained from the HR managers of these three public banks, which was made up of seven hundred and seventy-four (724) talented individuals.
The simple random sampling technique within the probability method (Thomas, 2004) was then used to select the participants. All of the talented employees in the three participating public commercial banks were arranged and put in a table
using a computer program, which automatically selected the respondents that were then given questionnaires to answer.
4.6.5 Sample Size
One of the main concerns regarding survey research relates to the appropriate sample size required to draw meaningful conclusions (Babbie, 2013). Indeed, Thomas (2004: 108) states that the question “how many units are needed?” is difficult to answer in the abstract. Thomas added that in a survey research sample sizes are likely to be influenced by both technical and practical considerations. On the technical side, as sample sizes increases, the magnitude of sampling errors decreases, although at a diminishing rate. On the practical side, funding and time constraints are likely to limit sample sizes (Thomas, 2004: 108).
The sample size for this study was largely driven by both technical and practical considerations. With respect to the technical consideration, the sample size was largely driven by SEM, which was the main data analysis method used in this study. Even though Tabachnick and Fidel (2014) did not categorically state the sample size, they indicated that SEM is a large sample technique. SEM requires a larger sample size relative to other multivariate methods (Hair et al., 2010). This is because some of the statistical algorithms used by SEM programs are unreliable with small samples.
Therefore, the critical question has been how large should the sample size be to produce trustworthy results. Opinions regarding the minimum sample size have varied (Fornell and Yi, 1992). Hair et al. (2010) suggested a sample size in the range of 100 to 400. They specifically stated the following sample sizes depending on the situation at hand: first, a minimum sample size of 100 for models containing five or fewer constructs with more than three items; second, a minimum sample size of 150 for models with seven or fewer constructs, modest communalities, and no under- identified constructs; third, a minimum sample size of 300 for models with seven or fewer constructs, lower communalities and/or multiple under identified constructs;
fourth, minimum sample size of 500 for models with larger numbers of constructs, some with lower communalities and/or having fewer than three measured items (Hair et al., 2010).
Therefore, five criteria have been identified as affecting the required sample size for SEM. The first is multivariate normality; second, the estimation technique;
third, model complexity; fourth, the amount of missing data; and fifth, the average error variance among the reflective indicators (Hair et al., 2010). From the practical point of view, funding and time are important here. Research degree students do not have all of the necessary funding or time to conduct large samples as in the context of large-scale government surveys.
Indeed, Thomas (2004: 108) argued that funding and time constraints are likely to limit sample sizes and these are likely to be quite tight in research degree studies. Based on the technical and practical considerations as well as on an examination of the relevant literature, a sample size of 300 was considered appropriate for this study. Careful consideration was giving to this decision because Creswell (2014) noted that the question of sample size is an equally-important decision to the sampling strategy in the data collection process. An appropriate sample size will ensure the validity and reliability of the study; hence the importance of sample size.
In fact, the importance of sample size is seen with respect to the fact that the larger is the sample size, the greater is the likelihood of obtaining a true representation of the population. This sample size was adequate because Thomas (2004) stated that samples of around 200 usually provide sufficient scope for analysis of survey data because those that have argued for sample sizes of 1000 and above are mostly government surveys, which are likely to be beyond the reach of the research student.
Again, the sample size of 300 almost represented 50 percent of the entire population of this study. Hence, Thomas (2004: 109) gave a general rule as follows: “as few as you must, as many as you can.” Table 4.4 provides the details of the banks’ total employees, employees in the talent pool, and the selected sample for this study.
Table 4.4 Population and Sample Size
Bank Total employees Talents Sample size
A 2,315 348 145
B 1,244 225 93
C 780 151 62
Total 4,339 724 300