CHAPTER THREE
3.3 Sampling Technique and sample description
According to Lind et al., (2008), sampling is the process of making inferences about a population using a representative sample that is selected using an appropriate sampling
54 technique. A sample as opposed to analysing the general population is convenient as it is time saving, cost effective and if the sample is adequately selected, the results of the research become reliable, stable and consistent (Lind et al., 2008).
3.3.1 Sampling
Probability and non probability sampling are the two types of sampling techniques applicable in social research. In probability sampling, elements in a population have a likelihood of being selected while in non- probability sampling, the elements in the population have no likelihood of being selected as sample subjects and hence their inclusion in the study is left to the discretion of the researcher (Miller and Salkind, 2002; Sekaran and Bougie, 2010).
Probability sampling techniques include simple random sampling, systematic, cluster, double and stratified sampling techniques. Convenience, purposive, judgement and quota sampling are techniques that are categorised under non-probability sampling (Lind et al., 2008). All these methods have their pros and cons and their applicability in research depends on the type of research being carried out. For example, in systematic sampling the researcher draws every nth element in the population with the 1st element being randomly chosen from the population (Sekaran and Bougie, 2010). According to Cooper et al., (2001), simple random sampling has the least bias and offers the most generalizability. However, simple random sampling can be cumbersome due to the fact that a population list (sample frame) may not always be easily available and the technique can be expensive to use (Cooper et al., 2001).
Purposive sampling was the sampling method used in this study. Purposive sampling involves the researcher obtaining information from specific target groups who conform to the researchers‟ specifications (Sekaran and Bougie, 2010). Respondents for this study were therefore selected on the basis of their suitability to the researchers‟ set research specifications and by judgement were best placed to provide the information that was needed by the researcher. Potential disadvantages of non- probability sampling techniques are that the results are not generalizable to the entire population (Hair et al., 2005). However as a sampling method, this method is usually considered the most logical and meaningful way to carry out a study (Sekaran and Bougie, 2010).
Initially the Business Womens‟ Association (BWASA) data base was considered a viable option to select the sample frame. This option was later dropped after it turned out that the
55 potential sample frame being women in entrepreneurial ventures could not suit this research.
Since there existed no adequate population frame to sample, a sample was drawn from female managers in their individual capacities working in organisations situated in the Durban Metropolitan area (DMA) with an additional sample being drawn from females in a similar capacity who were part of the University of Kwa-Zulu Natal (UKZN) MBA and alumni for which a Gate Keepers letter was obtained.
Snowball sampling was used to increase the number of respondents in this study. According to Bouma and Ling (2005), snowball sampling is used when access to a particular study group is needed but the researcher only knows a few of the respondents. The researcher then requests those he/she knows to nominate others that they may know who in turn are requested to nominate others in return. The sample consequently grows like a snowball with the most recently found contacts finding other potential contacts too (Cross and Linehan, 2006).
3.3.2 Sample description
The sample comprised female managers holding first line/supervisory, middle or senior management levels in their organisations. To be part of this study, respondents had to conform to the following specifications:
Respondents had to be females.
Respondents had to belong to either one of the following management tiers: first-line, middle or senior management.
They needed to have the desire to progress in their careers.
Be involved in some form of decision making.
Respondents needed to have a top down and bottom up reporting structure.
Selection of an appropriate target population to sample was essential to ensure the researcher successfully achieved the study objectives and hence answered the research questions posed.
3.3.2.1 Sample size
With regard to the number of study participants, the representativeness of the sample to the general population is dependent on time availability, degree of precision desired and the budget available to carry out the research (Bryman and Cramer, 1997). The sample should as
56 far as possible be representative of the general population. Statistically, the precision of the sample size selection is what leads to a smaller standard error deviation from the population mean hence enabling generalisations to be made with ease. However, with non-probability sampling as was the case in this study, the generalizability of the results to the whole population is not possible. According to Labour market shuts... (2010), 3, 245, 739 women occupy management positions. For a population of approximately 3, 000,000, an adequate sample size is 384 at the 95% confidence interval (CI) (MBA dissertation style guideline, 2011). For this study, a total of 117 completed responses were received from 290 surveys that were circulated. The non response rate was attributed to respondents who either viewed but did not participate in the survey or started the survey but did not complete it, resulting to a total response rate of 40%. The sample breakdown by management tier that was obtained is shown in Table 3.1.
Table 3.1 Sample size by management level Management level Sample (n) First line/supervisory 26 Middle line management 55 Senior/top management 32
Other 4
Total 117
From Table 3.1, the majority of respondents held middle management positions while first line had the least representation of respondents. The perceptions of respondents at the different levels of management resulted in increasing the variability within and between the responses obtained on some of the questionnaire variables.