CHAPTER 5: RESEARCH DESIGN AND METHODOLOGY
5.14 Sampling techniques
There are two sampling techniques used in research studies, and these are non-probability and probability sampling (Saunders et al., 2012).
5.14.1 Non-probability sampling
This sampling technique (non-probability sampling) is also known as deliberate sampling, purposive sampling or judgement sampling (Hennink et al., 2011). There is no deliberate selection of items by the researcher in a non-probability sampling. The choice concerning the items remains supreme, which means that the selection of cases is on the interviewer’s judgement.
Non-probability sampling is a sampling procedure that does not afford any basis for estimating the probability that each item in the population has been included in the sample (Saunders and Rojon, 2014).
Saunders and Rojon (2014) further state that non-probability techniques are utilised when the population is not known or not identifiable. This is why the selection of respondents is by other means, such as quota sampling, accidental sampling or judgemental sampling.
In other words, the actual selection of the items for the sample is to the interviewer’s discretion (Maxfield and Babbie, 2014). The primary advantage of non-probability is that technique is relatively inexpensive. In addition, the quota technique enables inferences to be drawn (Saunders and Rojon, 2014).
141
However, this technique has a number of disadvantages. However, there is mentioning of one disadvantage. The primary disadvantage is that the researcher may select a sample that will yield results that are favourable to their point of view and if that happen the whole, inquiry may be tainted (Maxfield and Babbie, 2014).
5.14.2 Probability sampling
Probability sampling is a sampling design in which every item in the universe has an equal chance of inclusion in the sample (Saunders and Rojon, 2014). There is an Individual picking up of units from the whole group, not deliberately, but by some mechanical process.
Obviously, in probability sampling, it is blind chance alone that determines whether there will be a selection of one item or the other for inclusion in the sample study (Hennink et al., 2011).
A further example of probability sampling is random sampling. Random sampling ensures the Law of Statistical Regularity (Hennink et al., 2011), which states that if, on average, the sample chosen is random; it will have the same composition and characteristics as the universe (Saunders and Rojon, 2014).
This is the reason why there is a consideration of random sampling in a number of studies.
This technique better suited to select a representative sample (Hennink et al., 2011). The sample is then a true reflection of the population, and can therefore be generalised to the population.
The main feature of this sampling method is that it allows for the elimination of any possible conscious or inherent bias, in those conducting the research study because the selection of samples is random (Saunders and Rojon, 2014).
There are a number of advantages to probability sampling, including that the results emerging from wherever this design is used, inference of results to the population and generalisation is possible (Saunders and Rojon, 2014). Furthermore, it allows data to be collected by means of a questionnaire. This greatly facilitates data collection (Saunders and Rojon, 2014).
However, there are also drawbacks, and one of these drawbacks is that there is a possibility that not all elements (some very important) of the population will be selected, some of which might have aided the researcher to describe the problem better (Saunders and Rojon, 2014).
142 5.14.3 Justification of the sampling technique
The researcher tenaciously read about the characteristics, advantages and disadvantages of the two sampling techniques, which are non-probability, namely quota sampling, where the researcher selects items for the sample deliberately. In a systematic sampling, every item of the universe has an equal chance of inclusion in the sample (Babbie, 2013b).
Probability sampling techniques nevertheless have their own drawbacks, and one of these drawbacks is that there is a high probability of unintentional exclusion of certain characteristics of the population in the data.
However, after weighing the drawbacks against the advantages of both non-probability and probability sampling techniques, the probability sampling technique was appropriate for this research study.
The reason for choosing this sampling technique (systematic sampling)is that it has many advantages. Firstly, probability sampling has a very strong link to quantitative research.
Secondly, there is an equal chance for each participant to form part of the research study.
5.14.4 Sample selection
A systematic sampling method was appropriate for this study to select the respondents. The systematic sampling method relies on organising the target population according to some ordered list or scheme, and thereafter selecting elements at regular intervals throughout that ordered list or scheme (Babbie, 2013b).
The reason for choosing this method is that the sample has an even spread of the sample across the entire population. The systematic sampling method is moreover relatively easy to apply (Kumar, 2012).
The lists used to select a sample was from the entire target population, sourced from the HRM departments of selected provincial legislatures, namely, Limpopo and Mpumalanga provincial legislatures. There was a compilation of a common list from the two lists. The reason for choosing these two provincial legislatures was that other provincial legislatures did not respond to the request to be part of this study.
After the list was combined, participants were listed in alphabetical order (i.e. surnames first) from A–Z, and each participant was allocated a number in that combined list. The reason for
143
consolidating the lists was to ensure that the study is not bias in choosing employees from either of the provincial legislatures.
The numbers allocated to participants were between 1 and 324, following the alphabetical order. The selection of respondents was between 1 and 3, the third participant was then selected.
The reason for choosing every third participant was to ensure that a sizeable number of participants took part in the study. The first participant was chosen randomly by the throw of a die for a number between 1 and 3.
The selection of respondents was from the alphabetically sorted combined list, the selection resulted in 50 respondents from the Limpopo legislature and 58 from the Mpumalanga legislature. The sample selected from the population (N=324) was (n=108). The reason for determining the exact numbers from each provincial legislature was to ensure that questionnaires to the provincial legislatures equal the number of respondents in that specific provincial legislature.
The questionnaires were completed by (n=90) respondents, with thirteen (13) questionnaires not returned and five (5) not completed in full, and subsequently excluded from data analysis.
Returned questionnaires represented a response rate of 28% (90/324 x 100), calculated from the entire population but calculating the response rate from the selected sample was 83%
(90/108 x 100). The response rate indicated the following respondents: female represented 61% and male represented 39%.
The advantage of systematic sampling is that it is relatively easy to apply, and there is an evenly spread over the entire reference population (Suresh, 2011). The drawback, though, is that it becomes a challenge when there is an estimation of variances (Suresh, 2011).
The advantage of systematic sampling is that it is relatively easy to select a suitable sampling frame as it is easy to identify the sample and the spread is even over the entire reference population. The drawback here is that there is a high probability of unintentional exclusion of certain characteristics of the population in the data (Suresh, 2011).
144 5.14.5 Sampled population
Table 5.1: Sampled population in table format
Sampled population
Provincial legislature
Total number of employees
Selected
employees using systematic
random sampling
Discarded (n)
Response rate (n)
Response rate %
Limpopo 150 50 9 41 46
Mpumalanga 174 58 9 49 54
Total 324 108 18 90 100
Table 5.1 indicate that the questionnaires were completed by (n=90) respondents, with thirteen (13) questionnaires not returned and five (5) not completed in full, and subsequently excluded from data analysis. Returned questionnaires represented a response rate of 28%
(90/324 x 100), calculated from the entire population but calculating the response rate from the selected sample was 83% (90/108 x 100). The response rate indicated the following respondents: female represented 61% and male represented 39%.