Sampling Procedure

Sampling design must begin with defining the target population. Malhotra (2007) suggests that the target population is the collection of elements or objects that: process the information sought by the researchers; and about which conclusions are going to be drawn upon. Target populations need to be defined precisely, from which the sampling frame can be determined. Saunders et al. (2007) suggest that the sampling frame for any probability sample is a complete list of all the cases in the population.

Samples can be extracted on a convenience basis, by referral, or by any of the other techniques for taking non-probability samples (Tull and Hawkins, 2003).

Sample surveys are defined by their ability to estimate characteristics about a population by obtaining data from a sub-section of its total (Dillman et al. 2009). The sampling techniques that are used to select the sample sub-section can be broadly divided into probability sampling and non-probability sampling. Saunders et al. (2007) suggest that probability or representative sampling is most commonly associated with survey-based research strategies where researchers need to make inferences from the sample about a population to answer research questions or to meet research objectives.

Figure 4.10 provides the comparison between non-probability sampling and probability sampling.

Figure 4.10 Non-Probability vs. Probability Sampling

Factors Non-Probability Sampling Probability Sampling

Nature of research Exploratory Conclusive

Relative magnitude of sampling and non-sampling errors

Non-sampling errors are larger Sampling errors are larger

Variability in the population Homogenous (low) Homogenous (high)

Statistical considerations Unfavourable Favourable

Operational considerations Favourable Unfavourable

Source: Saunders et al. (2007)

4.9.1 Probability Sampling

In probability sampling, every member in the target research population has a fixed and equal probabilistic chance of being selected by chance for the research sample (Webb, 1992; Tull and Hawkins, 1993). Random Sampling, Systematic Sampling, Stratified Sampling and Cluster Sampling are all probability sampling techniques (Malhotra, 2007).

In Random Sampling, every member of a research population has an equal and fair chance of being selected in the research sample (Schuman and Kalton, 1985).

Systematic Sampling selects a research sample by choosing each member from the population at evenly spaced intervals until the desired research sample size is obtained (Malhotra, 2007). Stratified Sampling selects a characteristically proportionate research sample by grouping members by characteristics and selecting them in proportionate numbers to the overall population (Schuman and Kalton, 1985). Finally, in Cluster Sampling, mutually and collectively exhaustive sub-populations are divided

within the target research population and then a random sample of these clusters is selected based on one of the three other probability sampling techniques (Webb, 1992).

4.9.2 Non-probability Sampling

In non-probability sampling, availability and judgement are key elements in creating a representative research sample (Churchill and Brown, 2007), eliminating an unknown unavailable and deemed unsuitable portion of the research populations. Hence, the degree to which the research sample differs from the research population remains, whereas in probability sampling, sampling error can be calculated (Webb, 1992).

Non-probability is applied in business research when it is difficult to specify the probability that any case will be included in the sample (Saunders et al. 2007).

Convenience Sampling, Judgement Sampling, Quota Sampling, and Snowball Sampling are all non-probability sampling techniques (Malhotra, 2007).

4.9.2.1 Convenience Sampling

Convenience Sampling uses whatever members of the research populations are available, subsequently eliminating a large proportion of the research population from the chance of being selected (Webb, 1992) and is one of the most commonly utilised non-probability sampling technique. Convenient elements are a key characteristic of a convenience sample, with selection the responsibility of the researcher who will select members of the research population who are in the right place at the right time (Churchill and Brown, 2007). Convenience samples tend to be groups of people that are easily accessible to the researcher, for example: students; church groups; members of social organisations, etc. (Dillon et al. 1994).

Advantages of selecting a research sample that is convenient are the relative in-expense in time and cost, their accessibility, ease of measurement and likely high level of co-operation (Webb, 1992). However, Convenience Sampling is ultimately unrepresentative of any definable research population (Webb, 1992) and results in a large degree of bias, in some cases respondent self-selection (Malhotra, 2007). It is therefore, not academically or theoretically viable to select a generalised research sample through Convenience Sampling (Churchill and Brown, 2007).

Judgement Sampling (or Purpose Sampling) selects a research sample that is representative of the research population or appropriate to the research topic based on the judgement/expertise of the researcher (Malhotra, 2007; McDaniel and Gates, 2007). This form of sampling does not require random selection (Fowler, 1984), or the researcher to be definite as to materiality and risk, and is relatively cost effective, convenient and quick (Webb, 1992).

4.9.2.3 Quota Sampling

Quota Sampling is a restricted version of Judgement Sampling and has two stages:

development of control categories/quotas of population elements; and selection of sample elements based on convenience or judgement (Schuman and Kalton, 1985).

This sampling method ensures that the research sample is proportionate to the research population with respect to the characteristics relevant to the research objectives (Churchill and Brown, 2007). The relevant characteristic proportions are determinants of the control categories/quotas, which may include: gender; age; race;

etc. and are identified on the basis of judgement (Schuman and Kalton, 1985).

As similar to the previously described non-probability sampling techniques, Quota Sampling is relatively cost effective and time efficient (Barnet, 1991). Although it is still not as representative of the research population as Probability Sampling, and sampling error cannot be determined (Fowler, 1984), Quota Sampling creates a research sample that provides results close to those provided by research samples created by Probability Sampling techniques.

4.9.2.4 Snowball Sampling

Snowball Sampling begins with random selection of some of its research sample members, who themselves identify other suitable members belonging to the research population, resulting in waves of referrals, thus creating a snowball effect (Dillon et al.

1994). It is a sampling technique used to sample from low incidence or rare populations which results in reduced sampling costs but also reduced sampling quality (McDaniel and Gates, 2007).