Level 3: Behaviour
5.8 SAMPLING PROCEDURE
Sampling techniques are the method used to choose a group from a wider population, as it is not possible to include the whole populationwhen conducting a survey. According to Bryman and Cramer (2001: 96), sampling is one of the most reliable methods of collecting statistics, particularly when the population is vague or exceptionally large.
Denscombe (2007:130) notes that even though data is collected in the form of a segment, what is found in that segment will be relevant to the rest of the population, although it would not be advisable to conclude that the result from the sample will simulate the entire sample population.The sample must be carefully considered; this will allow some level of confidence in its reliability and validity. Bless and Higson-Smith (2000: 85) identified the following attributes of effective samples:
A distinct targeted population;
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A sufficiently well selected data sample; and
A sample is an approximated representation of the entire population.
Jupp (2007: 312) observes that the sample, “must [be] a reflection of population strength (validity) which is the degree to which sample distributions reflect individuals of the population which the sample was collected from”. The sample for this study will represent the patient population as the study targets people admitted to KZN provincial hospitals.
The sampling hypothesis is based on the theory that inferences can be drawn from the targeted population from which the data is collected (Descombe, 2007: 271). Bless and Higson-Smith (2000: 84) concur with this statement and state that the sampling hypothesis can be also used as a combination of a population and samples drawn from it. The intention of this study is to establish various kinds of a certain population; one of the objectives of sampling is to draw inferences about the unidentified population parameters from known sample statistics.The population, as defined by Bless and Higson-Smith (2000: 85) is the “set of basic fundamentals that the research focuses upon and to which the results obtained by testing the sample should be generalized”. Other authors such as Bryman and Cramer (2001:
96) define a population as a separate cluster or unit which is used to study the data and not just populations in the predictable sense of the word.
Descombe (2007: 17) writes that prejudice is normally regarded as a non-constructive characteristic of study; the onus is on the researcher to try and avoid it. Prejudice can cause misrepresentation of the data or departure from the facts or even serious deviations from accepted research procedures. While research will always be affected by the researcher‟s own social position and ideology; in some sense, this may be conceived of an as organized fault;
however researchers should strive to remain impartial and should always commit themselves to ethical practice and avoid prejudice or bias in every possible way.
Bless andHigson-Smith (2000:140) harbour the view that during the research process, the values of the researchers, their religion and cultural attitudes and convictions may play an important role and could direct the researcher to choose a particular population, adopt a certain sample, ask or abstain from asking specific questions, and intentionally fail to take into account theories that disagree with their approach due to prejudice.Therefore, the researcher must make sure that bias is narrowed down to the smallest possible degree and that
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inconsistencies in research results can be explained after taking the shortcomings and limitations of the research into account.
5.8.1 Non-probability sampling
According to Bless andHigson-Smith (2000: 155) non-probability sampling is a sampling method where the prospect of each component of the population being included in a sample is unknown. According to Jupp (2006: 196), a number of techniques are associated with this approach, such as snowball, quota and convenience sampling.Denscombe (2007: 17) argues that when one uses non-probability sampling, the theory which underlies or simplifies probability sampling disappears; therefore each component of the study population stands an equivalent probability of being incorporated or included in the sample.
Denscombe(2007: 16) notes the following reasons for using non-probability sampling:
It is not realistic to include a large number of examplesin the study;
The researcher may not have adequate information about the population; or
It may prove extremely complicated to contact a sample chosen through conventional, probability sampling techniques.
5.8.2 Probability sampling
This method ensures that the likelihood of each component of the population being included in the sample can be determined. Probability sampling is defined by Jupp (2006:238) as anytechnique of sampling that uses random collection to ensure that all units in the population have an equivalent chance of being selected. The hypothesis is that, provided adequately large numbers of examples are selected, and the range has been authentically „at random‟, the results will be representative of the sample cross-section.
5.8.3 Stratified random sampling
This system of sampling is intended to ensure that the sample has definite descriptions, which are frequently representative of the population on key variables. Put another way, this means that the sampling is split into different populations and different groups, called strata so that each constituent of the population belongs to one stratum only. For example, the population
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may be stratified according to the criterion of gender, in which case two strata - of males and females - will be generated (Bryman& Cramer 2001: 98).
Denscombe (2007: 15) writes that an important benefit of stratified sampling over pure random sampling is that the researcher can exercise some degree of management of the choices of the test or sample for assurance purposes, and that crucial factors are covered in proportion to the way they exist in the wider population. This is supported by Bryman and Cramer (2001: 99), who agree that the advantage of stratified sampling is that it offers the possibility of better accuracy by ensuring that the groups which are created by a stratifying criterion are represented in the same proportions as in the population.
5.8.4 Sample size for correlation with acceptable absolute precision
Researchers normally work with a 95% level of confidence, meaning that if the sample was chosen 100 times, at least 95 of the subjects would be certain to represent the characteristics of the population (Saunders, Lewis &Thornhill, 2000: 155).The margin of inaccuracy describes the accuracy of the estimates of the population. Nichols (1991: 52) states that, in practical terms, cost is frequently the major issue influencing the sample size. Furthermore, when choosing a sample size, it is wise for a researcher to estimate assurance or confidence intervals in some of the most important variables he/she is studying.According to Gustavsson (2007: 28) the volume of the sample affects the possibility of making the correct inferences;
however, the technique used to select the sample is equally important.