Principales Entidades de comercio exterior en el Ecuador.

Garson (2012:5) defines sampling in the context of survey research as a method of choosing which subject to measure in a research report. In addition, Garson (2012:5) notes that while the subjects are usually people, they could also be objects, organisations, cities or even nations. Garson (2012:5) concludes that regardless of the subject, sampling will determine how much and how well the researcher may generalise the study findings. The Hillingdon Hospital Education Center (2006:1) defines sampling as the act, process, or technique of selecting a suitable sample, or a representative part of a population for the purpose of determining parameters or characteristics of the whole population. It results in the fact that conclusions from the sample may be extended to that about the entire population. In the same line Jawale (2012:183) defines sampling as technique, process or method of drawing a definite number of the individuals, cases or the observations from a particular universe, selecting part of a total group for investigation. In addition, Jawale (2012:183) states that sampling emerges whenever it is difficult to study the entire universe or total population.

According to Neville (2007:31), when seeking the views of a group of fifty or less in a positivistic study, the entire population should be surveyed and studied. However, to elicit the views of larger groups, some form of sampling is usually necessary to gather opinions that are likely to be representative of the whole group. Saunders, et al. (2009:212) share a related view that in some research questions it is possible to collect data from an entire population (census) provided that the population size is manageable. However, it should not be assumed that a census would necessarily provide more useful results than collecting data from a sample which represents the entire population. In addition, Saunders, et al. (2009:212) provide circumstances when sampling would provide a valid alternative to a census:

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 when it would be impracticable to survey the entire population;

 when the budget constraints prevent the researcher from surveying the entire population;

 when time constraints prevent the researcher from surveying the entire population; and

 when the researcher has collected all the data but needs the results quickly. According to Neville (2007:31), there are two basic sampling techniques, namely, probability and non-probability sampling. Neville (2007:31) explains that probability sampling is where the researcher has a significant measure of control over who is selected and on the selection methods for choosing them and such sampling methods allow for representative cross sections, or particular groups to be identified or targeted. Probability sampling method has also been defined as a method for selecting a target sample from a sampling frame in which the probability of occurrence for each and every possible study sample is a function of a set of design variables (Rust, 2004:6). In addition, Rust (2004:6) points out that an important property of a probability-based sampling method is that the probability of inclusion in the target sample is known for each and every element in the sampling frame. Neville (2007:32) states that non- probability sampling techniques are applicable where the researcher has little initial control over the choice of who is presented for selection, or where controlled selection of participants is not a critical factor.

For the purpose of the data collection for this study, probability sampling was used while non-probability sampling was used for the pilot study.

(A) PROBABILITY SAMPLING TECHNIQUES

Neville (2007:32) outlines the main methods under probability sampling as including simple random sampling, systematic sampling, stratified sampling and cluster sampling.

141 (i) Simple random sampling

Frerichs (2008:3), Indrayan (2008:116) and Jawale (2012:186) concur that simple random sampling is a sampling method where subjects in the population are sampled by a random process, using either a random number generator or a random number table, allowing each subject remaining in the population to have the same probability of being included in the sample. Therefore each subject has an equal probability of being selected and it is used in conjunction with all other probability sampling plans. It therefore serves as a foundation upon which all types of random samples are based. In addition, Indrayan (2008:116) notes that in order to select a random sample from a population, it is first necessary to identify all subjects from whom the selection will be made (the sampling frame), but warns that such a study may be time consuming, tedious and very costly (Frerichs, 2008:3). Indrayan (2008:116) concludes that the usual method of selecting a simple random sample from a list of subjects is to assign a number to each subject and then select certain numbers by reference to random number tables which are published in standard statistical textbooks.

In addition, Jawale (2012:186) notes that simple random sampling method is the easiest to apply of all probability plans and the researcher does not need to know the true composition of population beforehand. Furthermore, Jawale (2012:186) highlights that random sampling, although cumbersome, is considered the least biased method to provide a unique designation to every population member since it involves counting and numbering the population from the first to the last item.

It is thus noted from the above arguments that simple random sampling is a method that offers each member of the population an equal chance of being selected through establishing a sampling frame. It is however noted that the method is not ideal for larger populations as it requires counting each individual which may be time consuming and costly.

142 (ii) Systematic random sampling

Hayes (2012:8) and Jawale (2012:187) define systematic random sampling as a sampling method where subjects are selected from the sampling frame such that every Xth _{person in the sampling frame is selected. In further argument, Jawale (2012:187)}

points out that an element of randomness is usually introduced into this kind of sampling by using random numbers to identify the subject with which to start. This procedure is useful when the sampling frame is available in the form of a list. In concurrence with Hayes (2012:8), Jawale (2012:187) asserts that in such a design, the selection process starts by identifying some random point in the list and then every Xth _{subject is selected}

until the desired number of subjects is secured.

Ahmed (2009:42), Hayes (2012:8), Garson (2012:11) and Jawale (2012:187) state that the starting point is random while equal intervals are used to determine the next subject to be included in the sample. In addition, Garson (2012:11) explains that the basic arguments for systematic random sampling over simple random sampling are its simplicity and flexibility as it allows the researcher to add a degree of system or process into the random selection of subjects. Another advantage of systematic random sampling over simple random sampling is the assurance that the population will be evenly sampled. Garson (2012:11), however, warns that if the sampling technique coincides with the periodicity of the trait, it will no longer be random and the representativeness of the sample will be compromised.

It can thus be concluded that although systematic sampling is basically random, it possesses some element of non-randomness in that once the starting item is identified, the rest of the items to be included are predetermined. It can also be argued that systematic random sampling is fairer compared to simple random sampling as it considers specific intervals between potential sample elements.

(iii) Stratified random sampling

According to Jawale (2012:187), if the population from which a sample is to be drawn does not constitute a homogeneous group, then stratified sampling technique is applied

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to obtain a representative sample. Jawale (2012:187) explains that in this technique, the population is stratified into a number of non-overlapping strata (sub-populations) and sample subjects are randomly selected from each stratum.

In a related argument, Banerjee and Chaudhury (2010:64) state that if a condition is unevenly distributed in a population with respect to age, gender or some other variable, it may be prudent to choose a stratified random sampling method. Banerjee and Chaudhury (2010:64) illustrate that to obtain a stratified random sample according to age, the study population can be divided into age groups such as 0-5, 6-10, 11-14, 15- 20 and 21-25 depending on the requirement. A different proportion of each group can then be selected as a subsample either by simple random sampling or systematic sampling.

Levy and Lemeshow (2008:132) categorise stratified sampling into proportionate and disproportionate approaches. They argue that in the former, the number of subjects allocated to the various strata is proportional to the representation of the strata in the target population. Therefore the size of the sample drawn from each stratum is proportional to the relative size of that stratum in the target population. The same sampling fraction is applied to each stratum, giving every subject in the population an equal chance to be selected. The resulting sample is a self-weighting sample. This sampling procedure is used when the purpose of the research is to estimate a population’s parameters. Levy and Lemeshow (2008:132) describe disproportionate stratified sampling as a procedure in which the number of subjects sampled from each stratum is not proportional to their representation in the total population. Population subjects are not given an equal chance to be included in the sample. Hence the same sampling fraction is not applied to each stratum.

It is thus evident from the above discussion, that stratified sampling is highly applicable in surveys where the study population has varying characteristics in which case the population is divided into different strata based on the similarity of the characteristics of the subjects. It is also noted that where sampling is being conducted such that the

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sample statistics are intended to estimate population parameters, proportionate stratified sampling technique is ideal.

(iv) Cluster sampling

According to Jawale (2012:187), cluster sampling involves grouping the population into clusters and then selecting the clusters rather than individual subjects for inclusion in the sample. Rose, Grais, Coulombier and Ritter (2006:291) define cluster sampling as a technique used when natural but relatively homogeneous groupings are evident in a statistical population. In this technique, the total population is divided into these groups and a simple random sample of the groups is selected. Then the required information is collected from a further simple random sample of the subjects within each selected group. This may be done for every subject in these groups or a subsample of subjects may be selected within each group.

Jawale (2012:187) illustrates that if, for instance, some departmental store wishes to sample its credit card holders where it has issued its cards to 15,000 customers and the sample size is to be kept to 450, cluster sampling of this list of 15,000 card holders could be formed into 100 clusters of 150 card holders each. Three clusters might then be selected for the sample randomly. In addition, Jawale (2012:187) commends that this sampling procedure is relatively easy and increases the efficiency of field work, especially in the case of personal interviews.

In cluster sampling, a group of population subjects constitutes the sampling unit, instead of a single subject of the population. The main reason for cluster sampling is cost efficiency (economy and feasibility), but the variance estimation efficiency is compromised (Ahmed, 2009:42). In addition, Ahmed (2009:42) emphasises that generating a sampling frame for clusters is economical if a sampling frame is readily available at cluster level. The procedure is very economical as larger samples are selected for a similar fixed cost while it consumes less time to implement, and it is also suitable for a survey of institutions. A common motivation for cluster sampling is to reduce the total number of interviews and costs giving the desired accuracy. However,

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Ahmed (2009:42) warns that standard errors of the estimates are high, compared to other sampling designs with the same sample size.

Cluster sampling procedure is thus construed as referring to a sampling method adopted where the population is divided into natural groups and each group forms a cluster from which samples are randomly selected. The implication with this sampling method is that the procedure may have multiple levels hence sampling errors can be reduced through a detailed scrutiny of the study samples.

(B) NON-PROBABILITY SAMPLING TECHNIQUES

Non-probability sampling techniques include convenience sampling, purposive sampling, and snowball sampling (Neville, 2007:32). Skowronek and Duerr (2009:413) define convenience sampling as a type of non-probability method which involves the sample being drawn from that part of the population that is close to hand. Therefore a sample is selected because it is readily available and convenient, as researchers are drawing on relationships or networks to which they have easy access. Latham (2007:9) defines purposive sampling as the sample selection method that is based on the researcher’s knowledge of the study population, its elements, and the nature of the research aims. Therefore the population is non-randomly selected based on a particular characteristic and individuals are selected to answer questions about a certain matter or product. The researcher is then able to select participants based on internal knowledge of the said characteristic.

According to Morgan (2008:816), snowball sampling uses a small pool of initial informants to nominate, through their social networks, other participants who meet the eligibility criteria and could potentially contribute to a specific study. The term ‘snowball sampling’ reflects an analogy to a snowball increasing in size as it rolls downhill. It is a method used to obtain research and knowledge, from extended associations, through previous acquaintances.

Non-probability sampling techniques, namely, purposive and convenience sampling were used to identify the respondents to participate in the pilot study as the respondents

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should be from the same target population but should not be one of the 383 sample respondents. In this study, probability sampling techniques, namely, stratified, systematic, cluster sampling techniques were employed to select a total sample of 383 respondents. Each of the five divisions of Kampala district, namely, Central, Kawempe, Nakawa, Makindye and Rubaga divisions, formed a stratum. Therefore, cluster sampling was used to select the five divisions. From each stratum, using stratified random sampling, at least 67 respondents were selected ensuring that every division was equally represented. Within each stratum, clusters of related businesses were identified and from each cluster, businesses were randomly selected for inclusion in the study sample. Therefore cluster sampling and systematic random sampling were used.

5.7 DATA SOURCES AND DATA COLLECTION AND THE MEASURING