Sampling, Sekaran and Dougie (2010) write, is the process of selecting the right individuals, objects or events as representatives for the entire population. Sampling is commonly used by researchers for two main reasons. Firstly, sampling is done for feasibility purposes (Sarantakos, 2000) because practically it is impossible to collect data and test or examine each and every element where studies involve large numbers of people say 1000. Even in cases where this is practical, time, human, financial and material resources would be huge.
Secondly, sampling is done to enhance accuracy of results (Strydom, 2011; Sekaran and Dougie, 2010). Studying larger populations could yield large amounts of data. This could pose challenges in handling and analysis, a development that could ultimately lead to errors.
In contrast, the use of manageable samples could lead to more accurate results because “time, money and effort can be concentrated on producing better quality results, better instruments, more in-depth information, and better trained interviewers or observers” (Strydom, 2011, p.224).
4.6.1 Types of Sampling Techniques
There are two broad types of sampling techniques. These are probability and non-probability sampling. Techniques that fall under probability sampling include simple, systematic, stratified and cluster sampling. Those that fall under non-probability sampling include convenience, purposive, quota and snowball sampling. Probability and non-probability sampling differ in that in probability sampling, elements have an equal chance of being selected whilst this is not the case in non-probability sampling. The other difference between these methods is that results drawn from probability samples are capable of being generalised to the entire population whilst those from non-probability samples cannot (Sekaran and Dougie, 2010). This study discusses in detail simple random sampling, stratified sampling and purposive sampling because they were used in this study.
4.6.1.1 Simple Random Sampling
In this form of sampling, every element of the population has an equal chance of getting selected (Connaway and Powell, 2010; Jackson, 2003). Using this method, a sample can be drawn using lots or computer-generated random numbers. According to Sekaran and Dougie (2010), the advantage of this sampling design is that it has the least bias, and also offers the
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most generalisability. The drawback of this method is that the sampling process could become cumbersome and expensive. Furthermore, in some instances an updated listing of the population which acts as the sampling frame may not always be available leading to the use of older ones which may not be accurate.
4.6.1.2 Stratified Random Sampling
This sampling method involves a process of stratification or segregation, followed by random selection of subjects from each stratum (Sekaran and Dougie, 2010). This type of sampling is suitable for heterogeneous populations because the inclusion of small subgroups percentage- wise can be assured (Strydom, 2011). In using this method, the strata should be defined in such a way that each element appears in only one stratum. Selection within the different strata still occurs randomly.
The student and academic staff populations covered by this study were heterogeneous.
Among others, they were of different sexes and age groups; they belonged to different faculties and departments; and the students were at different levels of study (years 3, 4, 5 or postgraduate). This aspect made the use of stratified random sampling technique appropriate for this study because it gave an opportunity for each group or strata to be well represented in the sample (Israel, 2012). This helped the researcher to easily work out some correlation tests to find out the significance of certain aspects within the data collected.
4.6.1.3 Purposive Sampling Technique
In purposive sampling, elements are chosen from specific types of people who can provide the information either because they are the only ones who have it or conform to some criteria set by the researcher (Sekaran and Dougie, 2010). This form of sampling is often used when a limited number or category of people have the information that is being sought. Purposive sampling leads to greater depth of information from a smaller number of carefully selected cases (Tashakkori and Teddlie, 2009). University/college librarians and ICT directors were purposively sampled in the study because they conform to this criteria. The hope was that they would provide information that would shed more light on the issues that were investigated.
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4.6.1.4 Sampling Tables
Sampling tables present another popular way of sampling. Sampling tables provide the sample sizes for a given set of criteria (Israel, 2013). Among others, they specify samples that can be drawn for given population sizes at varying levels of precision and confidence intervals.
4.6.1.4.1 Sampling Methods Used in this Study
Published Table 4.2 below as provided by Israel (2013) was used to sample the academic staff and student populations who were administered with the questionnaire. The table contains scientifically worked out figures that specify a sample that can be drawn out from a specified population with precision levels of ±3%, ±5%, ±7% and ±10% where confidence level is 95% and P=.5. Using a precision level of ±5%, the researcher drew a sample of 370 for the student population and 255 for the academic staff. This sample was distributed across the study sites proportionately according to their population sizes. Full breakdown is provided in Table 4.3.
Table 4. 2: Table for Selecting Sample Sizes [An Abridged Version] (Source: Israel 2013)
Size of population Sample Size (n) for Precision (e) of:
±3% ±5% ±7% ±10%
500 A 222 145 83
600 A 240 152 86
700 A 255 158 88
4,000 870 364 194 98
5,000 909 370 196 98
6,000 938 375 197 98
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100,000 1,099 398 204 100
>100,000 1,111 400 204 100
a = Assumption of normal population is poor (Yamane, 1967). The entire population should be sampled.
Furthermore, purposive stratified sampling technique was applied to the student sample.
University or college librarians of the case study libraries and ICT directors were purposively selected and were interviewed.
Table 4. 3: Research Sample
Study site Students Academic staff University or College Librarian
IT
Directors Population Sample Population Sample
MZUNI 1189 81 176 61 1 1
KCN 361 24 71 24 1 1
Polytechnic 2176 147 211 73 1 1
COM 680 46 119 41 1 1
LUANAR 1059 72 164 56 1 1
Total 5465 370 741 255 5 5