STUDY AREA AND POPULATION

This section is focused on where the study was carried out and the objects of investigation. The study was carried out at the Federal Institute of Industrial Research Oshodi (FIIRO), Lagos, Nigeria. It serves as a federal research institute carrying out industrial research. It occupies a land area of five hectares. The area is multicultural. The researchers working at the institute have different academic backgrounds. The premises have buildings housing its research laboratories, engineering, administration, food pilot plants, staff clinic, staff canteen and other facilities

Schensul (2012:72) depicts a study population as the people that form the focus of analysis of the research questions of a project study. Neuman (2011:224) expresses a population to be the total collection of all units of analysis about which the researcher desires to make specific conclusions. In a similar fashion, Gray (2014:688) represents a population as the totality of people, organisations, objects or occurrences from which a sample is drawn. Babbie and Mouton (2012:173), Brink, Van der Walt and Van Rensburg (2012:131) and Polit and Beck (2012:738) depict a population as the entire group of persons who are of interest to the researcher and who meet the stipulated criteria that the research shows interest in studying, or a set of individuals having some common qualities. For instance, a population can assume different people or entities in different settings - it can be an organisation, a printed document, an online document, a social action that is measurable or a large, well-defined group.

Biemer and Lyberg (2003:29) describe a target population as “a group of persons or other units for whom the study results will apply”. FIIRO is a well-established research institute and one of the research institutes situated in Nigeria. The target population consisted of all the researchers of FIIRO. i.e. 165 researchers (excluding six Directors who are also researchers) that are found in all FIIRO departments as highlighted in section 1.2.4 and the library staff (consisting of five professional librarians and three

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professional librarians) of the FIIRO library. For the library, the study population was reduced to five professional librarians who work at the FIIRO library providing professional expertise to the researchers to meet their information needs. The six directors were excluded from the study because in the course of carrying out the pilot study the researcher observed the lackadaisical attitude of the Directors towards participating in the research largely due to the sensitivity of their position. They do not want to be identified with giving information that negatively portrays the mediocre approach of the government of Nigeria to the researchers and the FIIRO management.

They are afraid of being dismissed from the federal civil service possibly as a result of divulging sensitive information. The five professional librarians were chosen because of their expertise in the field considering their wealth of experience over those of the three other library staff that are non-experts.

4.6 SAMPLING

Kothari (2014:147) defines sampling as a statistical method or procedure of finding a representative population to collect data or information about an entire population by examining only an integral portion of it. From another viewpoint, Gravetter and Forzano (2009:144) describe sampling as “the process of selecting individuals to participate in a research study”. Similarly, Kumar (2011:397-398) opines that sampling is the procedure of selecting a few respondents (a sample) from a bigger group (population) to become the foundation for estimating the occurence of information of interest to one. Maree and Pietersen (2010:172) state that sampling can be divided into two types, namely probability or non-probability sampling. The two sampling methods were used for the purpose of this study.

4.6.1 Probability sampling

Bhattacherjee (2012:67) describes probability sampling as a technique in which every unit in the population has a chance (non-zero probability) of being selected in the sample, and this chance can be accurately determined. Additionally, Kumar (2011) observes that for a design to be called probability sampling, it is necessary that each element in the population should have an equal and independent chance of being selected in the sample with the term equal implying that the probability of selection of each element in the

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population is the same; that is, the choice of an element in the sample is not influenced by other considerations such as personal preference. Bhattacherjee (2012:67) summarily states that probability sampling has two attributes in common which are: every unit in the population has a known non-zero probability of being sampled and the sampling procedure will involve random selection at some point.

Teddlie and Tashakkori (2009:171), Neuman (2011:227) and Leedy and Ormrod (2010:205) ascertain five probability sampling methods summarised below:

 Simple random sampling - In this method, a researcher creates a sampling frame and uses a pure random process to select cases, this makes certain the addition of each and every sample of the population so that each sampling element will have an equal chance of being selected.

 Systematic sampling – It is a special kind of random sampling. It involves the selection of the first unit of the sample from the population based on the process called randomisation, and the remaining units of the sample are selected from the population at fixed intervals of n, where n is the sample size.

 Stratified sampling – In this method, the population is divided into several sub-populations (called ‘strata’) that are individually more homogeneous than the total population, and the items are selected from each stratum to constitute a sample.

Members within each stratum have similar attributes but the members between strata have dissimilar attributes.

 Cluster sampling - In this method, the total population is divided into a number of relatively small sub-divisions which are themselves clusters of still smaller units and then some of these clusters are randomly selected for inclusion into the overall sample. With this method, it is desirable for each cluster to be a miniature of the entire population so that the full variability of the population is captured.

 Multi-stage sampling - This method employs more than one stage to sample the population and helps in the design of a smaller sampling frame which will make a study realistic in terms of cost and time. The principle of this method makes allowance for economic considerations when the geographical area to be covered is very vast and travel costs need to be reduced.

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The probability sampling methods adopted for this study were random and stratified sampling. The researchers were placed into sub-populations (strata) according to departments in the institute, that is, according to departments (areas of research) of the research institute which are Food Technology Department, Project Design and Development Department, Biotechnology Department, Chemical Fibre and Environmental Technology Department, Production, Analytical and Laboratory Management Department and Planning, Technology Transfer and Information Management Department.

4.6.2 Non-probability sampling

Bhattacherjee (2012:69) explains non-probability sampling as a sampling technique in which some units of the population have zero chance of being selected or where the probability of selection cannot be accurately determined. Bhattacherjee further adds that it is typical that units are selected based on certain non-random criteria, such as quota or convenience and as a result may be subjected to a sampling bias. Kumar (2011) states that non-probability sampling design does not follow the theory of probability in the choice of elements from the sampling population and they are used when the number of elements in a population is either unknown or cannot be individually identified and, in such situations, the selection of elements is dependent upon other considerations.

Neuman (2011:220), Kothari (2004:59), Bhattercherjee (2012:69-70) and Leedy and Ormrod (2010:211-213) underscore convenience sampling, quota sampling, expert sampling, snowball sampling, and purposive or judgemental sampling as non-probability sampling techniques.

 Convenience sampling - This is a technique in which a sample is drawn from that part of the population that is close to hand, readily available, or convenient.

 Quota sampling - In this technique, the population is segmented into mutually exclusive subgroups (just as in stratified sampling), and then a non-random set of observations is chosen from each subgroup to meet a predefined quota.

 Expert sampling - This is a technique that has to do with respondents being chosen in a non-random manner based on their expertise on the phenomenon being studied.

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 Snowball sampling – In applying this technique, the researcher starts by identifying a few respondents that match the criteria for inclusion in his/her study, and then asks them to recommend others they know who also meet his/her selection criteria.

 Purposive sampling – In applying this technique, the researcher uses a wide range of methods to locate all possible cases of a highly specific and difficult to reach population.

The non-probability sampling technique adopted was expert sampling. In this sampling technique, five professional librarians of the FIIRO library were interviewed based on their knowledge about the study and the population of the study. The advantage of this approach is that experts tend to be more familiar with the subject matter than non-experts - opinions from a sample of experts are more credible than a sample that includes both experts and non-experts.

4.6.3 Sample frame

Bhattacherjee (2012:66) describes sample frame as an accessible section of the target population (normally a list with contact information) from which a sample is drawn. In this study, a sample frame was obtained from the records department of FIIRO. All researchers, except the six Directors who are also researchers, were selected for inclusion in the study.

4.6.4 Sample size

Burns and Grove (2009:721) describe sample size as the number of subjects or participants recruited and that consented to take part in a study. Ngulube (2005:134) identifies that sample size has to be representative of the population because a sample that is very small reduces the efficacy of results. Collins (2011:361) states that the larger the sample size selected, the smaller the error in estimating the characteristics of the population. In addition, Somekh and Lewin (2011:223) express that irrespective of the fact that a larger sample size will bring about accuracy in population characteristics estimate, it will have increased research cost. Neuman (2014:267) emphasises that a key notion of sample size is that the smaller the population, the larger the sampling ratio has

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to be for a sample that has a high probability of yielding the same results as the entire population.

For the researchers, the sample size was the target population which was 171 researchers minus the six Directors. This brought the sample size for researchers to 165. With this, the sample size is a representative one comprising all the researchers from the six departments at FIIRO. For the librarians, the sample size was the five professional librarian staff members out of the eight library staff members. Table 4.1 summarily shows the target population and the study population.

Table 4.1: Target and study population of the current study

Target population Study population

FIIRO’s researchers 165 165

FIIRO’s librarians 8 5