Introduction
Research can simply be described as a diligent and scientific enquiry or investigation into a subject or problem in order to come up with the latest discovery (ies) so as to resolve an identified societal problem. In any field of knowledge, research is very important as it enables the researcher to discover new ground to cover in meeting human needs and thereby makes society worth living. Research is the bedrock of any serious minded scholar which he/she has to carry out in order to salvage a decaying economy of a nation and also sustain development for his country. Adeniyi; Oyekanmi and Tijani (2011:49) posit research as “generally based on scientific inquiry in which available facts are closely examined or investigated” by a researcher. In making scientific enquiry and come up with a fact in which to base any meaningful research, it is pertinent to note that a researcher has to consult some educational resources and contact some group of people in order to ascertain the validity of his or research work. That group of individuals or respondents or elements or observable materials that a researcher has to contact is known as population of the study. Population in this sense, according to Adeniyi et al (2011) represents all conceivable elements, subjects or observations relating to a particular area of interest to the researcher.
However, it is quite not really possible for a researcher to make use of the entire large population for his study; this could be as a result of limited financial resources of the researcher, too large area to cover which may not be easily covered by the researcher or it could be as a result limited period allocated for such study. Hence, there is need to draw out some fraction known as sample size out of the entire population which can be easily managed by the researcher. Basically, Popoola (2011) posits that the major components of research methodology especially in Library and Information science or in any research work include: research design; the population of study; sampling procedure and sample size; research instrument(s); validity and reliability of the research instrument(s) and methods of data analysis.
Nevertheless in this paper, we shall be dealing with how to choose the best sampling technique and sample size for a study. The paper shall lay more emphasis on population of the study, also educate our readers on how to choose a sample technique and sample size for any study they are presently undergoing or intend to carry out in future.
nebulous type. The elements cannot all be identified by names or numbers e.g. all the men or women in a country.
Group population:This is a structured and listed population e.g. students offering a course of study may be classified into male and female; local and foreign; single or combined honours students; workers (junior, senior) management and supervisory staff.
Scattered population:Population of listed members who are found in different geographical locations e.g. members of Nigerian Library Association or lecturers of private universities of a country or lawyers in a country.
Clustered population:Population of unlisted individuals who are known to exist together in different locations e.g. Nigerian footballers in European and Asian countries.
Target population:This consists of all members of a group of under the investigation to which the result of the investigation can be applied. They are population of interest (e.g. university students) but all of whom are not of interest to the researcher.
Accessible population: This refers to the members of a target population, which are within the reach of the researcher, work with and obtain their sample for his study.
Furthermore, it is pertinent to note that characteristics of a population of scores are called parameters and characteristics of a sample of scores from a larger population are statistics. The mean of an entire population of scores is a parameter and the mean of a sample is a statistics (Akinade & Owolabi, 2009). However, when the population is relatively too large to be covered within the period allocated for the study and also with the limited human and material (fund) resources of the researcher, it is necessary for the researcher to take sample size from the entire population for the study.
Sample and Sample Size
A sample is a manageable section of a population but elements of which have common characteristics. Also, it refers to any portion of a population selected for the study and on whom information needed for the study is obtained ( Awoniyi; Aderanti & Tayo, 2011; Akinade & Owolabi, 2009; Adedokun, 2003). It is the elements making up the sample that are actually studied and generalizations or inferences about the population are made. This generalization of result based on the sample to the population is the major purpose of sampling and also a major concern in any scientific investigation. It can be reemphasized here that, to study the entire population may be cumbersome, time consuming and of course very costly, hence, a sample takes a fair portion as representative of the entire population. In statistical analysis, population of the study is being denoted with ‘N’ while sample size is denoted with ‘n’
Sample size is the number of elements that can be selected for a research. This number varies from one study to another. In homogenous population (where there is little variation) requires a small size. Experimental studies tend to use relatively small sample size. But for heterogeneous population (where there is a wider variation) requires a larger sample size. This is common in survey research as in education and behavioural sciences (Akinade & Owolabi, 2009).
Types of Samples
The following types of samples are adopted from Akinade & Owolabi (2009: 74), they include:
Random Sample: Is one in which all the elements have equal chances of being selected for a study. It is the basis of most of the other types of samples. For example, all the male students in a classroom.
Subjective Sample: This refers to a subset of an accessible population. It is deliberately or purposefully chosen. On the basis of researcher’s predetermined intention, selecting experimental subjects this way is open to bias. For instance, if a researcher wants to find out the reading habits of students in a university and uses the students he/she teaches. This sample is not representative of all the students in the university not to talk of the world. It is a sample that is neither random nor representative. It is a sample that the researcher has allowed his/her preferences to influence his choice of items to be selected for the study. It may not be a captive sample. However, generalization cannot be made from the results. Captive samples and biased samples are good for quick collection of data from the respondents.
Captive Sample:it is a sample obtained from a captive population such as students in a classroom or workers in an industry, patients in a psychiatric hospital ward. The elements have no population and so one cannot generate finding from it. It is not used in serious research. It may be useful for pilot study or where a researcher wants a quick result before commencing the actual study.
Purpose of Sampling
Basically, sampling is done to ensure that there is no bias or subjectivity in the selection process. It also helps the researcher to work with reasonable size of elements since it is difficult to do so with the entire population. It thus saves time spent on each research and also reduces cost of research operations (Akinboye & Akinboye, 1998 in Akinade & Owolabi, 2009).
However, according to Adeniyi et al (2011: 50) other reasons for sampling include:
In a large population, there could be some similarities and uniformities along the line of research investigation, drawing up a sample along these similarities will give a general result for the whole population.
It makes the researcher more thorough and affords more time for better study.
Sampling makes data analysis easier and more skillful with qualitative results.
Since sampling enables us to deal with a part of the population, it is obviously cheaper to study a sample rather than the entire population.
It enables us to obtain quicker results than covering an entire population with its attendant problems.
At times, it is practically impossible to take a complete and comprehensive study of the population because of the nature and pattern of distribution or dispersion of the population elements.
For research study involving practical enumeration of subjects, sampling is the only best option to achieve it.
Sampling helps the researcher to guard against incomplete and inaccurate instruments such as questionnaires.
Sampling makes it possible to study infinite population.
Nevertheless, limitations of sampling include the following. Some samples may be too small or heterogeneous to collect a representative sample. In some cases, the researcher may not be equipped with sufficient knowledge of diverse or relevant sampling methods in that case there may be bias (Akinade & Owolabi, 2009: 75).
Sampling Techniques
Sampling techniques can be classified into two main groups, namely: probability and non-probability sampling.
Probability Sampling Technique:
Here all the items/units in a population have equal chance of being selected as sample for a study. The advantages of this method of sampling is that it only when the items have been selected with known probabilities that one is able to evaluate the precision of the sampling result (Popoola, 2011; Adeniyi et al, 2011). The examples include:
Simple Random sampling
Systematic sampling
Stratified sampling
Cluster/Area sampling, and
Double sampling.
Each of the listed probability sampling techniques shall be discussed in turn:
Simple Random Sampling:
In simple random sampling, every member of the population has equal chance of being selected for a study. It is a method that gives each member of the population non-zero probability of being selected. This type of sampling technique is used when the population has similar characteristics (homogeneous population), the sampling frame is available and the population size is determinate or finite. To ensure a random sample, the selection of samples may be done through balloting, table of random numbers and computer simulation. In most cases, the sampling is done without replacement (Popoola, 2011).
Systematic Sampling:
This involves the selection of every nth subject or item from serially listed population subjects or units. Where n is any number determined from the population. For example, obtaining a systematic sample of 100 from a population of 800. The will be as follows:
Number the items serially up to 800.
Divide 800 by 100 i.e. N/n = 800/100 = 8
Randomly select a starting point, say number ‘8’ of the population list.
Then select every 8th unit after the first. The list will include the following: 8th, 16th, 24th, 32nd, 40th, 48th, 56th, 64th, 72nd etc. on the population list (Adeniyi et al, 2011).
Stratified Random Sampling:
This is a method in which the heterogeneous population is first stratified by dividing it into a set of mutually exclusive or non-overlapping sub-populations or strata, and thereafter random samples are then selected from each stratum for detailed study (Popoola, 2011). It must be noted that the stratified random sampling may involve stratifying the respondents into infected and uninfected; males and females; old and young; residents and non-residents; employed and unemployed; literates and illiterates; rich and poor. The author reiterates that four basic methods could be employed to determine the sample size per stratum or total sample size for all the strata. These include probability proportionate to size (PPS), optimum allocation, Neyman allocation and equal allocation.
Cluster/Area sampling:
This type of sampling technique is similar to stratified random sampling that had been discussed in this paper. Cluster or area sampling is otherwise known as multistage sampling in that the population is subdivided into units and a random sampling of smaller units; that is, it is a population that exists in clusters over a geographical area. The selection of individual cases in the group may also employ simple random technique (Awoniyi et al, 2011). This method is used when the population is very large and covers a large geographical area. For example a country can be divided into regions or states which constitute sampling units called clusters.
