A Presentation On Sampling By Members Of Team 2.
This chapter seeks to shed more light on sampling as a basic and essential tool in research. It explains how sampling design decisions are important aspects of research design and include both the sampling plan to be used and the sample size that will be needed.
Before going any further, we will need to define/ explain certain terms/words frequently used here;
Population
Population refers to the total number of people, events or things of interest that the researcher wishes to investigate.
Element
An element refers to a single unit of the people, event or things of things of interest in the population.
Population Frame
The population frame is a listing or directory of all the elements that make up the population from which tne sample is drawn.
One of the limitations of the population frame is that it might not always be current or updated.
Sample
A sample is a select group carved out from the population which is going to be used in making generalization on the entire population.
Subject
A subject is a single unit or number from the sample.
Then, what is Sampling?
From the explanations given above, we can refer to sampling as the process or method adopted in creating a select group of subjects to form a sample from the elements in the population.
Why Sampling?
1. Difficulty in gathering information from the entire population
2. The cost implication of gathering data from from the entire population where possible will be very heavy on the researcher
3. There is a tendency of producing more accurate results from sampling rather than the entire population size because of the probability of fewer errors from computing results from a smaller select group.
Types Of Sampling.
There are two major types of sampling designs;
1. Probability Sampling
a. Unrestricted or Simple Random Sampling
b. Restricted or Complex Probability Sampling
i. Systematic sampling
ii. Stratified Random Sampling
iii. Proportionate and Disproportionate Stratified
iv. Cluster Sampling
v. Area Sampling.
vi. Double Sampling.
2. Non-probability Sampling
a. Convenience Sampling
b. Purposive Sampling
i. Judgement Sampling
ii. Quota Sampling.
1.Probability Sampling.
In probability sampling, the elements in the population have a known chance of being selected as sample subjects. This type of sampling is used when the representativeness of the sample is of importance in the interest of the wider generalizability.
Probability sampling can further be broken down into two forms, Restricted or Unrestricted. The Unrestricted or Simple Random Sampling adopts the approach whereby every element in the population has an equal chance of being selected to the sample. However this design could become cumbersome or expensive in a large or complex population hence the development of the Restricted Sampling Design.
The systematic approach involving adopting a unified sequence in choosing subjects from the elements. While the Stratified approach can be adopted in a population whereby the elements in the population have parameters that are segmented or stratified hence he used a systematic design in choosing subjects from the various segments or stratum.
The proportionate or Disproportionate Stratified Sampling design is fallout of the stratified design. Researchers desiring to further create a sample out of each stratum are faced with the challenge of whether to adopt a proportionate or disproportionate design. A proportionate design adopts selecting a unified or proportionate number of subjects from each stratum(eg applying a unified % across of the strata) while the disproportionate adopts a one that isn’t unified.
2. Non-Probability Sampling.
There basically two main types of nonprobability sampling designs: convenience sampling and purposive sampling. Convenience sampling refers to the sampling done with information readily available to the researcher. It usually carried out when quick and timely results are needed. It’s major flaw is that it scores very low in terms of generalization. Purposive Sampling involves sampling from a specific target group and falls into two categories, Judgement and quota sampling design. Judgement sampling though limited in generalization is used when there’s only a select or limited population that can provide information for the research study. While Quota Sampling is adopted when there’s a constraint of either cost, time and the need to adequately represent minority elements in the population.
SAMPLING IN CROSS-CULTURAL RESEARCH.
Cross-Cultural research can basically be defined as the research carried out when comparing or dealing with issues that occur with two or more cultures/ countries/locations involved.
When carrying out sampling in a cross-cultural research the major issue the researcher is faced with is that of the precision and confidence in determining the sample size. Determining the sample size is a major issue any researcher has to deal with when confidently generalizing his/her findings to the population with a high tendency of precision.
What is precision in determining sample size?
Precision refers to how close our estimate is to the population characteristic. In achieving a greater level of precision the researcher has to increase the size of his sample
Confidence?
This refers to how close or certain the researcher is that the estimates will really hold true for the population.
Relationship between Sample Data, Precision & Confidence in Estimation.
The relationship among the sample data , precision & confidence in estimation cannot be overemphasized because the sample data is what is used in making inferences about the population. A good correlation enhances the accuracy of our estimation and in turn increases the confidence of our generalization.
In sum, the sample size is a function of the level of precision and confidence desired.
DETERMINING THE SAMPLE SIZE.
The major factors affecting decisions on sample size are as follows;
1. The extent of precision required
2. The acceptable risk in predicting that level of precision
3. The amount of variability in the population itself
4. The cost and time constraints
5. The size of the population itself
Efficiency in Sampling.
Efficiency in sampling is achieved when for a given level of precision, the sample size could be reduced or for a given sample size, the level of precision could be increased.
Team’s Comment.
Members of team two after intensively reviewing this chapter agrees sampling is a very delicate and key aspect of any thorough research work. Identifying the various sampling designs and the appropriateness of each for different research purposes is also very important.
Knowledge gained from this chapter would go along way in improving our efficiency in carrying out a detailed and useful research study.
