SAMPLING
Sampling is the process of selecting a sufficient number of elements from the population, so that a study of the sample and an understanding of its propreties.In learning how representative data (i.e., as reflected in the universe)can be collected, a few terms as described below ,have to be first understood.
SAMPLE: A sample is a subset of the population. It comprises some members selected from it. In other words, some, not all, elements of the population would form the sample.
POPULATION: Population refers to the entire group of people.
ELEMENT: An element is a single member of the population.
SUBJECT: A is a single member of the sample, just as an element is a single member of the population.
POPULATION FRAME: The population frame is a listing of all the elements in the population from which the sample is drawn
SAMPLING DESIGN
There are two types of sampling designs: probability and nonprobability sampling, in probability sampling, the elements in the population have some known chance or probability of being selected as sample subjects i.e. unrestricted (simple random sampling) or restricted (complex probability sampling. In nonprobability sampling, the elements do not have a known or predetermined chance of being selected as subjects. I.e. convenience sampling, purposive sampling, judgment sampling, quota sampling.
PROBANILITY SAMPLING: Advantages
High generalizability of findings.
Easy to use if population frame is available.
Most efficient among all probability designs.
All groups are adequately sampled and comparison among groups are possible.
In geography cluster, costs of data collection are low.
Area sampling is cost-effective. Useful for decision relating to a particular location.
Offer more detailed information on the topic of study.
Disadvantages
Not as efficient as stratified sampling
Systematic biases are possible
More time consuming than simple random sampling design since subsets of clusters are more homogeneous than heterogeneous.
Original biases, if any, will be carried over.
Individual may not be happy responding the second time.
NONPROBABILITY SAMPLING: Advantages
Quick and convenient
Less expensive
Sometimes the only meaningful way to investigate
Very useful where minority participation in a study is critical
Disadvantages
Not generalizable at all.
ISSEUS OF PRECISION AND CONVIDENCE IN DETERMINIG SAMPLING
Having discussed the various probability and nonprobability sampling designs, we now need to focus attention on the second aspects of the sampling design issue—sample size. Suppose we select 30 people from a population of 3,000through a simple random sampling procedure. Will we able to generalize our findings to the population with confidence, since we have chosen a probability design that has the most generalizability? What is the sample size that would be required to make reasonable precise generalizations with confidence? What do precision mean and confidence mean? These issues will be considered
PRECISION: this is refers to how close our estimate is to the true population characteristic. Usually, we would estimate the population parameter to fall within a range, based on the sample estimate.
CONFIDENCE: this denotes how certain we are that our estimate will really hold true for the population. In essence, confidence reflects the level of certainty with which we can state that our estimates of the population parameters, based on our sample statistics will hold true.
EFFICIENCY IN SAMPLING
Efficiency in sampling is attained when for a given level of precision (standard error), the sample size could be reduced, or for a given sample size (n), the level of precision could be increased. Some probability sampling designs are more efficient than others. The simple random sampling procedure is not always the more efficient plan to adopt; some other probability sampling designs are often more efficient. A stratified random sampling plan is often the most efficient, and a disproportionate stratified random sampling design has been shown to be more efficient than a proportionate sampling design in many cases. Cluster sampling is less efficient than simple random sampling because there is generally more homogeneity among the subjects in the cluster than is found in the elements in the population. Multistage cluster sampling is more efficient than single-stage cluster sampling when there is more heterogeneity found in the earlier stages. There is often a trade-off between time and cost efficiencies and precision efficiencies. The choice of sampling plan thus depends on the objectives of the research, as well as on the extent and nature of efficiency desire.
MANAGERIAL RELEVANCE
Awareness of sampling designs and sample size help managers to understand why a particular method of sampling is used by researcher. It also facilitates understanding of the cost implications of different designs, and the trade-off between precision and confidence vis-a-vis the costs. This enables the managers to understand the risk they take in implementing changes based on the results of the research study.
Lastly, 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. However, care should be taken not to overgeneralize the results of any study to population that are not represented by the sample. This is a problem common in some research studies.
Endnote
Sekaran, Uma. (2003).Research methods for business: A skill-building approach.
(4th Ed.) Hoboken, N. J.: Wiley.
Sampling is the process of selecting a sufficient number of elements from the population, so that a study of the sample and an understanding of its propreties.In learning how representative data (i.e., as reflected in the universe)can be collected, a few terms as described below ,have to be first understood.
SAMPLE: A sample is a subset of the population. It comprises some members selected from it. In other words, some, not all, elements of the population would form the sample.
POPULATION: Population refers to the entire group of people.
ELEMENT: An element is a single member of the population.
SUBJECT: A is a single member of the sample, just as an element is a single member of the population.
POPULATION FRAME: The population frame is a listing of all the elements in the population from which the sample is drawn
SAMPLING DESIGN
There are two types of sampling designs: probability and nonprobability sampling, in probability sampling, the elements in the population have some known chance or probability of being selected as sample subjects i.e. unrestricted (simple random sampling) or restricted (complex probability sampling. In nonprobability sampling, the elements do not have a known or predetermined chance of being selected as subjects. I.e. convenience sampling, purposive sampling, judgment sampling, quota sampling.
PROBANILITY SAMPLING: Advantages
High generalizability of findings.
Easy to use if population frame is available.
Most efficient among all probability designs.
All groups are adequately sampled and comparison among groups are possible.
In geography cluster, costs of data collection are low.
Area sampling is cost-effective. Useful for decision relating to a particular location.
Offer more detailed information on the topic of study.
Disadvantages
Not as efficient as stratified sampling
Systematic biases are possible
More time consuming than simple random sampling design since subsets of clusters are more homogeneous than heterogeneous.
Original biases, if any, will be carried over.
Individual may not be happy responding the second time.
NONPROBABILITY SAMPLING: Advantages
Quick and convenient
Less expensive
Sometimes the only meaningful way to investigate
Very useful where minority participation in a study is critical
Disadvantages
Not generalizable at all.
ISSEUS OF PRECISION AND CONVIDENCE IN DETERMINIG SAMPLING
Having discussed the various probability and nonprobability sampling designs, we now need to focus attention on the second aspects of the sampling design issue—sample size. Suppose we select 30 people from a population of 3,000through a simple random sampling procedure. Will we able to generalize our findings to the population with confidence, since we have chosen a probability design that has the most generalizability? What is the sample size that would be required to make reasonable precise generalizations with confidence? What do precision mean and confidence mean? These issues will be considered
PRECISION: this is refers to how close our estimate is to the true population characteristic. Usually, we would estimate the population parameter to fall within a range, based on the sample estimate.
CONFIDENCE: this denotes how certain we are that our estimate will really hold true for the population. In essence, confidence reflects the level of certainty with which we can state that our estimates of the population parameters, based on our sample statistics will hold true.
EFFICIENCY IN SAMPLING
Efficiency in sampling is attained when for a given level of precision (standard error), the sample size could be reduced, or for a given sample size (n), the level of precision could be increased. Some probability sampling designs are more efficient than others. The simple random sampling procedure is not always the more efficient plan to adopt; some other probability sampling designs are often more efficient. A stratified random sampling plan is often the most efficient, and a disproportionate stratified random sampling design has been shown to be more efficient than a proportionate sampling design in many cases. Cluster sampling is less efficient than simple random sampling because there is generally more homogeneity among the subjects in the cluster than is found in the elements in the population. Multistage cluster sampling is more efficient than single-stage cluster sampling when there is more heterogeneity found in the earlier stages. There is often a trade-off between time and cost efficiencies and precision efficiencies. The choice of sampling plan thus depends on the objectives of the research, as well as on the extent and nature of efficiency desire.
MANAGERIAL RELEVANCE
Awareness of sampling designs and sample size help managers to understand why a particular method of sampling is used by researcher. It also facilitates understanding of the cost implications of different designs, and the trade-off between precision and confidence vis-a-vis the costs. This enables the managers to understand the risk they take in implementing changes based on the results of the research study.
Lastly, 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. However, care should be taken not to overgeneralize the results of any study to population that are not represented by the sample. This is a problem common in some research studies.
Endnote
Sekaran, Uma. (2003).Research methods for business: A skill-building approach.
(4th Ed.) Hoboken, N. J.: Wiley.
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