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SAMPLING DESIGN

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The Nature of Sampling

• The basic idea of sampling is that by

selecting some of the elements in

population, we may draw conclusions

about the entire population

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Nature of Sampling

• A population element is the unit of study

• The unit of study might be a person or just about anything else

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Why Sample?

• Lower cost

• Greater accuracy of results

• Greater speed of data collection

• Availability of Population elements.

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What is Good Sample?

• How well it represents the characteristics of the population it purports to represent

• In measurement terms, the sample must be

valid.

• Validity of a sample depends on two considerations

• Accuracy and precision.

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Accuracy

• Degree to which bias is absent from the sample.

• Some sample elements underestimate the population values being studied and other overestimate them.

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How do Bring in Accuracy?

• Under-estimation and over-estimation offset each other and gives a sample value that is generally close to the population value.

• Offsetting requires large number of elements

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Precision

• No sample will fully represent its population in all respects

• Differences in the sample and population values occurs due to random fluctuations inherent in the sampling process.

• This is called sampling error and reflects the influences of chance in drawing the sample members.

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Sampling Error

• What is left after all known sources of systematic variance have been accounted for.

• In theory, sampling error consists of random fluctuations only

• Some unknown systematic variance may be included when too many or too few sample elements possess a particular characteristic.

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Precision

• Measured by the standard error of estimate

• Type of standard deviation measurement

• The smaller the standard error of estimate, the higher is the precision

• Samples of the same size can produce different amounts of error variance.

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Classification of Sample Techniques

Sampling Techniques

Probability Non-Probability

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Probability Sampling

Probability Sampling

Simple Random

Sampling

SystematicSampling

ClusterSampling

StratifiedRandom Sampling

Proportionate

DisProportion

ate

One-Stage

Two Stage

Multi-Stage

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Non-Probability

Non-Probability

Convenience Sampling

QuotaSampling

Judgment Sampling

Snowball Sampling

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Steps in Sampling Design

• What is the Relevant Population?

• The definition of the population

• Whether the population consists of individuals, households, families or a combination of these

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What are the Parameters of Interest?

• Population parameters are summary descriptors (proportion, mean, variance) of variables of interest in the population.

• Sample statistics are descriptors of the relevant variables computed from sample data.

• Sample statistics are used as estimators of population parameters

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What is the Sampling Frame?

• The sampling frame is closely related to the population.

• It is the list of elements from which the sample is actually drawn.

• Ideally, it is a complete and correct list of population members only.

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What is the Type of Sample?

• Choosing a probability sampling technique has several consequences.

• A researcher must follow appropriate procedures, so that :

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What is the Type of Sample?

• Interviewers cannot modify the selections made.

• Only those selected elements from the original sampling frame are included.

• Substitutions are excluded except as clearly specified and controlled according to pre-determined decisions rules.

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What Size Sample is Needed ?

• Some Myths

• A sample must be large or it is not representative.

• A sample should bear some proportional relationship to the size of the population from which it is drawn.

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Some principles that influence sample size include :

• The greater the dispersion or variance within the population, the larger the sample must be to provide estimation precision.

• The greater the desired precision of the estimate, the large the sample must be.

• The narrower the interval range, the larger the sample must be.

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Some Principles that Influence Sample Size Include :

• The higher the confidence level in the estimate, greater the sample size must be

• If the calculated sample size exceeds 5 percent of the population, sample size may be reduced without sacrificing precision.

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How Much Will it Cost?

• Almost all studies have some budgetary

constraint, and this may encourage a

researcher to use a non-probability sample

• Probability sample surveys incur list costs for sample frames, and other costs that are not necessary when more haphazard or arbitrary methods are used.

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Probability Sampling

• Based on the concept of random selection

• A controlled procedure

• Assures that each population element is given a known nonzero chance of selection.

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Non-probability Sampling

• In contrast, is arbitrary (nonrandom) and subjective

• Allowing interviewers to choose sample elements “at random”

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Probability Sampling- Simple Random Sampling

• Each element in the target population has an equal chance or probability of being selected in the population

• Numbers can be randomly generated by computers or picked out of a box

• In small population random sampling is done without replacement

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Requisites

• Target population size is small

• Homogeneous sampling frame is defined

• Not much information is available regarding the population

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Advantages

• Free of classification error

• Requires minimum advance knowledge about the population

• Elimination of human bias

• Non-dependency on the availability of the element

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Disadvantages

• Imperative to list every item in the population prior to sampling

• Requires constructing very large sampling frames

• Hence requires extensive sampling calculations

• Hence excessive costs

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Systematic Sampling

• Selecting every kth from a sampling frame

• K represents the skip interval

• Formula k = population size / Sample Size

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Advantages

• Used in industrial operations where equipments in the production are checked for defects

• Questioning people in a sample survey

• Necessary to select first element randomly and then apply k

• Economical and less time consuming

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Stratified Random Sampling

• Process of grouping members into relatively homogenous groups before sampling

• Each element of the population must be included in a stratum

• Strata should be exhaustive so as not to leave any element of the population

• Then random sampling is applied within each stratum

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Proportional Stratified sampling

• Number selected from each strata depends on the homogeneity and std dev of elements present in it

• Proportional Stratified sampling – A smaller sample can be drawn out of the stratum known to have the same value

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Disproportionate Stratified Sampling

• Samples can be drawn in a much higher proportion from another stratum where values are known to differ.

• Higher number of respondents are required to minimise sampling errors because of the high variability

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Advantages and Disadvantages

• Improves representativeness by reducing sampling error

• Greater statistical efficiency over simple random sampling

• Groups are represented when strata are combined

• There can be errors in designating bases due to time and cost factors

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Multistage Cluster Sampling

• Involves grouping the population into various clusters and then selecting a few clusters for study

• Clusters should be homogenous in nature

• Elements within each cluster should be heterogeneous

• Cluster should be similar to the population

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Multistage Cluster Sampling

• Suitable for studies that cover large geographic areas

• Researcher can go for 1, 2 or multi-stage cluster sampling

• In single stage- all elements from each cluster are studied

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Two Stage

• Two stage - uses random sampling to select a few elements from each of selected clusters

• Multi-stage - selecting a sample in 2 or more successive stages.

• Cluster / units is selected in the first stage and further divided into clusters / units

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Non- Probability Sampling