3.2 Types of Sampling

8/10/2019 3.2 Types of Sampling

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PROBABILITYSAMPLING DESIGN

1. Simple probability

sampling2. Stratified random

sampling

3. Systematic sampling

4. Cluster sampling/multi- stage sampling

NON-PROBABILITYSAMPLING

1. Deliberate sampling

2. Quota sampling3. Convenience

sampling


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Probability or random sampling gives allmembers of the population a known chanceof being selected for inclusion in the sample.

Selection of the specific units in the sampledepends entirely on chance.

It is possible to measure the sampling errorand hence we know the degree of precision.


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1. Simple random Sampling :This is the ideal

choice as it is a perfectrandom method.

Using this method, individuals are

randomly selected from a list of thepopulation and every single individual has

an equal chance of selection.

This method is ideal, but if it cannot beadopted, one of the following alternatives

may be chosen if any shortfall in accuracy.


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Stratified sampling methods also come intwo types proportionate anddisproportionate.

In proportionate sampling, the stratasample sizes are made proportional to the

strata population sizes.For example if thefirst strata is made up of males, then asthere are around 50% of males in the UKpopulation, the male strata will need torepresent around 50% of the total sample.

In disproportionate methods, the strataare not sampled according to thepopulation sizes, but higher proportionsare selected from some groups and notothers. This technique is typically used in anumber of distinct situations


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Systematic sampling is a frequently usedvariant of simple random sampling. Whenperforming systematic sampling, every kthelement from the list is selected (this is

referred to as the sample interval) from arandomly selected starting point. Forexample, if we have a listed population of6000 members and wish to draw a sampleof 200, we would select every 30th (6000

divided by 200) person from the list. Inpractice, we would randomly select anumber between 1 and 30 to act as ourstarting point.


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The one potential problem with this

method of sampling concerns the

arrangement of elements in the list.? If

the list is arranged in any kind of ordere.g. if every 30th house is smaller than

the others from which the sample is being

recruited, there is a possibility that the

sample produced could be seriouslybiased.


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Cluster sampling is a frequently-used, andusually more practical, random samplingmethod. It is particularly useful insituations for which no list of the elementswithin a population is available and

therefore cannot be selected directly. Asthis form of sampling is conducted byrandomly selecting subgroups of thepopulation, possibly in several stages, itshould produce results equivalent to asimple random sample.


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The sample is generally done by firstsampling at the higher level(s) e.g.randomly sampled countries, then samplingfrom subsequent levels in turn e.g. within

the selected countries sample counties,then within these postcodes, the withinthese households, until the final stage isreached, at which point the sampling is

done in a simple random manner e.g.sampling people within the selectedhouseholds. The levels in question aredefined by subgroups into which it isappropriate to subdivide your population.


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Cluster samples are generally used if: - No list of the population exists.

- Well-defined clusters, which will oftenbe geographic areas exist.

- A reasonable estimate of the numberof elements in each level of clusteringcan be made.- Often the total sample size must befairly large to enable cluster sampling tobe used effectively.


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The probability of in-decision of any

unit (of population) in a sample is

not known.

The selection of units within a

sample involves judgment rather

than pure chance.

The degree of accuracy is notknown.


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1. Deliberate/ purposive sampling: this is also called

judgement sampling. The investigator excercises hisdiscretion in selecting sample observations from theuniverse. As a result, there is an element of bias in theselection.


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2. Quota sampling: In a Market Research context, themost frequently-adopted form of non-probabilitysampling is known as quota sampling.? In some waysthis is similar to cluster sampling in that it requiresthe definition of key subgroups. The main difference

lies in the fact that quotas (i.e. the amount of peopleto be surveyed) within subgroups are set beforehand(e.g. 25% 16-24 yr olds, 30% 25-34 yr olds, 20% 35-55yr olds, and 25% 56+ yr olds) usually proportions areset to match known population distributions.Interviewers then select respondents according to

these criteria rather than at random. The subjectivenature of this selection means that only about aproportion of the population has a chance of beingselected in a typical quota sampling strategy.


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3. Convenience sampling: a sample

obtained from readily available

lists such as telephone directories

or other prepared populationstatements is called convenience

sample.


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Technique

Strengths

Weaknesses

Nonprobability SamplingConvenience sampling

Least expensive, leasttime-consuming, mostconvenient

Selection bias, sample notrepresentative, not recommended fordescriptive or causal research

Judgmental sampling Low cost, convenient,not time-consuming

Does not allow generalization,subjective

Quota sampling Sample can be controlledfor certain characteristics

Selection bias, no assurance ofrepresentativeness

Snowball sampling Can estimate rarecharacteristics

Time-consuming

Probability samplingSimple random sampling

(SRS)

Easily understood,results projectable

Difficult to construct samplingframe, expensive, lower precision,

no assurance of representativeness.Systematic sampling Can increase

representativeness,easier to implement than

SRS, sampling frame notnecessary

Can decrease representativeness

Stratified sampling Include all importantsubpopulations,

precision

Difficult to select relevantstratification variables, not feasible tostratify on many variables, expensive

Cluster sampling Easy to implement, cost

effective

Imprecise, difficult to compute and

interpret results

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3.2 Types of Sampling