050 sampling theory
DESCRIPTION
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A process of selecting units from a population
A process of selecting a sample to determine certain characteristics of a population
A sample is a subset of a larger population of objects individuals, households, businesses, organizations and so forth.
Concept of sampling
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Sampling enables researchers to make estimates of some unknown characteristics of the population in question
A finite group is called population whereas a non-finite (infinite) group is called universe
A census is a investigation of all the individual elements of a population
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PopulationPopulation
SampleSample
A sample is a subset of alarger population of objectsindividuals, households,businesses, organizationsand so forth.
Sampling enables researchersto make estimates of some unknown characteristics of the population in question
A finite group is called populationwhereas a non-finite (infinite) group is called universe
A census is a investigation of allthe individual elements of a population
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Get information about large populations
Less costs Less field time More accuracy i.e. Can Do A Better
Job of Data Collection When it’s impossible to study the
whole population
Why sampling
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Sampling Techniques
Classification of Sampling TechniquesClassification of Sampling Techniques
Non-probabilitySampling Techniques
ConvenienceSampling
ProbabilitySampling Techniques
JudgmentSamples
QuotaSampling
SnowballSampling
SystematicSampling
StratifiedSampling
ClusterSampling
Simple randomSampling
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Probability Sampling: utilizes some form of random selection. A probability sample is a sample in which every element of the population has a known and equal probability of being selected into the sample.
Non-probability sampling: does not involve random selection
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Simple random Stratified random Systematic random Cluster/area random Multi-stage random
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Non-probability Sampling are of following types
Convenience Sampling Judgment Sampling
Quota Sampling Snow ball Sampling
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Probability selected = ni/N When population is rather uniform (e.g.
school/college students, low-cost houses) Simplest, fastest, cheapest Could be unreliable, why?
A T Y W
B P G E S C K L
G N Q
B T
G K
Population
Sample
Population not uniform
Wrong procedure
?
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Pick any “element” Use random table
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Break population into “meaningful” strata and take random sample from each stratum
Can be proportionate or disproportionate within strata
When: * population is not very uniform (e.g. shoppers,
houses) * key sub-groups need to be represented → more precision * variability within group affects research results
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Population
Sample
Stratum 2 = even no.
Stratum 1 = odd no.
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Simple or stratified in nature Systematic in the “picking-up” of element.
E.g. every 5th. visitor, every 10th. House, every 15th. minute
Steps: * Number the population (1,…,N) * Decide on the sample size, n * Decide on the interval size, k = N/n * Select an integer between 1 and k * Take case for every kth. unit
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Research involves spatial issues (e.g. do prices
vary according to neighbourhood’s level of crime?) Sampling involves analysis of geographic
units Sampling involves extensive travelling → try
to minimise logistic and resources Steps: * Divide population into “clusters” (localities) * Choose clusters randomly (simple random, stratified, etc.) * Take all cases from each cluster Efficient from administrative perspective
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Section 5
Section 2Section 1
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Convenience Samples◦ Non-probability samples used primarily because
they are easy to collect.
Judgment Samples◦ Non-probability samples in which the selection
criteria are based on personal judgment that the element is representative of the population under study.
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Quota Samples◦ Non-probability samples in which population
subgroups are classified on the basis of researcher judgment.
Snowball Samples◦ Non-probability samples in which selection of
additional respondents is based on referrals from the initial respondents.
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Technique Strengths WeaknessesNonprobability Sampling Convenience 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 sampling Simple random sampling(SRS)
Easily understood,results projectable
Difficult to construct samplingframe, expensive, lower precision,no assurance of representativeness.
Systematic sampling Can increaserepresentativeness,Easier to implement thanSRS, 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, costeffective
Imprecise, difficult to compute andinterpret results
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Thank you!