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

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

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

Sampling Techniques

Classification of Sampling TechniquesClassification of Sampling Techniques

Non-probabilitySampling Techniques

ConvenienceSampling

ProbabilitySampling Techniques

JudgmentSamples

QuotaSampling

SnowballSampling

SystematicSampling

StratifiedSampling

ClusterSampling

Simple randomSampling

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

Simple random Stratified random Systematic random Cluster/area random Multi-stage random

Non-probability Sampling are of following types

Convenience Sampling Judgment Sampling

Quota Sampling Snow ball Sampling

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

?

Pick any “element” Use random table

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

1 4 8 12

3 6 13 2 10 20 15 7 14 11 16

3 7

10 16

Population

Sample

Stratum 2 = even no.

Stratum 1 = odd no.

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

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

Section 5

Section 2Section 1

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.

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.

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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