Sampling

Types of Sampling

Random Sampling

Non-Random Sampling

Types of Random Sampling

• Simple Random Sampling

• Stratified Random Sampling

• Systematic Sampling

• Cluster Sampling

Simple Random Sampling

• Can be used when population size is small, to get a representative sample

• For eg, steel producing companies in India

• Companies providing aviation service in India

• Not used in consumer research

• Not used when population is very large

• Not possible to get a representative sample

Stratified Random Sampling

• Population divided into strata which are mutually exclusive and collectively exhaustive.

• Based on demographic variables

• Gender, Income , Age, Marital Status

• Homogeneity within strata and heterogeneity between strata.

• From each strata, certain number of people are picked

• Proportionate & Disproportionate

Stratified Random Sampling

• Bank deposits

• Big Account holders (above 10 lakhs) -10%

• Medium Account holders (between 2 lakhsand 10 lakhs) – 60%

• Small Account holders (less than 2 lakhs) –30%

Systematic Sampling

• Sampling interval , k = N/n (convert to a whole number)

• A random number C selected from between 1 & k.

• From a list of people, the elements selected would be C, C+k, C+2k,…… till n elements of the sample have been picked.

Cluster Sampling

• Geography divided into areas/clusters such that there is heterogenity within clusters and homogeneity between clusters

• Cluster of test market cities

• Simple random sample/stratified random sampling

Cluster Sampling

• Departments on each floor of a building

• Collecting information on how much money is spent on entertainment by people in Departments.

• Might be difficult to form heterogenousclusters.

• Households in a neighbourhood will be homogenous rather than heterogenous

Types of Non-random sampling

• Convenience Sampling

• Judgemental Sampling

• Snowball Sampling

• Quota Sampling

Convenience Sampling

• Convenience of the researcher /investigator

• Obtain information quickly & inexpensively

• People interviewed in a shopping mall

• People coming out of a movie theatre for reviews about the movie

• Researcher visiting shops in neighbourhood to observe which brand of a product are people buying, to draw an estimate of market share.

Convenience Sampling

• Generally used in the pre-test phase of research study

• Used in exploratory research

• Not used in conclusive research

Judgemental Sampling

• Judgement of an expert is used to identify a representative sample

• Very difficult to produce satisfactory results

• May curtail generalisability of results

• Most common application is in B2B marketing

Snowball sampling

• Used when it is difficult to identify members

• Deep sea divers, people who play golf, neurosurgeons

• Problem is only in finding the first contact

• Ask for references

• Downside is that references might be similar

• Therefore, difficult to get a representative sample

• Also, problem in identifying a new case

Quota Sampling

• Sample includes minimum number from each subgroup

• Sample based on certain demographic variables as age, gender, education, occupation, income levels

• Quotas allotted to field workers

Quota Sampling - % composition

• Job satisfaction varies across levels of employees

Class 1 10%

Class 2 15%

Class 3 35%

Class 4 40%

Quota Sampling - % composition

• Job satisfaction influenced by Level and Education

Class 1 Class 2 Class 3 Class 4 Total

Postgraduation

8 5 5 0 18

Graduation 2 10 20 5 37

Higher Secondary

0 0 10 35 45

Total 10 15 35 40 100

Quota Vs. Stratified Random

• In stratified random sampling, the selection of sample from each stratum is random

• In quota sampling, the selection from each sub group can be done by judgement or convenience sampling

• The results of stratified random sampling can be generalised

Central Limit Theorem

• If samples of size n are drawn randomly from a population that has a mean µ and standard deviation σ, then the sample means will follow a normal distribution for sufficiently large sizes of the sample (x ≥ 30), regardless of the shape of the population distribution.