BUSINESS RESEARCH

METHODS

LECTURE 7

SAMPLING & SAMPLING TECHNIQUES

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KEY TERMINOLOGIES &

CONCEPTS IN SAMPLING

• Population – the universe of units from which

one selects the sample. The units can be people,

nations, cities, regions, organisations, etc.

– The population has much wider meaning than what is

ordinarily used to identify an entire population

• Sample – the section of the population one selects

for the study or investigation.

– It is a compartment or subset within the population

based on predetermined criterion

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KEY TERMINOLOGIES &

CONCEPTS IN SAMPLING CONT’D

• Sampling frame – The listing of all units in the population from which the sample will be chosen, e.g. Individuals, institutions, household

• Representative sample – a sample that truly mirrors the population so that it is seen as a microcosm of the population

• Sampling bias – a misrepresentation or distortion in the representativeness of the sample that results when members of the population have little or no chance of being chosen for inclusion the sample

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ILLUSTRATION

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FEATURES OF A POPULATION

– Homogeneous – all cases are similar, e.g. bottles of beer on a

production line;

– Stratified – contain strata or layers, e.g. people with different

levels of income: low, medium, high;

– Proportional stratified – contains strata of known proportions

e.g. percentages of different nationalities of students in a

university;

– Categorised by type – contains distinctive groups, e.g. of

apartment buildings – towers, slabs, villas, tenement blocks;

– Classified by location – different groups according to where

they are e.g. animals in different habitats – desert, equatorial

forest, savannah, tundra, etc.

KEY TERMINOLOGIES &

CONCEPTS IN SAMPLING CONT’D

• Probability sample – a sample that has been selected using random selection such that each unit in the population has a known chance of being chosen

• Non-probability sample – a sample that has not been selected using a random selection technique. This means that the selection of some units in the population are more probable than others

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PROBABILITY AND NON-

PROBABILITY SAMPLING FORMS

TYPES OF PROBABILITY

SAMPLE

TYPES OF NON-

PROBABILITY SAMPLE

•Simple Random

•

•Systematic Sample

•Stratified Random

Sampling

•Cluster Sampling

•Convenience Sampling

•Snowball Sampling

•Quota Sampling

•Judgemental 7

KEY TERMINOLOGIES &

CONCEPTS IN SAMPLING CONT’D

• Sampling error – the difference between a sample and the population from which it is selected, in spite of selecting a probability sample, e.g. unrepresentativeness of the taken sample

• Non-sampling error – Identifies the differences between the population and the sample that arise either from deficiencies in the sampling method. This can result from insufficient sampling frame or non-response rate due to such problems as poor interviewing, bad data processing or poor wording of questions.

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KEY TERMINOLOGIES &

CONCEPTS IN SAMPLING CONT’D

• Simple Random Sample – each unit of the population has an equal probability of inclusion in the sample

• Systematic Sample – Process of selecting units directly from the sample frame without using any sense of randomness, such as 6, 26, 46, 66, 86, etc.

• Stratified Sample – it is a proportional representation of the different categories within the selected unit.

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ILLUSTRATION OF STRATIFIED SAMPLING

TECHNIQUESource: Explorable.com

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PROBABILITY SAMPLING

• Cluster sampling – Involves the selection

groupings within the units but not the primary

sampling unit, e.g. 10 out 100 firms for

interviews involving 5000 employees in which

500 each of from the 10 companies would be

interviewed.

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EXAMPLE OF CLUSTER SAMPLINGsource: explorable.com

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KEY TERMINOLOGIES & CONCEPTS IN

SAMPLING CONT’D

• Convenience sampling – A sample that is readily available to the investigator in terms of accessibility

• Snowball sampling – a situation of making an initial contact with few small group of people who are important to the research topic. The researcher then uses the small group to get in touch with others

– Facilitated by current sample elements

– The sample may well not be representative

– It is not advisable to use in quantitative study, rather it can perfectly be used in a qualitative research strategy

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KEY TERMINOLOGIES &

CONCEPTS IN SAMPLING CONT’D

• Quota sampling – Necessary features or characteristics are applied to divide the sample to enhance its representativeness

– Normally used in market research or political opinion poll

– The rationale of this sampling technique is to produce a sample that reflects a population in terms of the relative proportion in different categories such as age groups, gender, tribal groupings, etc.

• Judgemental – Intentionally choose sample to conform to some criteria or conditions of the study

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KEY TERMINOLOGIES &

CONCEPTS IN SAMPLING CONT’D

• Non-response – a source of non-sampling

error that is particularly likely to happen when

individuals are being sampled. This happens

whenever some members of the sample refuse

to cooperate, cannot be reached or cannot

provide the needed data for some reason.

• Census – The process of enumerating the total

population

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KEY TERMINOLOGIES &

CONCEPTS IN SAMPLING CONT’D

• Response rate – the percentage of a sample that

does actually participate. Calculating the response

rate is not pretty straightforward.

• One should bear the following in mind:

– Not every response will be included, e.g. Uncompleted

questionnaire, suspicion of lack of seriousness,

• It can be worked out thus:

Total Number of responses x100

Total number in sample – ineligible

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EXAMPLE OF RESPONSE RATE

CALCULATION

• Kofi administered an email questionnaire to students who had left the supply chain and information systems department over the past 10 years. He obtained a list of the 520 past students from the registrar over this period and selected 50% sample. He obtained a response from 140. In addition, his list of the former students was inaccurate and ten of those he contacted were ineligible to respond, having let the department over five years earlier.

• What would Kofi’s response rate be?

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SOLUTION

Kofi’s total response rate can be calculated thus:

Response rate = Total Number of responses x100

Total number in sample – ineligible

140 = 140x100 = 56%

260 – 10 250

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