Lecture 9.ppt

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Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009
Sampling Techniques-Quantitative data
Lecture No. 9

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Data Collection
• In research, statisticians use data in many
different ways.
• Data can be used to describe situations.
• Data can be collected in a variety of ways,
BUT if the sample data is not collected in
an appropriate way, the data may be so
completely useless that no amount of
statistical torturing can salvage them.

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Selecting samples
Population, sample and individual cases
Source: Saunders et al. (2009)

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The need to sample
Sampling- a valid alternative to a census when
• A survey of the entire population is impracticable
• Budget constraints restrict data collection
• Time constraints restrict data collection
• Results from data collection are needed quickly

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Sampling Basics
• A sample is a “part of a whole to show what
the rest is like”.
• Sampling helps to determine the
corresponding value of the population and
plays a vital role in research.

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Sample offers many benefits
• Save costs
• Less expensive to study the sample than the
population
• Save time
• Less time needed to study the sample than the
population
• Accuracy
• Since sampling is done with care and studies are
conducted by skilled and qualified interviewers,
the results are expected to be accurate

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Sampling Process
Defining the
population
Developing a
sample frame
Developing a
sample size
Specific
Sampling
Methiod
Selecing the
sample

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Steps in Sampling

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Step 1 | Defining the Universe
• Universe or population is the whole mass under study.
• How to define a universe:
• What constitutes the units of analysis (HDB apartments)?
• What are the sampling units (HDB apartments occupied in
the last three months)?
• What is the specific designation of the units to be covered
(HDB in town area)?

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Step 2 | Establishing the Sampling
Frame
• A sample frame is the list of all elements in the
population (such as telephone directories, electoral
registers, club membership etc.) from which the
samples are drawn.
• A sample frame which does not fully represent an
intended population will result in frame error and
affect the degree of reliability of sample result.

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Step 3 | Determination of Sample Size
• Sample size may be determined by using: Subjective
methods (less sophisticated methods)
1. The rule of thumb approach: eg. 5% of population
2. Conventional approach: eg. Average of sample sizes of
similar other studies;
3. Cost basis approach: The number that can be studied with
the available funds;
4. Statistical formulae (more sophisticated methods)

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Sample Size Determination

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Overview of sampling techniques
Sampling techniques
Source: Saunders et al. (2009)
Judgmental

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Probability Sampling
• Probability of each case / unit being selected
from the population is known (and usually
equal to all cases).

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Non Probability Sample
• Total Population is in accessible.
Researcher can not reach the target
population in a specified time period.

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Techniques in Probability Sampling

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Techniques to apply probability sampling
• Five main techniques used for a probability
sample
• 1. Simple random
• 2. Stratified random
• 3. Systematic
• 4. Cluster
• 5. Multi-stage

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1. Simple Random
Selected by using chance or random numbers
– Each individual subject (human or otherwise) has
an equal chance of being selected
– Examples:
• Drawing names from a hat
• Random Numbers

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2. Stratified Random Sampling
Divide the population into at least two different
groups with common characteristic(s), then draw
SOME subjects from each group (group is called
strata or stratum)
Results in a more representative sample
• A random sample (simple or systematic) is then drawn
from each of the strata.

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3. Systematic sampling
• Systematic sampling involves you selecting
the sample at regular intervals from the
sampling frame.
– Select a random starting point and then select
every kth subject in the population
– Simple to use so it is used often

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4. Cluster Sampling
• Similar to stratified, devide the population into groups (called
clusters), randomly select some of the groups, and then collect data
from ALL members of the selected groups
• Used extensively by government and private research
organizations Examples: Exit Polls

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5. Multi Stage Sampling
• Multistage sampling is a complex form of cluster
sampling. Cluster sampling is a type of sampling which
involves dividing the population into groups (or
clusters). Then, one or more clusters are chosen at
random and everyone within the chosen cluster is
sampled.

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• In traditional cluster sampling, a total population of interest is
first divided into ‘clusters’ (for example, a total population into
geographic regions, household income levels, etc), and from
each cluster individual subjects are selected by random
sampling.
• This approach however, may be considered overly-expensive or
time consuming for the investigator. Using multi-stage
sampling, investigators can instead divide these first-stage
clusters further into second-stage cluster using a second element
(for example, first ‘clustering’ a total population by geographic
region, and next dividing each regional cluster into second-stage
clusters by neighborhood). Multi-stage sampling begins first
with the construction of the clusters. Next, the investigator
identifies which elements to sample from within the clusters,
and so on until they are ready to survey.

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Techniques in Non Probability
Sampling

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1. Quota Sampling
• A sampling method of
gathering representative data from a group. As
opposed to random sampling, quota sampling
requires that representative individuals are
chosen out of a specific subgroup. For example, a
researcher might ask for a sample of 100 females,
or 100 individuals between the ages of 20-30

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• A quota sample a type of non-probability sample in which the
researcher selects people according to some fixed quota.
• That is, units are selected into a sample on the basis of pre-
specified characteristics so that the total sample has the same
distribution of characteristics assumed to exist in the population
being studied.
• For example, if you are a researcher conducting a national quota
sample, you might need to know what proportion of the population
is male and what proportion is female as well as what proportions
of each gender fall into different age categories, race or ethnic
categories, educational categories, etc.
• The researcher would then collect a sample with the same
proportions as the national population.

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2. Purposive Sampling
• Purposive or judgmental sampling enables you to use your
judgment to select cases that will best enable you to answer your
research question(s) and to meet your objectives.
• This form of sample is often used when working with very small
samples such as in case research and when you wish to select
cases that are particularly informative.
• Purposive sampling can also be used by researchers adopting the
grounded theory strategy. For such research, findings from data
collected from your initial sample inform the way you extend
your sample into subsequent cases.

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3. Snowball Sampling
• Snowball sampling can happen in a number
of ways, but generally it is when a group of
people recommends potential participants
for a study, or directly recruits them for the
study.
• Those participants then recommend
additional participants, and so on, thus
building up like a snowball rolling down a
hill.

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4. Self Selecting Sampling
• It occurs when you allow each case usually
individuals, to identify their desire to take
part in the research you therefore
• Publicize your need for cases, either by
advertising through appropriate media or by
asking them to take part.
• Collect data from those who respond

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5. Convenience Sampling
• Convenience sampling (or haphazard sampling) involves
selecting haphazardly those cases that are easiest to obtain
for your sample, such as the person interviewed at random
in a shopping centre for a television programme or the
book about entrepreneurship you find at the airport.
• The sample selection process is continued until your
required sample size has been reached.
• Although this technique of sampling is used widely, it is
prone to bias and influences that are beyond your control,
as the cases appear in the sample only because of the ease
of obtaining them.

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Summary
• Choice of sampling techniques depends upon the research question(s) and
their objectives
• Factors affecting sample size include:
- confidence needed in the findings
- accuracy required
- likely categories for analysis
• Probability sampling requires a sampling frame and can be more time
consuming
• When a sampling frame is not possible, non- probability sampling is used
• Many research projects use a combination of sampling techniques

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Thank you

Lecture 9.ppt

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