SamplingLecture 9
Prof. Development and Research
Lecturer: R. Milyankova
Objectives of this session:
To understand the need for sampling in B&M research
To be aware of a range of probability and non-probability sampling techniques
To be able to select, to justify and to use a range of sampling techniques
To be able to assess the representativeness of respondents
To be able to apply the knowledge, skills and understanding gained to your own research project
Sampling terminology Census – counting of the population Population – the full set of cases from which
a sample is taken Sampling techniques – range of methods that
enable you to reduce the amount of data you collect
Sample
Population
Case or element
Need to sampleSampling provides a valid alternative when: It would be impracticable for you to survey the
entire population Your budget constraints prevent you from surveying
the entire population Your time constraints prevent you from surveying
the entire population You have collected all the data but need the results
very quickly
Major types of sampling methods Probability or representative sampling- The probability for each case is known and is usually equal for
all cases- Uses some form of random selection- Requires that each unit has a known (often equal) probability of
being selected- Used more for survey-based than for experiment research Non-probability or judgemental sampling- The probability of the separate cases is not known preliminary- Selection is systematic or haphazard, but not random- More frequently used for case study research
SamplingTechniques probability
non-probability
SamplingSampling
Simplerandom
Systematic
Stratif iedrandom
Cluster
Multi-stage
Quota
Purposive
Snow ball
Self-selection
Convenience
Extreme case
Heterogeneous
Homogeneous
Critical case
Ty pical case
Extreme case
Heterogeneous
Homogeneous
Critical case
Typical case
Probability sampling: Stages1. Identify a suitable sampling frame based on your research
questions and/or objectives (unbiased, current and accurate)
Checklist for selecting a sample frame o Are cases listed in the sampling frame relevant to your
research topic, are they current? Does the sampling frame include all cases, is it complete? Does the sampling frame exclude the irrelevant cases, is it
precise? Can you establish control precisely how he sample will be
selected? (when purchased lists of samples)
Probability sampling: Stages2. Decide on a suitable sample size – the larger the
sampling size, the lower the error (the sampling is a compromise between the accuracy of your findings and the amount of time and money you invest in collecting data)
The confidence you need to have in your data (the level of certainty)
The margin of error that you can tolerate The types of analyses you are going to undertake The size of the total population from which your
sample is being drawn
Probability sampling: Stages Minimum number of cases – 30 (The
Economist). Less than 30 – use all cases + expert system
Level of certainty – 95 % The margin of error depends on response
rates (see Saunders, M. et all, 2003, Table 6.1, page 156)
Probability sampling: StagesReasons for non-response: Refusal to respond Illegibility to respond Inability to locate respondents Respondent located but unable to make contact
Total response rate = total number of responses
total number of sample – ineligible
Active response rate = total number of responses
total number of responses–(ineligible+unreachable)
Probability sampling: StagesSelect the most appropriate sampling technique and select the sample Simple random – accurate and easily accessible, concentrate on face-to face contact
otherwise does not matter, difficult to explain to support workers, high costClose your eyes and choose the number Systematic - accurate and easily accessible, suitable for all sizes, concentrate on face-to face
contact otherwise does not matter, relatively easy to explain, low costEvery third case for example Stratified random - accurate and easily accessible, suitable for all sizes, concentrate on face-
to face contact otherwise does not matter, relatively difficult to explain, low costDivide the population into strata (men-women, retail-corporate) Cluster – as large as practicable, quick but reduced precisionDiscrete groups=clusters (geographical areas, town regions) Multi-stage – substantial errors possibleIt is a development of the cluster sampling
Sampling fraction = actual sample size total population
Probability sampling: StagesChecking the sample is representative for the
population Compare with samples, done for the needs of
marketing or other sources for the population researched
Groups in SamplingGroups in SamplingThe Theoretical The Theoretical
PopulationPopulationThe Theoretical The Theoretical
PopulationPopulation
How to id
entify th
e suita
ble sa
mplin
g
fram
e?
What population can What population can you get access to?you get access to?
(Telephone directory)(Telephone directory)
What population can What population can you get access to?you get access to?
(Telephone directory)(Telephone directory)
Groups in SamplingGroups in Sampling
The Theoretical The Theoretical PopulationPopulation
The Theoretical The Theoretical PopulationPopulation
Groups in SamplingGroups in Sampling
The Theoretical The Theoretical PopulationPopulation
The Theoretical The Theoretical PopulationPopulation
The Study The Study PopulationPopulationThe Study The Study PopulationPopulation
How can you get How can you get access to them?access to them?
(methods of research)(methods of research)
How can you get How can you get access to them?access to them?
(methods of research)(methods of research)
Groups in SamplingGroups in Sampling
The Theoretical The Theoretical PopulationPopulation
The Theoretical The Theoretical PopulationPopulation
The Study The Study PopulationPopulationThe Study The Study PopulationPopulation
Groups in SamplingGroups in Sampling
The Theoretical The Theoretical PopulationPopulation
The Theoretical The Theoretical PopulationPopulation
The Study The Study PopulationPopulationThe Study The Study PopulationPopulation
The Sampling Frame The Sampling Frame --complete list of all the cases complete list of all the cases
in the populationin the population
The Sampling Frame The Sampling Frame --complete list of all the cases complete list of all the cases
in the populationin the population
Who is in your study?Who is in your study?
The sampleThe sample
Who is in your study?Who is in your study?
The sampleThe sample
Groups in SamplingGroups in Sampling
The Theoretical The Theoretical PopulationPopulation
The Theoretical The Theoretical PopulationPopulation
The Study The Study PopulationPopulationThe Study The Study PopulationPopulation
The Sampling The Sampling FrameFrame
The Sampling The Sampling FrameFrame
Deciding on a suitable sampling sizeThe larger your sampling size the lower the error
The confidence you need to have in your data – the level of certainty that the characteristics of data collected will represent the characteristics of the total population
The margin of error that you can tolerate – the accuracy you require for any estimates made from your sample
The types of analysis you are going to undertake – The size of the total population from which your
sample is being drawn
The sampleThe sampleThe sampleThe sample
Where Can We Go Wrong?Where Can We Go Wrong?
The Theoretical The Theoretical PopulationPopulation
The Theoretical The Theoretical PopulationPopulation
The Study The Study PopulationPopulationThe Study The Study PopulationPopulation
The Sampling The Sampling FrameFrame
The Sampling The Sampling FrameFrame
Sample sizes for different sizes of population at a 95% level of certainty
Margin of error
Population 5% 3% 2% 1%50 44 48 49 50
100 79 91 96 99
150 108 132 141 148
200 132 168 185 196
250 151 203 226 244
300 168 234 267 291
400 196 291 334 384
500 217 340 414 475
750 254 440 571 696
1000 278 516 706 906
2000 322 696 1091 1655
5000 357 879 1622 3288
10000 370 964 1936 4899
100000 383 1056 2345 8762
1000000 384 1066 2395 9513
SamplingTechniques probability
non-probability
SamplingSampling
Simplerandom
Systematic
Stratif iedrandom
Cluster
Multi-stage
Quota
Purposive
Snow ball
Self-selection
Convenience
Extreme case
Heterogeneous
Homogeneous
Critical case
Ty pical case
Extreme case
Heterogeneous
Homogeneous
Critical case
Typical case
Non-probability sampling Quota sampling – non-random, used for interview surveys,
the population is divided into specific groups, stratified, less costly, can be set up very quickly
Purposive (judgmental) sampling – - extreme case or deviant sampling- heterogeneous or maximum variation sampling- homogeneous all sample members are similar- critical case sampling – selected either because they are
important or because they are different- typical case sampling -
Non-probability sampling Snowball sampling – when it is difficult to
identify members of the desired population Self-selection sampling – participate if they
want Convenience (haphazard) sampling – select
those cases that are easier to obtain for your sample
11 22 33 44 55
Statistical Terms in SamplingStatistical Terms in Sampling
VariableVariableVariableVariable self esteemself esteem
11 22 33 44 55
Statistical Terms in SamplingStatistical Terms in Sampling
VariableVariableVariableVariable
StatisticStatisticStatisticStatistic
self esteemself esteem
Average = 3.72Average = 3.72samplesample
11 22 33 44 55
Statistical Terms in SamplingStatistical Terms in Sampling
VariableVariableVariableVariable
StatisticStatisticStatisticStatistic
ParameterParameterParameterParameter
self esteemself esteem
Average = 3.72Average = 3.72
Average = 3.75Average = 3.75
samplesample
populationpopulation
The Sampling DistributionThe Sampling DistributionThe Sampling DistributionThe Sampling Distribution
samplesample
4.44.24.03.83.63.43.23.0
5
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5
0
samplesample
4.44.24.03.83.63.43.23.0
5
0
5
0
samplesample
4.44.24.03.83.63.43.23.0
5
0
5
0
AverageAverageAverageAverage AverageAverageAverageAverage AverageAverageAverageAverage
4.44.24.03.83.63.43.23.0
15
10
5
0
The Sampling The Sampling Distribution...Distribution...The Sampling The Sampling Distribution...Distribution...
...is the distribution of a ...is the distribution of a statistic across an statistic across an infinite number of infinite number of
samplessamples