Sampling Lecture 9 Prof. Development and Research Lecturer: R. Milyankova

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 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)









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





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?






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


VariableVariableVariableVariable

StatisticStatisticStatisticStatistic

self esteemself esteem

Average = 3.72Average = 3.72samplesample

11 22 33 44 55


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

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samplesample

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samplesample

4.44.24.03.83.63.43.23.0

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AverageAverageAverageAverage AverageAverageAverageAverage AverageAverageAverageAverage

4.44.24.03.83.63.43.23.0

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10

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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

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Sampling Lecture 9 Prof. Development and Research Lecturer: R. Milyankova