2 introduction to sampling and survey design

7/28/2019 2 Introduction to Sampling and Survey Design

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Introduction tosampling andsurvey design

Dr James Abdey

Overview

Stages of market research

Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics

Probability (or random)

sampling

Non-probability (or

non-random) sampling

Applied Marketing

(Market Research Methods)

Topic 2:

Introduction to samplingand survey design

Dr James Abdey
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Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling

Non-probability (or


Overview

Aim:

To establish a common language and understandingof setting up a commercial market research / samplesurvey
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Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling

Non-probability (or



1. Research brief

2. Research proposal

3. Data collection

4. Data analysis and evaluation

5. Preparation and presentation of research report
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Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling

Non-probability (or


Stage 1: Research brief

Define the marketing problem

Exploratory research

Consultation

Informal discussions

Developing survey objectives

Target population
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Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling

Non-probability (or


Stage 2: Research proposal

Nature of the problem

Population involved

Methodology to be adopted

Estimate time and costs

Example: New packaging of a product
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Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling

Non-probability (or


Stage 3: Data collection

Survey design (sampling scheme) Questionnaire design

Mode of data collection

Sample size

Sources of error Sampling error Measurement errors

Aim: To produce meaningful numbers relating to the

population studied

Bad data will produce bad and useless results!
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Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling

Non-probability (or


Stage 4: Data analysis and

evaluation

Descriptive statistics (means, proportions, variances,

ratios, etc.)

Plots and graphs

Cross-tabulations

Statistical modelling (regression analysis)

Multivariate analysis

Time series analysis
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Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling

Non-probability (or


Stage 5: Presentation of results

Tables

Graphs

Report

Output from statistical analysis should be clearly and

appropriately discussed

Results discussed must answer the main research

questions set in Stage 1


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Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling

Non-probability (or


Glossary

Population: Group of people or objects that we

would like to study in a particular survey

Target population

Survey population

Census: Total enumeration / study of the population

Sample: A number of sampling units selected from a

population and studied

Elementary sampling unit: The element being

sampled from a population (individuals, supermarket,

hospitals, etc.)
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Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling

Non-probability (or


Sample surveys

Surveys involve collecting new data about a

population

They are based on samples rather than censuses

Surveys are conducted in the real world under

circumstances that cannot be fully controlled

The procedures of sampling are explicit,

systematic and standardised

The sample sizes can be large

They require much planning and teamwork
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Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling

Non-probability (or


Examples of sample surveys

Demography: study of births, deaths, family

formation, etc.

Social psychology: study of perceptions, attitudes,

individual and group behaviour

Political science: study of voting

Market research: study of customer perceptions,

choice of products and services, use of services, etc.

Government: employment, education, health,

income, housing, crime, etc.


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Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling

Non-probability (or


Something to think about

The manager of a multiplex cinema is anxious to

know how it is perceived, i.e. the image people have

of the cinema

Identify three possible populations that may be

surveyed to obtain this information


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Dr James Abdey

OverviewStages of market research

Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling

Non-probability (or


Definitions

The aim of sampling is to select a representative

sample from a population

It is important to understand the mechanisms,

limitations and advantages of sampling


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Dr James Abdey


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling

Non-probability (or


Survey objective 1: Fairness

It would be difficult to justify a method that was

biased

For this reason sampling survey methodology places

a great emphasis on unbiased surveys and

unbiased estimators

Bias can arise from

Sample selection The use of the wrong sampling frame Non-response Other

I d i
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Dr James Abdey


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling

Non-probability (or


Selection bias

Selection bias is avoided with the use of random, or

probability, sample designs

The basic method of simple random sampling

(SRS) is used to justify fairness
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Introduction toS bj i 3 C


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Dr James Abdey


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling

Non-probability (or


Survey objective 3: Cost

A widely used method for reducing cost is cluster

sampling

Once again this uses SRS to ensure fairness but the

survey is conducted in such a way to reduce

sampling costs due to travel and time

Introduction toS li f
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Dr James Abdey


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling

Non-probability (or


Sampling frame

A sampling frame is a list of all population elements(for example, register of electors, postcode

addresses, etc.)

Issues to consider

Adequacy Completeness (missing units) No duplication Accuracy (dynamic populations) Convenience (accessibility)

Introduction toS f ti l li f
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Dr James Abdey


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling

Non-probability (ornon-random) sampling

Sources of national sampling frames

in UK

Registor of electors

Compiled in October, published in February 7% of eligible voters are not on the register Names in alphabetical order, not by address No age information

Postcode Address File (PAF)

Used in Family Expenditure Survey General Household Survey Labour Force Survey National Food Survey

Introduction toPostcode Address File (PAF)
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Dr James Abdey


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling




UK

120 Areas

2700 Districts

8900 Sectors

1.5 million post codes

(22 million addresses)

1.3 million small users1700000 large users

Introduction toSomething for you to think about
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Dr James Abdey


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling


Something for you to think about

The chamber of commerce in a large town wishes to

conduct a random sample of retail outlets in the town

It is proposing to use a local commercial directory as

a sampling frame

To what extent could this sampling frame lead to

bias?

How might such bias be minimised?

Introduction toStatistic/estimator and parameter
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sampling andsurvey design

Dr James Abdey


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling


Statistic/estimator and parameter

Statistic or estimator: any quantity calculated from

a sample to estimate a population parameter (for

example, sample mean, sample variance, sample

proportion, sample percentage)

Parameter: The value of an attribute calculated in

the population (for example, population mean,

population variance, population proportion,

population percentage)

Introduction toSampling variability and error
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Dr James Abdey


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling


Sampling variability and error

Different samples drawn from a fixed population have

different statistics

In contrast, population parameters are fixed

(constant)

Hence we typically experience sampling variability

Sampling error refers to the difference between a

sample estimate (such as the sample mean, x) and

the value of the population parameter (such as thepopulation mean, )

x

Introduction toSampling distribution of a sample
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Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling


Sampling distribution of a sample

statistic

Estimator bias is the difference between the mean

(expected) value of the sampling distribution and the

true parameter value

E(X)

Sample variance is the spread of the sampling

distribution, denoted

Variance(X) = Var(X)

Introduction toli dStatistics and parameters
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Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling


Statistics and parameters

Sample statistics (calculated using sample data)

Sample mean: X Sample variance: S2

Sample standard deviation: S

Population parameters (to be estimated using

sample statistics)

Population mean: Population variance: 2

Population standard deviation:

Introduction toli dDesirable properties for the
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Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling


Desirable properties for the

estimators

Unbiasedness for example, E(X) =

Efficiency small standard errors

Consistency sample statistic approaches the

population parameter that is to be estimated as the

sample size increases

Introduction tosampling andProbability (or random) sampling
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Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling


Probability (or random) sampling

In population (or random) sampling, every

population element has a known, non-zeroprobability of being selected in the sample

Expensive and time consuming

Needs a sampling frame

Aims:

To minimise random sampling error

To minimise systematic sampling bias

Introduction tosampling andProbability sampling designs
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Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling


Probability sampling designs

Simple random sampling

Systematic sampling

Stratified sampling

Cluster sampling

Multistage sampling

Introduction tosampling andSimple random sampling (SRS)
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Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling


Simple random sampling (SRS)

Definition: every population element, as well as sets

of elements, has the same probability of being

selected in the sample

Selection is done using a random device, such as a

pseudo-random number generator

Introduction tosampling andSimple random sampling (SRS)
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Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling


Simple random sampling (SRS)

Example

Suppose a population consists of 4 individuals:

Anna, Ben, Claire and David There are 6 possible samples of size n= 2 from a

population of size N= 4

Samples Probability

AB 1/6AC 1/6

AD 1/6

BC 1/6

BD 1/6

CD 1/6

The probability of each element (at the first draw) is

P(A) = P(B) = P(C) = P(D) = 1/4
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Introduction tosampling andFormulae for SRS


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p gsurvey design

Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling


Sample mean:

X=

n

i=1 Xi

n

Sample variance:

S2 =

n

i=1(Xi X)2n

1

Standard error of X:

S.E.(X) =n

Estimated standard error of X: ( unknown)

E.S.E.(X) =Sn

Introduction tosampling andSample size calculation
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p gsurvey design

Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling


p

Select the sample size such that the standard error

of a variable is less than or equal to some value k

n k n

k

2

Introduction tosampling andExample: Sample size how many
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survey design

Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling


p p y

elements to select?

Population Size, N, Sample size, n

China > 1 billion ?UK 65 million ?City 4 million ?

Town 50,000 ?

Introduction tosampling andStratified random sampling
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survey design

Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling


p g

This technique achieves greater accuracy by taking

advantage of known natural groupings of the

population

These are called strata and they should be chosenso that units are homogeneous (similar) within strata,

but heterogeneous (different) between strata

It makes use of SRS to sample units within strata

thus giving unbiased estimators

Introduction tosampling and

d iStratified random sampling
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survey design

Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling


Population elements are divided into strata (groups)

based on stratification factors (related to the

survey objectives)

Stratification factors should be relevant to the survey

(for example age, gender or social group)

Groups are homogeneous within and heterogeneousbetween groups

A number of elements ni is randomly selected from

each of the L strata, i = 1, . . . , L

Final sample size is

n= n1 + n2 + . . . + nL

where n1 is the number of elements selected from

stratum 1, etc.


Example stratification factor: Age
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survey design

Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling


Population:Students of Market Research

Methods

N= N1 + N2 + N3

Stratum 1Age group

18-22N1

n1

Stratum 2Age group

22-26N2

n2

Stratum 3Age group

26+N3

n3


Stratification sampling allocation
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survey design

Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling


Stratum Stratum Equal Weight Proportional

size allocation allocation

1 N1 = n1 = 250N1N

= 12 n1 = 500100,000

2 N2 = n2 = 250N2N

= 14 n2 = 250

50,0003 N3 = n3 = 250

N3N

= 18 n3 = 12525,000

4 N4 = n4 = 250N4N

= 18 n4 = 12525,000

N= n=1,000 n=1,000200,000
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Stratified random sampling


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survey design

Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling


Advantages:

Most precise and efficient random design (smallersampling errors)

Most representative, significant characteristics of thepopulation are represented adequately in the

different strata

Disadvantages:

Most expensive

Stratification information

Most difficult to administer


Cluster sampling
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survey design

Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling

Non-probability (or


Cluster sampling is aimed at economy (cost saving)

when the population is widely dispersed

The population is again subdivided, but this time into

clusters

Ideally the clusters are such that each is as variableas the main population and represents it well

Only a certain number of the clusters are sampled

using SRS sampling only a proportion of the

clusters is expected to result in economy

Within these randomly selected areas of clusters,

every unit is sampled


Cluster sampling
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y g

Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling

Non-probability (or


Clusters should be heterogeneous

Suitable when sampling frame does not exist for the

whole population

Least efficient random design

When a subsample is taken from a selected cluster it

is called a multistage design
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Systematic sampling


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Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling

Non-probability (or


Systematic sampling is used when the units of the

population are ordered in some meaningful way

either in position or time

Units are sampled at equal distances throughout

the population and the first unit is sampled randomly

to give unbiasedness

This method can result in greater economy and

accuracy than other methods based on the same

sample size

Uses a sampling interval for selection, for example

elements 4, 44, 84, . . . , using sampling interval of 40

It is important to check how the list has been

compiled for any cyclicality


Multistage sampling
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Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling

Non-probability (or


In multistage sampling, sample selection at two or

more successive stages

This technique is often adopted in large surveys

At the first stage, large compound units are sampled(primary units)

At the second stage, smaller units are sampled from

these (secondary units), and several samplingstages of this type may be performed until we at last

sample the basic units


Multistage sampling
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Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling

Non-probability (or


This technique is commonly used in cluster sampling

so that we are at first sampling main clusters, and

then clusters within clusters, etc.

We can also use multistage sampling with mixed

techniques; i.e. cluster sampling at Stage 1, then

stratified sampling at Stage 2, etc.

Example: National survey of salesmen in a

company, sales areas could be identified

A random selection is taken of these, then instead ofinterviewing every person in the cluster, onlyrandomly selected people within the cluster will beinterviewed


Non-probability (or non-random)

sampling
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Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling

Non-probability (or


sampling

Non-probability (or non-random) sampling

involves judgement or purposive sampling or expert

choice

Some units in the population do not have a chance of

selection

Individual units in the population do not have a

known chance of being selected


Quota sampling
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Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling

Non-probability (or


Quota sampling means selection is controlled to

some extent by stratification, weighting and the

settings of quotas for each stratum

Quotas are set by researchers

Selection of the respondents is the responsibility ofthe interviewers

Hence quota sampling is a type of stratified sampling

in which the selection of sampling units within strata(such as age, gender or social group) is done by

interviewers on a non-random basis, controlled by

quotas allocated to different strata


Quota sampling
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Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling

Non-probability (or


With quota sampling it is not possible to estimate the

sampling error

It is fast, cheap, and has administrative simplicity

The efficiency of the design depends on howrepresentative the sample is

Identify stratification factors that are important to thesubject of the survey

Proportions of factors within the sample shouldreflect their actual distribution in the population


Quota sampling: example
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Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling

Non-probability (or


Control Population Sample

characteristic: composition composition Number

SexMale 48% 48% 480

Female 52% 52% 520

Total 100% 100% 1000


Types of quota sampling
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Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling

Non-probability (or


Independent quota sample results in biased

selection, hence is unreliable

The interviewer could fulfil the quota entirely fromone age group or social class

Interrelated quota sample controls the types of

respondents to be interviewed, hence is more

reliable

The interviewer has a more difficult job


Types of quota sampling
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Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling

Non-probability (or


Random route or random walk

Start at specified points and call on households atspecified intervals expensive and inaccurate

Grid sampling

District selection based on ACORN classification http://acorn.caci.co.uk/

Region, television area, neighbourhood types


Probability vs. non-probability

sampling designs
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Dr James Abdey

Overview


Glossary

Sample surveys

Definitions

Survey objectives

Sampling frame

Statistics


sampling

Non-probability (or


sampling designs

Probability sampling makes it possible to estimate

the margins of sampling error

All statistical techniques can be applied

Non-probability sampling is sometimes more

convenient, cheaper and faster
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2 introduction to sampling and survey design

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