sampling: design, procedures and statistical considerations

1- 1 Malhotra Hall Shaw Oppenheim Essentials of Marketing Research © Copyright 2004 Pearson Education Australia

PART THREE

Chapter 8

Sampling: Design,

Procedures and

Statistical Considerations

8-2 Malhotra Hall Shaw Oppenheim Essentials of Marketing Research © Copyright 2004 Pearson Education Australia

Chapter Objectives

After reading this chapter, you should be able to:

Differentiate a sample from a census and identify the conditions that favour the use of a sample versus a census.

Discuss the sampling design process.

Classify sampling techniques as non-probability and probability techniques.

Describe the non-probability sampling technique.

Describe the probability sampling technique.

Identify the conditions that favour the use of non-probability sampling sampling versus probability sampling.


Sample or Census

Population

(Census)

Sample

Aggregate of all the elements that

share some common set of

characteristic and that comprise

the universe for the purpose of the

marketing research problem.

A subgroup of

the population

selected for

participation in

the study.


Table 8.1 Conditions Favouring the Use of

Sample vs Census

Sample Census

Budget

Time available

Population size

Variance in the characteristics

Cost of sampling errors

Cost of non-sampling errors

Nature of measurement

Attention to individual cases

Small

Short

Large

Small

Low

High

Destructive

Yes

Large

Long

Small

Large

High

Low

Non-destructive

No


Figure 8.1 The Sampling Design Process

Define the target population

Determine the sampling frame

Select sampling technique(s)

Determine the sample size

Execute the sampling process


Precise statement of who should and should not be included in the sample

Do we only want to interview those that participate in sport at the professional level? Should we ignore amateur sports people?

Elements

An element about which or from which the information is desired.

eg. respondent: male, female, over 18, main grocery buyer, decision maker.

Define the Target Population


Define the Target Population cont.

Sampling units

An element or a unit containing the element.

eg. households, small businesses, schools

Mall-intercept and personal interviews are

special cases where the element is the sampling

unit.


Define the Target Population cont.

Extent

Geographical boundaries

e.g. western metropolitan region of

Melbourne, national study, study of two

countries (Malaysia and Australia)

Time

Period under consideration


Identify the element, sampling unit , extent and time in each

of the following descriptions of the target population.

Females between the age of 18 – 55 are

interviewed at Chadstone shopping centre in

July of 2004 to determine their attitude to a new

range of ‘natural’ cosmetics.

The Directors of small to medium sized

manufacturing companies in the western

suburbs of Sydney are interviewed to gain an

understanding why some manufacturers have

been successful in export goods.


Determine the Sampling Frame

A list or set of directions for identifying the target

population.

Telephone book [white or yellow pages]

An association directory

[MRSA list of research organisations or members]

Mailing list [purchased from a commercial

business, membership list]

City directory or map

Random digit dialling [RDD]


Sampling Frame Error

A list that may omit some elements of the population or include other elements which do not belong.

Eg. Perth WhitepagesTM may omit people with an incorrect listing, silent numbers, and people outside the metropolitan area.

Eg. If our target population are people who purchased car tyres in the last 3 months, the WhitepagesTM would also include people who have not purchased car tyres (in the last 3 months).

Overcome sampling frame error by:

Redefining the population in terms of the sampling frame.

Screen respondents according to demographics, familiarity, product usage.

Adjust the data collected by a weighting scheme.


Select a sampling technique

Bayesian

Elements are selected sequentially

After each element is added to the sample, data

is collected, sample statistics computed,

sampling costs determined.

Not used widely in marketing research.

Traditional

Entire sample is selected before data collection

begins.


Select a sampling technique cont.

Sampling with replacement

An element is selected from the sampling frame

and appropriate data is obtained

Element is placed back in the sampling frame

Sampling without replacement

Once an element is selected for inclusion in the

sample, it is removed from the sampling frame

and therefore cannot be selected again

Use randomisation (i.e. next birthday) when more

than one person is eligible to participate.


Determine the Sample Size

The number of elements to be included in the study

Qualitative factors

Importance of the decision

Number of variables

Nature of the analysis

Sample size used in similar studies

Incidence rates

Completion rates

Resource constraints

Incidence rates

Anticipated refusals


Determine the Sample Size cont.

Quantitative factors

2

D

Zn

Sample size formula when the key

variable produces a mean value

n is the sample size

Z is the number of standard deviation

from the mean

is the standard deviation

D is the maximum permissable error

(precision)


Determine the Sample Size cont.

Quantitative factors

Sample size formula when the key

variable produces a proportion

value

n is the sample size

Z is the number of standard

deviation from the mean

is the population proportion (if not

available use (1 - ) = .25)

D is the maximum permissable error

(precision)

2

2

D

)1(Zn


Table 2 Area under normal curve


25.0)1(

09.01.0x9.0

16.02.0x8.0

21.03.0x7.0

24.04.0x6.0

25.05.0x5.0

24.06.0x4.0

21.07.0x3.0

16.08.0x2.0

09.09.0x1.0

Why use ?

Highest combination

Better to overestimate

than underestimate, as it

produces a higher n.


Execute the Sampling Process

Detailed specifications of how the sampling,

design decisions with respect to the population,

sampling frame, sampling units, sampling

techniques and sample size are to be

implemented

Develop guidelines for ‘not at homes’

i.e. Do you re-contact them?


Figure 8.2 A Classification of Sampling Techniques


Sampling Techniques

Non-probability

Personal judgement of the researcher is used rather

than chance to select elements

Difficult to generalise result to the population

Used in studies where projection to the population is

not necessary

eg. concept tests, package tests, and copy tests


Sampling Techniques cont.

Probability

Sampling units are selected by chance

Pre-specifying every potential sample of a given size

that could be drawn from the population

Require precise definition of the target population

and sampling frame

Able to make inferences about the target population

Used when there is a need to estimate market share

or provide information on product category, brand

usage rates, psychographic and demographic

profiles of users


Non-Probability Sampling Techniques

Convenience sampling

Selection of sampling units is left to the interviewer

[right place, right time]

Inexpensive, quick, can be used for exploratory

research

Selection bias present, not representative, can not

generalise to the population

e.g. students at uni, shopping centres without qualifying

respondents, questionnaires in magazines or restaurants


Non-Probability Sampling Techniques cont.

Judgmental sampling

Selection based on researcher judgement

Inexpensive, convenient, quick

Can not generalise to specific populations

e.g. selection of test markets



Quota sampling

1st – develop quotas based on relevant

characteristics and determine the distribution in

relation to the population proportion.

Eg. Age: 18 – 25 (25%),…

2nd – sample elements are then selected based on

convenience or researcher judgement

May not be representative of the population but could

be relevant

Selection and self-selection bias possible

Lower cost and greater convenience



Snowballing sampling

Initial group of respondents is selected at random,

then asked to identify others who belong to the target

population of interest

Referrals will have demographic and psychographic

characteristics that are more similar to person

referring than would be by chance.

eg. minority groups, widowed males under 35,

people involved in a specialised craft

Substantial increase likelihood of locating desired

sample, results in low sampling variance and cost.


Probability Sampling Techniques

Simple random sampling (SRS)

Each element in the population has a known and

equal chance of selection

A sample is drawn by a random procedure from a

sampling frame

Easily understood

Generalisation to the population is possible

Difficult to construct a sampling frame

Samples may be spread over large geographical

areas, hence high time and cost in data collection

Lower precision (large standard errors).

May or may not result in representative sample


Sampling Frame

1. Avril Levine

2. Jennifer Lopez

3. Justin Timberlake

4. J West

5. Missy Elliot

6. Robbie Williams

7. Kylie Minogue

N = 893


Table of random numbers


Probability Sampling Techniques cont.

Systematic sampling

Sample is chosen by selecting a random starting

point and then picking every ith element in

succession from the sampling frame

[eg. telephone book]

i = N/n

Commonly used in telephone and mall-intercept

interviews



Stratified sampling

Population split into sub-populations

Strata are mutually exclusive and collectively

exhaustive

Then SRS from each stratum to select the elements

Within stratum – homogeneous

Each stratum - heterogeneous

Age: 18 - 25 year olds would have similar characteristic than

would 46 – 55 year olds.

Proportionate vs disproportionate?



Cluster sampling

Target population is split into mutually exclusive and

collectively exhaustive sub-populations

Then random sample of clusters is selected based on

SRS

A sample from each (selected) cluster is selected

Within cluster – homogeneous

Each cluster - heterogeneous

eg. Area sampling

One stage, two-stage or multi-stage sampling?


Differences between Stratified and

Cluster sampling

Only one sample of subpopulations (cluster) is

chosen, whereas all subpopulations (strata) are

selected for further sampling.

The objective of cluster sampling is to increase

efficiency by decreasing costs, whereas the objective

of stratified sampling is to increase precision.

Homogeneity and heterogeneity criteria is different.


Table 8.2 Strengths and Weaknesses of basic

sampling techniques


Table 8.3 Choosing non-probability vs

probability sampling

Factors Non-probability

sampling

Probability

sampling

Nature of research Exploratory Conclusive

Relative magnitude

of sampling and

non-sampling errors

Non-sampling errors are

larger

Sampling errors are

large

Variability in the

population

Homogeneous (low) Heterogeneous (high)

Statistical

considerations

Unfavourable Favourable

Operational

considerations

Favourable Unfavourable

sampling: design, procedures and statistical considerations

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