Marketing Research Ch 11

8/12/2019 Marketing Research Ch 11

1/30

Chapter Eleven

Sampling:

Design and Procedures

2007 Prentice Hall 11-1


2/30


SAMPLE OR CENSUS

Population: A population is the entire group we areinterested in, which we wish to describe or drawconclusions about. For example, the population for astudy of infant health might be all children born inBangladesh in the 2006's.

A census involves a complete enumeration of the

elements of a population.

Sample: A sample is a group of units selected from alarger group (the population). For example, the samplemight be all babies born on 7th May in the 1980's.

Sample characteristics, called statistics used to makeinferences about population parameters.


3/30


The Sampling Design Process

Define the Population

Determine the Sampling Frame

Select Sampling Technique(s)

Determine the Sample Size

Execute the Sampling Process


4/30


Define the Target Population

The target population is the collection of elements orobjects that possess the information sought by theresearcher and about which inferences are to be made.The target population should be defined in terms ofelements, sampling units, extent, and time.

An elementis the object about which or from whichthe information is desired, e.g., male or female headof the household responsible for most of the shoppingat department stores.

A sampling unitis an element, or a unit containing

the element, that is available for selection at somestage of the sampling process. e.g., households Extentrefers to the geographical boundaries.

Metropolitan city Timeis the time period under consideration. 2006


5/30


Determine the sampling frame

A sampling frame consists of a list or set ofdirections for identifying the target population.E.g. telephone book

Select a sampling techniqueThe researcher must decide whether to use

nonprobability or probability sampling.


6/30


Determine the Sample Size

Sample size refers to the number of elements tobe included in the study.

Qualitative Factors to be considered: The importance of the decision: more important, more larger

The nature of the research: exploratorysmall, conclusivelarge

The number of variables: manylarge

The nature of analysis: sophisticated analysis of the datausing multivariate techniquelarge

Sample sizes used in similar studiesrefer to next slide

Resource constraints: limited money and time - small


7/30 2007 Prentice Hall 11-7

Sample Sizes Used in MarketingResearch Studies

Type of Study Minimum Size Typical Range

Problem identification research(e.g. market potential)

500 1,000-2,500

Problem-solving research (e.g.pricing)

200 300-500

Product tests 200 300-500

Test marketing studies 200 300-500

TV, radio, or print advertising (percommercial or ad tested)

150 200-300

Test-market audits 10 stores 10-20 stores

Focus groups 2 groups 6-15 groups



Classification of Sampling Techniques

Sampling Techniques

Nonprobability

Sampling Techniques

Probability

Sampling Techniques

ConvenienceSampling

JudgmentalSampling

QuotaSampling

SnowballSampling

SystematicSampling

StratifiedSampling

ClusterSampling

Other SamplingTechniques

Simple RandomSampling



Nonprobability Sampling

Nonprobability sampling relies on the personaljudgment of the researcher rather than chanceto select sample elements.

Probability Sampling

Sampling units are selected by chance.



Convenience Sampling

Convenience samplingattempts to obtain asample of convenient elements. Often, respondentsare selected because they happen to be in the rightplace at the right time.

use of students, and members of socialorganizations

mall intercept interviews without qualifying therespondents

department stores using charge account lists

people on the street interviews



A Graphical Illustration ofConvenience Sampling

A B C D E

1 6 11 16 21

2 7 12 17 22

3 8 13 18 23

4 9 14 19 24

5 10 15 20 25

Group D happens to

assemble at a

convenient time and

place. So all theelements in this

Group are selected.

The resulting sample

consists of elements

16, 17, 18, 19 and 20.

Note, no elements are

selected from group

A, B, C and E.



Judgmental Sampling

Judgmental samplingis a form ofconvenience sampling in which thepopulation elements are selectedbased on the judgment of theresearcher.

purchase engineers selected inindustrial marketing research



Graphical Illustration of JudgmentalSampling

A B C D E

1 6 11 16 21

2 7 12 17 22

3 8 13 18 23

4 9 14 19 24

5 10 15 20 25

The researcher considers

groups B, C and Eto betypical and convenient.

Within each of thesegroups one or two

elements are selectedbased on typicality and

convenience. The

resulting sampleconsists of elements 8,

10, 11,13, and 24. Note,no elements are selected

from groups A and D.



Quota Sampling

Quota samplingmay be viewed as two-stage restricted judgmentalsampling.

The first stage consists of developing control categories, or quotas,of population elements.

In the second stage, sample elements are selected based onconvenience or judgment.

Population Samplecomposition composition

Control

Characteristic Percentage Percentage NumberSexMale 48 48 480Female 52 52 520

____ ____ ____100 100 1000



A Graphical Illustration ofQuota Sampling

A B C D E

1 6 11 16 21

2 7 12 17 22

3 8 13 18 23

4 9 14 19 24

5 10 15 20 25

A quota of one

element from each

group, A to E, is

imposed. Within each

group, oneelement isselected based on

judgment or

convenience. The

resulting sample

consistsof elements3, 6, 13, 20 and 22.

Note, one element is

selected from each

column or group.



Snowball Sampling

In snowball sampling, an initial group ofrespondents is selected, usually at random.

After being interviewed, these respondents areasked to identify others who belong to the targetpopulation of interest.

Subsequent respondents are selected based onthe referrals.



A Graphical Illustration ofSnowball Sampling

A B C D E

1 6 11 16 21

2 7 12 17 22

3 8 13 18 23

4 9 14 19 24

5 10 15 20 25

Elements 2 and 9 are

selected randomly

from groups A and B.

Element 2 referselements 12 and 13.

Element 9 refers

element 18. The

resulting sample

consistsof elements2, 9, 12, 13, and 18.

Note, there are no

element from group E.

Random

Selection Referrals



Simple Random Sampling

Each element in the population hasan equal probability of selection.



A Graphical Illustration ofSimple Random Sampling

A B C D E

1 6 11 16 21

2 7 12 17 22

3 8 13 18 23

4 9 14 19 24

5 10 15 20 25

Select five

random numbers

from 1 to 25. Theresulting sample

consists of

population

elements 3, 7, 9,

16, and 24. Note,there is no

element from

Group C.



Systematic Sampling

The sample is chosen by selecting a randomstarting point and then picking every ithelement in succession from the sampling

frame.

The sampling interval, i, is determined bydividing the population size N by the sample

size n and rounding to the nearest integer.



Systematic Sampling

If there are 100,000 elements in thepopulation and a sample of 1,000 is desired.In this case the sampling interval, i, is 100.

A random number between 1 and 100 isselected. If, for example, this number is 23,the sample consists of elements 23, 123,

223, 323, 423, 523, and so on.



A Graphical Illustration ofSystematic Sampling

A B C D E

1 6 11 16 21

2 7 12 17 22

3 8 13 18 23

4 9 14 19 24

5 10 15 20 25

Select a random

number between 1 to

5, say 2.The resulting sample

consists of

population 2,

(2+5=) 7, (2+5x2=) 12,

(2+5x3=)17, and

(2+5x4=) 22.Note, allthe elements are

selected from a

single row.


23/30


Stratified Sampling

A two-step process in which the population ispartitioned into subpopulations, or strata.

Next, elements are selected from eachstratum by a random procedure, usually SRS.

The elements within a stratum should be as

homogeneous as possible, but the elements indifferent strata should be as heterogeneousas possible.


24/30


A Graphical Illustration ofStratified Sampling

A B C D E

1 6 11 16 21

2 7 12 17 22

3 8 13 18 23

4 9 14 19 24

5 10 15 20 25

Randomly select a

number from 1 to 5

for each stratum, A toE. The resultingsample consists of

population elements4, 7, 13, 19 and 21.

Note, one elementis selected from each

column.


25/30


Cluster Sampling

The target population is first divided into mutuallyexclusive subpopulations, or clusters.

Then a random sample of clusters is selected, basedon a probability sampling technique such as SRS.

For each selected cluster, either all the elements are

included in the sample (one-stage) or a sample ofelements is drawn probabilistically (two-stage).


26/30


Cluster Sampling

Elements within a cluster should be asheterogeneous as possible, but

clusters themselves should be ashomogeneous as possible.


27/30


A Graphical Illustration ofCluster Sampling (2-Stage)

A B C D E

1 6 11 16 21

2 7 12 17 22

3 8 13 18 23

4 9 14 19 24

5 10 15 20 25

Randomly select 3

clusters, B, D and E.

Within each cluster,

randomly select oneor two elements. The

resulting sample

consists of

population elements

7, 18, 20,21, and 23.Note, no elements

are selected from

clusters A and C.


28/30


Types of Cluster Sampling

Cluster Sampling

One-StageSampling

MultistageSampling

Two-StageSampling

Simple ClusterSampling

ProbabilityProportionate

to Size Sampling

Strengths and Weaknesses of


29/30


Technique Strengths WeaknessesNonprobability Sampling

Convenience samplingLeast expensive, leasttime-consuming, mostconvenient

Selection bias, sample notrepresentative, not recommended fordescriptive or causal research

Judgmental sampling Low cost, convenient,not time-consuming

Does not allow generalization,subjective

Quota sampling Sample can be controlledfor certain characteristics

Selection bias, no assurance ofrepresentativeness

Snowball sampling Can estimate rarecharacteristics

Time-consuming

Probability samplingSimple random sampling(SRS)

Easily understood,results projectable

Difficult to construct samplingframe, expensive, lower precision,no assurance of representativeness.

Systematic sampling Can increaserepresentativeness,easier to implement thanSRS, sampling frame notnecessary

Can decrease representativeness

Stratified sampling Include all importantsubpopulations,

precision

Difficult to select relevantstratification variables, not feasible tostratify on many variables, expensive

Cluster sampling Easy to implement, costeffective

Imprecise, difficult to compute andinterpret results

Strengths and Weaknesses ofBasic Sampling Techniques

Choosing Nonprobability Vs


30/30

Choosing Nonprobability Vs.Probability Sampling

Conditions Favoring the Use ofFactors Nonprobability

samplingProbabilitysampling

Nature of research Exploratory Conclusive

Relative magnitude of samplingand nonsampling errors

Nonsamplingerrors arelarger

Samplingerrors arelarger

Variability in the population Homogeneous

(low)

Heterogeneou

s (high)

Statistical considerations Unfavorable Favorable

Operational considerations Favorable Unfavorable

Documents

Marketing Research Ch 11