Files-2-Presentations Malhotra ORGNL Ppt 11

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© 2007 Prentice Hall 11-2

Chapter Outline

1) Overview

2) Sample or Census

3) The Sampling Design Process

i. Define the Target Population

ii. Determine the Sampling Frame

iii. Select a Sampling Technique

iv. Determine the Sample Size

v. Execute the Sampling Process




Chapter Outline

4) A Classification of Sampling Techniquesi. Nonprobability Sampling Techniques

a. Convenience Sampling

b. Judgmental Sampling

c. Quota Sampling

d. Snowball Sampling

ii. Probability Sampling Techniques

a. Simple Random Sampling

b. Systematic Sampling

c. Stratified Sampling

d. Cluster Sampling

e. Other Probability Sampling Techniques




Chapter Outline

5. Choosing Nonprobability Versus ProbabilitySampling

6. Uses of Nonprobability Versus ProbabilitySampling

7. Internet Sampling

8. International Marketing Research

9. Ethics in Marketing Research

10. Summary




Sample Vs. CensusTable 11.1

Conditions Favoring the Use of

Type of Study Sample Census

1. Budget Small Large

2. Time available Short Long

3. Population size Large Small

4. Variance in the characteristic Small Large

5. Cost of sampling errors Low High

6. Cost of nonsampling errors High Low

7. Nature of measurement Destructive Nondestructive

8. Attention to individual cases Yes No




The Sampling Design Process

Fig. 11.1

Define the Population

Determine the Sampling Frame

Select Sampling Technique(s)

Determine the Sample Size

Execute the Sampling Process




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 of elements, sampling units, extent, and time.

An element is the object about which or from whichthe information is desired, e.g., the respondent.

A sampling unit is an element, or a unit containing

the element, that is available for selection at somestage of the sampling process.

Extent refers to the geographical boundaries.

Time is the time period under consideration.




Define the Target Population

Important qualitative factors in determining thesample size are:

the importance of the decision the nature of the research

the number of variables

the nature of the analysis

sample sizes used in similar studies incidence rates

completion rates

resource constraints






Classification of Sampling Techniques

Sampling Techniques

NonprobabilitySampling Techniques

Probability

Sampling Techniques

ConvenienceSampling

JudgmentalSampling

QuotaSampling

SnowballSampling

SystematicSampling

StratifiedSampling

ClusterSampling

Other SamplingTechniques

Simple RandomSampling

Fig. 11.2




Convenience Sampling

Convenience sampling attempts to obtain asample of convenient elements. Often, respondentsare selected because they happen to be in the right place 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

Fig. 11.3

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 2 25

Group D happens to

assemble at a

convenient time and

place. So all the

elements 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 sampling is a form of conveniencesampling in which the population elements areselected based on the judgment of the researcher.

test markets

purchase engineers selected in industrialmarketing research

bellwether precincts selected in voting behaviorresearch

expert witnesses used in court




Graphical Illustration of JudgmentalSampling

Fig. 11.3 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 1 15 20 25

The researcher considers

groups B, C and E to be

typical and convenient.

Within each of these

groups one or two

elements are selected

based 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 sampling may be viewed as two-stage restricted judgmentalsampling.

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

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

Population Samplecomposition composition

ControlCharacteristic Percentage Percentage NumberSexMale 48 48 480Female 52 52 520

____ ____ ____100 100 1000




A Graphical Illustration ofQuota Sampling

Fig. 11.3 A B C D E

1 6 11 16 21

2 7 12 17 22

38

1318 23

4 9 14 19 24

5 10 15

225

A quota of one

element from each

group, A to E, is

imposed. Within eachgroup, one element is

selected based on

judgment or

convenience. The

resulting sample


3, 6, 13, 20 and 22.

Note, one element is

selected from each

column or group.




Snowball Sampling

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

After being interviewed, these respondents areasked to identify others who belong to the target population 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 1823

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


2, 9, 12, 13, and 18.

Note, there are no

element from group E.

Random

Selection Referrals




Simple Random Sampling

Each element in the population has a known andequal probability of selection.

Each possible sample of a given size (n) has aknown and equal probability of being the sampleactually selected.

This implies that every element is selectedindependently of every other element.




A Graphical Illustration ofSimple Random Sampling

Fig. 11.4 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 random startingpoint and then picking every ith element insuccession from the sampling frame.

The sampling interval, i, is determined by dividing thepopulation size N by the sample size n and roundingto the nearest integer.

When the ordering of the elements is related to thecharacteristic of interest, systematic samplingincreases the representativeness of the sample.




Systematic Sampling

If the ordering of the elements produces a cyclicalpattern, systematic sampling may decrease therepresentativeness of the sample.

For example, there are 100,000 elements in thepopulation and a sample of 1,000 is desired. In thiscase the sampling interval, i, is 100. A randomnumber between 1 and 100 is selected. If, forexample, this number is 23, the sample consists of

elements 23, 123, 223, 323, 423, 523, and so on.




A Graphical Illustration ofSystematic Sampling

Fig. 11.4 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 sampleconsists of

population 2,

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

(2+5x3=)17, and

(2+5x4=) 22. Note, all

the elements are

selected from a

single row.






Stratified Sampling

The elements within a stratum should be ashomogeneous as possible, but the elements indifferent strata should be as heterogeneous as

possible.

The stratification variables should also be closelyrelated to the characteristic of interest.

Finally, the variables should decrease the cost of the stratification process by being easy to measureand apply.




Stratified Sampling

In proportionate stratified sampling, the size of the sample drawn from each stratum is proportionate

to the relative size of that stratum in the totalpopulation.

In disproportionate stratified sampling, the sizeof the sample from each stratum is proportionate to

the relative size of that stratum and to the standarddeviation of the distribution of the characteristic of interest among all the elements in that stratum.




A Graphical Illustration ofStratified Sampling

Fig. 11.4 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 resulting

sample consists of

population elements

4, 7, 13, 19 and 21.

Note, one elementis selected from each

column.




Cluster Sampling

The target population is first divided into mutuallyexclusive and collectively exhaustive 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 areincluded in the sample (one-stage) or a sample of elements is drawn probabilistically (two-stage).




Cluster Sampling

Elements within a cluster should be asheterogeneous as possible, but clusters themselvesshould be as homogeneous as possible. Ideally,

each cluster should be a small-scale representationof the population.

In probability proportionate to size sampling,the clusters are sampled with probabilityproportional to size. In the second stage, theprobability of selecting a sampling unit in a selectedcluster varies inversely with the size of the cluster.




A Graphical Illustration ofCluster Sampling 2 Stage

Fig. 11.4 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

225

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.




Types of Cluster Sampling

Fig 11.5 Cluster Sampling

One-Stage

Sampling

Multistage

Sampling

Two-Stage

Sampling

Simple ClusterSampling

ProbabilityProportionate

to Size Sampling




Technique Strengths Weaknesses

Nonprobability Sampling Convenience sampling

Least expensive, leasttime-consuming, mostconvenient

Selection bias, sample notrepresentative, not recommended for descriptive or causal research

Judgmental sampling Low cost, convenient,not time-consuming

Does not allow generalization,subjective

Quota sampling Sample can be controlled

for certain characteristics

Selection bias, no assurance of

representativenessSnowball sampling Can estimate rare

characteristicsTime-consuming

Probability sampling Simple 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

Table 11.3

Strengths and Weaknesses ofBasic Sampling Techniques




A Classification of Internet Sampling

Fig. 11.6

Internet Sampling

Online Intercept Sampling

Recruited OnlineSampling

Other Techniques

Nonrandom Random Panel Nonpanel

RecruitedPanels

Opt-inPanels

Opt-in List Rentals




Procedures for DrawingProbability Samples

Exhibit 11.1

Simple Random

Sampling

1. Select a suitable sampling frame

2. Each element is assigned a number from 1 to N(pop. size)

3. Generate n (sample size) different random numbersbetween 1 and N

4. The numbers generated denote the elements that should be included in the sample





Exhibit 11.1, cont.Systematic

Sampling

1. Select a suitable sampling frame

2. Each element is assigned a number from 1 to N (pop. size)

3. Determine the sampling interval i:i=N/n. If i is a fraction,round to the nearest integer

4. Select a random number, r, between 1 and i, as explained in

simple random sampling

5. The elements with the following numbers will comprise thesystematic random sample: r, r+i,r+2i,r+3i,r+4i,...,r+(n-1)i





1. Select a suitable frame

2. Select the stratification variable(s) and the number of strata, H

3. Divide the entire population into H strata. Based on theclassification variable, each element of the population is assignedto one of the H strata

4. In each stratum, number the elements from 1 to Nh (the pop.size of stratum h)

5. Determine the sample size of each stratum, nh, based onproportionate or disproportionate stratified sampling, where

6. In each stratum, select a simple random sample of size nh

Exhibit 11.1, cont.

nh = nh=1

H

StratifiedSampling







Repeat the process until each of the remaining

clusters has a population less than the sampling

interval. If b clusters have been selected withcertainty, select the remaining c-b clusters

according to steps 1 through 7. The fraction of units

to be sampled with certainty is the overall sampling

fraction = n/N. Thus, for clusters selected withcertainty, we would select ns=(n/N)(N1+N2+...+Nb)

units. The units selected from clusters selected

under two-stage sampling will therefore be n*=n- ns.

Cluster Sampling

Exhibit 11.1,cont.






Tennis' Systematic SamplingReturns a Smash

Tennis magazine conducted a mail survey of its subscribers togain a better understanding of its market. Systematic samplingwas employed to select a sample of 1,472 subscribers from thepublication's domestic circulation list. If we assume that thesubscriber list had 1,472,000 names, the sampling intervalwould be 1,000 (1,472,000/1,472). A number from 1 to 1,000

was drawn at random. Beginning with that number, every1,000th subscriber was selected.

A brand-new dollar bill was included with the questionnaire asan incentive to respondents. An alert postcard was mailed oneweek before the survey. A second, follow-up, questionnaire

was sent to the whole sample ten days after the initialquestionnaire. There were 76 post office returns, so the net effective mailing was 1,396. Six weeks after the first mailing,778 completed questionnaires were returned, yielding aresponse rate of 56%.

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