Topic Sampling Ppts

7/31/2019 Topic Sampling Ppts

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Sampling in Marketing Research


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Basics of sampling I

A sample is apart of a whole

to show what the

rest is like.

Sampling helps to

determine the

corresponding

value of the

population and

plays a vital role in

marketing

research.

Samples offer many benefits: Save costs:Less expensive to study the

sample than the population.

Save time:Less time needed to study the

sample than the population .

Accuracy:Since sampling is done with

care and studies are conducted by skilled

and qualified interviewers, the results are

expected to be accurate.

Destructive nature of elements:For someelements, sampling is the way to test, since

tests destroy the element itself.


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Basics of sampling II

Limitations of Sampling

Demands more rigid control

in undertaking sample

operation.

Minority and smallness in

number of sub-groups oftenrender study to be

suspected.

Accuracy level may be

affected when data is

subjected to weighing. Sample results are good

approximations at best.

Sampling Process

Defining thepopulation

Developinga sampling

Frame

DeterminingSample

Size

SpecifyingSampleMethod

SELECTING THE SAMPLE


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Sampling: Step 1

Defining the Universe

Universe or population is thewhole mass under study.

How to define a universe:

What constitutes the units of

analysis (HDB apartments)?

What are the sampling units

(HDB apartments occupied in

the last three months)?

What is the specific designation

of the units to be covered (HDB

in town area)? What time period does the data

refer to (December 31, 1995)

Sampling: Step 2

Establishing the SamplingFrame

A sample frame is the list of all

elements in the population

(such as telephone directories,

electoral registers, club

membership etc.) from whichthe samples are drawn.

A sample frame which does not

fully represent an intended

population will result inframe

error and affect the degree of

reliability of sample result.


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Step - 3

Determination of Sample Size

Sample size may be determined by using:

Subjective methods (less sophisticated methods)

The rule of thumb approach: eg. 5% of population

Conventional approach: eg. Average of sample sizes ofsimilar other studies;

Cost basis approach: The number that can be studied

with the available funds;

Statistical formulae (more sophisticated methods)Confidence interval approach.


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Conventional approach of Sample size determination using

Sample sizes used in different marketing research studies

TYPE OF STUDY MINIMUM

SIZE

TYPICAL

RANGE

dentifying a problem (e.g.market

segmentation) 500 1000-2500

roblem-solving (e.g., promotion) 200 300-500

roduct tests 200 300-500

dvertising (TV, Radio, or print Media

per commercial or ad tested) 150 200-300

est marketing 200 300-500

est market audits 10stores/outlets

10-20stores/outlets

ocus groups 2 groups 4-12 groups


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Sample size determination using statistical formulae:

The confidence interval approach

To determine sample sizes using statistical formulae,

researchers use the confidence interval approach based on the

following factors:

Desired level of data precision or accuracy;

Amount of variability in the population (homogeneity); Level of confidence required in the estimates of population values.

Availability of resources such as money, manpower and time

may prompt the researcher to modify the computed sample

size. Students are encouraged to consult any standard marketing

research textbook to have an understanding of these formulae.


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Step 4:

Specifying the sampling method

Probability Sampling

Every element in the target population or universe [sampling

frame] has equal probability of being chosen in the sample for

the survey being conducted.

Scientific, operationally convenient and simple in theory. Results may be generalized.

Non-Probability Sampling

Every element in the universe [sampling frame] does not have

equal probability of being chosen in the sample.

Operationally convenient and simple in theory.

Results may not be generalized.


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Probability sampling

Appropriate for

homogeneous population

Simple random sampling

Requires the use of a random

number table.

Systematic sampling

Requires the sample frame

only,

No random number table isnecessary

Appropriate for

heterogeneous population

Stratified sampling

Use of random number

table may be necessary

Cluster sampling

Use of random number

table may be necessary

Four types of probability sampling


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Non-probability sampling

Four types of non-probability samplingtechniques

Very simple types, based on subjective criteria

Convenient sampling

Judgmental sampling

More systematic and formal

Quota sampling

Special type

Snowball Sampling


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Simple Random Sampling

Also called random

sampling

Simplest method of

probability

sampling

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

1 37 75 10 49 98 66 03 86 34 80 98 44 22 22 45 83 53 86 23 51

2 50 91 56 41 52 82 98 11 57 96 27 10 27 16 35 34 47 01 36 08

3 99 14 23 50 21 01 03 25 79 07 80 54 55 41 12 15 15 03 68 56

4 70 72 01 00 33 25 19 16 23 58 03 78 47 43 77 88 15 02 55 67

5 18 46 06 49 47 32 58 08 75 29 63 66 89 09 22 35 97 74 30 80

6 65 76 34 11 33 60 95 03 53 72 06 78 28 14 51 78 76 45 26 45

7 83 76 95 25 70 60 13 32 52 11 87 38 49 01 82 84 99 02 64 00

8 58 90 07 84 20 98 57 93 36 65 10 71 83 93 42 46 34 61 44 01

9 54 74 67 11 15 78 21 96 43 14 11 22 74 17 02 54 51 78 76 76

10 56 81 92 73 40 07 20 05 26 63 57 86 48 51 59 15 46 09 75 64

11 34 99 06 21 22 38 22 32 85 26 37 00 62 27 74 46 02 61 59 81

12 02 26 92 27 95 87 59 38 18 30 95 38 36 78 23 20 19 65 48 5013 43 04 25 36 00 45 73 80 02 61 31 10 06 72 39 02 00 47 06 98

14 92 56 51 22 11 06 86 88 77 86 59 57 66 13 82 33 97 21 31 61

15 67 42 43 26 20 60 84 18 68 48 85 00 00 48 35 48 57 63 38 84

Need to use

Random

Number Table


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How to Use Random Number Tables

_______________________________________________1. Assign a unique number to each population element in the

sampling frame. Start with serial number 1, or 01, or 001,

etc. upwards depending on the number of digits required.

2. Choose a random starting position.

3. Select serial numbers systematically across rows or downcolumns.

4. Discard numbers that are not assigned to any population

element and ignore numbers that have already been

selected.

5. Repeat the selection process until the required number ofsample elements is selected.


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How to Use a Table of Random Numbers to Select a Sample

Your marketing research lecturer wants to randomly select 20 students from

your class of 100 students. Here is how he can do it using a random number table.Step 1: Assign all the 100 members of the population a unique number.You may

identify each element by assigning a two-digit number. Assign 01 to the first name

on the list, and 00 to the last name. If this is done, then the task of selecting the

sample will be easier as you would be able to use a 2-digit random number table.

NAME NUMBER NAME NUMBER

Adam, Tan 01 Tan Teck Wah 42

Carrol, Chan 08 Tay Thiam Soon 61

. .. Jerry Lewis 18 Teo Tai Meng 87

. .

Lim Chin Nam 26 . Yeo Teck Lan 99

Singh, Arun 30 Zailani bt Samat 00


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Step 2: Select any starting point in the Random Number Table and find the first number that

corresponds to a number on the list of your population. In the example below, # 08 has been

chosen as the starting point and the first student chosen is Carol Chan.

10 09 73 25 33 76

37 54 20 48 05 64

08 42 26 89 53 19

90 01 90 25 29 09

12 80 79 99 70 8066 06 57 47 17 34

31 06 01 08 05 45

Step 3: Move to the next number, 42 and select the person corresponding to that number into

the sample. #87 Tan Teck Wah

Step 4: Continue to the next number that qualifies and select that person into the sample.

# 26 -- Jerry Lewis, followed by #89, #53 and #19Step 5: After you have selected the student # 19, go to the next line and choose #90. Continue

in the same manner until the full sample is selected. If you encounter a number selected

earlier (e.g., 90, 06 in this example) simply skip over it and choose the next number.

Starting point:move right to the end

of the row, then downto the next row row;move left to the end,then down to the next

row, and so on.

How to use random number table to select a random sample


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Systematic sampling

Very similar to simple random sampling with one exception.

In systematic sampling only one random number is needed throughout the

entire sampling process.

To use systematic sampling, a researcher needs:

[i] a sampling frame of the population; and is needed.

[ii] a skip interval calculated as follows:

Skip interval = population list size

Sample size

Names are selected using the skip interval.

If a researcher were to select a sample of 1000 people using the local telephone

directory containing 215,000 listings as the sampling frame, skip interval is

[215,000/1000], or 215. The researcher can select every 215th name of the entire

directory [samplingframe], and select his sample.


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Example: How to Take a Systematic Sample

Step 1: Select a listing of the population, say the City Telephone Directory, from which to

sample. Remember that the list will have an acceptable level of sample frame error.

Step 2: Compute the skip interval by dividing the number of entries in the directory by thedesired sample size.

Example: 250,000 names in the phone book, desired a sample size of 2500,

So skip interval = every 100th name

Step 3: Using random number(s), determine a starting position for sampling the list.

Example: Select: Random number for page number. (page 01)

Select: Random number of column on that page. (col. 03)

Select: Random number for name position in that column (#38, say, A..Mahadeva)Step 4: Apply the skip interval to determine which names on the list will be in the sample.

Example: A. Mahadeva (Skip 100 names), new name chosen is A Rahman b Ahmad.

Step 5: Consider the list as circular; that is, the first name on the list is now the initial name

you selected, and the last name is now the name just prior to the initially selected one.

Example: When you come to the end of the phone book names (Zs), just continue on

through the beginning (As).


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Stratified sampling I

A three-stage process:

Step 1- Divide the population into

homogeneous, mutually exclusive

and collectively exhaustive subgroups

or strata using some stratification

variable;

Step 2- Select an independent simplerandom sample from each stratum.

Step 3- Form the final sample by

consolidating all sample elements

chosen in step 2.

May yield smaller standard errors ofestimators than does the simple random

sampling. Thus precision can be gained

with smaller sample sizes.

Stratified samples can be:

Proportionate: involving the

selection of sample elements

from each stratum, such that

the ratio of sample elements

from each stratum to the

sample size equals that of thepopulation elements within

each stratum to the total

number of population

elements.

Disproportionate: the sample

is disproportionate when the

above mentioned ratio is

unequal.


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To select a proportionate stratified sample of 20 members of the Island Video Club which has

100 members belonging to three language based groups of viewers i.e., English (E), Mandarin

(M) and Others (X).

Step 1: Identify each member from the membership list by his or her respective language groups00 (E ) 20 (M) 40 (E ) 60 ( X ) 80 (M)

01 (E ) 21 ( X ) 41 ( X ) 61 (M) 81 (E )

02 ( X ) 22 (E ) 42 ( X ) 62 (M) 82 (E )

03 (E ) 23 ( X ) 43 (E ) 63 (E ) 83 (M)

04 (E ) 24 (E ) 44 (M) 64 (E ) 84 ( X )

05 (E ) 25 (M) 45 (E ) 65 ( X ) 85 (E )

06 (M) 26 (E ) 46 ( X ) 66 (M) 86 (E )

07 (M) 27 (M) 47 (M) 67 (E ) 87 (M)08 (E ) 28 ( X ) 48 (E ) 68 (M) 88 ( X )

09 (E ) 29 (E ) 49 (E ) 69 (E ) 89 (E )

10 (M) 30 (E ) 50 (E ) 70 (E ) 90 ( X )

11 (E ) 31 (E ) 51 (M) 71 (E ) 91 (E )

12 ( X ) 32 (E ) 52 ( X ) 72 (M) 92 (M)

13 (M) 33 (M) 53 (M) 73 (E ) 93 (E )

14 (E ) 34 (E ) 54 (E ) 74 ( X ) 94 (E )

15 (M) 35 (M) 55 (E ) 75 (E ) 95 ( X )16 (E ) 36 (E ) 56 (M) 76 (E ) 96 (E )

17 ( X ) 37 (E ) 57 (E ) 77 (M) 97 (E )

18 ( X ) 38 ( X ) 58 (M) 78 (M) 98 (M)

19 (M) 39 ( X ) 59 (M) 79 (E ) 99 (E )

Selection of a proportionate Stratified Sample


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Step 2: Sub-divide the club members into three homogeneous sub-groups or strata by the

language groups: English, Mandarin and others.

EnglishLanguage Mandarin Language Other LanguageStratum Stratum Stratum .

00 22 40 64 82 06 35 66 02 42

01 24 43 67 85 07 44 68 12 46

03 26 45 69 86 10 47 72 17 52

04 29 48 70 89 13 51 77 18 60

05 30 49 71 91 15 53 78 21 65

08 31 50 73 93 19 56 80 23 7409 32 54 75 94 20 58 83 28 84

11 34 55 76 96 25 59 87 38 88

14 36 57 79 97 27 61 92 39 90

16 37 63 81 99 33 62 98 41 95

1.Calculate the overall sampling fraction, f, in the following manner:

f = n = 20 = 1 =N 100 5

where n = sample size and N = population size

0.2

Selection of a proportionate stratified sample II


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Determine the number of sample elements (n1) to be selected from the English

language stratum. In this example, n1 = 50 x f = 50 x 0.2 =10. By using a simplerandom sampling method [using a random number table] members whose numbers

are 01, 03, 16, 30, 43, 48, 50, 54, 55, 75, are selected.

Next, determine the number of sample elements (n2) from the Mandarin language

stratum. In this example, n2 = 30 x f = 30 X 0.2 = 6. By using a simple random

sampling method as before, members having numbers 10,15, 27, 51, 59, 87 areselected from the Mandarin language stratum.

In the same manner, the number of sample elements (n3) from the Other language

stratum is calculated. In this example, n3 = 20 x f = 20 X 0.2 = 4. For this stratum,

members whose numbers are 17, 18, 28, 38 are selected

These three different sets of numbers are now aggregated to obtain the ultimate

stratified sample as shown below.

S = (01, 03, 10, 15, 16, 17, 18, 27, 28, 30, 38, 43, 48, 50, 51, 54, 55, 59, 75, 87)

Selection of a proportionate stratified sample III


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Cluster sampling

Is a type of sampling in which clusters or groups of

elements are sampled at the same time.

Such a procedure is economic, and it retains the

characteristics of probability sampling.

A two-step-process: Step 1- Defined population is divided into number of mutually

exclusive and collectively exhaustive subgroups or clusters;

Step 2- Select an independent simple random sample of clusters.

One special type of cluster sampling is called area sampling, where

pieces of geographical areas are selected.


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Example : One-stage and two-stage Cluster sampling

Consider the same Island Video Club example involving 100 club members:

Step 1: Sub-divide the club members into 5 clusters, each cluster containing 20 members.

Cluster

No. English Mandarin Others

1 00, 22, 40, 64, 82 06, 35, 66 02, 42

01, 24, 43, 67, 85 07, 44, 68 12, 46

2 03, 26, 45, 69, 86 10, 47, 72 17, 52

04, 29, 48, 70, 89 13, 51, 77 18, 60

3 05, 30, 49, 71, 91 15, 53, 78 21, 65

08, 31, 50, 73, 93 19, 56, 80 23, 744 09, 32, 54, 75, 94 20, 58, 83 28, 84

11, 34, 55, 76, 96 25, 59, 87 38, 88

5 14, 36, 57, 79, 97 27, 61, 92 39, 90

16, 37, 63, 81, 99 33, 62, 98 41, 95

Step 2: Select one of the 5 clusters. If cluster 4 is selected, then all its elements (i.e. Club

Members with numbers 09, 11, 32, 34, 54, 55, 75, 76, 94, 96, 20, 25, 58, 59, 83, 87, 28, 38, 84,

88) are selected.

Step 3: If a two-stage cluster sampling is desired, the researcher may randomly select 4 members

from each of the five clusters. In this case, the sample will be different from that shown in step 2

above.


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Stratified Sampling vs Cluster Sampling

Stratified Sampling Cluster Sampling1.The target population is sub-divided

into a few subgroups or strata, each

containing a large number of elements.

1.The target population is sub-

divided into a large number of

sub-population or clusters, each

containing a few elements.

2.Within each stratum, the elements are

homogeneous. However, high degree ofheterogeneity exists between strata.

2.Within each cluster, the elements

are heterogeneous. Betweenclusters, there is a high degree of

homogeneity.

3.A sample element is selected each time. 3.A cluster is selected each time.

4.Less sampling error. 4.More prone to sampling error.

5.Objective is to increase precision. 5.Objective is to increase samplingefficiency by decreasing cost.

AREA SAMPLING


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AREA SAMPLING

A common form of cluster sampling where clusters consist of geographic areas, such as

districts, housing blocks or townships. Area sampling could be one-stage, two-stage, or

multi-stage.

How to Take an Area Sample Using Subdivisions

Your company wants to conduct a survey on the expected patronage of its new outlet in a new

housing estate. The company wants to use area sampling to select the sample households to be

interviewed. The sample may be drawn in the manner outlined below.

___________________________________________________________________________________

Step 1: Determine the geographic area to be surveyed, and identify its subdivisions. Each

subdivision cluster should be highly similar to all others. For example, choose ten housing

blocks within 2 kilometers of the proposed site [say, Model Town ] for your new retail outlet;

assign each a number.

Step 2: Decide on the use of one-step or two-step cluster sampling. Assume that you decide to

use a two-stage cluster sampling.

Step 3: Using random numbers, select the housing blocks to be sampled. Here, you select 4

blocks randomly, say numbers #102, #104, #106, and #108.Step 4: Using some probability method of sample selection, select the households in each of the

chosen housing block to be included in the sample. Identify a random starting point (say,

apartment no. 103), instruct field workers to drop off the survey at every fifth house

(systematic sampling).


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Non-probability samples

Convenience sampling

Drawn at the convenience of the researcher. Common in exploratory research.

Does not lead to any conclusion.

Judgmental sampling

Sampling based on some judgment, gut-feelings or experience of the researcher.

Common in commercial marketing research projects. If inference drawing is not

necessary, these samples are quite useful. Quota sampling

An extension of judgmental sampling. It is something like a two-stage judgmental

sampling. Quite difficult to draw.

Snowball sampling

Used in studies involving respondents who are rare to find. To start with, the

researcher compiles a short list of sample units from various sources. Each of

these respondents are contacted to provide names of other probable respondents.


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Quota Sampling

To select a quota sample comprising 3000 persons in country X using three control

characteristics: sex, age and level of education.

Here, the three control characteristics are considered independently of one another.

In order to calculate the desired number of sample elements possessing the various

attributes of the specified control characteristics, the distribution pattern of the

general population in country X in terms of each control characteristics is examined.

Control

Characteristics Population Distribution Sample Elements .

Gender: .... Male...................... 50.7% Male 3000 x 50.7% = 1521

................. Female .................. 49.3% Female 3000 x 49.3% = 1479

Age: ......... 20-29 years ........... 13.4% 20-29 years 3000 x 13.4% = 402

................. 30-39 years ........... 53.3% 30-39 years 3000 x 52.3% = 1569

................. 40 years & over .... 33.3% 40 years & over 3000 x 34.3% = 1029

Religion: .. Christianity ........... 76.4% Christianity 3000 x 76.4% = 2292

................. Islam ..................... 14.8% Islam 3000 x 14.8% = 444

................. Hinduism .............. 6.6% Hinduism 3000 x 6.6% = 198

................. Others ................... 2.2% Others 3000 x 2.2% = 66

__________________________________________________________________________________


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Sampling vs non-sampling errors

Sampling Error [SE] Non-sampling Error [NSE]

Very small sampleSize

Larger sample size

Still larger sample

Complete census


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Choosing probability vs. non-probability sampling

Probability Evaluation Criteria Non-probability

sampling samplingConclusive Nature of research Exploratory

Larger sampling Relative magnitude Larger non-sampling

errors sampling vs. error

non-sampling error

High Population variability Low

[Heterogeneous] [Homogeneous]

Favorable Statistical Considerations Unfavorable

High Sophistication Needed Low

Relatively Longer Time Relatively shorter

High Budget Needed Low


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Sampling Examples I

Topic: A Comparative empirical studybetween Public & Private life insurancecompanies

Population: Public & Private life insuranceholders

Sample: Sample of 100 selected using

Judgment & convenient method where 76belongs to LIC and 24 Private sector


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Example II

Topic: Challenges faced by working womenin Bangladesh A Study on Khulna City

Population: Working women in Khulna City(Public & Pvt sector banks, Insurance,MNCs, NGOs, Govt Organizations)

Sampling Method: Stratified Random

Sample ( Proportional allocation)


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Banks

40

Govt Org

10

NGOs

15

MNCS

15

Others

20


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Example III

Topic: Performance Management in RetailSector in India An Empirical Study

Population: Employees of Retail Industry ofIndore city

Source List: Big Bazaar, Pantaloons,Reliance Fresh, West side, Treasure Island

Sample Size: 104 front line employees areselected using Simple Random Sample


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Example IV

Topic: Effect of e-CRM on BusinessOpportunities: A Study with reference to smalland medium scale Enterprises in India

Population: Consumers of SME Location: Bangalore ( Major SME Locations

identified and from each location one SME isconsidered for the study. Homogeneity in size,

structure, other demographic & organizationalfactors are considered in selection)


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Further Criterion; SMEs must posses a website and a valid email Id, because, this isminimum technological infrastructure to

implement e-CRM.

Sample Size: From each SME 10consumers are selected at random.

Total size: 10 * 10 = 100


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Example V

Topic: The Production & Labour problems ofsmall scale Entrepreneurs

Location: Madurai Region of Tamil Nadu

Source: Tamil Nadu Small IndustriesDevelopment Corporation (TNSIDCO) Manual& Web site

Sample: Divided Madurai region into 5Industrial estates. Stratified Random Sampling

(Proportional allocation) method is followed inselecting sample.


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Region Population Sample

Kappalur 169 56

Andipatti 05 02Theni 37 12

Pudur 74 25

Uranganpatti 147 49Total 432 144

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