A perspective on sampling in india

Perspective on Market Research
Study Sampling in India

Juxt for whoever interested (Public)

1

India is a really big country for any
researcher…
Let me Introduce India to you…


2

Map Visual: http://www.economist.com/content/indian-summary

3

35 States & UTs
640 districts

7,935+ towns
640,867+ villages
1.2billion individuals
247million households

4

Ideally, how many homogenous
cells the country (universe) should be
divided in to for capturing the
diversity (sampling & weighting) and fully
understand it (infer)?
As a researcher my three key challenges been:
- To know the actual size of the universe, and size of the cell and the
number of members in the final homogenous cell
- To draw appropriate random sample from that cell
- To project/correct the findings back to the population
- Then, infer it with/without enough local knowledge

5

Can be any number, but a valid
argument can be:
33,600 cells of Urban India =
35S/UT * 8TC * 5SEC * 6Age * 2Gender * 2Language

37,800 Cells of Rural India =
35S/UT * 3VC * 3DT * 5SEC * 6Age * 2Gender * 2Language

But realistically, that’s utopian and
may be unnecessary…

6

Some more points to ponder
 Most of the mass product and service marketers (who are

research savvy) have a pan India reach – a distribution
network that reaches out to many towns and villages, if not
complete India
More than 50% business of these marketers’ business probably
come from beyond the top 8 to 10 cities
 Consumption growths are also higher on cities beyond the top 8
to 10 cities


 All mindshare creation effort (advertising/promotion) by

mass marketers today is pan India (spends on geo-locations is
possible only if advertiser is very heavy on print, else TV is
all pan India)

 Then, why are regular market research studies (tracking)

limited to just 8 to 10 cities for most of the product/service
categories?


7

Some of the sampling practices
 Big budget nation representative studies
NSSO: 88 Geographical Regions – Stratified random Sampling
 NCAER (CMCR): Stratified Random Sampling
 IRS: Probability proportionate to size (PPA) - Sample allocated
proportionate to 12 years+ Universe of a geographic unit
 National Family Health Survey


 Regular Market Research/ Smaller studies/ Monthly

Tracking

Judgmental, Quota Sampling by Target Group
 Sometimes post survey correction weighting on 1 or 2 variables
with universe estimates from IRS



8

Big Question 1:

What is better, natural (gen. pop.)
proportion of users & intenders vs.
TG wise quotas?
Quota sampling sessions in schools always start with “it is not a advisable method, avoid as much as you
can?
Why do we need quota… no reason other than to have “an analyzable base for a segment”?

9

Big Question 2:

Why 100 to 200 sample per town?
How can 100 sample be representative for a 3million+ population town?
Even after collecting 200 odd sample with what confidence one will analyze the data at an Individual
town level to take inferences, especially in India where there is a lot of diversity?

10

Is there a more practical way
of dividing India?
Before that let’s understand India a little more…

11

Urbanization in India



Percentage of urban population is good enough indicator for clubbing
of towns together as high level homogenous groups with varied access
to infrastructure, social development and consumerism



However all 100,000+ population towns can’t be similar

12

Let’s not forget about the growth
of 1million+ towns

 As per 2011 census there are 47 towns# (1 million+ population

towns) accounting for 116 million population accounting for around
30% of all Indian Urban population


# Source: http://en.wikipedia.org/wiki/List_of_most_populous_cities_in_India

13

Emerging agglomerates of India

 The emergence of small agglomerates such as these is changing

the urban setting in respect of rural access to services. Goods
and service facilities are coming nearer to the consumer, and
services of new kinds are emerging, inducing a diversification of
jobs, notably in construction, food supply and processing, and
groceries.


14

Territorial network is bringing urban
agglomerations closer to towns



In some much-urbanized regions, such as Tamil Nadu, the average maximum
distance to the nearest city is already only 6 km and will drop to 5.6 km in
2011. For 2011, we can expect 12.3 km for Madhya Pradesh (13.7 km in 2001),
9.2 km for Gujarat (10.2 km in 2001), and 8.1 km for Andhra Pradesh (8.8 km
in 2001).


15

Is town class a substitute to town
selection for smaller and regular
studies?
Possibly yes… but not the way Indian Census divides it… various studies
provide us enough clue for right course of action.

16

Way forward for typical small sample study
 Smaller studies can still remained urban focused

yet bring in relatively better national
representation with even 2000 odd sample, if a at a
segment level roughly 377 (95% confidence level &
5% error margin) sample is collected then we can
collect sample for 6 town classes instead of 6 to 8
towns
 We must not mix up all the 1,000,000 population

towns as one and try to group them in 3 sub-groups
 30 lakh+ (3mn+) (Top 10 Towns, if required they should be

studied individually)
 10lakh to 30lakh (1 to 3million) (37 more towns)
 5lakh to 10lakh (0.5 to 1 million) (45 more towns)
 1 to 5 lakh towns (404 more towns)
 Below 1lakh population towns (7531 more towns)

17

An easier, practical and manageable way
for national representative sampling would
be:

2,880 cells of Urban India =
4Z * 6TC * 5SEC * 6Age * 2Gender * 2Language

1,440 Cells of Rural India =
4Z * 3VC * 5SEC * 6Age * 2Gender * 2Language

Or depending on objective & budget we
can reduce the no. of cells further…

18

Thank you
www.juxtconsult.com

www.getcounted.net
19

A perspective on sampling in india

Recomendados

Recomendados

Mais conteúdo relacionado

Semelhante a A perspective on sampling in india

Semelhante a A perspective on sampling in india (20)

Mais de JuxtConsult

Mais de JuxtConsult (20)

Último

Último (20)

A perspective on sampling in india