Sampling

What is sampling

• Process of selecting a sample

Why do we need to do sampling

• Speed• Economical• Better control

Basics of Sampling

• Defining universe• Selection of Sampling Frame• Selection of Samples

Importance of Sampling

• Samples are representative of the universe• Unbiased selection of samples

Sample Design

What does Sample Design mean?

1. TG Definition & Sub-segments

2. Sample Size and Spread

3. Sampling Methodology

4. Contact Process

TG Definition

• TG Definition = Identifying the Universe / Population

• Needed beyond the primary definition of (say) product usage

• How does one describe the target respondent?

– Could be described by the client – consider following for comparability with other researches

– Else, develop yourself

TG Definition

• Use a very large syndicated database– Check what the IRS / TAM says

• Else, take information from client, basis their other researches

• Understand the Core Audience

• Normal Descriptors of Target Respondent– Age, SEC, Gender– Product Usage – if needed– Lifestyle basis – if needed

Reporting Units

• Need clarity on what you need to report for– State or Zone– Total or Urban / Rural– SEC wise?– Age Wise?– Brand Wise?– . . . . – . . .

Choosing Sample SizeMust be representative

Must be adequate

Depends on

• Sample size requirement depends on heterogeneity of universe (not size)

• Heterogeneity is with reference to a measure

• Before calculating sample size, need to understand– heterogeneity (variance)– accuracy desired (error)– Importance of decision (confidence %)– Reporting Units - Requirement of sub-group analysis

• Cost of error / Risk

• Fixed costs of the research

Sample Size Based on Sampling Error

• Standard Error- reduces with sample size - also depends on result value

• Confidence level- relates to risk of wrong decision - Industry practice 90% - 95 %

• Need for sub-group analysis- young vs old smokers- metros vs smaller towns etc.

• To arrive at sample size, we need to predefine- extent of error acceptable ( absolute or relative to the result value )- expected value- confidence level- sub-group size estimates

Choosing Type of SamplingMinimise Variance

Minimise Bias

• Universe Size and Heterogeneity (Variance)

• Timeliness

• Relative cost and effort vs Risk

• Non-sampling errors

Define TG

Decide Sample Design

Sample Size & Spread

TIME & COSTS

Steps involved in Sampling• Defining Universe.• Selection of Sampling Frame• Selection of samples/ starting points• Fieldwork from the randomly selected household• Selection of subsequent samples following RHR• Following skipping instructions, if any• Selection of respondent in a household• Replacement due to refusals, non availability etc.

Sampling Terminology

Some Terminology

• Population - The term population is not only used in its common sense but for any ‘universe’ of interest: households, shops, farms, other types of business, bus users etc.

• Sample - a selected subset of the population

• Units / Elements - The members of a population

• Sampling Frame - A list of units from which the sample is drawn e.g., electoral register, customer list (Frame Errors)

Sampling Frame

• Choice of Sampling Frame– Voter List / Electoral Rolls– Telephone Directory– Map– Ward List– Area List– Data Base

Types of Sampling Methodology

• Probability– Each unit has a known

probability of being chosen– Can estimate sampling error– Independent of person

measuring – no control given to field

– Callbacks to maximize response rate

• Non-Probability– No known probability of being

chosen– Hence, cannot estimate error

or perform statistical tests (technically speaking!!)

– Has a selection bias, due to interviewer discretion

– No call backs – higher NSE

Which to use when?Accuracy vs. Costs

Types of Sampling Methodology

• Probability– Simple Random– Systematic Random – Stratified (proportionate

vs. disproportionate)– Cluster / Multi-stage

• Non-probability– Quota (Proportionate vs

Disproportionate)– Convenience– Judgemental– Snowballing

For many MR purposes the sample design is a ‘mix’ of the various types

Types of Probability Sampling

• Simple random sample– A credit card company randomly selects 500 customers from its customer data

file to participate in a mail survey

• Systematic sample (every nth)– A credit card company selects every 2,000th customer from its customer data

file to participate in a mail survey– Statistically similar to simple random sampling– Frame must be ordered– Increases representative ness in some cases (hi-volume, low volume)– Decreases representative ness in some cases (1st of every month) – though

rarely

Systematic SamplingAn Example

Universe = 100,000Sample = 200Sampling interval = 100,000 / 200 = 500Step 1: Select one random no. between 1 and 500

(=292, say)Step 2 : Select every 500th item after that (292, 792, 1292…)

Types of Probability Sampling

• Stratified sampling

– When the universe is known to be heterogeneous

– Segment the universe – heterogeneous across strata and homogeneous

within

– Random Samples from each (simple, systematic, cluster)

– e.g. the country is divided into regions, the sample size for each is determined and sampling is conducted separately for each; all strata are represented

– If the sample size is a fixed % of the region population, this is proportionate stratified sampling (PSS)

– If the %s vary between regions, this is disproportionate stratified sampling

Types of Probability Sampling

• Cluster/multi-stage sampling– Cluster sampling involves more than one stage– Groups or clusters of elements are selected first, then chosen

clusters are sub sampled• e.g. 1 select companies, then directors• e.g. 2 select city blocks, then households; this is an example of

‘area sampling’– Not all clusters are selected (in contrast to strata, which are all

represented)

FOR LOGISTICAL CONVENIENCE

Types of Non-Probability Sampling

• Quota sampling– Decide sample size in each cell– Leave to Interviewer’s discretion – Major reasons for using quota sampling are:

• to ensure that enough respondents with certain characteristics are interviewed for sub-analysis etc.

• to ensure variety of response where a sufficient random sample size is not possible;

in other words, where a smaller random survey might be more likely “by chance” to be skewed or limited

• to reduce costs and fieldwork time!

Quota Sampling

• E.g. 200 users and 300 non-users of a product category with half of each user group aged 20-34 and half aged 35-39

• The major shortcomings are that control (quota) variables may be unrelated to problem of interest

• May need many interlocking control (quota) variables e.g., age, sex, income and education

• N.B. Interlocking quotas improve control, but reduce the cost/time advantage of quota sampling. To be used with care!

Quota Sampling – Proportionate vs. Disproportionate

• Proportionate – when individual quotas are in line with population proportions

• Disproportionate – when they are not. E.g. a mobile users study, where there is a need to study teenage mobile users separately

• What is the difference between– Proportionate / Disproportionate Quota Sampling and– Proportionate / Disproportionate Stratified Random Sampling?

Types of Non-Probability Sampling

• Convenience sampling

– Respondents are selected on the basis of convenience of the researcher

• Street/mall intercepts• Professors of psychology conducting experiments on

students– These samples may be informative but are not

projectable to the population

Types of non-probability samples

• Judgemental sampling– sample selected to include elements believed will

provide vital information• Experts interviewed by a news reporter • Cities selected for test marketing

– again, not projectable

Types of non-probability samples

• Snowball sampling

– A referral system– Typically used for hard-to-find respondents– In built bias over-ridden by the cost and time

savings

Sampling Errors

What do we mean by accuracy?

• We have used this term several times in its usual sense

• But survey sampling uses two more precise terms :– Variation (variability, variance)– Bias

Sample Size and Reliability of Results

• Sources of error- Sampling error- Non sampling error/ bias

• Sampling error- will always be present in a probability sample survey

• Non-sampling error- can be controlled (to some extent ) by controlling the research process

Sampling Error

TE = SE + NSE

• SE Sample Size (n) Expected heterogeneity

• NSE Random Error Systematic Error

Non Sampling Error

• NSE– Random Error (miscoding, errors in punching etc)

– Systematic Error• Measurement Error (biased question)• Selection Error (skipping, not using Kish grid etc)• Non-response Error (Unavailable, Refusal)

Sample Design

In Practice

What is Random?Degrees of Randomness

• Is your entire design random?

OR

• Are your sample sizes quota’ed with random contact pattern (purposive)

Quota / Convenience

PPS with Cluster Sampling

Know your universe- A Classic case of random sampling

• Proportionate Stratified Sampling (also called PPS)

• A combination of cluster and stratified random sampling

• Cover the UA of every city

Know your universeIRS

• Starting Points– Choice of SPs from electoral rolls

• Kish Grid Selection

• 3 callbacks, before termination

Instructions to choose SPs

• Choose 60 SPs . . . . How ?? Purposively?

• Different ways of choosing SPs– Electoral Rolls– Ward wise– Post Box wise– FO / FC’s discretion / experience– Geographical Clusters

Instructions to choose SPs

• Understand the NSE

– SEC AB TG . . . Safe to choose purposively? OK to miss out (say) SEC B HHs in certain downmarket localities?

– UA vs MC – Safe to do only MC? Are patterns likely to be different in the rest of UA?

– Rules of Substitution

# of Clusters and Size of Cluster

• How many SPs? How many interviews per SP?

• Depends on– TG (SEC) – Size and Heterogeneity– Expected productivity– Balance between efficiency and representation

• Size of cluster defined by– Main Interview OR– Listing ???

Skipping Pattern

• Reduce NSE . . . specify skipping to as much detail as possible

• Skipping pattern will decide the size of the cluster

• # of HH to be skipped - depends upon # of SPs and interviews/SP

What should I skip?

• Skip after . . – Every door knock– Every listing (successful or unsuccessful)– Every completed interview

• How many– In buildings– In row houses– In bungalow colonies

Selection of Respondent

• Kish Grid

• Birthday method

• Available respondents – to meet quota

Map Sampling

• Population of Town = No.of blocks on the Map -------------------- 2000

• Length x Breadth of Map = Area

Map Sampling

• Area of Map ------------------- = Area of each block No.of blocks

• Area of block = Length x Breadth of block

Map Sampling

• Draw blocks on the map• Number the populated blocks• Randomly select blocks• Start field work from the top right hand corner of the

selected block

Map Sampling

x x x 1 2 3 4 x

x x 5 6 7 8 9 10

x x 11 12 13 14 15 16

x x 17 18 19 20 21 22

x 23 24 25 26 27 28 29

Length 30 31 32 33 34 35 36 37

38 39 40 41 42 43 44 45

46 47 48 49 50 51 52 x

x 53 54 55 56 57 58 x

x x 59 60 61 62 63 x

x x 64 65 66 67 x x

x x 68 69 70 x x x

Breadth

Trade Survey Sampling

• Divide city into 12 blocks• Divide each block into 2 Sub-blocks• i..e. 12 x 2 = 24 Sub-blocks• In each Sub- block select 2 busy intersections i.e. 24 x

2 = 48 Intersections• List all shops at each intersection (Main Shop listing)

Trade Survey Sampling

• Walk in a pre-specified direction away from the intersection for 5 minutes (Point 1)

• Walk again using RHR for further 5 minutes (Point 2)• List all shops between point 1 & 2 ( Away shop

listings )

Trade Survey Sampling

• Now we have 48 intersections x 2 Shop listing ( Main & Away ) = 96 Shop Listings

• Select one shop randomly using Random No.Table for each Listing

SELECTION OF INDIVIDUAL THROUGH KISH GRID

• The Kish grid is a ready reckoner for randomly selecting an individual from the household members

KISH GRID

HOUSEHOLD SERIAL NUMBERNO OF 12+ 1 2 3 4 5 6MEMBERS

1 1 1 1 1 1 12 1 2 1 2 1 23 2 3 1 2 3 14 4 1 2 3 4 15 4 5 1 2 3 46 4 5 6 1 2 37 5 6 7 1 2 38 5 6 7 8 1 29 9 1 2 3 4 510 7 8 9 10 1 2

Replacement

• Sex• Age group• Occupation• Religion etc.

Thank You