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Chapter 14. Sampling. Learning Objectives. Understand . . . The two premises on which sampling theory is based. The accuracy and precision for measuring sample validity. The five questions that must be answered to develop a sampling plan. Learning Objectives. Understand . . .

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Chapter 14

Chapter 14


Learning objectives
Learning Objectives

Understand . . .

  • The two premises on which sampling theory is based.

  • The accuracy and precision for measuring sample validity.

  • The five questions that must be answered to develop a sampling plan.

Learning objectives1
Learning Objectives

Understand . . .

  • The two categories of sampling techniques and the variety of sampling techniques within each category.

  • The various sampling techniques and when each is used.

Pull quote
Pull Quote

“We have to hear what’s being said in a natural environment, and social media is an obvious place to do this, but we also have to go and discover the opinions that are not being openly shared. Only then can we understand the dichotomy between the public and private persona.”

Ben Leet, sales director


The nature of sampling


Population Element

Sampling Frame



The Nature of Sampling

Why sample

Greater speed

Why Sample?

Lower cost

Availability of elements



Greater accuracy

What is a sufficiently large sample
What Is a Sufficiently Large Sample?

“In recent Gallup ‘Poll on polls,’ . . . When asked about the scientific sampling foundation on which polls are based . . . most said that a survey of 1,500 – 2,000 respondents—a larger than average sample size for national polls—cannot represent the views of all Americans.”

Frank Newport

The Gallup Poll editor in chief

The Gallup Organization

When is a census appropriate
When Is a Census Appropriate?



What is a valid sample
What Is a Valid Sample?



Sampling design within the research process
Sampling Design within the Research Process

Steps in sampling design
Steps in Sampling Design

What is the target population?

What are the parameters of interest?

What is the sampling frame?

What is the appropriate sampling method?

What size sample is needed?

When to use larger sample

Population variance

Desired precision

Number of subgroups

Confidence level

Small error range

When to Use Larger Sample?

Simple random
Simple Random


  • Easy to implement

    with random dialing


  • Requires list of population elements

  • Time consuming

  • Larger sample needed

  • Produces larger errors

  • High cost

Sample frame
Sample Frame

List of elements in population

Complete and correct

Error rate increases over time

May include elements that

must be screened out

International frames

most problematic



  • Simple to design

  • Easier than simple random

  • Easy to determine sampling distribution of mean or proportion


  • Periodicity within population may skew sample and results

  • Trends in list may bias results

  • Moderate cost



  • Control of sample size in strata

  • Increased statistical efficiency

  • Provides data to represent and analyze subgroups

  • Enables use of different methods in strata


  • Increased error if subgroups are selected at different rates

  • Especially expensive if strata on population must be created

  • High cost



  • Provides an unbiased estimate of population parameters if properly done

  • Economically more efficient than simple random

  • Lowest cost per sample

  • Easy to do without list


  • Often lower statistical efficiency due to subgroups being homogeneous rather than heterogeneous

  • Moderate cost

Stratified and cluster sampling
Stratified and Cluster Sampling


  • Population divided into few subgroups

  • Homogeneity within subgroups

  • Heterogeneity between subgroups

  • Choice of elements from within each subgroup


  • Population divided into many subgroups

  • Heterogeneity within subgroups

  • Homogeneity between subgroups

  • Random choice of subgroups

Area sampling
Area Sampling

Well defined political or

geographical boundaries

Low cost

Frequently used

Double sampling
Double Sampling


  • May reduce costs if first stage results in enough data to stratify or cluster the population


  • Increased costs if discriminately used

Nonprobability samples


Nonprobability Samples

No need to generalize


Limited objectives


Nonprobability sampling methods
Nonprobability Sampling Methods





Key terms
Key Terms

  • Area sampling

  • Census

  • Cluster sampling

  • Convenience sampling

  • Disproportionate stratified sampling

  • Double sampling

  • Judgment sampling

  • Multiphase sampling

  • Nonprobability sampling

  • Population

  • Population element

  • Population parameters

  • Population proportion of incidence

  • Probability sampling

Key terms1
Key Terms

  • Proportionate stratified sampling

  • Quota sampling

  • Sample statistics

  • Sampling

  • Sampling error

  • Sampling frame

  • Sequential sampling

  • Simple random sample

  • Skip interval

  • Snowball sampling

  • Stratified random sampling

  • Systematic sampling

  • Systematic variance

Chapter 141

Chapter 14

Additional Discussion opportunities

Snapshot ford s new sample
Snapshot: Ford’s New Sample

Dealers control 75% of advertising

Recruited 30 influential dealers

Morpace conducted focus groups

72-hour marathon of questions

Gave voice to important group

Closeup keynote experiment cont
CloseUp: Keynote Experiment (cont.)

Pull quote1
Pull Quote

“The proof of the pudding is in the eating. By a small sample we may judge of the whole piece.”

Miguel de Cervantes Saavedra


Pulsepoint research revelation
PulsePoint: Research Revelation


The average number of text messages sent per day by

American teens.

Chapter 142

Chapter 14