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McGraw-Hill/Irwin Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. SAMPLING Chapter 14

McGraw-Hill/IrwinCopyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. SAMPLING Chapter 14

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14-3 The Nature of Sampling Population Population Element Sampling Frame Census Sample

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Page 1: McGraw-Hill/IrwinCopyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. SAMPLING Chapter 14

McGraw-Hill/Irwin Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved.

SAMPLING

Chapter 14

Page 2: McGraw-Hill/IrwinCopyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. SAMPLING Chapter 14

14-2

Learning Objectives

Understand . . .The accuracy and precision for

measuring sample validity.The two categories of sampling

techniques and the variety of sampling techniques within each category.

The various sampling techniques and when each is used.

Page 3: McGraw-Hill/IrwinCopyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. SAMPLING Chapter 14

14-3

The Nature of Sampling

PopulationPopulation ElementSampling FrameCensusSample

Page 4: McGraw-Hill/IrwinCopyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. SAMPLING Chapter 14

14-4

Why Sample?

Greater accuracy

Availability of elements

Greater speed

Sampling provides

Lower cost

Page 5: McGraw-Hill/IrwinCopyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. SAMPLING Chapter 14

14-5

When Is a Census Appropriate?

NecessaryFeasible

Page 6: McGraw-Hill/IrwinCopyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. SAMPLING Chapter 14

14-6

What Is a Valid Sample?

Accurate Precise

Page 7: McGraw-Hill/IrwinCopyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. SAMPLING Chapter 14

14-7

Sampling Design within the Research Process

Page 8: McGraw-Hill/IrwinCopyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. SAMPLING Chapter 14

14-8

Types of Sampling Designs

Probability Nonprobability

• Simple random • Convenience• Systematic Random • Judgement• Cluster• Stratified

• Quota• Snowball

Page 9: McGraw-Hill/IrwinCopyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. SAMPLING Chapter 14

14-9

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?

Page 10: McGraw-Hill/IrwinCopyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. SAMPLING Chapter 14

14-10

When to Use Larger Sample?

Desired precision

Number of subgroups

Confidence level

Population variance

Small error range

Page 11: McGraw-Hill/IrwinCopyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. SAMPLING Chapter 14

14-11

Simple Random

AdvantagesEasy to implement with random dialing

DisadvantagesRequires list of population elements

Time consumingLarger sample needed

Produces larger errors

High cost

Page 12: McGraw-Hill/IrwinCopyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. SAMPLING Chapter 14

14-12

How to Choose a Random Sample

Page 13: McGraw-Hill/IrwinCopyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. SAMPLING Chapter 14

14-13

Systematic

AdvantagesSimple to designEasier than simple

randomEasy to determine

sampling distribution of mean or proportion

DisadvantagesPeriodicity within

population may skew sample and results

Trends in list may bias results

Moderate cost

Page 14: McGraw-Hill/IrwinCopyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. SAMPLING Chapter 14

14-14

Stratified

AdvantagesControl of sample size

in strataIncreased statistical

efficiencyProvides data to

represent and analyze subgroups

Enables use of different methods in strata

DisadvantagesIncreased error if

subgroups are selected at different rates

Especially expensive if strata on population must be created

High cost

Page 15: McGraw-Hill/IrwinCopyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. SAMPLING Chapter 14

14-15

Cluster

AdvantagesProvides an unbiased

estimate of population parameters if properly done

Economically more efficient than simple random

Lowest cost per sample

Easy to do without list

DisadvantagesOften lower statistical

efficiency due to subgroups being homogeneous rather

than heterogeneous

Moderate cost

Page 16: McGraw-Hill/IrwinCopyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. SAMPLING Chapter 14

14-16

Stratified and Cluster SamplingStratified Population divided

into few subgroups Homogeneity within

subgroups Heterogeneity

between subgroups Choice of elements

from within each subgroup

Cluster Population divided

into many subgroups

Heterogeneity within subgroups

Homogeneity between subgroups

Random choice of subgroups

Page 17: McGraw-Hill/IrwinCopyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. SAMPLING Chapter 14

14-17

Nonprobability Samples

Cost

Feasibility

Time

No need to generalize

Limited objectives

Page 18: McGraw-Hill/IrwinCopyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. SAMPLING Chapter 14

14-18

Nonprobability Sampling Methods

Convenience

Judgment

Quota

Snowball

Page 19: McGraw-Hill/IrwinCopyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. SAMPLING Chapter 14

14-19

Sample Size 19

Page 20: McGraw-Hill/IrwinCopyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. SAMPLING Chapter 14

14-20

Key Terms

CensusCluster samplingConvenience

samplingstratified samplingJudgment sampling

Nonprobability sampling

PopulationPopulation elementProbability

sampling

Page 21: McGraw-Hill/IrwinCopyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved. SAMPLING Chapter 14

14-21

Key Terms

Quota samplingSamplingSampling errorSampling frame

Simple random sample

Skip intervalSnowball samplingStratified random

samplingSystematic

sampling


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