11. Sampling (Print).ppt

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Chapter 15

Sampling

15-1


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Learning Objectives

Understand . . .

two premises on which sampling theory is

based

accuracy and precision for measuring sample

validity

five questions that must be answered todevelop a sampling plan

15-2


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Learning Objectives

Understand . . .

two categories of sampling techniques and the

variety of sampling techniques within each

category

various sampling techniques and when each is

used

15-3


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The Nature of Sampling

Sampling Population Element

Population

Census (count of all the

elements in a

population)

Sampling frame (listing

of all populationelements from which

the sample will be

drawn)

15-4


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Why Sample?

15-5

Greater

accuracy

Availability of

elements

Greater

speed

Samplingprovides

Lower cost


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When Is A Census Appropriate?

15-6

NecessaryFeasible


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What Is A Good Sample?

15-7

PreciseAccurate


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Exhibit 15-1

Sampling Design

within the

Research Process

15-8


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Exhibit 15-2 Types of Sampling Designs

Element

Selection

Probability Nonprobability

Unrestricted Simple random Convenience

Restricted Complex random Purposive

Systematic Judgment

Cluster Quota

Stratified Snowball

Double15-9


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Steps in Sampling Design

15-10

What is the target population?

What are the parameters of

interest?

What is the sampling frame?

What is the appropriate sampling

method?

What sample size is needed?


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Larger Sample Sizes

15-11

Small errorrange

Number of

subgroups

Confidencelevel

When

Population

variance

Desired

precision

The greater the dispersion

or variance within the

population, the larger thesample must be to provide

estimation precision.

The greater the number of subgroups of interest within

a sample, the greater the sample size must be.


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Simple Random

Advantages Easy to implement with

random dialing

Disadvantages Requires list of

population elements

Time consuming Uses larger sample sizes

Produces larger errors

High cost

15-12

The probability of selection is equal to the sample

size divided by the population size.


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How to choose a random sample

The steps are as follows:

1. Assign each element within the sampling frame

a unique number.

2. Identify a random start from the random

number table.

3. Determine how the digits in the random number

table will be assigned to the sampling frame.

4. Select the sample elements from the sampling

frame.

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Systematic

Advantages

Simple to design

Easier than simple

random

Easy to determine

sampling distribution of

mean or proportion

Disadvantages

Periodicity within

population may skew

sample and results Trends in list may bias

results

Moderate cost

15-14


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How to choose a Systematic sample

The steps are as follows:

1. Identify, list, and number the elements in the

population

2. Identify the skip interval

3. Identify the random start

4. Draw a sample by choosing every kth entry.

To protect against subtle biases, the research can1)Randomize the population before sampling,

2)Change the random start several times in the process, and

3)Replicate a selection of different samples.

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Stratified

Advantages

Control of sample size in

strata

Increased statistical

efficiency

Provides data to represent

and analyze subgroups

Enables use of different

methods in strata

Disadvantages

Increased error will result if

subgroups are selected at

different rates

Especially expensive if strata

on population must be

created

High cost

15-16


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Cluster

Advantages

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

Disadvantages

Often lower statistical

efficiency due to subgroups

being homogeneous rather

than heterogeneous

Moderate cost

15-17


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Exhibit 15-5 Stratified and Cluster Sampling

Stratified

Population divided intofew subgroups

Homogeneity withinsubgroups

Heterogeneity betweensubgroups

Choice of elementsfrom within eachsubgroup

Cluster

Population divided intomany subgroups

Heterogeneity withinsubgroups

Homogeneity betweensubgroups

Random choice ofsubgroups

15-18


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Area Sampling

15-19


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Double

Advantages

May reduce costs if first

stage results in enough

data to stratify orcluster the population

Disadvantages

Increased costs if

discriminately used

15-20


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Nonprobability Samples

15-21

Cost

Feasibility

Time

Issues

No need to

generalize

Limited

objectives


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Nonprobability

Sampling Methods

15-22

Convenience

Judgment

Quota

Snowball


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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

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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

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11. Sampling (Print).ppt

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