Sampling - MBA -B -Section

8/7/2019 Sampling - MBA -B -Section

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Business Mathematics & Analytics

Module 5Sampling


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"The secret of success is to know something

nobody else knows. "

Aristotle Onassis


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How will you differentiate?

Data

Information


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Uncertainty

I dont know

if we

should

offer on-site

child care?


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Information Reduces uncertainty

Helps focus decision making

Research is

a systematic Inquiry whose objective is to provide informationto solve managerial problems


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Follow the Yellow Brick Road ofFollow the Yellow Brick Road of

the Research Processthe Research Process

Problem Discovery

and Definition

ResearchDesign

Sampling

Data

Gathering

Data Processing

and Analysis

Conclusions and

Report

Discovery and

Definition

and so on


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SamplingT

erminology Population or universe

Population element

Census

Sample

Sampling


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Population Any complete group

People

Sales territories

Stores


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Census Investigation of all individual elements that

make up a population


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POPULATIONPOPULATION

SAMPLESAMPLESample:S

ample: subsetsubsetof a larger populationof a larger population..

Selecting a Sample


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SamplingSampling is the process of selecting part of a

larger group of participants with the intent

of generalizing from the smaller group.


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Theories of sampling

The law of inertia of large numbers

The law of statistical regularity

The law of persistence

The law of optimization

T

he law of validity


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Why sample?

1.Budget and Time constraints

2.Complete population inaccessible

3. Accurate and reliable results

4. Whenever destruction of test units is

involved.


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Define the target population

Select a sampling frame

Conduct fieldwork

Determine if a probability or nonprobability

sampling method will be chosen

Plan procedure

for selecting sampling units

Determine sample size

Select actual sampling units

Stages in the

Selection

of a Sample


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Two Major Categories of

Sampling Probability sampling

Known, nonzero probability for every

element

Nonprobability sampling (Purposive

method)

Probability of selecting any particularmember is unknown


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Nonprobability Sampling Convenience

Judgment

Quota

Snowball


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Convenience Sampling

Also called haphazard or accidental

sampling

The sampling procedure of obtaining the

people or units that are most conveniently

available


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Convenience

Advantages

No need for list of

populaiton

Disadvantages

Variability and bias of

estimates cannot be

measured or controlled. Projecting data beyond

sample is inappropriate.


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Judgment Sampling Also called purposive sampling

An experienced individual selects the

sample based on his or her judgment about

some appropriate characteristics required of

the sample member


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Judgment

Advantages

Useful for certain types of

forecasting

Sample guaranteed to meeta specific objective.

Disadvantages

Bias due to experts beliefs

may make sample

unrepresentative. Projecting data beyond



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Quota Sampling Ensures that the various subgroups in a

population are represented on pertinent

sample characteristics

To the exact extent that the investigators

desire

It should not be confused with stratifiedsampling.


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Quota

Advantages

Introduces some

stratification of population

Requires no list ofpopulation

Disadvantages

Introduces bias in

researchers classification of

subjects Non random selection

within classes means error

from population cannot be

estimated Projecting data beyond



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Snowball Sampling A variety of procedures

Initial respondents are selected by

probability methods

Additional respondents are obtained from

information provided by the initial

respondents


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Snowball

Advantages

Useful in locating members

of rare populations.

Disadvantages

High bias because sample

units are not independent

Projecting data beyondsample is inappropriate.


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Nonprobability SamplingReasons to use

Procedure satisfactorily meets the sampling

objectives

Lower Cost

Limited Time

Not as much human error as selecting a

completely random sample

Total list population not available


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Probability Sampling Simple random sample

Systematic sample

Stratified sample

Cluster sample

Multistage Multiphase

Area sampling


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Simple Random Sampling A sampling procedure that ensures that each

element in the population will have an equal

chance of being included in the sample.


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From a college of 2000 students, select a

sample of 20 students without replacement.


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Simple random

Advantages

Only minimal advance

knowledge of population

needed Easy to analyze data and

compute error

Disadvantages

Requires sampling frame to

work from

Does not use knowledge ofpopulation that researcher

may have

Larger errors for same

sample size than withstratified sampling

Respondents may be widely

dispersed

H

ence cost may be higher


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Systematic Sampling A simple process

Every nth name from the list will be drawn


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Systematic

Advantages

Simple to draw sample

Easy to check

Disadvantages

If sampling interval is

related to a periodic

ordering of the population,may introduce increased

variability


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Stratified Sampling Subsamples are drawn within different

strata

Each stratum is more or less equal on some

characteristic

Do not confuse with quota sample


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Stratified

Advantages

Assures representation of all

groups in sample

Characteristics of eachstratum can be estimated

and comparisons made

Reduces variability for

same sample size

Disadvantages

Requires accurate

information on proportion in

each stratum If stratified lists are not

already available, they can

be costly to prepare.


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Cluster Sampling The purpose of cluster sampling is to

sample economically while retaining the

characteristics of a probability sample.

The primary sampling unit is no longer the

individual element in the population

The primary sampling unit is a larger clusterof elements located in proximity to one

another


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Cluster

Advantages

If clusters geographically

defined

Yields lowest field cost

Requires listing of all

clusters but of individuals

only within clusters

Can estimate characteristicsof clusters as well as of

population

Disadvantages

Larger error for comparable

size than other probability

samples Researcher must be able to

assign population members

to unique cluster or else

duplication or omission ofindividual results


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COMPLETELY

CERTAIN

ABSOLUTE

AMBIGUITY

CAUSAL,

COMPARATIVE,

ASSOCIATIONAL,

OR DESCRIPTIVE

EXPLORATORY

Uncertainty Influences

The Type OfResearch


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Exploratory Research Descriptive Research Causal Research

(Unaware of Problem) (Aware of Problem) (Problem Clearly Defined)

Our sales are declining and What kind of people are buying Will buyers purchase more of

we dont know why. our product? Who buys our our products in a new package?

competitors product?

Would people be interested Which of two advertising

in our new product idea?

campaigns is more effective?

Degree of Problem Definition


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Descriptive Research

Examples

WeightWatchersaverage customer

W

oman about 40years old

Household incomeof about $50,000

At least some

college educationTrying to juggle

children and a job


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Causal Study

Studying the effects of a training program onemployee performance


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Sampling errors Difference between

the results of studying a sample and

inferring a result about the population

and the results of the census of the whole

population


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Causes of sampling errors

Unrepresentative sample

Small sample size

Indeterminacy in principle


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Non sampling errors Non sampling errors are errors that occur in

acquiring, recording or tabulating statistical

data that cannot be ascribed to samplingerror.

They may arise in either a census or a

sample. Two types Systematic (Biased) and

Unsystematic


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T

hank you

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Sampling - MBA -B -Section