chapter8 sampling

7/29/2019 Chapter8 Sampling

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Business

Research Methods

Sample Designs and Sampling

Procedures


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Fundamental Idea of Sampling

The fundamental idea of sampling is that by

selecting a few elements from a population,

we can make conclusion about the whole

population.


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Inferential Process

Population

Sample

Sample

Statistics

Estimation

& Test

),( spX


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Parameter and Statistic

Example

The average height of MBA students is

150 cms.

Parameter

The average height of students in the

AGW 621 class is 150 cms.

Statistic

Population

Sample


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SAMPLING TERMINOLOGY

Population

All items of interest

Element

Members of a population

Sample Portion chosen from population

Subject

Members of a sample

Parameter

Characteristic of a population Statistic

Characteristic of a sample

Census

Population is very small

There is a lot of variation among elements


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Why Sample

Cost

Time

Destruction of unit More accurate


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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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TARGET POPULATION

RELEVANT POPULATION

OPERATIONALLY DEFINE

COMIC BOOK READER?


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SAMPLING FRAME

A LIST OF ELEMENTS FROM WHICH

THE SAMPLE MAY BE DRAWN

WORKING POPULATION

MAILING LISTS - DATA BASE

MARKETERS

SAMPLING FRAME ERROR


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SAMPLING UNITS

GROUP SELECTED FOR THE SAMPLE

PRIMARY SAMPLING UNITS (PSU)

SECONDARY SAMPLING UNITS

TERTIARY SAMPLING UNITS


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RANDOM SAMPLING ERROR

THE DIFFERENCE BETWEEN THE

SAMPLE RESULT SAND THE RESULT

OF A CENSUS CONDUCTED USINGIDENTICAL PROCEDURES

STATISTICAL FLUCTUATION DUE TO

CHANCE VARIATIONS


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SYSTEMATIC ERRORS

NONSAMPLING ERRORS

UNREPRESENTATIVE SAMPLE

RESULTS

NOT DUE TO CHANCE

DUE TO STUDY DESIGN OR

IMPERFECTIONS IN EXECUTION


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ERRORS ASSOCIATED WITH

SAMPLING

SAMPLING FRAME ERROR

RANDOM SAMPLING ERROR

NONRESPONSE ERROR


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TWO MAJOR CATEGORIES OF

SAMPLING

PROBABILITY SAMPLING KNOWN, NONZERO PROBABLITY FOR

EVERY ELEMENT

NONPROBABLITY SAMPLING PROBABLITY OF SELECTING ANY

PARTICULAR MEMBER IS UNKNOWN


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Sampling Design

Judgment

Quota

Convenience

Judgment

Single Stage

Multi-Stage

Simple Random

Systematic

Cluster

Stratified

Simple Random

Stratified

Combination

Non-probability

design

Probability design


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NONPROBABLITY SAMPLING

CONVENIENCE

JUDGMENT

QUOTA

SNOWBALL


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PROBABLITY SAMPLING

SIMPLE RANDOM SAMPLE

SYSTEMATIC SAMPLE

STRATIFIED SAMPLE

CLUSTER SAMPLE

MULTISTAGE AREA SAMPLE


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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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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

e.g., Test market


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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 stratified

sampling


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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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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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SYSTEMATIC SAMPLING

A simple process

every nth name from the list will be drawn


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STRATIFIED SAMPLING

Probability sample

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 SAMPLING

All Students

Commuters Residents

Sample

1. Divide population into

homogeneous subgroups

Mutually exclusive Exhaustive

2. Select proportionate

simple random

samples from subgroups

3. More accurately reflect

characteristics of the

population


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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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WHAT IS THE APPROPRIATE

SAMPLE DESIGN

Homogeneity of sampling units - variations

Confidence level required

Precision required - sampling error

Analytical procedure

Cost, time, personnel

Advanced knowledge of the population

National versus local


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AFTER THE SAMPLE DESIGN

IS SELECTED

DETERMINE SAMPLE SIZE

SELECT ACTUAL SAMPLE UNITS

CONDUCT FIELDWORK

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