Research Method for Business chapter 10

Chapter Ten

Sampling:

Design and Procedures

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Sampling

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Sampling

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Chapter Outline

1) Overview

2) Sample or Census

3) The Sampling Design Process

i. Define the Target Population

ii. Determine the Sampling Frame

iii. Select a Sampling Technique

iv. Determine the Sample Size

v. Execute the Sampling Process

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Chapter Outline

4) A Classification of Sampling Techniques

i. Nonprobability Sampling Techniques

a. Convenience Sampling

b. Judgmental Sampling

c. Quota Sampling

d. Snowball Sampling

ii. Probability Sampling Techniques

a. Simple Random Sampling

b. Systematic Sampling

c. Stratified Sampling

d. Cluster Sampling

e. Other Probability Sampling Techniques

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Sampling

Sampling;

• The process of selecting a small number of elements

from a larger defined target group of elements the

information gathered from the small group will allow

judgments to be made about the larger groups

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Population

Sample

Sampling

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Sample

The Sample

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Census1

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List of Units

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Census

Sample or Census

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Sample vs. Census

Conditions Favoring the Use of

Type of Study

Sample Census

1. Budget

Small

Large

2. Time available

Short Long

3. Population size

Large Small

4. Variance in the characteristic

Small Large

8. Attention to individual cases Yes No

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• Budget and time constraints (in case of large populations)

• High degree of accuracy and reliability (if sample is

representative of population)

• Sampling may sometimes produce more accurate results

than taking a census as in the latter

• There are more risks for making interviewer and other

errors due to the high volume of persons contacted and

the number of census takers

• Some of whom may not be well-trained

Reasons for Sampling

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Who is the target group

for the study?

This is called the study

population

Who in the target group

should be surveyed?This is called the sample.

How many people should

be surveyed?

This is called the sample

size.

How should the people to

be surveyed by selected?

This is called the sampling

method.

The Sampling

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Acknowledgments to Uma Sekaran

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• A sample is a subset of a larger

population of objects individuals,

households, businesses,

organizations and so forth.

• Sampling enables researchers to

make estimates of some unknown

characteristics of the population in

question

• A finite group is called population

whereas a non-finite (infinite) group is

called universe

• A census is a investigation of all the

individual elements of a Population

Population

Sample

Sampling

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Classification of Sampling Techniques

Sampling Techniques

Non-probability

Sampling Techniques

Probability

Sampling Techniques

Convenience

Sampling

Judgmental

Sampling

Quota

Sampling

Snowball

Sampling

Systematic

Sampling

Stratified

Sampling

Cluster

Sampling

Other Sampling

Techniques

Simple Random

Sampling

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The Sampling Design Process

Fig. 11.1

Define the Population

Determine the Sampling Frame

Select Sampling Technique(s)

Determine the Sample Size

Execute the Sampling Process

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Define the Target population

Select a Sampling Frame

Determine if a probability

or non-probability sampling

method will be chosen

Plan procedure for

selecting sampling units

Determine sample size

Select actual sampling units

Conduct fieldwork1

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The Sampling Process

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Defining the Target Population

• The target population is that complete group whose relevant characteristics are to be determined through the sampling and a target population may be, for example,

• All faculty members in the PRESTON University Islamabad,

• All housewives in Islamabad

• All pre-college students in Rawalpindi

• All medical doctors in Pakistan

• The target group should be clearly defined if possible, for example, do all pre-college students include only primary and secondary students or also students in other specialized educational institutions?

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Define the Target Population

The target population;

The target population should be defined in terms of elements, sampling units and time.

• An element is the object about which or from which the information is desired, e.g., the respondent.

• A sampling unit is an element, or a unit containing the element, that is available for selection at some stage of the sampling process.

• Time is the time period under consideration.

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• A list of population elements (people, companies, houses, cities, etc.) from which units to be sampled can be selected.

• Examples;

• A student telephone directory (for the student population),

• The list of companies on the stock exchange,

• The directory of medical doctors and specialists,

• The yellow pages (for businesses)

• Difficult to get an accurate list.

• Sample frame error occurs when certain elements of the population are accidentally omitted or not included in list

• Information relating to sampling frames can be obtained from commercial organizations

The Sampling Frame

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List of

Units1

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Sample

The Sampling Frame

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• The sampling unit is a single element – or group of

elements – subject to selection in a sample.

• Examples:

Every student at PRESTON whose first name begins

with the letter “F”

All child passengers under 18 years of age who are

traveling in a train from destination X to destination Y

All jeweler shops in sectors F-6, F-7 and F-8 in

Islamabad

The Sampling Units

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Classification of Sampling Techniques

Sampling Techniques

Non-probability

Sampling Techniques

Probability

Sampling Techniques

Convenience

Sampling

Judgmental

Sampling

Quota

Sampling

Snowball

Sampling

Systematic

Sampling

Stratified

Sampling

Cluster

Sampling

Other Sampling

Techniques

Simple Random

Sampling

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Sampling

Probability sampling;

1. Simple Random Sampling;

2. Systematic Sampling;

3. Stratified Sampling;

4. Cluster Sampling;

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Simple Random Sampling

Simple random sampling;

• SRS is a method of probability sampling in which

every unit has an equal nonzero chance of being

selected

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Systematic Random Sampling

Systematic random sampling;

• A method of probability sampling in which the

defined target population is ordered the sample is

selected according to position using a skip interval

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Systematic Sampling

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Systematic Sampling

The sample is chosen by selecting a random starting point and then picking every ith element in succession from the sampling frame.

The sampling interval, is determined by dividing the population size N by the sample size n and rounding to the nearest integer.

When the ordering of the elements is related to the characteristic of interest, systematic sampling increases the representativeness of the sample.

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Systematic Sampling

If the ordering of the elements produces a cyclical pattern, systematic sampling may decrease the representativeness of the sample.

For example, there are 100,000 elements in the population and a sample of 1,000 is desired. In this case the sampling interval, that is 100. A random number between 1 and 100 is selected. If, for example, this number is 23, the sample consists of elements 23, 123, 223, 323, 423, 523, and so on.

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Systematic Sampling

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Stratified Sampling

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Stratified Random Sampling

Stratified random sampling;

• A method of probability sampling in which the

population is divided into different subgroups and

samples are selected from each subgroup.

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Stratified Sampling

Stratified Sampling;

A two-step process in which the population is partitioned

into subpopulations, or strata

The strata should be mutually exclusive and collectively

exhaustive in that every population element should be

assigned to one and only one stratum and no population

elements should be omitted

Next, elements are selected from each stratum by a

random procedure, usually SRS.

A major objective of stratified sampling is to increase

precision without increasing cost.

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Stratified Sampling

The elements within a stratum should be as homogeneous as

possible, but the elements in different strata should be as

heterogeneous as possible.

In proportionate stratified sampling, the size of the sample

drawn from each stratum is proportionate to the relative size of

that stratum in the total population.

In disproportionate stratified sampling, the size of the sample

from each stratum is proportionate to the relative size of that

stratum and to the standard deviation of the distribution of the

characteristic of interest among all the elements in that

stratum.

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Cluster Sampling

The target population is first divided into mutually exclusive

and collectively exhaustive subpopulations, or clusters.

Then a random sample of clusters is selected, based on a

probability sampling technique such as SRS.

For each selected cluster, either all the elements are

included in the sample (one-stage) or a sample of elements

is drawn probabilistically (two-stage).

In probability proportionate to size sampling, the clusters

are sampled with probability proportional to size.

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Types of Cluster Sampling

Fig. 11.3Cluster Sampling

One-Stage

Sampling

Multistage

Sampling

Two-Stage

Sampling

Simple Cluster

SamplingProbability

Proportionate

to Size Sampling

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Random Sampling Error ;–

• The “difference between the sample result and the result of a census conducted using identical procedures” and is the result of chance variation in the selection of sampling units

• If samples are selected properly (for e.g. through the technique of

randomization), the sample is usually supposed to be a good approximation of the population and thus capable of delivering an accurate result

• Usually, the random sampling error arising from statistical fluctuation is small, but sometimes the margin of error can be significant

The Sampling Errors (1)

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Systematic (Non-Sampling) Errors;

• These errors result from factors such as an

• Improper research design that causes response error or

• Or errors committed in the execution of the research

• Errors in recording responses

• Non-responses from individuals who were not contacted

or who refused to participate

• Both Random sampling errors and systematic (non-

sampling) errors reduce the representativeness of a

sample and consequently the value of the information

which is derived by business researchers from it

The Sampling Errors (2)

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Total Population

Sampling Frame Error

Random Sampling Error

Sampling Frame

Planned

Sample

Non-Response Error

Respondents

(actual

sample)

The Sampling Errors

Yes

Choose one of the

PROBABILITY sampling

designs.

If purpose of study

mainly is for:

No

Generalizability

.

Assessing

differential

parameters in

subgroups of

population.

Collecting

information

in a localized

area.

Gathering more

information

from a subset of

the sample

To obtain quick,

even if unreliable

information

To obtain

information relevant

to and available only

with certain groups

Choose one of the

NONPROBABILITY

sampling designs.

If purpose of study

mainly is:

Choose

cluster

sampling

if not

enough $.

Choose

systematic

sampling.

Choose

simple

random

sampling.

All subgroups have

equal number of

elements?

Yes No

Choose

disproportionate

stratified random

sampling

Choose

proportionate

stratified random

sampling.

Choose

convenience

sampling.

Looking for

information that

only a few “experts”

can provide?

Need responses of

special interest

minority groups?

Choose

judgment

sampling.

Choose

quota

sampling.

Choose area

sampling.

Choose

double

sampling.

Is REPRESENTATIVENESS

of sample critical for the study?

Diagram 11.3

Choice Points in Sampling Design.

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Acknowledgments to Uma Sekaran

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Sampling

Non-probability sampling;

1. Convenience sampling;

2. Judgmental sampling;

3. Quota sampling;

4. Snowball sampling;

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Convenience Sampling

Convenience sampling;

• Attempts to obtain a sample of convenient elements.

• Respondents are selected because they happen to be

in the right place at the right time

• Use of students, and members of social organizations

• Mall intercept interviews without qualifying the

respondents

• Department stores using charge account lists

• “people on the street” interviews

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Judgmental Sampling

Judgmental sampling;

A form of convenience sampling in which the population

elements are selected based on the judgment of the

researcher.

Test markets

Purchase engineers selected in industrial marketing

research

Expert witnesses used in court

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Quota Sampling

Quota sampling;

may be viewed as two-stage restricted judgmental sampling

The first stage consists of developing control categories, or quotas, of population elements.

In the second stage, sample elements are selected based on convenience or judgment.

Population Samplecomposition composition

ControlCharacteristic Percentage Percentage NumberSex

Male 48 48 480Female 52 52 520

100 100 1000

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Snowball Sampling

Snowball sampling;

An initial group of respondents is selected, usually at random

After being interviewed, these respondents are asked to

identify others who belong to the target population of interest

Subsequent respondents are selected based on the referrals

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Snowball Sampling

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Snowball Sampling

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2. For smaller samples (N ‹ 100), there is

little point in sampling. Survey the

entire population.

1. The larger the population size, the

smaller the percentage of the

population required to get a

representative sample

Rules of thumb for determining the

sample size...

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4. If the population size is around 1500,

20% should be sampled.

3. If the population size is around 500

(give or take 100), 50% should be

sampled.

5. Beyond a certain point (N = 5000),

the population size is almost

irrelevant and a sample size of 400

may be adequate.

Rules of thumb for determining the

sample size...

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Technique Strengths Weaknesses

Nonprobability SamplingConvenience sampling

Least expensive, leasttime-consuming, mostconvenient

Selection bias, sample notrepresentative, not recommended fordescriptive or causal research

Judgmental sampling Low cost, convenient,not time-consuming

Does not allow generalization,subjective

Quota sampling Sample can be controlledfor certain characteristics

Selection bias, no assurance ofrepresentativeness

Snowball sampling Can estimate rarecharacteristics

Time-consuming

Probability samplingSimple random sampling(SRS)

Easily understood,results projectable

Difficult to construct samplingframe, expensive, lower precision,no assurance of representativeness.

Systematic sampling Can increaserepresentativeness,easier to implement thanSRS, sampling frame notnecessary

Can decrease representativeness

Stratified sampling Include all importantsubpopulations,precision

Difficult to select relevantstratification variables, not feasible tostratify on many variables, expensive

Cluster sampling Easy to implement, costeffective

Imprecise, difficult to compute andinterpret results

Table 11.3

Strengths and Weaknesses of Basic Sampling Techniques

Research Method for Business chapter 10

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