07 Sampling

02/10/2013

1

Zaki Rashidi

Sampling Techniques

Slide 7.2

Saunders, Lewis and Thornhill, Research Methods for Business Students, 5th Edition, © Mark Saunders, Philip Lewis and Adrian Thornhill 2009

Selecting samples

Population, sample and individual cases

Source: Saunders et al. (2009)

Figure 7.1 Population, sample and individual cases

Slide 7.3


The need to sample

Sampling- a valid alternative to a census when

A survey of the entire population is impracticable

Budget constraints restrict data collection

Time constraints restrict data collection

Results from data collection are needed quickly

Slide 7.4


Basics of sampling I

A sample is a

“part of a whole

to show what the

rest is like”.

Sampling helps to

determine the

corresponding

value of the

population and

plays a vital role

in esearch.

Samples offer many benefits:

Save costs: Less expensive to study the sample than the population.

Save time: Less time needed to study the sample than the population .

Accuracy: Since sampling is done with care and studies are conducted by skilled and qualified interviewers, the results are expected to be accurate.

Destructive nature of elements: For some elements, sampling is the way to test, since tests destroy the element itself.

Slide 7.5


Basics of sampling II

Limitations of Sampling

Demands more rigid control in undertaking sample operation.

Minority and smallness in number of sub-groups often render study to be suspected.

Accuracy level may be affected when data is subjected to weighing.

Sample results are good approximations at best.

Sampling Process

Defining the

population

Developing

a sampling

Frame

Determining

Sample

Size

Specifying

Sample

Method

SELECTING THE SAMPLE

Slide 7.6


Sampling Cycle

Population

Draw a Sample

Compute Statistics

Apply inference

Estimate Parameter

02/10/2013

2

Slide 7.7


The sampling frame

The sampling frame for any probability sample is

a complete list of all the cases / units in the

population from which your sample will be

drown.

What is the difference between population and

sampling frame?

Slide 7.8


Sampling: Step 1

Defining the Universe

Universe or population is the

whole mass under study.

How to define a universe:

What constitutes the units of

analysis (HDB apartments)?

What are the sampling units

(HDB apartments occupied in

the last three months)?

What is the specific designation

of the units to be covered (HDB

in town area)?

What time period does the data

refer to (December 31, 1995)

Sampling: Step 2 Establishing the Sampling

Frame

A sample frame is the list of all elements in the population (such as telephone directories, electoral registers, club membership etc.) from which the samples are drawn.

A sample frame which does not fully represent an intended population will result in frame error and affect the degree of

reliability of sample result.

Slide 7.9


Step - 3

Determination of Sample Size

Sample size may be determined by using:

Subjective methods (less sophisticated methods)

The rule of thumb approach: eg. 5% of population

Conventional approach: eg. Average of sample sizes of similar

other studies;

Cost basis approach: The number that can be studied with the

available funds;

Statistical formulae (more sophisticated methods)

Confidence interval approach.

Slide 7.10


Choice of Sample Size - Large

Populations Sample Sizes

% Margin of Error 95% Confidence 99% Confidence

± 1 9,604 16,590

± 2 2,401 4,148

± 3 1,068 1,844

± 4 601 1,037

± 5 385 664

± 6 267 461

± 7 196 339

± 8 151 260

± 9 119 250

± 10 97 166

Source :Parker & Rea, Designing and Conducting Research

Table 1

Slide 7.11


Choice of Sample Size - Small Populations

Sample Sizes

95% Level of Confidence 99% Level of Confidence

N ± 3% ± 5% ± 10% ± 3% ± 5% ± 10%

500 250 218 81 250 250 124

1000 500 278 88 500 399 143

1500 624 306 91 750 460 150

2,000 696 323 92 959 498 154

3,000 788 341 94 1,142 544 158

5,000 880 357 95 1,347 586 161

10,000 965 370 96 1,556 622 164

20,000 1,014 377 96 1,687 642 165

50,000 1,045 382 96 1,777 655 166

100,000 1,058 383 96 1,809 659 166

Source : Parker & Rea, Designing and Conducting Research

Table 2

Slide 7.12


Sample size determination using statistical formulae:

The confidence interval approach

To determine sample sizes using statistical formulae, researchers

use the confidence interval approach based on the following

factors:

Desired level of data precision or accuracy;

Amount of variability in the population (homogeneity);

Level of confidence required in the estimates of population values.

Availability of resources such as money, manpower and time

may prompt the researcher to modify the computed sample size.

Students are encouraged to consult any standard marketing

research textbook to have an understanding of these formulae.

02/10/2013

3

Slide 7.13


Probability sampling Probability of each case / unit being selected from

the population is known (and usually equal to all

cases).

This means that it is possible to answer research

questions and to achieve objectives that require you

to estimate statistically the characteristics of the

population from the sample.

Consequently, probability sampling is often

associated with survey and experimental research

strategies.

Slide 7.14


Non probability samples The probability of each case being selected from the

total population is not known and it is impossible to

answer research questions or to address research

objectives that require you to make statistical

inferences about the characteristics of the

population.

You may still be able to generalize from non

probability samples about the population, but not on

statistical grounds

Slide 7.15


Overview of sampling techniques

Sampling techniques

Source: Saunders et al. (2009) Figure 7.2 Sampling techniques

Probability Sampling

Slide 7.17


Probability sampling

The four stage process

1. Identify sampling frame from research objectives

2. Decide on a suitable sample size

3. Select the appropriate technique and the sample

4. Check that the sample is representative

Slide 7.18


Identifying a suitable sampling frame

Key points to consider

Problems of using existing databases

Extent of possible generalisation from the sample

Validity and reliability

Avoidance of bias

02/10/2013

4

Slide 7.19


Sample size

Choice of sample size is influenced by

Confidence needed in the data

Margin of error that can be tolerated

Types of analyses to be undertaken

Size of the sample population and distribution

Slide 7.20


The importance of response rate

Key considerations

Non- respondents and analysis of refusals

Obtaining a representative sample

Calculating the active response rate

Estimating response rate and sample size

Slide 7.21


Selecting a sampling technique

Five main techniques used for a probability sample

1. Simple random

2. Stratified random

3. Systematic

4. Cluster

5. Multi-stage

Slide 7.22


Simple random(Random sampling) Involves you selecting at random frame using either random

number tables, a computer or an online random number

generator such as Research Randomizer.

Slide 7.23


Stratified random sampling Stratified random sampling is a modification of random sampling

in which you divide the population into two or more relevant and

significant strata (groups) based on a one or a number of

attributes.

Sampling frame is divided into a number of subsets.

A random sample (simple or systematic) is then drawn from each

of the strata.

Consequently stratified sampling shares many of the advantages

and disadvantages of simple random or systematic sampling

Slide 7.24


Three approaches

a) Proportional Allocation

b) Disproportional Allocation

c) Neyman’s Allocation

02/10/2013

5

Slide 7.25


Systematic sampling

Systematic sampling involves you selecting the sample at regular

intervals from the sampling frame.

1. Number each of the cases in your sampling frame with a unique

number . The first is numbered 0, the second 1 and so on.

2. Select the first case using a random number.

3. Calculate the sample fraction.

4. Select subsequent cases systematically using the sample fraction to

determine the frequency of selection

Slide 7.26


Cluster Sampling Similar to stratified as you need to divide the population into

discrete groups prior to sampling.

The groups are termed clusters in this form of sampling and

can be based in any naturally occurring grouping.

For example, you could group your data by type of

manufacturing firm or geographical area

Slide 7.27


Cluster Sampling For cluster sampling your sampling frame is the complete list

of clusters rather than complete list of individual cases within

population, you then select a few cluster normally using

simple random sampling.

Data are then collected from every case within the selected

clusters.

What is the difference in the groups of

stratified sampling and cluster sampling?

Slide 7.28


Multi-stage sampling

(multi-stage cluster sampling It is a development of cluster sampling

It is normally used to overcome problems associated with a

geographically dispersed population when face to face contact is

needed or where it is expensive and time consuming to construct

a sampling frame for a large geographical area.

However, like cluster sampling you can use it for any discrete

groups, including those not are geographically based.

The technique involves taking a series of cluster samples, each

involving some form of random sampling method.

Non Probability Sampling

Slide 7.30


Non-Probability Sampling

1. Quota Sampling

2. Purposive Sampling

1. Extreme case Sampling

2. Heterogeneous /Maximum Variation

3. Homogeneous Sampling

4. Critical case Sampling

5. Typical case Sampling

3. Snowball Sampling

4. Self-selection Sampling

5. Convenience Sampling

02/10/2013

6

Slide 7.31


Non- probability sampling

Key considerations

Deciding on a suitable sample size

Selecting the appropriate technique

Slide 7.32


Quota sampling It is entirely non random and it is normally used for

interview surveys.

It is based on the premise that your sample will represent

the population as the variability in your sample for

various quota variables is the same as that in population.

Quota sampling is therefore a type of stratified sample in

which selection of cases within strata is entirely non-

random

Slide 7.33


Quota sampling Divide the population into specific groups.

Calculate a quota for each group based on relevant and available

data.

Give each interviewer an ‘assignment', which states the number of

cases in each quota from which they must collect data.

Combine the data collected by interviewers to provide the full

sample.

Slide 7.34


Purposive sampling

Purposive or judgmental sampling enables you to use your

judgment to select cases that will best enable you to answer your

research question(s) and to meet your objectives.

This form of sample is often used when working with very small

samples such as in case research and when you wish to select cases

that are particularly informative.

Purposive sampling can also be used by researchers adopting the

grounded theory strategy. For such research, findings from data

collected from your initial sample inform the way you extend your

sample into subsequent cases.

Such samples, however can not be considered to be statistically

representative of the total population.

Slide 7.35


Continued The logic on which you base your strategy for selecting cases

for a purposive sample should be dependent on your research

question(s)and objectives.

Select information-rich cases in purposive sampling in

contrast to need to be statistically representative in

probability sampling.

Slide 7.36


Extreme case or deviant sampling

Extreme case or deviant sampling focuses on unusual or

special cases

You will learn the most to answer your research

question(s) and to meet your objects more effectively.

02/10/2013

7

Slide 7.37


Heterogeneous or maximum variation

sampling

Heterogeneous or maximum variation sampling enables

you to collect data to explain and describe the key

themes that can be observed.

To ensure maximum variation within a sample it is

suggested to identify diverse characteristics (sample

selection criteria) prior to selecting your sample.

Slide 7.38


Homogenous Sampling In direct contrast to heterogeneous sampling , homogenous

sampling focuses on one particular sub-group in which all the

sample members are similar.

This enables you to study the group in great depth.

Slide 7.39


Critical Case Sampling Critical case sampling selects critical cases on the bases that

they can make a point dramatically or because they are

important.

The focus of data collections to understand what is happening

in each critical case so that logical generalizations can be

made.

Slide 7.40


Continued A number of clues that suggest critical cases can be

summarized by the questions such as:

If it happens there, will it happen everywhere?

If they are having problems, can you be sure that

everyone will have problems?

If they cannot understand the process, is it likely that no

one will be able to understand the process?

Slide 7.41


Typical case sampling In contrast of critical case sampling, typical case sampling is

usually used as a part of a research project to provide an

illustrative profile using a representative case.

Such a sample enables you to provide an illustration of what is

‘typical’ to those who will be reading your research report and

may be unfamiliar with the subject matter.

Slide 7.42


Snowball sampling Is commonly used when it is difficult to identify members of

desired population. For example people who are working while

claiming unemployment benefit you therefore, need to:

1. Make contact with one or two cases in the population.

2. Ask these cases to identify further cases.

3. Ask theses new cases to identify further new cases (and so on)

4. Stop when either no new cases are given or the sample is as

large as manageable

02/10/2013

8

Slide 7.43


Self selecting sampling It occurs when you allow each case usually individuals, to

identify their desire to take part in the research you therefore

1. Publicize your need for cases, either by advertising through

appropriate media or by asking them to take part.

2. Collect data from those who respond

Slide 7.44


Convenience sampling Convenience sampling (or haphazard sampling) involves selecting

haphazardly those cases that are easiest to obtain for your sample,

such as the person interviewed at random in a shopping centre for a

television programme or the book about entrepreneurship you find

at the airport.

The sample selection process is continued until your required

sample size has been reached.

Although this technique of sampling is used widely, it is prone to

bias and influences that are beyond your control, as the cases appear

in the sample only because of the ease of obtaining them.

Slide 7.45


Choosing probability vs. non-probability sampling

Probability Evaluation Criteria Non-probability sampling sampling

Conclusive Nature of research Exploratory

Larger sampling Relative magnitude Larger non-sampling

errors sampling vs. error

non-sampling error

High Population variability Low

[Heterogeneous] [Homogeneous]

Favorable Statistical Considerations Unfavorable

High Sophistication Needed Low

Relatively Longer Time Relatively shorter

High Budget Needed Low

Slide 7.46


Sampling vs non-sampling errors

Sampling Error [SE] Non-sampling Error [NSE]

Very small sample Size

Larger sample size

Still larger sample

Complete census

Slide 7.47


07 Sampling

Documents

Transcript of 07 Sampling