Download SAMPLE SIZE???

RESEARCH DESIGN (PART 2)
Siti Rohaida Bt Mohamed Zainal, PhD
School of Management
[email protected]
What is Sampling?
“Sampling is a
process by which
we study a small
part of a population
to make judgments
about the entire
population.”
Why Sampling is Needed?



Lower cost
Greater speed of data collection
Greater accuracy
Factors to Consider in Sample Design
Research objectives
Resources
Knowledge of
target population
Statistical analysis needs
Degree of accuracy
Time frame
Research scope
The Nature of Sampling
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Population
Population element
Sampling frame
Sample
Subject
Parameter
Statistics
Sampling error
The Nature of Sampling
• Population - total collection of elements about which we
wish to make some inferences.
• Population element - the individual participant or object
on which the measurement is taken--the unit of study.
• Sampling frame - the listing of population elements from
which the sample will be drawn—i.e., master lists,
directories etc
The Nature of Sampling
• Sample - a part of the population from which we actually
collect information which is used to draw conclusions
about the whole population
• Subject - a single member of the sample
• Parameter - characteristics of the population
• Statistics - characteristics of the sample
• Sampling error: any error in a survey that occurs
because of the sample
Inference Process
Estimation &
Hypothesis
Testing
Population
Sample
Statistics
( X , ps)
Sample
Parameter and Statistics: Example
“Average income of engineers in
Malaysia is RM5000”
Parameter
Population
“Average income of engineers in
Penang is RM5000”
Statistic
Sample
The Sampling Design Process
Define the Population
Determine the Sampling Frame
Select Sampling Techniques
Determine the Sample Size
Execute the Sampling Process
Define the Target Population
Important factors in determining the sample size:

the importance of the decision
 the nature of the research
 the number of variables
 the nature of the analysis
 sample sizes used in similar studies
 resource constraints-time and cost
SAMPLE
SIZE???
Sampling Error
Sampling error is any type of bias
that is attributable to mistakes
in either drawing a sample or
determining the sample size
SAMPLE
SIZE???
Two Basic Sampling Methods

Probability samples: ones in which
members of the population have a known
chance (probability) of being selected into
the sample

Non-probability samples: instances in which
the chances (probability) of selecting
members from the population into the
sample are unknown
Classification of Sampling Techniques
SAMPLING TECHNIQUES
Non-Probability
Sampling Techniques
Convenience
Sampling
Judgmental
Sampling
Simple Random
Sampling
Systematic
Sampling
Probability
Sampling Techniques
Quota
Sampling
Stratified
Sampling
Snowball
Sampling
Cluster
Sampling
Double
Sampling
NON-PROBABILITY
SAMPLING
Non-probability Samples
Reasons to use:




Procedure satisfactorily meets the sampling
objectives
Lower Cost
Limited Time
Total list population not available
Non-probability Samples
No need to
generalize
Feasibility
Time
Limited
objectives
Cost
Non-probability Sampling
Methods
Convenience
Based on ease of accessibility
Judgmental
Deliberately select sample to
conform to some criterion
Quota
Snowball
Relevant characteristics are used
to segregate the sample to
improve its representativeness
Referred by current sample
elements
Convenience Sampling
Convenience sampling – sample is selected base on
ease of accessibility.
Normally use in the early stage of exploratory study
Often, 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
 “people on the street” interviews
 pool of friends and contacts
Judgmental Sampling

Judgmental sampling is a form of convenience
sampling in which the population elements are selected
based on the judgment of the researcher or those
conform to some criterion of interest.

Useful when looking for information that only a few
“experts” can provide.
Example:
 Academic expertise
 Purchase engineers selected in industrial marketing
research
 Expert witnesses used in court
Quota Sampling
Quota sampling – relevant characteristics
are used to stratify the sample.
 The
first stage consists of developing
categories of population elements.
 In
the second stage, sample elements are
selected based on convenience or
judgment.
 Example:
gender, religion, ethnicity, etc
Quota Sampling
Characteristic
Postgraduate
MA
PhD
Population
composition
Sample
composition
Percentage
Percentage
60%
40%
____
100
60%
40%
____
100
Number
600
400
____
1000
Snowball Sampling
In 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.
PROBABILITY
SAMPLING
Simple Random Sampling
All elements in the population are considered and each has
equal chance to be selected.
Each possible sample of a given size (n) has a known and
equal probability of being the sample actually selected.
Advantages
• High generalisability of
the findings
• Easy to implement with
random number table.
Disadvantages
• Requires list of population
elements
• Time consuming
• Uses larger sample sizes
Systematic Random Sampling
 The sample is chosen by selecting a random starting
point and then picking every kth element from the
sampling frame.
 To draw 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 element.
* kth element is the skip interval
Systematic Random Sampling
EXAMPLE:
There are 100,000 elements in the population and a sample
of 1,000 is desired. In this case the sampling interval, k, 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.
Advantages
• Simple to design
• Easier than simple
random if population
frame is available
Disadvantages
• Systematic biases are
possible
Stratified Sampling
All Postgraduates
• Population is divided into
sub-population and
subjects are selected
randomly.
• Homogeneity within group
and heterogeneity across
groups.
Masters
PhD
Sample
Stratified Sampling
 A two-step process in which the population is
partitioned into sub-population.
 Elements are selected from each sub-population by a
random procedure, usually simple random sampling.
 The elements within each sub-population should be as
homogeneous as possible, but the elements across
sub-population should be as heterogeneous as
possible.
 The stratification variables should also be closely
related to the characteristic of interest.
Stratified Sampling
Example:
University students can be divided into:
 Gender
 Race
 School/department
 Class level: undergraduate and postgraduate
 Off campus and on-campus
Stratified Sampling
Advantages
Disadvantages
• Most efficient among all
probability designs.
• Increased statistical
efficiency
• Provides data to
represent subgroups
• Stratification must be
meaningful
• Time consuming
Cluster Sampling
All Managers in Malaysia
• Population is
divided into
clusters.
• Heterogeneity
within group and
homogeneity
across groups.
Kuala Lumpur
Penang
Sample
Johor
Cluster Sampling
Population Element
Possible Clusters in Malaysia
Malaysian adult population
States
Districts
Metropolitan Statistical Area
Housing Area
Households
Cluster Sampling
 The target population is first divided into mutually
exclusive clusters.
 Then a random sample of clusters is selected, based
on a probability sampling technique
 Elements within a cluster should be as heterogeneous
as possible, but clusters themselves should be as
homogeneous as possible.
 Ideally, each cluster should be a small-scale
representation of the population.
Area Sampling (example of cluster)
A cluster sampling technique applied to a population
with well-defined political or geographic boundaries.
Double Sampling

The same sample or a subset of the sample is
studied twice.

Double and multiple sampling plans were invented to
give a questionable lot another chance.

For example:
a structured interview might indicate that a subgroup
of the respondents has more insights into a problem
in the organization, then, these respondents might be
approached again with additional questions.
Double Sampling
What Is a Valid Sample?
Accurate
The degree to
which bias is
absent from the
sample. The
sample is drawn
properly.
Precise
The degree to
which the
sample
selected closely
represent the
population.
What Is a Valid Sample?
High accuracy but low
precision
High precision but low
accuracy
RULE OF THUMB FOR SAMPLE SIZE:
According to ROSCOE (1975):
(1) Sample size larger than 30 and less than 500
are appropriate for most research.
(2) Where samples are to be broken into
subsamples (male/female, masters/PhD etc), a
minimum sample size of 30 for each category is
necessary.
(3) In multivariate research, sample size should be,
preferably, 10 times (or more) as large as the
number of variables in the study.
LET’S
RECAP...
Nonprobability Sampling Methods
Convenience sampling relies
upon convenience and access
Judgment sampling relies upon belief
that participants fit characteristics
Quota sampling emphasizes representation
of specific characteristics
Snowball sampling relies upon respondent
referrals of others with like characteristics
Exercise 1
A researcher wants a sample of 35 households from a
total population of 260 houses in Medan, Indonesia. He
samples every 7th house starting from a random number
of 1 to 7. He then choose houses numbered 7, 14, 21, 28
and so on. What type of sampling technique does the
researcher adopt?
a)
b)
c)
d)
a simple random sampling
a stratified random sampling
a cluster sampling
a systematic random sampling
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Exercise 2
A pharmaceutical company wants to trace the effects of a
new drug on patients with specific health problems. It
then contacts such individuals and with the group of
voluntarily consenting patients, tests the drugs. What type
of sampling is appropriate?
a)
b)
c)
d)
a simple random sample
a stratified random sample
a cluster sample
a judgmental sample
44
Exercise 3
The director of human resources of a manufacturing firm
wants to offer stress management seminars to the
personnel who are exposed to high levels of stress. He
predicts that three groups are most prone to stress; (1)
those who handle dangerous chemicals, (2) counselors
who listen to problems, and (3) those who handle
production line. What type of sampling is most
appropriate in this case?
a)
b)
c)
d)
a simple random sample
a stratified random sample
a cluster sample
a judgmental sample
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The end
Questions?