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Population and Sampling


Probability Sampling
Non-probability Sampling
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Definition
A group of potential participants
to whom you want to generalize
the results of a study.
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Generalize :
The key to a successful study;
because it is only the results
that can be generalized from a
sample to a population; that
research results have meaning
beyond the limited setting.
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Not generalize : The sample
selected is not an accurate
representation of the
population.
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 Population
the a group of people or things you are
interested in.
 Census
is a measurement of all the units in the
population
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 PP
= number that results from measuring all the
units in the population.
 Statistic = number that results from measuring all
the units in the sample; statistics from samples
are used to estimate PP.
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 SF
= specific data from which sample is
drawn, e.g., a phone book.
 UA
= type of object of interest, e.g.,
arsons, fire departments, firefighters.
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 Is
a list or quasi list of the members of a
population.
 Resource used in the selection of a sample.
 A sample’s representativeness depends
directly on the extent to which a sampling
frame contains all the members of the total
population that the sample is intented to
represent.
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The
data for this research were
obtained from a random sample of
parents of children in the third
grade in government primary schools
in Selangor.
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Definition :
Sample is a subset of the
population.
 Good
sampling : include maximizing
the degree to which this selected
group represent the population.
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POPULATION
Sample
Sample
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Types of sampling
Probability sampling
2. Non probability
sampling
1.
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



Allows use of statistics, tests hypotheses.
Can estimate population parameter.
Eliminates bias.
Must have random selections of units.
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Exploratory
research, generates hypotheses.
Population parameters not of interests.
Adequacy of sample unknown.
Cheaper, easier, quicker to carry out.
Cant generalized findings.
Non-representative.
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A
type of sampling where the
likelihood of any one member
of the population being
selected is known.
Commonly
used because the
selection of participants is
determined by chance.
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 e.g.,
if there are 4,500
students in the Faculty of
Human Ecology, and if there
are 1,000 seniors, the odds of
selecting one senior as part of
the sample is 1000:4,500 or
0.22.
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 Where
the likelihood of selecting any one
member from the population or where
the probability of selecting a single
individual is not known.
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 e.g.,
if you do not know how
many seniors in the Faculty of
Human Ecology, the likelihood
of anyone being selected
cannot be computed.
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1.
2.
3.
4.
Simple Random Sampling
Systematic Sampling
Stratified Random Sampling
Cluster Sampling
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1. Simple
Random Sampling
When the population’s
members are similar to one
another.
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http://www.google.com.my/search?q=cluster+sampling+design+ppt&ie=utf-8&oe=utf8&aq=t&rls=org.mozilla:en-US:official&client=firefox-a
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Adv:
Ensures a high degree of
representativeness
Disadv:
Time consuming and tedious
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 Let's
assume that we have a population of
185 students and each student has been
assigned a number from 1 to 185. Suppose
we wish to sample 5 students (although
we would normally sample more, we will
use 5 for this example).
 Since
we have a population of 185 and
185 is a three digit number, we need to
use the first three digits of the numbers
listed on the chart.
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 We
close our eyes and randomly point to a
spot on the chart. For this example, we will
assume that we selected 20631 in the first
column.
 We
interpret that number as 206 (first three
digits). Since we don't have a member of our
population with that number, we go to the
next number 899 (89990). Once again we
don't have someone with that number, so we
continue at the top of the next column.
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 As
we work down the column, we find that the
first number to match our population is 100
(actually 10005 on the chart). Student number
100 would be in our sample. Continuing down
the chart, we see that the other four subjects in
our sample would be students 049, 082, 153,
and 005.
http://www.google.com/imgres?imgurl=http://www.gifted.uconn.edu/sieg
le/research/Samples/RANTBLE.JPG&imgrefurl
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2. Systematic Sampling
When the population’s members
are similar to one another.
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http://www.google.com.my/search?q=cluster+sampling+design+ppt&ie=utf-8&oe=utf8&aq=t&rls=org.mozilla:en-US:official&client=firefox-a
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Adv :

Ensures a high degree of
representativeness; no need to
use a table of random numbers.
Disadv :

Less truly random than simple
random sampling
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3. Stratified
Random Sampling
When the population is heterogeneous in
nature and contains several different
groups.
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http://www.google.com.my/search?q=cluster+sampling+design+ppt&ie=utf-8&oe=utf8&aq=t&rls=org.mozilla:en-US:official&client=firefox-a
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Adv :
 Ensures a high degree of
representativeness of all the
strata in the population.
Disadv :
 Time consuming and tedious
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 Proportionate
SRM
 Non-Proportionate SRM
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 Sampel
selected is in proportion to the size of
each stratum in the population
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 Population
= 100
 Layer 1 = 40 males
 Layer 2 = 60 females
 For a sample size of 10, you will take 4 males
+ 6 females.
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 Selection
of sample is not according to size of
stratum in the population
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 Population
= 100
 Layer 1 = 40 males
 Layer 2 = 60 females
 For a sample size of 10, you will take 5
males + 5 females.
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4.
Cluster Sampling
When the population consist of units
rather than individuals.
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http://www.google.com.my/search?q=cluster+sampling+design+ppt&ie=utf-8&oe=utf8&aq=t&rls=org.mozilla:en-US:official&client=firefox-a
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http://www.google.com.my/search?q=cluster+sampling+design+ppt&ie=utf-8&oe=utf8&aq=t&rls=org.mozilla:en-US:official&client=firefox-a
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Adv :

Easy and convenient
Disadv :

Possibility that members of units
are different from one another,
decreasing the sampling’s
effectiveness
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1.
2.
3.
4.
Convenience Sampling
Quota sampling
Purposive Sampling
Snowball sampling
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1. Convenience Sampling
When the sample is captive.
 Adv

:
convenient and inexpensive
 Disadv

:
results in questionable
representativeness.
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2. Quota sampling
When strata are present, and stratified,
sampling is not possible

Adv :


Ensures some degree of representativeness of all the
strata in the population
Disadv :

Results in questionable representativeness
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3. Purposive


Sampling
Researcher uses own judgment in the
selection of sample members
Sometimes called a judgmental sample.
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4. Snowball
sampling
A technique often used in rare
populations; each subject
interviewed is asked to identify
others.
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 Lack
of fit between the sample and the
population.
 The
difference between the
characteristics of the sample and the
characteristics of the population from
which the sample was selected.
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Reducing
sampling error is the
major goal of any selection
technique.
Larger
error.
sample, lower sampling
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




How big?
Depends on type of research design.
Desired confidence level of results.
Amount of accuracy wanted.
Characteristics of population of interest.
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 Big
enough to answer research question.
 But not so big that the process of sampling
becomes uneconomical.
 Heterogeneous
sample = bigger size
 Homogeneous sample = smaller size
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General
Rule of Thumb
30 participants/ respondents
in each group.
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1.
2.
Larger sample, smaller sampling error,
better representativeness.
If using several subgroups, starts with
large enough subjects to account for the
eventual breaking down of subject groups.
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3.
4.
If mailing out surveys or
questionnaires, increase
sample size by 40-50% to
account for lost mails or
uncooperative subjects.
Big is good, but appropriate is
better.
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 Students
will discuss and state what they have
learned in Lecture 8.
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