Sampling Techniques: Lesson 7

Lesson 7
SAMPLING TECHNIQUES
Sampling
 A process of choosing a
representative portion of a
population or some elements in a
population that will represent the
entire population.
Population
• total number of individuals
Population element
• an individual
Target population
 The population for which representative
information is desired
Sampling population
The population for which a sample is

actually drawn
Sampling frame
 list of all the individuals in a population;
the sample is drawn from this
Sample
an individual or group of individuals
Why study a sample?
 It is cheaper.
 It is faster.
 It is more accurate
 It can yield more
comprehensive information.
Basic types of sampling
Non-probability sampling (non-random)
 Data gatherers chose sample

cases “as they wish” or “wherever
they find them.”
Non-probability sampling
1. Accidental or Convenience sampling
 Selects the sample as they become available.
2. Purposive sampling
 The investigator uses a specific purpose in
selecting a sample
3. Quota sampling
 Target a number based on the population
and when you reach that number of
research subjects/respondents, that will
serve as your samples.
4. Snowball sampling
 follows like a networking process
Basic types of sampling
Probability sampling (random)
 Random selection, a procedure
that assures that all elements are
in the population are given equal
chance of being selected as a
sample unit.
Systematic sampling with a random start
 A method of selecting a sample from a population by
taking the kth units from an ordered population, from
the first unit which is selected at random.
 K is the sampling interval
 Derived by dividing the total population by
the desired sample size
 To use this technique, an ordered list of
population element is required
Population: 30 insulin-dependent diabetic patients
Sampling frame: list of names of eligible patients
Sampling unit: insulin-dependent diabetic patients
Desired # of samples: 15 insulin-dependent
diabetic patients
Steps in drawing the sample patients:
1. List the 30 eligible patients in alphabetical order
and number them from 1 to 30.
2. Determine the sampling interval (K) by dividing the
sample size of the population by the desired # of
samples: K = 30÷15 = 2.
3. Select a random start by picking at random any #
from 1-30.
4. For example you picked # 10, then start at # 10.
Take every second name in the list. When you
reach # 30, go back to # 1 and continue drawing
your sample units until you have drawn 15
numbers.
Probability sampling
1. Simple random
 giving all the elements a chance to be
included in the population
 Done either using drawing of lots or fishbowl
technique
 Using table of random numbers
 Random sampling maybe:
• Restricted type
• Unrestricted type
Population: 24 third year BSMT students
Sampling frame: list of 24 third year
BSMT students
Sampling unit: student
Desired # of samples: 10 students
Steps in Simple Random Sampling:
1. Make a list of the students in alphabet
and give them a corresponding number
2. Write a number on a small piece of paper
and roll it. Place in a container and
shake it.
3. Pick one piece of paper at a time until
you have picked 10 rolled papers
Stratified random sampling
The process of selecting a random

sample from subgroups or strata into
which a population has been subdivided.
Population: All 4, 110 BSMT students
Sampling frame: List of students per

year level
Sampling unit: Students
Steps in drawing the sample:
1. Classify the 4, 110 students
2. Determine the over-all sample size
using Slovin formula. n= ?
3. Allocate the needed sample size (n) among
the three strata either equally or
proportionately. To do this, divide the
stratum size by the population size (N) and
multiply the quotient by the needed sample
size (n).
JBLFMU-Arevalo Summary Statistical Report
(First Sem, 2011-2012)
Year Level Population (N) Sampling Fraction Sample Size (n)
1st 1, 139 1, 139/4, 110 x n n1 = ?
2nd 1, 422 1, 422/4, 110 x n n2 = ?
3rd 1, 209 1, 209/4, 110 x n n3 = ?
(Ladd) 1st 340 340/4, 110 x n n4 = ?
Total 4, 110 Real n = ?
4. With the sub-sample size determined, select the

sample from each stratum, using either simple
random sampling or systemic sampling with a
random start.
Cluster sampling
 Selecting a sample of subgroups or clusters of
elements.
 Clusters are usually exclusive sub-populations,
which together comprise a population.
 Each cluster consists of heterogeneous

elements and each is typical of the population.
Population: All the 10 classes in the covered
court classrooms
Sampling frame: List of 10 clusters or groups
Sampling unit: 1 group with 10 members

Steps in selecting the sample clusters:
1. Number the 10 groups consecutively from 1-10.
2. Using the simple random sampling, draw 5
numbers. Suppose numbers 2, 6, 5, 9 and 1 are
chosen.
3. Identify the groups represented by the numbers drawn.
4. Study all the students in the five sample classes.

Two-stage Random Sampling
 The selection of the sample is accomplished in two or
more (multi) stages.
 The population is first divided into a number of first

stage units from which a sample is drawn.
 Then, the population in the sampled first stage

units is divided into second stage units.
Population: All men/fathers with 0-6 year old
children in the province
Sampling frame: List of men with 0-6 year old

children
Sampling unit: A man/father with 0-6 year

old children
Steps in drawing the sample:
1. Draw 3 sample towns out of 7 towns in the province.
2. Secure a list of all barangays in each of the 3 sample

towns. Using the simple random sampling draw 3
sample barangays in each of the 3 sample towns.
3. Draw a sample of married men in the sample
barangays. List the names of the men with 0-6 year old
children in each of the 3 sample barangays in the 3
sample towns. Using the simple random sampling, or
systematic sampling, select the sample men in each of
the sample barangays.
Two-stage Random Sampling
 Combination of cluster random sampling with individual
random sampling
 Another example: Computed sample size is 100
respondents
3000 students in 100 classes
89 classes
4 students in each class

Determination of sample size
The size of the sample may be determined by using the
Slovin formula defined as:
N
n = ----------
1 + N (e)2
where: n= sample size

N = population size
e = margin of error
Minimum acceptable sizes for various types of
research (Fraenkel and Wallen, 2010)
 Descriptive research- minimum of 100 respondents
 Correlational research- at least 50 respondents
 Experimental research- minimum of 30 respondents

per group; 15 respondents per group if tightly
controlled
Always remember….
 Different research aims and needs such
as those observed between quantitative
and qualitative research call for different
sampling technique.
 In qualitative research, sampling is generally

purposive.

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Lesson 7

The population for which a sample is

 Data gatherers chose sample

The process of selecting a random

Sampling frame: List of students per

4. With the sub-sample size determined, select the

 Each cluster consists of heterogeneous

Sampling frame: List of 10 clusters or groups

Sampling unit: 1 group with 10 members

4. Study all the students in the five sample classes.

 The population is first divided into a number of first

 Then, the population in the sampled first stage

Sampling frame: List of men with 0-6 year old

Sampling unit: A man/father with 0-6 year

2. Secure a list of all barangays in each of the 3 sample

4 students in each class

where: n= sample size

 Descriptive research- minimum of 100 respondents

 Correlational research- at least 50 respondents

 Experimental research- minimum of 30 respondents

 In qualitative research, sampling is generally

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