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Lecture 5
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It is done to ensure the questions asked
would generate the data that would answer
the research questions n research objectives
The respondents of pretest or pilot study
must be representative of the population
The number of respondents for pretest of
survey research is at least 30, because this
will allow for minimal size of statistical tests
The way the respondents respond to the
questionnaire must be similar as data
collection i.e. self-administered or interview
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Do frequency run for all items in the
questionnaire to check for types of data
obtained from the respondents
Conduct appropriate statistical tests to
answer the research questions n research
objectives
Improve the questions according to the
responses obtained for better data
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After we had selected the research problem,
developed the research questions and
outlined the research objective, then we have
to identify how and from where we are going
to collect the data to answer the research
questions and research objectives
Thus, we need to identify and define the
population of our research so that the data
collected from them would be the ‘correct’
source of information
Population is the entire group being
observed
 Almost always assumed to be infinite
in size, too large and difficult to
measure
 The total collection of all cases in
which the researcher is interested and
wishes to understand
 Sometime known as universe
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Population is the group of interest
to the researcher, the group to
which he/she would like the result
of the study to be generalized (Gay
& Airasian, 2000)
 Population is a group of potential
participants to whom you want to
generalize the result of the study
(Salkind, 2009)
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Defining the population of study would help
to reduce the size and make it more
relevant to the study (e.g. working adults,
graduate students, career women,
Malaysian society etc.)
Study population is the aggregation of
elements from which the sample is actually
selected (e.g. working adults in private
sector, career women in business, redisents
in Bandar Baru Nilai etc.)
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are too costly and time
consuming to study and the research
Population
may not be able to get the data from all of
the population…then the only choice is to
select sample
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Sample – is a subset of the population
that is representative of the entire
population
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Sample is subset of
population. It
comprises some
members selected
from population.
Elements of
population would be
there at the sample
Population
...
..
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Sample
Sample size
Population
Who will be
selected
But we must ensure
Sample represent
population
How many n
who to
choose ?
Collecting data from population is
called census
 Sampling will save cost, time,
personnel and yield better data for
statistical analysis
 Issues in sampling include
representativeness, adequate sample
size and randomization
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Representativeness – a sample is
representative of the population from
which it is selected if the aggregate
characteristics of the sample closely
approximate those same aggregate
characteristics in the population that
are relevant to the substantive interest
of the study
Adequate sample size is necessary for
statistical analysis for power and
generalization
 In getting the sample size, always give
allowance for substitutes so that
minimum sample size can be obtained
 Take note certain statistical
procedures require minimum sample
size
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To get the adequate number of
sample, one can refer to sample size
table that gives the minimum number
at predetermined significant level
 Another way is to calculate the
minimum sample size by using the
appropriate formula that takes into
account the significant level
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Randomization – the application of
the principles of random sampling to
a chunk from a population in which a
number of equivalent groups are to
be established whose differences
from one another can be only
attributed to chance
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Sampling is a process of selecting a
number of individual for a study in
such way that they represent the
larger group from which they were
selected (Gay & Airasian, 2000)
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Probability sampling
◦ The elements in the population
have some chance or
probability to be selected as a
sample (Sekaran, 2003)
◦ Probability sampling is
crucial to generalize
Two major type of
Sampling methods
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Non-Probability
sampling
◦ The elements do not have a
known or predetermine
chance of being selected as
a subject.
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Non-probability
sampling
◦ Convenience sampling
◦ Purposive sampling
◦ Quota sampling
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Probability sampling
◦ Simple random
◦ Systematic random
sampling
◦ Stratified random
sampling
◦ Cluster or Multi-stage
random sampling
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Probability sampling – sample selected
according to mathematical guidelines
whereby each unit chance for selection is
known
Non-probability sampling does not follow the
mathematical probability
Probability allows researcher to calculate the
amount of sampling error present in a study,
while non-probability sampling does not
If sample is chosen according to
proper guidelines and is
representative of the population, then
the results of the sample can be
generalized to the population
 A sample does not provide the exact
data that a population would, the
potential error must be taken into
account
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Measurement error occurs when there
is inconsistencies produced by testing
or evaluation is present
 Sampling error is the degree of the
measurement of the unit or subject
selected differ from those of the
population as a whole
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Sampling error occurs when measurements
taken form a sample do not correspond to
what exist in the population
Involves confidence level and confidence
interval
When conducting research, the researcher
estimates the accuracy of the results in terms
of a level of confidence that results within a
specific interval
Has to be decided before conducting
research.
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In social science the confidence level or
significance level is normally 0.05
This means that the researcher is 95 %
confident (confidence level) that his/her
finding is within + - 5 % (confidence interval)
of the true population percentage
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Four types of probability sampling:
- Simple random sampling (SRS): a
method of choosing cases form a
population by which every case has
equal chance of being selected
independently. Most commonly used
in research. Requires complete
population list. E.g. use of random
number table, numbers in hat
- Systematic sampling: a method of
sampling by which the first case from
a list of population is randomly
selected and then every kth case is
selected. Also requires a complete
population list.
Sampling interval= population
size/sample size
- Stratified sampling: a method of
sampling by which cases are randomly
selected from sub-lists of the
population. The sampling plan can be
either proportional or disproportional.
Stratification depends on variables
and select sample from each strata
randomly.
Requires a complete sub-list of the
population or stratum
- Cluster sampling: a method of
sampling by which geographical units
are randomly selected and from
selected areas, randomly select
samples proportionately.
Requires a complete list of
geographical units
Non-probability sampling is done
according to certain criteria – the best
possible way
 The samples must be independent
 There should be adequate number of
sample
 Results cannot be generalized to the
population
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Four issues of using non-probability
sampling:
* purpose of study – not to be generalize
results to population
* cost vs. value – cost too high for sample
* time constraints – sponsors research that
need quick results
* amount of accepted error – study where
error control is not a prime factor
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Types of non-probability sampling:
- Purposive or judgmental sampling: a
method of sampling based on certain
judgment or specific purpose
- Quota sampling: a method of sampling to
include proportionate number of all parties
involved
- Bias sampling : selected on the basis where
researcher favors certain characteristics
- Convenient sampling: a method of
sampling done as one pleases and feels
appropriate
- Volunteer sampling: a method of
sampling done based on those who likes to
participate
- Snow ball sampling: a method of
sampling done by getting suggestion for
next sample from a current sample
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Purpose of qualitative research
* Produce information-rich data
* Depth rather than breadth
* Insight rather than generalisation
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Conceptual rather than numerical
considerations
* Choose information-rich sites and respondents
Number of respondent is not an issue
Non-probability sampling like snowball
technique until saturation
Type of research
 Purpose of research
 Research complexity
 Amount of error tolerated
 Time constraint
 Financial constraint
 Previous research in the area
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Identify the
population
Determine
Sample
size
Choose
Sampling
technique
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The what or whom being studied (Babbie,
2013)
In social science, the most typical units of
analysis are the individual people
It is the unit that we want to compare, e.g.
organizations, households, families, and
interactions
Make sure that the correct source of
information (respondent) will generate the
right data to be considered as unit of analysis