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29-01-2019
Objectives
 Students will be understanding the sample techniques
in research methodology.
Introduction
 Researchers commonly examine traits or
characteristics (parameters) of populations in their
studies. A population is a group of individual units
with some commonality. For example, a researcher
may want to study characteristics of female smokers in
Pakistan. This would be the population being analyzed
in the study, but it would be impossible to collect
information from all female smokers in Pakistan.
 Therefore, the researcher would select individuals
from which to collect the data. This is
called sampling. The group from which the data is
drawn is a representative sample of the population the
results of the study can be generalized to the
population as a whole.
 The sample will be representative of the population if
the researcher uses a random selection procedure to
choose participants. The group of units or individuals
who have a legitimate chance of being selected are
sometimes referred to as the sampling frame. If a
researcher studied developmental milestones of
preschool children and target licensed preschools to
collect the data, the sampling frame would be all
preschool aged children in those preschools.
 Students in those preschools could then be selected at
random through a systematic method to participate in
the study. This does, however, lead to a discussion
of biases in research. For example, low-income
children may be less likely to be enrolled in preschool
and therefore, may be excluded from the study. Extra
care has to be taken to control biases when
determining sampling techniques.
 There are two main types of sampling: probability and
non-probability sampling. The difference between the
two types is whether or not the sampling selection
involves randomization. Randomization occurs when
all members of the sampling frame have an equal
opportunity of being selected for the study. Following
is a discussion of probability and non-probability
sampling and the different types of each.
Probability Sampling
 Uses randomization and takes steps to ensure all
members of a population have a chance of being
selected. There are several variations on this type of
sampling and following is a list of ways probability
sampling may occur:
 Random sampling – every member has an equal
chance
 Stratified sampling – population divided into
subgroups (strata) and members are randomly
selected from each group
 Systematic sampling – uses a specific system to select
members such as every 10th person on an alphabetized
list
 Cluster random sampling – divides the population into
clusters, clusters are randomly selected and all
members of the cluster selected are sampled
 Multi-stage random sampling – a combination of one
or more of the above methods
Non-probability Sampling
 Does not rely on the use of randomization techniques
to select members. This is typically done in studies
where randomization is not possible in order to obtain
a representative sample. Bias is more of a concern with
this type of sampling. The different types of non-
probability sampling are as follows:
 Convenience or accidental sampling – members or
units are selected based on availability
 Purposive sampling – members of a particular group
are purposefully sought after
 Modal instance sampling – members or units are the
most common within a defined group and therefore
are sought after
 Expert sampling – members considered to be of high
quality are chosen for participation
 Proportional and non-proportional quota sampling –
members are sampled until exact proportions of
certain types of data are obtained or until sufficient
data in different categories is collected
 Diversity sampling – members are selected
intentionally across the possible types of responses to
capture all possibilities
 Snowball sampling – members are sampled and then
asked to help identify other members to sample and
this process continues until enough samples are
collected
 Thank You

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Sampling techniques

  • 2. Objectives  Students will be understanding the sample techniques in research methodology.
  • 3. Introduction  Researchers commonly examine traits or characteristics (parameters) of populations in their studies. A population is a group of individual units with some commonality. For example, a researcher may want to study characteristics of female smokers in Pakistan. This would be the population being analyzed in the study, but it would be impossible to collect information from all female smokers in Pakistan.
  • 4.  Therefore, the researcher would select individuals from which to collect the data. This is called sampling. The group from which the data is drawn is a representative sample of the population the results of the study can be generalized to the population as a whole.
  • 5.  The sample will be representative of the population if the researcher uses a random selection procedure to choose participants. The group of units or individuals who have a legitimate chance of being selected are sometimes referred to as the sampling frame. If a researcher studied developmental milestones of preschool children and target licensed preschools to collect the data, the sampling frame would be all preschool aged children in those preschools.
  • 6.  Students in those preschools could then be selected at random through a systematic method to participate in the study. This does, however, lead to a discussion of biases in research. For example, low-income children may be less likely to be enrolled in preschool and therefore, may be excluded from the study. Extra care has to be taken to control biases when determining sampling techniques.
  • 7.  There are two main types of sampling: probability and non-probability sampling. The difference between the two types is whether or not the sampling selection involves randomization. Randomization occurs when all members of the sampling frame have an equal opportunity of being selected for the study. Following is a discussion of probability and non-probability sampling and the different types of each.
  • 8. Probability Sampling  Uses randomization and takes steps to ensure all members of a population have a chance of being selected. There are several variations on this type of sampling and following is a list of ways probability sampling may occur:  Random sampling – every member has an equal chance  Stratified sampling – population divided into subgroups (strata) and members are randomly selected from each group
  • 9.  Systematic sampling – uses a specific system to select members such as every 10th person on an alphabetized list  Cluster random sampling – divides the population into clusters, clusters are randomly selected and all members of the cluster selected are sampled  Multi-stage random sampling – a combination of one or more of the above methods
  • 10. Non-probability Sampling  Does not rely on the use of randomization techniques to select members. This is typically done in studies where randomization is not possible in order to obtain a representative sample. Bias is more of a concern with this type of sampling. The different types of non- probability sampling are as follows:
  • 11.  Convenience or accidental sampling – members or units are selected based on availability  Purposive sampling – members of a particular group are purposefully sought after  Modal instance sampling – members or units are the most common within a defined group and therefore are sought after  Expert sampling – members considered to be of high quality are chosen for participation
  • 12.  Proportional and non-proportional quota sampling – members are sampled until exact proportions of certain types of data are obtained or until sufficient data in different categories is collected  Diversity sampling – members are selected intentionally across the possible types of responses to capture all possibilities  Snowball sampling – members are sampled and then asked to help identify other members to sample and this process continues until enough samples are collected