This document discusses sampling methods for research. It defines a sample as a subset of a population that is selected for measurement. There are random and non-random sampling methods. Random methods include simple random sampling, stratified random sampling, and cluster sampling, which aim to select representative samples. Non-random methods like convenience sampling are not as rigorous but can still be used. The document recommends a minimum sample size of 100 for descriptive studies. Representativeness of the sample is important for generalizing results to the target population.

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Sampling
Chapter Six

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Sampling
Chapter Six

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What is a Sample?
 Sampling is the process of selecting a number of
individuals from a population, preferably in a way
that the individuals are representative of the larger
group from which they were selected.
 A sample is any group on which information is
obtained.

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Defining the Population
 A population refers to all the members of a particular
group.
 The first task in selecting a sample is to define the
population of interest.
 In Educational Research, the population of interest is a
group of persons who possess certain characteristics.
 A target population is the actual population that the
researcher would like to generalize.
 Considered rarely available
 The accessible population would be the group that is
available (realistic choice)

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Representative vs. Non-representative Samples (Fig. 6.1)

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Two Main Types of Sampling
 Sampling may be either random or non-random
 Random sampling is a method of selecting subjects from
a population by chance, so that biases do not alter the
sample.
 The 3 most common ways of obtaining this type of
sample are:
 Simple Random Sampling
 Stratified Random Sampling
 Cluster Sampling

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Part of a Table of Random Numbers
(Table 6.1)
011723 223456 222167 032762 062281 565451
912334 379156 233989 109238 934128 987678
086401 016265 411148 251287 602345 659080
059397 022334 080675 454555 011563 237873
666278 106590 879809 899030 909876 198905
051965 004571 036900 037700 500098 046660
063045 786326 098000 510379 024358 145678
560132 345678 356789 033460 050521 342021
727009 344870 889567 324588 400567 989657
000037 121191 258700 088909 015460 223350
667899 234345 076567 090076 345121 121348
042397 045645 030032 657112 675897 079326
987650 568799 070070 143188 198789 097451
091126 021557 102322 209312 909036 342045

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Simple Random Sampling
 A Simple Random Sampling is a sample selected from a population
in such a manner that all members have an equal chance of being
selected
 If the sample is large, it is the best method to obtain a sample
representative of the population from which it has been selected
 The larger the sample size, the more it is likely to represent the
population
 Any differences that occur are the result of chance rather than bias
on the part of the researcher
 Disadvantages of this method are: 1) the difficulty of performing the
sampling and, 2) this method does not ensure that subgroups are
present in the sampling in the same proportion as they are in a
population

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Stratified Random Sampling
 A Stratified Random Sampling is a sample selected so
that certain characteristics are represented in the sample
in the same proportion as they occur in the population
 The term strata refers to sub-groups
 The advantage of stratified random sampling is that it
increases the likelihood of representation, especially if
the sample size is small
 It virtually ensures that any key characteristics of
individuals in the population are included in the same
proportions in the sample size
 The disadvantage is that it requires still more effort on
the part of the researcher

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Selecting a Stratified Sample (Figure 6.2)

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Cluster Random Sampling
 A Cluster Random Sampling is a sample obtained by
using groups as the sampling unit (cluster), rather than
individuals
 There are instances where it is not possible to select a
sample of individuals from a population
 This is considered more effective with large numbers of
clusters
 Advantages include more efficient and easier to
implement in schools
 Its disadvantage is that there is a great chance of
selecting a sample that is not representative of the
population

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Random Sampling Methods (Figure 6.3)

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Two-Stage Random Sampling
 This method selects groups randomly and then
chooses individuals randomly from these
groups.
 This becomes a combination of a cluster
random sampling with individual random
sampling.
 Considered less time consuming but allows for
a good representation of the groups at
random.

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Nonrandom Sampling Methods
 There are 3 main types of nonrandom sampling methods used in
Educational Research
 A Systematic Sample is a sample obtained by selecting every nth
name in a population
 A Convenience Sample is any group of individuals that is
conveniently available to be studied
 Are not considered representative of the population and should be avoided,
if possible
 A Purposive Sample is a sample selected because the individuals
have special qualifications of some sort, or because of prior
evidence of representation
 Personal judgment is used for selection purposes
 A major disadvantage is that the researcher’s judgment could be in error

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Convenience Sampling (Figure 6.4)

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Nonrandom Sampling Method (Figure 6.5)

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Sample Size
 The question remains as to what constitutes an adequate sample
size.
 Samples should be as large as a researcher can obtain with a
reasonable expenditure of time and energy.
 The recommended minimum number of subjects are as follows for
the following types of studies:
 100 for a Descriptive Study
 50 for a Correlational Study
 30 in each group for Experimental and Causal-Comparative Study
The use of 15 subjects per group should probably be replicated

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External Validity, a.k.a.
Generalizability
 The whole notion of science is built on generalizing.
 External Validity refers to the extent that the results of
a study can be generalized from a sample to a
population.
 Population generalizability is the degree to which a
sample represents the population of interest.
 Obtaining a representative sample becomes very important
 Ecological generalizability refers to the extent to which
the results of a study can be generalized to conditions
or settings other than those that prevailed in the study.

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Population as Opposed to Ecological Generalizing
(Figure 6.6)