1.
Educational Research
Chapter 4
Selecting a Sample
Gay, Mills, and Airasian
2.
Topics Discussed in this Chapter
Quantitative sampling
Selecting random samples
Selecting non-random samples
Qualitative sampling
Selecting purposive samples
3.
Quantitative Sampling
Purpose – to identify participants from
whom to seek some information
Issues
Nature of the sample
Size of the sample
Method of selecting the sample
4.
Quantitative Sampling
Terminology
Population: all members of a specified group
Target population – the population to which the
researcher ideally wants to generalize
Accessible population – the population to which the
researcher has access
Sample: a subset of a population
Subject: a specific individual participating in a
study
Sampling technique: the specific method used to
select a sample from a population
Obj. 1.1, 1.2, & 1.3
5.
Quantitative Sampling
Important issues
Representation – the extent to which the sample is
representative of the population
Demographic characteristics
Personal characteristics
Specific traits
Generalization – the extent to which the results of
the study can be reasonably extended from the
sample to the population
Obj. 1.4
6.
Quantitative Sampling
Important issues (continued)
Sampling error
The chance occurrence that a randomly
selected sample is not representative of the
population due to errors inherent in the
sampling technique
Random nature of errors
Controlled by selecting large samples
Obj. 6.1
7.
Quantitative Sampling
Important issues (continued)
Sampling bias
Some aspect of the researcher’s sampling design
creates bias in the data
Non-random nature of errors
Controlled by being aware of sources of sampling bias
and avoiding them
Examples
Surveying only students who attend additional help
sessions in a class
Using data returned from only 25% of those sent a
questionnaire
Obj. 6.2
8.
Quantitative Sampling
Important issues (continued)
Three fundamental steps
Identify a population
Define the sample size
Select the sample
Obj. 1.5
9.
Quantitative Sampling
Important issues (continued)
General rules for sample size
As many subjects as possible
Thirty (30) subjects per group for correlational,
causal-comparative, and true experimental
designs
Ten (10) to twenty (20) percent of the
population for descriptive designs
Obj. 1.8
10.
Quantitative Sampling
Important issues (continued)
General rules for sample size (continued)
See Table 4.2 for additional guidelines for survey
research
The larger the population size, the smaller the percentage
of the population needed to get a representative sample
For population of less than 100, use the entire population
If the population is about 500, sample 50%
If the population is about 1,500, sample 20%
If the population is larger than 5,000, sample 400
Obj. 1.9
11.
Selecting Random Samples
Known as probability sampling
Best method to achieve a
representative sample
Four techniques
Random
Stratified random
Cluster
Systematic
Obj. 1.7
12.
Selecting Random Samples
Random sampling
Selecting subjects so that all members of a
population have an equal and independent chance
of being selected
Advantages
Easy to conduct
High probability of achieving a representative sample
Meets assumptions of many statistical procedures
Disadvantages
Identification of all members of the population can be
difficult
Contacting all members of the sample can be difficult
Obj. 1.6, 2.2, & 4.9
13.
Selecting Random Samples
Random sampling (continued)
Selection process
Identify and define the population
Determine the desired sample size
List all members of the population
Assign all members on the list a consecutive number
Select an arbitrary starting point from a table of random
numbers and read the appropriate number of digits
If the number corresponds to a number assigned to an
individual in the population, that individual is in the
sample; if not, ignore the number
Continue until the desired number of subjects have been
selected
Obj. 2.3
14.
Selecting Random Samples
Random sampling (continued)
Selection issues
Use a table of random numbers
Need to list all members of the population
Ignore duplicates and numbers out of range when sampled
Potentially time consuming and frustrating
Use SPSS-Windows or other software to select a
random sample
Create a SPSS-Windows data set of the population or their
identification numbers
Pull-down commands
Data, select cases, random sample, approximate or
exact
15.
Selecting Random Samples
Stratified random sampling
Selecting subjects so that relevant subgroups in
the population (i.e., strata) are guaranteed
representation
A strata represents a variable on which the
researcher would like to see representation in the
sample
Gender
Ethnicity
Grade level
Obj. 3.1 & 3.3
16.
Selecting Random Samples
Stratified random sampling (continued)
Proportional and non-proportional (i.e., equal size)
Proportional – same proportion of subgroups in the
sample as in the population
If a population has 45% females and 55% males, the
sample should have 45% females and 55% males
Non-proportional – different, often equal, proportions of
subgroups
Selecting the same number of children from each of the
five grades in a school even though there are different
numbers of children in each grade
Obj. 3.4
17.
Selecting Random Samples
Stratified random sampling (continued)
Advantages
More precise sample
Can be used for both proportional and non-proportional
samples
Representation of subgroups in the sample
Disadvantages
Identification of all members of the population can be
difficult
Identifying members of all subgroups can be difficult
Obj. 3.2 & 4.9
18.
Selecting Random Samples
Stratified random sampling (continued)
Selection process
Identify and define the population
Determine the desired sample size
Identify the variable and subgroups (i.e., strata)
for which you want to guarantee appropriate
representation
Classify all members of the population as
members of one of the identified subgroups
Obj. 4.1
19.
Selecting Random Samples
Stratified random sampling (continued)
Selection process (continued)
For proportional stratified samples
Randomly select a number of individuals from each
subgroup so the proportion of these individuals in the
sample is the same as that in the population
For non-proportional stratified samples
Randomly select an equal number of individuals from
each subgroup
Obj. 4.1
20.
Selecting Random Samples
Stratified random sampling (continued)
Selection process for proportional samples
Identify and define the population
Determine the desired sample size
Identify the variable and subgroups (i.e., strata) for which
you want to guarantee appropriate representation
Classify all members of the population as members of
one of the identified subgroups
Randomly select an equal number of individuals from
each subgroup
Obj. 4.1
21.
Selecting Random Samples
Cluster sampling
Selecting subjects by using groups that have
similar characteristics and in which subjects can
be found
Clusters are locations within which an intact group of
members of the population can be found
Examples
Neighborhoods
School districts
Schools
Classrooms
Obj. 4.3
22.
Selecting Random Samples
Cluster sampling (continued)
Multistage sampling involves the use of
two or more sets of clusters
Randomly select a number of school districts
from a population of districts
Randomly select a number of schools from
within each of the school districts
Randomly select a number of classrooms from
within each school
Obj. 4.6
23.
Selecting Random Samples
Cluster sampling (continued)
Advantages
Very useful when populations are large and spread over
a large geographic region
Convenient and expedient
Do not need the names of everyone in the population
Disadvantages
Representation is likely to become an issue
Assumptions of some statistical procedures can be
violated
Obj. 4.9
24.
Selecting Random Samples
Cluster sampling (continued)
Selection process
Identify and define the population
Determine the desired sample size
Identify and define a logical cluster
List all clusters that make up the population of clusters
Estimate the average number of population members per
cluster
Determine the number of clusters needed by dividing the
sample size by the estimated size of a cluster
Randomly select the needed numbers of clusters
Include in the study all individuals in each selected
cluster Obj. 4.4
25.
Selecting Random Samples
Systematic sampling
Selecting every Kth
subject from a list of the
members of the population
Advantage
Very easily done
Disadvantages
Susceptible to systematic exclusion of some subgroups
Some members of the population don’t have an equal
chance of being included
Obj. 4.7 & 4.9
26.
Selecting Random Samples
Systematic sampling (continued)
Selection process
Identify and define the population
Determine the desired sample size
Obtain a list of the population
Determine what K is equal to by dividing the size of the
population by the desired sample size
Start at some random place in the population list
Take every Kth
individual on the list
If the end of the list is reached before the desired sample
is reached, go back to the top of the list
Obj. 4.8
27.
Selecting Non-Random Samples
Known as non-probability sampling
Use of methods that do not have random
sampling at any stage
Useful when the population cannot be
described
Three techniques
Convenience
Purposive
Quota
Obj. 5.1
28.
Selecting Non-Random Samples
Convenience sampling
Selection based on the availability of
subjects
Volunteers
Pre-existing groups
Concerns related to representation and
generalizability
Obj. 5.2 & 5.3
29.
Selecting Non-Random Samples
Purposive sampling
Selection based on the researcher’s experience
and knowledge of the individuals being sampled
Usually selected for some specific reason
Knowledge and use of a particular instructional strategy
Experience
Being in a specific setting such as a school changing to a
teacher-based decision-making process
Need for clear criteria for describing and defending
the sample
Concerns related to representation and
generalizability
Obj. 5.2 & 5.4
30.
Selecting Non-Random Samples
Quota sampling
Selection based on the exact
characteristics and quotas of subjects in
the sample when it is impossible to list all
members of the population
Concerns with accessibility, representation,
and generalizability
Obj. 5.2 & 5.5
31.
Quantitative Sampling Comments
Both probability and non-random sampling
techniques are used in quantitative research
Probability models are desired due to the selection
of a representative sample and the ease with
which the results can be generalized to the
population
Non-random (i.e., non-probability) models are
frequently used due the reality of the situations in
which the research is being conducted
Concerns with representation
Concerns with generalization
32.
Qualitative Sampling
Unique characteristics of qualitative research
In-depth inquiry
Immersion in the setting
Importance of context
Appreciation of participant’s perspectives
Description of a single setting
The need for alternative sampling strategies
Obj. 7.2
33.
Qualitative Sampling
Purposive techniques – relying on the
experience and insight of the
researcher to select participants
Intensity – compare differences of two or
more levels of the topics
Students with extremely positive and extremely
negative attitudes
Effective and ineffective teachers
Obj. 7.3
34.
Qualitative Sampling
Purposive techniques (continued)
Homogeneous – small groups of
participants who fit a narrow homogeneous
topic
Criterion – all participants who meet a
defined criteria
Snowball – initial participants lead to other
participants
Obj. 7.4, 7.5, & 7.6
35.
Qualitative Sampling
Purposive techniques (continued)
Random purposive – given a pool of
participants, random selection of a small
sample
Combinations of techniques
Inherent concerns related to
generalizability and representation
Obj. 7.7 & 7.8
36.
Qualitative Sampling
Sample size
Generally very small samples given the
nature of the data collection methods and
the data itself
Two general guidelines
Redundancy of the information collected from
participants
Representation of the range of potential
participants in the setting
Obj. 7.9
37.
Generalizability
Probability sampling
Begins with a population
and selects a sample
from it
Generalizability to the
population is relatively
easy
Non-probability and
purposive sampling
Begins with a sample
that is NOT selected
from some larger
population
Must consider the
population hypothetical
as it is based on the
characteristics of the
sample
Generalizability is often
very limited Obj. 7.10
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