This document discusses different methods for selecting samples in quantitative and qualitative research. For quantitative research, it describes random sampling techniques like simple random sampling, stratified random sampling, cluster sampling, and systematic sampling which aim to select representative samples. It also discusses non-random sampling techniques like convenience sampling and purposive sampling which are used when the researcher cannot describe the entire population. The document provides details on how to implement each sampling technique and discusses their advantages and disadvantages.

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