educational research chapter 5 selecting a sample gay, mills, and airasian 10 th edition
TRANSCRIPT
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Educational Research
Chapter 5Selecting a Sample
Gay, Mills, and Airasian10th Edition
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Topics Discussed in this Chapter
Quantitative sampling Selecting random samples Selecting non-random samples
Qualitative sampling Selecting purposive samples
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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
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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
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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
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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
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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
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Quantitative Sampling
Important issues (continued) Three fundamental steps
Identify a population Define the sample size Select the sample
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Quantitative Sampling
Important issues (continued) General rules for sample size
As many subjects as possible At least thirty (30) subjects per group for
correlational, causal-comparative, and true experimental designs
At least ten (10) to twenty (20) percent of the population for descriptive designs
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Quantitative Sampling
Important issues (continued) General rules for sample size (continued)
See Table 4.2 (see NEXT SLIDE) 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
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Qualitative Sampling
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Selecting Random Samples Known as probability sampling:
Everyone has probability of getting chosen
Best method to achieve a representative sample
Four techniques Random Stratified random Cluster Systematic
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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
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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
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Selecting Random Samples Random sampling (continued)
Selection issues Use a table of random numbers (page 562)
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
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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
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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
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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
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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
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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
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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
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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
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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
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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 (you don’t need to know which ones in this class)
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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
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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
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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
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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
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Selecting Non-Random Samples
Convenience sampling Selection based on the availability of
subjects Volunteers Pre-existing groups
Concerns related to representation and generalizability
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Selecting Non-Random Samples Purposive sampling
Researcher believes that this is a representative sample or an appropriate sample.
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
Need for clear criteria for describing and defending the sample
Concerns related to representation and generalizability
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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
Example: “I need 35 unemployed mothers and 35 employed mothers.”
Concerns with accessibility, representation, and generalizability
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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
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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
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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
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Qualitative Sampling Purposive techniques (continued)
Random purposive – given a pool of participants, random selection of a small sample
Inherent concerns related to generalizability and representation
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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: Once you are hearing the same thing from everyone, you are done collecting that data.
Representation of the range of potential participants in the setting. Make sure that you select someone from every part of the population that you want to examine.
More subjects does not mean “better.” More than 20 is often unusual.
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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