Educational Research

1
Educational Research

• Chapter 5
• Selecting a Sample
• Gay, Mills, and Airasian
• 10th Edition

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

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

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

7
Quantitative Sampling

• Important issues (continued)
• Sampling bias
• Some aspect of the researchers 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

8
Quantitative Sampling

• Important issues (continued)
• Three fundamental steps
• Identify a population
• Define the sample size
• Select the sample

9
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

10
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

11
Qualitative Sampling
12
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

13
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

14
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

15
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

16
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

17
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

18
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

19
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

20
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

21
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

22
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

23
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

24
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 dont need to know which ones in
  this class)

25
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

26
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 dont have an
  equal chance of being included

27
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

28
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

29
Selecting Non-Random Samples

• Convenience sampling
• Selection based on the availability of subjects
• Volunteers
• Pre-existing groups
• Concerns related to representation and
  generalizability

30
Selecting Non-Random Samples

• Purposive sampling
• Researcher believes that this is a representative
  sample or an appropriate sample.
• Selection based on the researchers 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

31
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

32
Qualitative Sampling

• Unique characteristics of qualitative research
• In-depth inquiry
• Immersion in the setting
• Importance of context
• Appreciation of participants perspectives
• Description of a single setting
• The need for alternative sampling strategies

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

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

35
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

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 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.

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