Educational Research
Chapter 4
Selecting a Sample
Gay, Mills, and Airasian
Topics Discussed in this Chapter
 Quantitative sampling
 Selecting random samples
 Selecting non-random samples
 Quali...
Quantitative Sampling
 Purpose – to identify participants from
whom to seek some information
 Issues
 Nature of the sam...
Quantitative Sampling
 Terminology
 Population: all members of a specified group

Target population – the population to...
Quantitative Sampling
 Important issues
 Representation – the extent to which the sample is
representative of the popula...
Quantitative Sampling
 Important issues (continued)
 Sampling error

The chance occurrence that a randomly
selected sam...
Quantitative Sampling
 Important issues (continued)
 Sampling bias

Some aspect of the researcher’s sampling design
cre...
Quantitative Sampling
 Important issues (continued)
 Three fundamental steps

Identify a population

Define the sample...
Quantitative Sampling
 Important issues (continued)
 General rules for sample size

As many subjects as possible

Thir...
Quantitative Sampling
 Important issues (continued)
 General rules for sample size (continued)

See Table 4.2 for addit...
Selecting Random Samples
 Known as probability sampling
 Best method to achieve a
representative sample
 Four technique...
Selecting Random Samples
 Random sampling
 Selecting subjects so that all members of a
population have an equal and inde...
Selecting Random Samples
 Random sampling (continued)
 Selection process

Identify and define the population

Determin...
Selecting Random Samples
 Random sampling (continued)
 Selection issues

Use a table of random numbers
 Need to list a...
Selecting Random Samples
 Stratified random sampling
 Selecting subjects so that relevant subgroups in
the population (i...
Selecting Random Samples
 Stratified random sampling (continued)
 Proportional and non-proportional (i.e., equal size)
...
Selecting Random Samples
 Stratified random sampling (continued)
 Advantages

More precise sample

Can be used for bot...
Selecting Random Samples
 Stratified random sampling (continued)
 Selection process

Identify and define the population...
Selecting Random Samples
 Stratified random sampling (continued)
 Selection process (continued)

For proportional strat...
Selecting Random Samples
 Stratified random sampling (continued)
 Selection process for proportional samples

Identify ...
Selecting Random Samples
 Cluster sampling
 Selecting subjects by using groups that have
similar characteristics and in ...
Selecting Random Samples
 Cluster sampling (continued)
 Multistage sampling involves the use of
two or more sets of clus...
Selecting Random Samples
 Cluster sampling (continued)
 Advantages

Very useful when populations are large and spread o...
Selecting Random Samples
 Cluster sampling (continued)
 Selection process

Identify and define the population

Determi...
Selecting Random Samples
 Systematic sampling
 Selecting every Kth
subject from a list of the
members of the population
...
Selecting Random Samples
 Systematic sampling (continued)
 Selection process

Identify and define the population

Dete...
Selecting Non-Random Samples
 Known as non-probability sampling
 Use of methods that do not have random
sampling at any ...
Selecting Non-Random Samples
 Convenience sampling
 Selection based on the availability of
subjects

Volunteers

Pre-e...
Selecting Non-Random Samples
 Purposive sampling
 Selection based on the researcher’s experience
and knowledge of the in...
Selecting Non-Random Samples
 Quota sampling
 Selection based on the exact
characteristics and quotas of subjects in
the...
Quantitative Sampling Comments
 Both probability and non-random sampling
techniques are used in quantitative research
 P...
Qualitative Sampling
 Unique characteristics of qualitative research
 In-depth inquiry
 Immersion in the setting
 Impo...
Qualitative Sampling
 Purposive techniques – relying on the
experience and insight of the
researcher to select participan...
Qualitative Sampling
 Purposive techniques (continued)
 Homogeneous – small groups of
participants who fit a narrow homo...
Qualitative Sampling
 Purposive techniques (continued)
 Random purposive – given a pool of
participants, random selectio...
Qualitative Sampling
 Sample size
 Generally very small samples given the
nature of the data collection methods and
the ...
Generalizability
 Probability sampling
 Begins with a population
and selects a sample
from it
 Generalizability to the
...
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Ch04 sampling

  1. 1. Educational Research Chapter 4 Selecting a Sample Gay, Mills, and Airasian
  2. 2. Topics Discussed in this Chapter  Quantitative sampling  Selecting random samples  Selecting non-random samples  Qualitative sampling  Selecting purposive samples
  3. 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. 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. 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. 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. 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. 8. Quantitative Sampling  Important issues (continued)  Three fundamental steps  Identify a population  Define the sample size  Select the sample Obj. 1.5
  9. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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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