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 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

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 don’t 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 don’t 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 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

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 participant’s 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. 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 Generalizability