Educational Research

1. Educational Research Selecting a Sample Lecture 6

2. Population (universe): The entire group of people about whom information is needed. Census: a collection of data on all possible members of a population or universe Sampling: the process of obtaining information from a subset of a larger group. Goal: to develop a sample that mirrors or represents the population of interest. The Concept of Sampling

3. Developing a Sampling Plan • Define the Population of Interest • Identify a Sampling Frame (if possible) • Select a Sampling Method • Determine Sample Size • Execute the Sampling Plan

4. Defining Population of Interest • Population of interest is entirely dependent on Management Problem, Research Problems, and Research Design. • Some Bases for Defining Population: • Geographic Area • Demographics • Usage/Lifestyle • Awareness

5. Sampling Frame • A list of population elements (people, companies, houses, cities, etc.) from which units to be sampled can be selected. • Difficult to get an accurate list. • Sample frame error occurs when certain elements of the population are accidentally omitted or not included on the list. • See Survey Sampling International for some good examples http://www.ssisamples.com/

6. Sampling Methods • Probability samples: sample that gives every member of the population a known chance of being selected • Simple random sample: basic type of probability sample in which every individual in the relevant universe has an equal opportunity of selection • Stratified sample: probability sample constructed to represent randomly selected subsamples of different groups within the total sample. May be Proportional or Disproportional • Cluster sample: probability sample in which researchers select geographic areas or clusters, and all of the chosen individuals within this area become respondents

7. Sampling Methods • Non-probability samples: arbitrary grouping that produces data unsuited for most standard statistical tests • Convenience sample: nonprobability sample selected from among readily available respondents • Quota sample: nonprobability sample divided to insure representation of different segments or groups in the total sample

8. Determining Sample Size • How many completed questionnaires do we need to have a representative sample? • Generally the larger the better, but that takes more time and money. • Answer depends on: • How different or dispersed the population is. • Desired level of confidence. • Desired degree of accuracy.

9. Common Methods for Determining Sample Size • Common Methods: • Budget/time available • Executive decision • Statistical methods • Historical data/guidelines

10. Topics Discussed • General sampling issues • Quantitative sampling • Random • Non-random • Qualitative sampling

11. General Sampling Issues • Purpose – to identify participants from whom to seek some information • Issues • Nature of the sample • Size of the sample • Method of selecting the sample

12. General Sampling Issues • 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

13. 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

14. 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 • 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

15. Quantitative Sampling • Important issues (continued) • Three fundamental steps • Identify a population • Define the sample size • Select the sample • 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 (see Table 4.2)

16. Selecting Random Samples • Known as probability sampling • Best method to achieve a representative sample • Four techniques • Random • Stratified random • Cluster • Systematic

17. Selecting Random Samples • Random sampling • Selecting subjects so that all members of a population have an equal and independent chance of being selected • Advantages • 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

18. Selecting Random Samples • Random sampling (continued) • Selection issues • Use a table of random numbers • Need to list all members of the population • Assign consecutive numbers to all members • Ignore duplicates and number out of range when sampled • Use of SPSS • Data, select cases, random sample, approximate or exact • Need for an electronic SPSS data set

19. Selecting Random Samples • Stratified random sampling • Selecting subjects so that relevant subgroups in the population (i.e., strata) are guaranteed representation • Proportional and non-proportional • Proportional – same proportion of subgroups • Non-proportional – different, often equal, proportions of subgroups • Advantage – representation of subgroups in the sample

20. Selecting Random Samples • Stratified random sampling (continued) • Disadvantages • Identification of all members of the population can be difficult • Identifying members of all subgroups can be difficult • Selection issues • Identification of relevant strata • Coding subjects regarding strata • Selecting randomly from within each level of the strata

21. Selecting Random Samples • Cluster sampling • Selecting subjects by using groups that have similar characteristics and in which subjects can be found • School districts • Schools • Classrooms • Advantages • Very useful when populations are large and spread over a large geographic region • Convenient and expedient

22. Selecting Random Samples • Cluster sampling (continued) • Disadvantages • Representation is likely to become an issue • Assumptions of some statistical procedures can be violated • Selection issues • Identify logical clusters and the average number of population members per cluster • Determine the number of clusters needed • Randomly select necessary clusters • Multi-stage sampling

23. 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 inclusion of some subgroups • Some members of the population don’t have an equal chance of being included

24. Selecting Random Samples • Systematic sampling (continued) • Selection issues • Listing all members of the population can be difficult • Determining the value of K • Starting at the top of the list again if the desired sample size is not reached

25. 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

26. Selecting Non-Random Samples • Convenience sampling • Selection based on the availability of subjects • Volunteers • Pre-existing groups • Concerns related to representation and generalizability

27. Selecting Non-Random Samples • Purposive sampling • Selection based on the researcher’s experience and knowledge of the group being sampled • Need for clear criteria for describing and defending the sample • Concerns related to representation and generalizability

28. 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

29. 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

30. 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

31. 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

32. 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