Participants, Subjects, and SamplingDiscussion TopicsSubjects, Participants, and SamplesSampling ProceduresProbability SamplingSlide 6Slide 7Slide 8Slide 9Slide 10Slide 11Non-Probability SamplingSlide 13Slide 14Slide 15Slide 16Slide 17Using Sampling ProceduresSampling and ResultsSlide 20Slide 21Slide 22Slide 23Slide 24Slide 25Criteria for Evaluating Sampling ProceduresSlide 27Slide 28Chapter 5Copyright © Allyn & Bacon 2008This multimedia product and its contents are protected under copyright law. The following are prohibited by law:• Any public performance or display, including transmission of any image over a network;• Preparation of any derivative work, including the extraction, in whole or in part, of any images;• Any rental, lease, or lending of the program.Discussion TopicsParticipants, subjects, and samplesProbability samplingNon-probability samplingIssues related to samplingCriteria for evaluating sampling proceduresCopyright © Allyn & Bacon 2008Subjects, Participants, and SamplesParticipant or SubjectPerson from whom data are collectedThe term “subject” is gradually being phased outIt is being replaced by “participant” and “source of data”Sample – the collective group of subjects or participants from whom data are collectedCopyright © Allyn & Bacon 2008Sampling ProceduresTwo types of proceduresProbabilityStatistically driven sampling techniques where the probability of being selected is knownPurpose is to select a group of participants representative of the larger group of subjects from which they are selectedNon-probabilityPragmatically driven sampling techniques where the probability of being selected is not knownPurpose is to select participants who can be particularly informative about the research issuesCopyright © Allyn & Bacon 2008Probability SamplingMethod of sampling in which participants are selected randomly from a population in such a way that the researcher knows the probability of selecting each participant.In a sample of 10 from a population of 100, each subject has a 10% chance of being included in the sampleIn a sample of 50 from a population of 100, each participant has a 50% chance of being in included in the sampleCopyright © Allyn & Bacon 2008Probability SamplingPopulation: a large group of individuals to whom the results of a study can be generalizedTarget population: the group to whom the results are intended to be generalizedSampling frame (i.e., survey population or accessible population)The group to whom the researcher has access and from which the actual sample will be drawnOften the sampling frame and the target population are differentCopyright © Allyn & Bacon 2008Probability SamplingThe goal of probability sampling is to select a sample that is representative of the population from which it is selectedSampling error - the difference between the “true” result and the “observed” result that can be attributed to using samples rather than populationsSampling bias - the difference between the “observed” and “true” results that is attributed to the sampling mistakes of the researcher.Copyright © Allyn & Bacon 2008Probability SamplingTypes of probability techniquesSimple random - a number is assigned to each subject in the population and a table of random numbers or a computer is used to select subjects randomly from the populationSystematic - a number is assigned to each subject in the population, and every nth member of the population is selectedCopyright © Allyn & Bacon 2008Probability SamplingTypes of probability techniquesStratified sampling - similar to random sampling with the exception that subjects are selected randomly from strata, or subgroups, of the populationStrata: homogeneous subgroups within a populationMales and femalesCertified and non-certified teachersProportional stratified sampleDisproportional stratified samplingCopyright © Allyn & Bacon 2008Probability SamplingTypes of probability techniquesCluster sampling: similar to random sampling except that naturally occurring groups are randomly selected first, then subjects are randomly selected from these sampled groupsUseful when it is impossible to identify all of the individuals in a populationTypical educational clusters are districts, schools, or classroomsCopyright © Allyn & Bacon 2008Probability SamplingFive steps in selecting probability samplesDefine the target populationIdentify the sampling frameDetermine the sample sizeSelect the sampling strategy (i.e., procedure)Select the sampleCopyright © Allyn & Bacon 2008Non-Probability SamplingMethod of sampling in which the probability of selecting a participant is unknownIt is often not possible to use probability sampling techniques due to access, time, resource or financial constraintsIt is often desirable to select subjects who can be particularly informative about the research issuesCopyright © Allyn & Bacon 2008Non-Probability SamplingThree categories of non-probability sampling proceduresConvenience samplingPurposiveQuotaCopyright © Allyn & Bacon 2008Non-Probability SamplingConvenience sampling: selecting a participant or group of participants based on their availability to the researcherTypical of much educational research given the constraints under which it is conductedThe major concern is the limited generalizability of the results from the sample to any populationExamplesStudents enrolled in the researcher’s classesFourth-grade students in two local, parochial schools to which the researcher has accessCopyright © Allyn & Bacon 2008Non-Probability SamplingPurposive sampling: selection of particularly informative or useful participantsTypically selects a few information-rich participants who are studied in-depthAlso known as purposeful samplingExamplesIt is reasonable to select “expert” teachers if one is trying to understand how teachers use effective instructional strategiesIt is reasonable to select physically fit individuals if one is trying to identify effective exercise behaviorsCopyright © Allyn & Bacon 2008Non-Probability SamplingQuota sampling: non-random sampling representative of a larger populationUsed when the researcher cannot use probability sampling procedures but does want a sample that is somewhat representative of the populationSimilar