Educational ResearchChapter 5Selecting a Sample

Gay, Mills, and Airasian10th Edition

Topics Discussed in this ChapterQuantitative samplingSelecting random samplesSelecting non-random samplesQualitative samplingSelecting purposive samples

Quantitative SamplingPurpose to identify participants from whom to seek some informationIssuesNature of the sampleSize of the sampleMethod of selecting the sample

Quantitative SamplingTerminologyPopulation: all members of a specified groupTarget population the population to which the researcher ideally wants to generalizeAccessible population the population to which the researcher has accessSample: a subset of a populationSubject: a specific individual participating in a studySampling technique: the specific method used to select a sample from a population

Quantitative SamplingImportant issuesRepresentation the extent to which the sample is representative of the populationDemographic characteristicsPersonal characteristicsSpecific traitsGeneralization the extent to which the results of the study can be reasonably extended from the sample to the population

Quantitative SamplingImportant issues (continued)Sampling errorThe chance occurrence that a randomly selected sample is not representative of the population due to errors inherent in the sampling techniqueRandom nature of errorsControlled by selecting large samples

Quantitative SamplingImportant issues (continued)Sampling biasSome aspect of the researchers sampling design creates bias in the dataNon-random nature of errorsControlled by being aware of sources of sampling bias and avoiding themExamplesSurveying only students who attend additional help sessions in a classUsing data returned from only 25% of those sent a questionnaire

Quantitative SamplingImportant issues (continued)Three fundamental stepsIdentify a populationDefine the sample sizeSelect the sample

Quantitative SamplingImportant issues (continued)General rules for sample sizeAs many subjects as possibleAt least thirty (30) subjects per group for correlational, causal-comparative, and true experimental designsAt least ten (10) to twenty (20) percent of the population for descriptive designs

Quantitative SamplingImportant issues (continued)General rules for sample size (continued)See Table 4.2 (see NEXT SLIDE) for additional guidelines for survey researchThe larger the population size, the smaller the percentage of the population needed to get a representative sampleFor population of less than 100, use the entire populationIf 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

Qualitative Sampling

Selecting Random SamplesKnown as probability sampling: Everyone has probability of getting chosenBest method to achieve a representative sampleFour techniquesRandomStratified randomClusterSystematic

Selecting Random SamplesRandom samplingSelecting subjects so that all members of a population have an equal and independent chance of being selectedAdvantagesEasy to conductHigh probability of achieving a representative sampleMeets assumptions of many statistical proceduresDisadvantagesIdentification of all members of the population can be difficultContacting all members of the sample can be difficult

Selecting Random SamplesRandom sampling (continued)Selection processIdentify and define the populationDetermine the desired sample sizeList all members of the populationAssign all members on the list a consecutive numberSelect an arbitrary starting point from a table of random numbers and read the appropriate number of digitsIf the number corresponds to a number assigned to an individual in the population, that individual is in the sample; if not, ignore the numberContinue until the desired number of subjects have been selected

Selecting Random SamplesRandom sampling (continued)Selection issuesUse a table of random numbers (page 562)Need to list all members of the populationIgnore duplicates and numbers out of range when sampledPotentially time consuming and frustratingUse SPSS-Windows or other software to select a random sampleCreate a SPSS-Windows data set of the population or their identification numbersPull-down commandsData, select cases, random sample, approximate or exact

Selecting Random SamplesStratified random samplingSelecting subjects so that relevant subgroups in the population (i.e., strata) are guaranteed representationA strata represents a variable on which the researcher would like to see representation in the sampleGenderEthnicityGrade level

Selecting Random SamplesStratified random sampling (continued)Proportional and non-proportional (i.e., equal size)Proportional same proportion of subgroups in the sample as in the populationIf a population has 45% females and 55% males, the sample should have 45% females and 55% malesNon-proportional different, often equal, proportions of subgroupsSelecting 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

Selecting Random SamplesStratified random sampling (continued)AdvantagesMore precise sampleCan be used for both proportional and non-proportional samplesRepresentation of subgroups in the sampleDisadvantagesIdentification of all members of the population can be difficultIdentifying members of all subgroups can be difficult

Selecting Random SamplesStratified random sampling (continued)Selection process Identify and define the populationDetermine the desired sample sizeIdentify the variable and subgroups (i.e., strata) for which you want to guarantee appropriate representationClassify all members of the population as members of one of the identified subgroups

Selecting Random SamplesStratified random sampling (continued)Selection process (continued)For proportional stratified samplesRandomly select a number of individuals from each subgroup so the proportion of these individuals in the sample is the same as that in the populationFor non-proportional stratified samplesRandomly select an equal number of individuals from each subgroup

Selecting Random SamplesStratified random sampling (continued)Selection process for proportional samplesIdentify and define the populationDetermine the desired sample sizeIdentify the variable and subgroups (i.e., strata) for which you want to guarantee appropriate representationClassify all members of the population as members of one of the identified subgroupsRandomly select an equal number of individuals from each subgroup

Selecting Random SamplesCluster samplingSelecting subjects by using groups that have similar characteristics and in which subjects can be foundClusters are locations within which an intact group of members of the population can be foundExamplesNeighborhoodsSchool districtsSchoolsClassrooms

Selecting Random SamplesCluster sampling (continued)Multistage sampling involves the use of two or more sets of clustersRandomly select a number of school districts from a population of districtsRandomly select a number of schools from within each of the school districtsRandomly select a number of classrooms from within each school

Selecting Random SamplesCluster sampling (continued)AdvantagesVery useful when populations are large and spread over a large geographic regionConvenient and expedientDo not need the names of everyone in the populationDisadvantagesRepresentation is likely to become an issueAssumptions of some statistical procedures can be violated (you dont need to know which ones in this class)

Selecting Random SamplesCluster sampling (continued)Selection processIdentify and define the populationDetermine the desired sample sizeIdentify and define a logical cluster List all clusters that make up the population of clustersEstimate the average number of population members per clusterDetermine the number of clusters needed by dividing the sample size by the estimated size of a clusterRandomly select the needed numbers of clustersInclude in the study all individuals in each selected cluster

Selecting Random SamplesSystematic samplingSelecting every Kth subject from a list of the members of the populationAdvantageVery easily doneDisadvantagesSusceptible to systematic exclusion of some subgroupsSome members of the population dont have an equal chance of being included

Selecting Random SamplesSystematic sampling (continued)Selection processIdentify and define the populationDetermine the desired sample sizeObtain a list of the populationDetermine what K is equal to by dividing the size of the population by the desired sample sizeStart at some random place in the population listTake every Kth individual on the listIf the end of the list is reached before the desired sample is reached, go back to the top of the list

Selecting Non-Random SamplesKnown as non-probability samplingUse of methods that do not have random sampling at any stageUseful when the population cannot be describedThree techniquesConveniencePurposiveQuota

Selecting Non-Random SamplesConvenience samplingSelection based on the availability of subjectsVolunteersPre-existing groupsConcerns related to representation and generalizability

Selecting Non-Random SamplesPurposive samplingResearcher believes that this is a representative sample or an appropriate sample.Selection based on the researchers experience and knowledge of the individuals being sampledUsually selected for some specific reasonKnowledge and use of a particular instructional strategyExperienceNeed for clear criteria for describing and defending the sampleConcerns related to representation and generalizability

Selecting Non-Random SamplesQuota samplingSelection based on the exact characteristics and quotas of subjects in the sample when it is impossible to list all members of the populationExample: I need 35 unemployed mothers and 35 employed mothers.Concerns with accessibility, representation, and generalizability

Qualitative SamplingUnique characteristics of qualitative researchIn-depth inquiryImmersion in the settingImportance of contextAppreciation of participants perspectivesDescription of a single settingThe need for alternative sampling strategies

Qualitative SamplingPurposive techniques relying on the experience and insight of the researcher to select participantsIntensity compare differences of two or more levels of the topicsStudents with extremely positive and extremely negative attitudesEffective and ineffective teachers

Qualitative SamplingPurposive techniques (continued)Homogeneous small groups of participants who fit a narrow homogeneous topicCriterion all participants who meet a defined criteriaSnowball initial participants lead to other participants

Qualitative SamplingPurposive techniques (continued)Random purposive given a pool of participants, random selection of a small sampleInherent concerns related to generalizability and representation

Qualitative SamplingSample sizeGenerally very small samples given the nature of the data collection methods and the data itselfTwo general guidelinesRedundancy 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.

GeneralizabilityProbability samplingBegins with a population and selects a sample from itGeneralizability to the population is relatively easyNon-probability and purposive samplingBegins with a sample that is NOT selected from some larger populationMust consider the population hypothetical as it is based on the characteristics of the sampleGeneralizability is often very limited