Presentation on theme: "Faculty of Allied Medical Science Biostatistics MLST-201"— Presentation transcript:
1 Faculty of Allied Medical Science Biostatistics MLST-201
2 Populations and samples Supervision: Prof. Dr.Ramez N. Bedwani
3 OutcomeImportance of choosing sampling type in clinical work
4 Target populationAll people with the disease/condition of interest to whom the conclusions of the study will be applied
5 Study populationThe people actually available and accessible for study — this is usually limited to the subgroup of people with the same disease/condition of interest attending the clinics where the study is being conducted.
6 A representative sample Has (within reasonable limits) the same characteristics as the target clinical population— so accurately reflects the variations(both random and systematic) within that target populationCan usually be obtained by choosing a sufficiently large random sample
7 Sampling Frame The list or procedure defining the POPULATION. (From which the sample will be drawn.)Distinguish sampling frame from sample.Examples:– Telephone book– Voter list– Random digit dialing• Essential for probability sampling, but can bedefined for nonprobability sampling
8 Types of Probability Samples Simple RandomStratified RandomSystematic RandomRandom ClusterComplex Multi-stage Random (various kinds)
9 Simple Random Sampling Each element in the population has an equal probability of selection AND each combinationof elements has an equal probability of selection
10 Stratified Random Sampling- 1 Divide population intogroups that differ in important waysBasis for grouping must be known before samplingSelect random sample from within each group
11 Systematic Random Sampling Each element has an equal probabilityof selection, but combinations ofelements have different probabilities.• Population size N, desired sample sizen, sampling interval k=N/n.• Randomly select a number j between 1and k, sample element j and then everykth element thereafter, j+k, j+2k, etc.• Example: N=64, n=8, k=64/8=8.Random j=3.
12 Random Cluster Sampling - 1 Done correctly, this is a form of random samplingPopulation is divided into groups, usually geographic or organizationalSome of the groups are randomly chosenIn pure cluster sampling, whole cluster is sampledIn simple multistage cluster, there is random sampling within each randomly chosen cluster
13 Random Cluster Sampling - 2 Population is divided into groupsSome of the groups are randomly selectedFor given sample size, a cluster sample has more error than a simple random sampleCost savings of clustering maypermit larger sampleError is smaller if the clusters are similar to each other
14 Random Cluster Sampling - 3 Cluster sampling has veryhigh error if the clusters aredifferent from each otherCluster sampling is NOTdesirable if the clusters aredifferent
15 Stratification vs. Clustering • Divide population intogroups different from eachother: sexes, races, ages• Sample randomly fromeach group• Less error compared tosimple random• More expensive to obtainstratification informationbefore samplingClustering• Divide population intocomparable groups:schools, cities• Randomly sample some ofthe groups• More error compared tosimple random• Reduces costs to sampleonly some areas ororganizations
16 Multi-stage Probability Samples –1 Large national probability samples involve severalstages of stratified cluster samplingThe whole country is divided into geographic clusters, metropolitan and ruralSome large metropolitan areas are selected withcertaintyOther areas are formed into strata of areas (e.g.middle-sized cities, rural counties); clusters areselected randomly from these strata
17 Multi-stage Probability Samples –2 Within each sampled area, the clusters aredefined, and the process is repeated, perhaps several times, until blocks or telephone exchanges are selectedAt the last step, households and individualswithin household are randomly selected
19 Convenience Sample Subjects selected because it is easy to access them No reason tied to purposes of researchStudents in your class, people on State, street, friends are taken as sample
20 Purposive SamplesSubjects selected for a good reason tied to purposes of researchSmall samples < 30, not large enough for power of probability samplingNature of research requires small sampleChoose subjects with appropriate variability in what you are studyingHard-to-get populations that cannot be found through screening general population
21 Quota Sampling Pre-plan number of subjects in specified categories (e.g. 100 men, 100 women)In uncontrolled quota sampling, the subjects chosenfor those categories are a convenience sample, selected any way the interviewer choosesIn controlled quota sampling, restrictions are imposed to limit interviewer’s choiceNo call-backs or other features to eliminateconvenience factors in sample selection
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