# Module 9: Sampling

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Module 9: Sampling

IPDETModule 9:SamplingSamplingInterventionorPolicyEvaluationQuestionsDesignApproachesDataCollectionIntroConceptsTypesConfidence/Precision?How Large?

Introduction• Introduction to Sampling• Sample Concepts• Types of Samples: Random and Non-Random• How Confident and Precise Do YouNeed to Be?• How Large a Sample Do You Need?IPDET 2 2

Sampling• Is it possible to collect data from theentire population? (census)– If so, we can talk about what is true for theentire population– Often we cannot (time/cost)– If not, we can use a smaller subset:a SAMPLEIPDET 3 3

Concepts• Population– the total set of units•Sample– a subset of the population• Sampling Frame– list from which to select your sampleIPDET 4 4

More Sampling Concepts• Sample Design– methods of sampling (probability or nonprobability)• Parameter– characteristic of the population• Statistic– characteristic of a sampleIPDET 5 5

Random Sample•A random sample allows us to makeestimates about the larger populationbased on what we learn from the subset• Advantages:– eliminates selection bias– able to generalize to the population– cost-effectiveIPDET 6 6

Types of RandomSamples• Simple random sample• Stratified random sample• Multi-stage random sample• Cluster random sampleIPDET 7 7

Simple Random Sample• Simplest• Establish a sample size and proceed torandomly select units until we reach thesample sizeIPDET 8 8

Stratified RandomSample• Use when specific groups must beincluded that might otherwise be missedby using a simple random sample– usually a small proportion of the populationIPDET 9 9

Stratified RandomSampleTotalPopulationsubpopulationsubpopulationsimple randomsamplesimple randomsamplesub-populationsimple randomsampleIPDET 10 10

Cluster Sample• Another form of random sampling• Any naturally occurring aggregate of the unitsthat are to be sampled that are used when:– you do not have a complete list of everyone in thepopulation of interest but have a list of the clustersin which they occur or– you have a complete list of everyone, but they areso widely distributed that it would be too timeconsuming and expensive to send data collectorsout to a simple random sampleIPDET 11 11

Multi-stage Sample• Combines two or more forms of randomsampling• Most commonly, it begins with randomcluster sampling and then appliessample random sampling or stratifiedrandom samplingIPDET 12 12

Drawback of Cluster andMulti-stage Sampling• May not yield an accuraterepresentation of the populationIPDET 13 13

Summary of RandomSampling ProcessStepProcess1 Obtain a complete listing of the entire population2 Assign each case a number3 Randomly select the sample using a randomnumbers table4 When no numbered listing exists or is notpractical to create:• take a random start• select every n th caseIPDET 14 14

Non-Random Samples• Can be more focused• Can help make sure a small sample isrepresentative• Cannot make inferences to a largerpopulationIPDET 15 15

Types of Non-RandomSamplesquotasnowballjudgmentalconvenienceset number from each subsetask people who else you shouldinterviewset criteria to achieve a specific mixof participantswhoever is easiest to contact orwhatever is easiest to observeIPDET 16 16

Non-Random SamplingIssues• People selected in a biased way?• Are they substantially different from therest of the population?– collect some data to show that the peopleselected are fairly similar to the largerpopulation (e.g. demographics)IPDET 17 17

Combinations: Randomand Non-Random• Example:– Non-randomly select two schoolsfrom poorest communities and twofrom the wealthiest communities– Select a random sample of studentsfrom these four schoolsIPDET 18 18

Possibility of Error• Sample different from the population?• Statistics: data derived from randomsamplesIPDET 19 19

How Confident Do YouWish to Be?• Confidence level– E.g., 90% (90% certain your sample resultsare an estimate of the population as awhole)• The higher confidence level, the largersample neededIPDET 20 20

Confidence Standard• Standard is 95%– 19 of 20 samples would have found similarresults– we are 95% certain that the populationparameter is somewhere between thelower and upper confidence intervalcalculated from the sampleIPDET 21 21

Confidence Interval• Sometimes called sampling error ormargin of error• Example:– in polls 48% for, 52% against, with (+/- 3%)– actually means 45% to 51% for and49% to 55% against• Also known as precision or margin oferrorIPDET 22 22

Sample Size• By increasing sample size, youincrease accuracy and decreasemargin of error• The smaller the population, thelarger the needed ratio of thesample size to the population size• Aim for is a 95% confidence leveland a ±5% confidence levelIPDET 23 23

Sample Sizes for LargePopulationsPrecisionConfidence Level99%95%90%±1%16,5769,6046,765±2%4,1442,4011,691±3%1,8481,067752±5%666384271IPDET 24 24