2Objectives: sampling To understand: Why we use sampling Definitions in samplingConcept of representativityMain methods of samplingSampling errors
3Definition of sampling Procedure by which some membersof a given population are selected as representatives of the entire population in terms of the desired characteristicsSampling is the use of a subset of the population to represent the whole population, in order to extrapolate the study results to the population from which the sample is drawn.
4Why bother in the first place? Get information from large populations with:Reduced costsReduced field timeIncreased accuracyWhy do we use sampling?We want to obtain information from large populations at:Reduced costsReduced field timeIncreased accuracy
5Definition of sampling terms Sampling unit (element)Subject under observation on which information is collectedExample: children <5 years, hospital discharges, health events…Sampling fractionRatio between sample size and population sizeExample: 100 out of 2000 (5%)There are several terms used in sampling which are important to understand:First the sampling unit or element of sampling:The sampling unit is the subject under observation on which information is collected, for example: children <5 years, hospital discharges, health events…Secondly, the sampling fraction is the ratio between sample size and population size, for example: 100 out of 2000 (5%)
6Definition of sampling terms Sampling frameList of all the sampling units from which sample is drawnLists: e.g. all children < 5 years of age, households, health care units…Sampling schemeMethod of selecting sampling units from sampling frameRandomly, convenience sample…To choose the sampling units you will need a sampling frame, which is a list of all the sampling units from which sample is drawn, for example all children < 5 years of age, households, health care units…And finally the sampling scheme is the method of selecting sampling units from sampling frame, for example randomly, convenience sample…
7Survey errors Systematic error (or bias) Sampling error (random error) Representativeness (validity)Information biasSampling error (random error)PrecisionBefore I will present the different sampling schemes or methods, I would like to mention some of the survey errors.First of all, your study can have a systematic error (or bias). This would be the case when your sample is not typical of the population you are looking at. This could happen through obtaining inaccurate response (information bias) or selecting people not representative of the population you are studying.A Sampling error or random error could also be possible.
8ValiditySample should accurately reflect the distribution of relevant variable in populationPerson (age, sex)Place (urban vs. rural)Time (seasonality)Representativeness essential to generaliseEnsure representativeness before startingConfirm once completedOur sample should accurately reflect the distribution of relevant variables in the population, regarding person (age, sex), place e.g. urban vs. rural and time e.g. seasonality. Is this the case for my example?The representativeness is essential to be able to generalise our results.We should ensure that our sample is representative before starting and confirm once completed the sampling
9Information bias Systematic problem in collecting information Inaccurate measuringScales (weight), ultrasound, lab tests (dubious results)Badly asked questionsAmbiguous, not offering right options…Systematic problem in collecting informationInaccurate measuringScales, ultrasound, lab tests…Badly asked questionsAmbiguous, not offering right options…
10Sampling error (random error) No sample is an exact mirror image of the populationStandard error depends onsize of the sampledistribution of character of interest in populationSize of errorcan be measured in probability samplesstandard errorEven if we have made sure that we have a representative sample, we can have a sampling error.Was does this mean?We should not forget that our sample is just a gross reflection of the reality. There will always be a random difference between our sample and the population from which the sample was drawn. This is also what we mean with precision.So the five persons I have chosen as my sample might not completely reflect all participants. Even if I have about the same age and gender distribution, it will not be exactly the same.We can measure the size of the sampling error in probability samples, and this is expressed by standard error of mean, proportion, differences, etcThe size of the sampling error depends on the sample size (the bigger the sample, the smaller the error).Also the sampling error depends upon how often a characteristic is present in the population. For very rare characteristics we might need to aim for a better sample error or precision.
11Survey errors: example Measuring height:Measuring tape held differently by different investigators→ loss of precision→ large standard errorTape too short→ systematic error→ bias (cannot be corrected retrospectively)Just to illustrate this by an example: We would like to measure the height of all participants of this course.We have one tape which we would use. All facilitators will be asked to measure the height of the participants in their group. Some facilitators will hold the tape to the right, the others to the left, so that even when they take the height of the same person, they would end up having different numbers. This would correspond to the standard error.However, if some of us are using an incorrect tape, which is actually not 2 m but only 1,98 m we will have a systematic error and our results will be biased in one direction. We will not be able to correct this after we took the measurements.Is that clear?
12Types of sampling Non-probability samples Probability samples Convenience samplesBiasedSubjective samplesBased on knowledgeIn the presence of time/resource constraintsProbability samplesRandomonly method that allows valid conclusions about population and measurements of sampling errorThere are two types of sampling, the non-probability samples and the probability samples which ensures each sampling unit has the same chance to be selected. In this presentation, we will focus on probability sampling techniques.12
13Non-probability samples Convenience samples (ease of access)Snowball sampling (friend of friend….etc.)Purposive sampling (judgemental)You chose who you think should be in the studyNormally sample should reflect the population structure. A convenience sample does not! In a convenience sample some persons have a higher chance of being sampled than others. For example, if we choose people on the main shopping street as a sample for a city ward, those who are frequently doing their shopping there are at higher risk of being chosen than those working during the day, bedriddenNonprobability sampling techniques cannot be used to infer from the sample to the general population. Any generalizations obtained from a nonprobability sample must be filtered through one's knowledge of the topic being studied. Performing nonprobability sampling is considerably less expensive than doing probability sampling, but the results are of limited value.Examples of nonprobability sampling include:Convenience sampling - members of the population are chosen based on their relative ease of access. To sample friends, co-workers, or shoppers at a single mall, are all examples of convenience sampling.Snowball sampling - The first respondent refers a friend. The friend also referes a friend, etc.Judgmental sampling or Purposive sampling - The researcher chooses the sample based on who they think would be appropriate for the study. This is used primarily when there is a limited number of people that have expertise in the area being researched.Case study - The research is limited to one group, often with a similar characteristic or of small size.ad hoc quotas - A quota is established (say 65% women) and researchers are free to choose any respondent they wish as long as the quota is metEven studies intended to be probability studies sometimes end up being non-probability studies due to unintentional or unavoidable characteristics of the sampling method. In public opinion polling by private companies (or organizations unable to require response), the sample can be self-selected rather than random. This often introduces an important type of error: self-selection error. This error sometimes makes it unlikely that the sample will accurately represent the broader population. Volunteering for the sample may be determined by characteristics such as submissiveness or availability. The samples in such surveys should be treated as non-probability samples of the population, and the validity of the estimates of parameters based on them unknownProbability of being chosen is unknownCheaper- but unable to generalise, potential for bias
14Example of a non-probability sample Take a sample of the population of a Greek island to ask about possible exposures following a gastroenteritis outbreakSampling frame: people walking around the port at high noon on a Monday
16Probability samples Random sampling Each unit has a known probability of being selectedAllows application of statistical sampling theory to results in order to:GeneraliseTest hypothesesLet’s look at the probability samplesRandom samplingEach subject has a known probability of being selectedAllows application of statistical sampling theory to results to:GeneraliseTest hypotheses
17Methods used in probability samples Simple random samplingSystematic samplingStratified samplingMulti-stage samplingCluster samplingThere are several ways of performing random sampling and I will present you the following:Simple random samplingSystematic samplingStratified samplingMulti-stage samplingCluster sampling
18Simple random sampling PrincipleEqual chance/probability of each unit being drawnProcedureTake sampling populationNeed listing of all sampling units (“sampling frame”)Number all unitsRandomly draw unitsSimple random samplingIn a simple random sampling, there is an equal chance/probability of drawing each unit. X has the same probability as y to be chosen.The Procedure is simple: Take the sampling population, establish a listing of all sampling units (“sampling frame”), number all units and randomly draw units
19Simple random sampling AdvantagesSimpleSampling error easily measuredDisadvantagesNeed complete list of unitsUnits may be scattered and poorly accessibleHeterogeneous population important minorities might not be taken into accountThe advantages are that the method is simple and the sampling error easily measured.The disadvantages are that you need complete list of units. This can be a problem. Here in the course we have the total list, but what when you need to sample from a larger population? Where do you get the listing from?Also, this method does not always achieve the best representativeness, e.g telephone numbers- is everyone in the telephone book? Are there people without phones? Or non-functioning phones… Or the population registry. Is it up to date?If we are not using telephones but door-to-door interviews: The units may be scattered and poorly accessible. Because units may be scattered may be more time consuming to perform the study.
20Systematic sampling Principle Procedure Select sampling units at regular intervals (e.g. every 20th unit)ProcedureArrange the units in some kind of sequenceDivide total sampling population by the designated sample size (eg 1200/60=20)Choose a random starting point (for 20, the starting point will be a random number between 1 and 20)Select units at regular intervals (in this case, every 20th unit), i.e. 4th, 24th, 44th etc.For convenience, selection from the sampling frame is sometimes carried out systematically rather than randomly, by taking individuals at regular intervals down the list. The principle of systematic sampling is to select sampling units at regular intervals.The process for drawing a systematic sample is as follows:You arrange the units in some kind of sequenceDivide the total sampling population by the sample size (say 1200/60=20)Choose a random starting point (e.g. for 20 the starting point will be chosen randomly from 1 to 20)Select units at regular intervals (for 20 this will be every 20th unit).The advantages are that it is simple to do and the sampling error can be easily measured.The disadvantages are that you need a complete list of units. If this list of units is not well mixed and there may be an underlying pattern in the sampling frame, eg. if we choose every second person in this room and men-women are always seated in the same order, the second person will be only eg. women. So if the sampling units are grouped such that particular characteristics occur at regular intervals, bias can occur.20
21Systematic sampling Advantages Disadvantages Ensures representativity across listEasy to implementDisadvantagesNeed complete list of unitsPeriodicity-underlying pattern may be a problem (characteristics occurring at regular intervals)The advantages are that the method is simple and the sampling error easily measured.-The disadvantages are that you need complete list of units. This can be a problem eg in a country with no census data or central population register.-The population may be widely dispersed and so a simple random sample would be impractical, time-consuming and costly (e.g. in an Afriacan country with remote villages and bad roads, a simple random sample would require much travelling to sample a few individuals from many remote villages).-If the population is made up of different types of groups (eg small minorities, a simple random will not be representative of the population- unless you have a very large number)Also, this method does not always achieve the best representativeness, e.g telephone numbers- is everyone in the telephone book? Are there people without phones? Or non-functioning phones… Or the population registry. Is it up to date?21
23Stratified sampling When to use Procedure Population with distinct subgroupsProcedureDivide (stratify) sampling frame into homogeneous subgroups (strata) e.g. age-group, urban/rural areas, regions, occupationsDraw random sample within each stratumStratified sampling is used when the population is made up of groups with different characteristics and expected big differences in the feature under study (eg minorities, urban/rural areas). Simple random sample will not be representative of the population, unless you have a very large sample.This is because the big differences that may occur between the separate groups will cause more variation in the overal estimate and it will be less precise. Eg. to estimate the prevalence of nosocomial infections, I can stratify by wards which have different levels of risk for hospital-acquired infections.We divide the sampling frame into homogeneous subgroups (strata) that have common characteristics e.g. age-group, occupation, area of residence;We draw a random sample in each stratum.In most situations, the number of people in each subgroup will not be the same. To ensure that all subjects have the same chance of being selected, the same proportion of subjects should be sampled from each group. In other words, the same sampling fraction is used in each stratum.23
24Selecting a sample with probability proportional to size Stratified samplingSelecting a sample with probability proportional to sizeArea Population Proportion Sample size Samplingsize fractionUrban %1000 x 0.7 = 70010 %Rural %1000 x 0.3 = 30010 %1000Total24
25Stratified sampling Advantages Disadvantages Can acquire information about whole population and individual strataPrecision increased if variability within strata is smaller (homogenous) than between strataDisadvantagesSampling error is difficult to measureDifferent strata can be difficult to identifyLoss of precision if small numbers in individual strata (resolved by sampling proportional to stratum population)The advantages of stratified sampling are that you can acquire information about whole population and individual strata.The precision will increase if variability within strata is less (homogenous) than between strataDisadvantagesCan be difficult to identify strataLoss of precision if small numbers in individual strataresolve by sampling proportionate to stratum population25
28Cluster sampling Principle Whole population divided into groups e.g. neighbourhoodsA type of multi-stage sampling where all units at the lower level are included in the sampleRandom sample taken of these groups (“clusters”)Within selected clusters, all units e.g. households included (or random sample of these units)Provides logistical advantagePrincipleWhole population divided into groups e.g. neighbourhoodsRandom sample taken of these groups (“clusters”)Within selected clusters, all units e.g. households included (or random sample of these units)
34Stage 3: Selection of the sampling unit Second-stage units => HouseholdsThird-stage unit => Individuals34
35All third-stage units might be included in the sample Stage 3: Selection of the sampling unitAll third-stage units might be included in the sample35
36Cluster sampling Advantages Simple as complete list of sampling units within population not requiredLess travel/resources requiredDisadvantagesCluster members may be more alike than those in another cluster (homogeneous)this “dependence” needs to be taken into account in the sample size and in the analysis (“design effect”)AdvantagesSimple as complete list of sampling units within population not requiredLess travel/resources requiredDisadvantagesPotential problem is that cluster members are more likely to be alike, than those in another cluster (homogeneous)….This “dependence” needs to be taken into account in the sample size….and the analysis (“design effect”)
37Selecting a sampling method Population to be studiedSize/geographical distributionHeterogeneity with respect to variableAvailability of list of sampling unitsLevel of precision requiredResources availableWhich one is the right sampling method?This depends first on the population to be studied: How large is the target population? Small population you can use a simple random sampling, for a large population you mkight need more sophisticated methods. Also, a population scattered in remote areas will require a different sampling method that the population of a large city.If you expect a large heterogeneity regarding your variable of interest, you may have to choose a stratified sample.It also depends on the availability of a list of sampling units, the level of precision required and last but not least the resources available.
38Conclusions Probability samples are the best Ensure ValidityPrecision…..within available constraints