2 Different sampling methods Probability & non probability sampling LEARNING OBJECTIVESReasons for samplingDifferent sampling methodsProbability & non probability samplingAdvantages & disadvantages of each sampling method
3 SAMPLING A sample is a smaller collection of units from a population Used to learn about that populationSampling frame errors: university versus personal addresses; changing class rosters; are all students in your population of interest represented?
5 SAMPLING FRAMEThe list from which the potential respondents are drawnRegistrar’s officeClass rostersElements=Members of population whose characteristics are measured
6 Population What is your population of interest? To whom do you want to generalize your results?All doctorsSchool childrenIndiansWomen aged yearsOther…How do we determine our population of interest?Administrators can tell usWe notice anecdotally or through qualitative research that a particular subgroup of students is experiencing higher riskWe decide to do everyone and go from there3 factors that influence sample representativenessSampling procedureSample sizeParticipation (response)When might you sample the entire population?When your population is very smallWhen you have extensive resourcesWhen you don’t expect a very high response
12 PROBABILITY SAMPLINGEvery unit in population has a chance (greater than zero) of being selected into sampleProbability of being selected can be determinedEvery element in population has same probability of selection= ‘Equal Probability of Selection' (EPS) design
13 PROBABILITY SAMPLING INCLULDES 1. Simple Random Sampling2. Systematic Sampling3. Stratified Random Sampling4. Cluster Sampling
14 1. SIMPLE RANDOM SAMPLING When population is:SmallHomogeneousReadily availableEach element of the frame has equal probability of selectionProvides for greatest number of possible samples.Assigning number to each unit in sampling frameA table of random numbers or lottery system is used to determine which units are selected
15 SIMPLE RANDOM SAMPLING DisadvantagesIf sampling frame is large, method impracticalMinority subgroups of interest in population may not be present in sample in sufficient numbers for study
16 2/11 2. SYSTEMATIC SAMPLING Elements of population are put in a list Then every kth element in list is chosen (systematically) for inclusion in sampleFor example, if population of study contained 2,000 students at a high school and the researcher wanted a sample of 100 students,
17 SYSTEMATIC SAMPLING Students are put in a list Then every 20th student is selected for inclusion in sampleTo ensure against human bias:Researcher should select first individual at random.‘Systematic sample with a Random start'
18 SYSTEMATIC SAMPLINGEPS method, because all elements have the same probability of selection (In the example, 1 in 20)
19 Another ExampleA researcher wants to select a systematic random sample of 10 people from a population of 100. If he or she has a list of all 100 people, he would assign each person a number from 1 to 100.Researcher then picks a random number, 6, as the starting number.He or she would then select every tenth person for the sample (because the sampling interval = 100/10 = 10).The final sample would contain those individuals who were assigned the following numbers: 6, 16, 26, 36, 46, 56, 66, 76, 86, 96.
20 SYSTEMATIC SAMPLING ADVANTAGES: Simple Guaranteed that population will be evenly sampledDISADVANTAGE:Sample may be biased if hidden periodicity in population coincides with that of selection.
21 3. STRATIFIED SAMPLINGPopulation contains a number of categoriesSampling frame can be organized into separate "strata“Each stratum is sampled as an independent sub-populationEvery unit in a stratum has same chance of being selected.
22 STRATIFIED SAMPLINGDraw a sample from each stratum
24 STRATIFIED SAMPLING Benefits: Using same sampling fraction for all strata ensures proportionate representation in sampleAdequate representation of minority subgroups of interest can be ensured by stratificationDrawbacks:Sampling frame of entire population has to be prepared separately for each stratumIn some cases (designs with a large number of strata, or with a specified minimum sample size per group), stratified sampling can potentially require a larger sample than other methods
25 4. Cluster Sampling http://www.youtube.com/watch?v=QOxXy-I6ogs Advantage of cluster sampling:CheapQuickEasy1. Researcher can allocate resources to a few randomly selected clusters2. Researcher can have a larger sample size than using simple random sampling. Take one sample from a number of clusters
27 NON PROBABILITY SAMPLING Any sampling method where some elements of population have no chance of selection orWhere probability of selection cannot be accurately determinedSelection of elements based on assumptions regarding population of interest
28 NON PROBABILITY SAMPLING Example: Visit every household in a given street, andInterview the first person to answer the door.In any household with more than one occupant, this is a nonprobability sample,Some people are more likely to answer the door (e.g. an unemployed person vs employed housemate)
29 NONPROBABILITY SAMPLING Nonprobability Sampling includes: Convenience Sampling, Quota Sampling and Purposive Sampling.In addition, non-response effects may turn any probability design into a nonprobability design if the characteristics of nonresponse are not well understoodNon-response effectively modifies each element's probability of being sampled
30 1. CONVENIENCE SAMPLINGUse results that are easy to get3030
31 CONVENIENCE SAMPLING Sometimes known as: GrabOpportunityAccidental orHaphazard samplingNonprobability sampling which involves sample drawn from part of population that is close.That is, readily available=ConvenientResearcher cannot scientifically make generalizations about total population from this sampleSample not representative
32 CONVENIENCE SAMPLINGExample: Interviewer conducts survey at shopping center early in morning on a given dayPeople he/she could interview limited to those in shopping center at that time on that day
33 This type of sampling is most useful for pilot testing. CONVENIENCE SAMPLINGSample would not represent views of other people in that area who might be at the mall at different times of day or different days of the weekThis type of sampling is most useful for pilot testing.
34 2. QUOTA SAMPLING1. Population is segmented into mutually exclusive sub-groups2. Judgment used to select subjects or units from each segment based on a specified proportion.3. For example, an interviewer may be told to sample 200 females and 300 males between ages of 45 and 60.This step makes the technique non- probability sampling.
35 QUOTA SAMPLING In quota sampling, selection of sample is non-random. For example: Interviewers might be tempted to interview those who look most helpful.Problem: Samples may be biased because not everyone gets a chance of selection.Greatest weakness
36 3. Purposive SampleSample is selected based on researchers’ knowledge of a population and purpose of the study.Subjects selected because of some characteristic.Field researchers often interested in studying extreme or deviant casesCases that don’t fit into regular patterns of attitudes and behaviors
37 Purposive SampleStudying deviant cases, researchers often gain a better understanding of regular patterns of behaviorThis is where purposive sampling often takes place.For instance, if a researcher is interested in learning more about students at the top of their class,Sample those students who fall into the "top of the class" category.They will be purposively selected because they meet a certain characteristic.
38 Purposive SampleCan be very useful for situations where you need to reach a targeted sample quickly andWhere sampling for proportionality is not the main concern.
39 Purposive Sample Example: Researchers (typically market researchers) who you might often see at a mall carrying a clipboard and stopping various people to interviewOften conducting research using purposive sampling.May be looking for and stopping only those people who meet certain characteristics.