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So you’re going to run a statistical study… … How will you choose the SAMPLE ?

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Sampling technique is a big deal Time – How long will it take to make 10 phone calls? 100 phone calls? 1000 phone calls? – How long will it take to lookup and read through 10 medical records? 100? 1000? And get permission to do so?

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Sampling technique is a big deal Money – Your assistants will expect to be paid – Or you’ll hire an expensive consulting firm to do it – Or your students will expect extra credit in return for their efforts

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Sampling technique is a big deal The quality of your results – A biased sample can invalidate your results – Your professional reputation could be damaged And if you’re building on previous studies from others, are those studies any good? – Solid rock? – Or shifting sand?

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1-3 Data Collection and Sampling Techniques Some Sampling Techniques Random Random – random number generator Systematic Systematic – every k th subject Stratified Stratified – divide population into “layers” Cluster Cluster – use intact groups Convenient Convenient – mall surveys 5 Bluman Chapter 1, © McGrawwHill

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A Sample of Current Darton Students Suppose we’re doing a study about Darton students Academic, Consumer, Personal, whatever subject the study might be about We’ll illustrate the various sampling techniques using this population Suppose that we have 5,000 students on the list and we want a sample of 100.

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Random Sampling We have a list of the students numbered from 1 to 5000. We ask the computer “Give me 100 different random numbers from 1 to 5000.” And the computer says, for instance, #1929, #3158, #495, #4927, #6, #292, #1887, etc. So we take #1929 on the list, #3158 on the list, #495 on the list, etc.

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Systematic Sampling We have our list of 5000 students, again. 5000 population divided by 100 population equals 50. We take every 50 th student. We could take #50, #100, #150, …, #5000 Or better, pick a random starting number from 1 to 50 and then count by 50s. Example: if it’s #22, our sample is #22, #72, #122, …, #4972

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Stratified Sampling Split our 5000 students into subgroups based on some status Made-up example: – 2300 age 19 and under (46%) – 1300 age 20-29 (26%) – 900 age 30-39 (18%) – 500 age 40+ (10%)

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Stratified Sampling Made-up example: – 2300 age 19 and under (46%) – 1300 age 20-29 (26%) – 900 age 30-39 (18%) – 500 age 40+ (10%) So we take 46% of our sample from age <= 19 And 26% of our sample from age 20-29, etc. What’s the advantage of doing this?

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Cluster Sampling Suppose the survey had to be done by visiting each student’s home. We want to minimize travel time. Have the computer organize them by zip code and by neighborhood. Choose only a few neighborhoods and random And our sample consists of the students in those neighborhoods only. What drawbacks might there be?

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Convenience Sampling Stand outside the entrance and survey students coming in and going out. What’s the advantage? What are the drawbacks?

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Convenience Sampling Another variation of convenience sampling is the self-selected sample “Click on this link to voice your opinion.” What are the advantages and disadvantages?

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