1. Sampling Methods Copyright (c) 2003 by The McGraw-Hill Companies. This material is solely for educational use by licensed users of LearningStats. It may not be copied or resold for profit.

2. Sampling Methods - I Simple random sample List of population items must be available Random numbers are used to choose items Each item has same chance of being selected Systematic sample Continuous process non-enumerable population (no list) Choose every K th item (e.g., every 10th voter at poll exit) Use a random starting point (e.g., the 8th voter) Unbiased unless data are in non-random order

3. Sampling Methods - II Stratified sample Each stratum is a defined population sub group (e.g., male/female) May be many strata (e.g., gender, race, occupation) Weight sample estimates by strata size (strata % must be known) Corrects for possible under-representation of groups Cluster sample Like stratification except based on geography (e.g., school districts) Two-stage is common (random cluster, random items in cluster) Reduces travel cost for in-person interviews

4. Sampling Methods - III Judgment sample Experts in the field select the sample (e.g., which firms) Utilizes domain knowledge of experts (e.g., software engineers) May avoid wasting time on atypical or unimportant respondents But introduces subjectivity Convenience sample Asking co-workers opinions "because they're handy" Using a data set that happens to exist already Subjective Unknowable biases Alas – many key business decisions are made this way!

5. Random Sample Problem The professor wants to call on a student at random. How can this be done? Question What sort of biases might a professor have in choosing a student "at random"?

6. Simple Random Sample Problem There are 200 college freshmen in a large lecture room. Choose 10 at random by picking random rows and columns. Note If you chose the sample, you might try to avoid adjacent items, or might try to "include" each row and column. That would not be random. Pro Unbiased, easy to understand. Con You need a list, and many populations are not listed (e.g., shoppers in a mall) But couldn't the same name come up more than once?

7. Systematic Sample Problem There are 200 college freshmen in a large lecture room. Choose 10 at random by picking every 20th student starting in row 12, column 3 and going down and across. Note For a finite population, we set k  N/n, and specify a method of recursion if we reach the end of the list. This method is random unless there is hidden periodicity in the population order. Pro No list required, easy to understand. Con Starting point must be randomized, may be hard "in the field."

8. Cluster Sample Data are freshman students to be interviewed about campus food. Each cluster is a dormitory. There are 11 dorms (clusters). Choose 3 clusters at random, then select 3 persons from each selected cluster. But Barbara came up twice, and so did Thelma! Pro Saves interviewer travel time. Con Complex, often not worth the trouble.

9. General Advice Do a cost/benefit before sampling Define the purpose before you plunge Expert advice can help with The sampling plan The sample sizes required Analysis of the sample