1. The Information School of the University of Washington LIS 470 Data & Sampling LIS 570 Session 4.1 [Many of the slides and graphics adapted from Harry Bruce’s Spring 2005 Class]

2. The Information School of the University of Washington LIS 570_Data & SamplingMason; p. 2 Objectives Understand the options in, and goals of, sampling techniques Reinforce knowledge of vocabulary and basic principles

3. The Information School of the University of Washington LIS 570_Data & SamplingMason; p. 3 Agenda Warm-up exercise: review of principles Discussion of sampling goals and methods Hypothetical research exercise

4. The Information School of the University of Washington LIS 570_Data & SamplingMason; p. 4 What Happened in 1997? Graduating Class Year Est. Average 1 st Year Earnings Projected Est. Average Total 5 year Earnings 1994$28,100$154,550 1995$29,200$160,600 1996$30,400$167,200 1997$50,500$339,800 FSU MIS Graduates

5. The Information School of the University of Washington LIS 570_Data & SamplingMason; p. 5 Possible Explanations Beginning of dot com boom Beginning of Y2K fears and staffing frenzy Other…? Peter Boulware first round NFL pick –Overall no. 4 pick by Baltimore Ravens –$800,000 1 st year salary, $1M signing bonus –$17M total 5 year package

6. The Information School of the University of Washington LIS 570_Data & SamplingMason; p. 6 Summary Sampling - the process of selecting observations –random; non-random –probability; non-probability You don’t have to eat the whole ox to know that the meat is tough

7. The Information School of the University of Washington LIS 570_Data & SamplingMason; p. 7 Aim A representative sample: a sample which accurately reflects its population Avoid (unconscious) bias

8. The Information School of the University of Washington LIS 570_Data & SamplingMason; p. 8 Basic terminology Population [universe] - the entire group of objects about which information is wanted Unit [element] - any individual member of the population Sample - a part or subset of the population used to gain information about the whole Sampling frame - the list of units [subset of the universe] from which the sample is chosen Variable - a characteristic of a unit, to be measured for those units in the sample

9. The Information School of the University of Washington LIS 570_Data & SamplingMason; p. 9 Step 1: Identify the Population The units of the population about whom or which you want to know Define the population concretely; no ambiguity Example: “Adult Residents of Seattle” –How is “adult” defined? –What is the exact boundary of Seattle? –As of what date? –Can the population be identified completely? (e.g., are the homeless included as “residents?”)

10. The Information School of the University of Washington LIS 570_Data & SamplingMason; p. 10 2. Decide on a Census or a Sample Census –Observe each unit –An “attempt” to sample the entire population –Not foolproof (example: issues of US census) Sample: observe a sub-group of the population

11. The Information School of the University of Washington LIS 570_Data & SamplingMason; p. 11 3. Decide on Sampling Approach

12. The Information School of the University of Washington LIS 570_Data & SamplingMason; p. 12 Random sampling Random (Probability) Sampling Each unit (element) has the same chance (probability) of being in the sample Chance or luck of the draw determines who is in the sample (random)

13. The Information School of the University of Washington LIS 570_Data & SamplingMason; p. 13 Each unit has a known probability or chance of being included in the sample An objective way of selecting units Random Sampling is not haphazard or unplanned sampling Random samples

14. The Information School of the University of Washington LIS 570_Data & SamplingMason; p. 14 Types of random sampling Simple random sample Systematic sampling Stratified sampling Cluster sampling

15. The Information School of the University of Washington LIS 570_Data & SamplingMason; p. 15 How to choose The nature of the research problem Availability of a sampling frame Money Desired level of accuracy Data collection method

16. The Information School of the University of Washington LIS 570_Data & SamplingMason; p. 16 Simple random samples Obtain a complete sampling frame Give each case a unique number starting with one Decide on the required sample size Select that many numbers from a table of random numbers Select the cases which correspond to the randomly chosen numbers

17. The Information School of the University of Washington LIS 570_Data & SamplingMason; p. 17 Systematic sampling Sample fraction: divide the population size by the desired sample size Select from the sampling frame according to the sample fraction—e.g., sample faction of 1/5 means that we select one element for every five in the population Must decide where to start

18. The Information School of the University of Washington LIS 570_Data & SamplingMason; p. 18 Stratified sampling Premise: if a sample is to be representative, then proportions for various groups in the sample should be the same as in the population Stratifying variable: characteristic on which we want to ensure correct representation in the sample –Order sampling frame into groups –Use systematic sampling to select appropriate proportion of people from each strata

19. The Information School of the University of Washington LIS 570_Data & SamplingMason; p. 19 Cluster sampling Involves drawing several different samples –draw a sample of areas –start with large areas then progressively sample smaller areas within the larger—e.g., example of city population Divide city into districts - select SRS sample of districts Divide sample of districts into blocks - select SRS sample of blocks Draw list of households in each block - select SRS sample of households

20. The Information School of the University of Washington LIS 570_Data & SamplingMason; p. 20 Random Samples Advantages –Ability to generalise from sample to population using statistical techniques—inferential statistics –High probability that sample generally representative of the population on variables of interest

21. The Information School of the University of Washington LIS 570_Data & SamplingMason; p. 21 Non-random Samples Purposive Quota Accidental Generalizability based on “argument” –Replication –Sample “like” the population

22. The Information School of the University of Washington LIS 570_Data & SamplingMason; p. 22 Selecting a sampling method Depends on the population Problem and aims of the research Existence of sampling frame

23. The Information School of the University of Washington LIS 570_Data & SamplingMason; p. 23 Conclusion The purpose of sampling is to select a set of elements from the population in such a way that what we learn about the sample can be generalised to the population from which it was selected The sampling method used determines the generalizability of findings Random samples Non-random sample

24. The Information School of the University of Washington LIS 470 Data & Sampling Research Exercise 10-12 minutes: work alone 15 minutes: in teams--compare solutions 15 minutes: discussion