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Friday, May 7, 20041 Descriptive Research Week 8 Lecture 2

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Friday, May 7, 2004 ISYS3015 Analytical Methods for IS Professionals School of IT, The University of Sydney 2 Agenda Basic sampling concepts Probability sampling Non-probability sampling

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Friday, May 7, 2004 ISYS3015 Analytical Methods for IS Professionals School of IT, The University of Sydney 3 Sampling in everyday life Eating at one of a chain of restaurant –=> All the restaurant in the chain serve poor food Decision on taking some course –<= opinions of friends who have already taken it Basic idea behind sampling –We seek knowledge or information about a whole class of similar objects or events (population) –We observe some of these (sample) –We extend our findings to the entire class What’s the problem?

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Friday, May 7, 2004 ISYS3015 Analytical Methods for IS Professionals School of IT, The University of Sydney 4 Why sample? Heterogeneity of target population (study only one or two cases is not practicable) Save time and money (study of whole population is not possible) Samples may be more accurate than censuses

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Friday, May 7, 2004 ISYS3015 Analytical Methods for IS Professionals School of IT, The University of Sydney 5 Sampling stages Define the population –Target population: specific pool of cases researchers want to study. –Example: All full-time students enrolled in university of Sydney between 1999 and present. All admissions to public or private hospitals in Sydney between December 2000 and December 2003 Identify the sampling frame –The list(s) from which you draw a sample –Sampling frame may not reflect the population perfectly Select a sampling procedure –Probability/Non-probability Determine the sample size Select the sample units

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Friday, May 7, 2004 ISYS3015 Analytical Methods for IS Professionals School of IT, The University of Sydney 6 Simple random sample Each unit in the population has an equal chance of being included Obtain a complete list of population Traditional way: –Number the units, use a table of random number Random-Digit-Dial (RDD) Simple random sample by excel

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Friday, May 7, 2004 ISYS3015 Analytical Methods for IS Professionals School of IT, The University of Sydney 7 Simple random sample by excel

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Friday, May 7, 2004 ISYS3015 Analytical Methods for IS Professionals School of IT, The University of Sydney 8 Stratified random sampling The target population is divided into two or more mutually exclusive segment (strata) A simple random sample of units is chosen independently from each stratum Why stratify? –You will be able to talk about subgroups (stratum) –Every stratum gets a better representation –Give higher precision with the same sample size How to form strata?

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Friday, May 7, 2004 ISYS3015 Analytical Methods for IS Professionals School of IT, The University of Sydney 9 Stratified sampling illustration

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Friday, May 7, 2004 ISYS3015 Analytical Methods for IS Professionals School of IT, The University of Sydney 10 Proportional stratified sampling

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Friday, May 7, 2004 ISYS3015 Analytical Methods for IS Professionals School of IT, The University of Sydney 11 Cluster sampling The target population spread out over a larger area. –is broken down into mutually exhaustive subsets (clusters) –Natural groupings, universities, countries, states, cities, blocks A random sample of clusters are selected –One stage –Two stages Stratum is homogeneous, cluster should be as heterogeneous as possible Do not need a complete frame of the population Cost saving

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Friday, May 7, 2004 ISYS3015 Analytical Methods for IS Professionals School of IT, The University of Sydney 12 Cluster sampling-- illustration

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Friday, May 7, 2004 ISYS3015 Analytical Methods for IS Professionals School of IT, The University of Sydney 13 Systematic sampling Select every nth unit after a random start Units in the population can be ordered in some way –A telephone list, student list –Houses that are ordered along a road –Customers who walk one by one through an entrance Advantage: a frame is not always needed

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Friday, May 7, 2004 ISYS3015 Analytical Methods for IS Professionals School of IT, The University of Sydney 14 Systematic sampling -- illustration

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Friday, May 7, 2004 ISYS3015 Analytical Methods for IS Professionals School of IT, The University of Sydney 15 Mini workshop Suppose you would like to do a survey of students on your univerisity to find out how much time on the average they spend studying per week. You obtain from the registrar a list of all students currently enrolled and draw your sample from this list. –What is your target population? –What is your sampling frame? –Assume that the registrar’s list also contains information about each student’s major, year of enrollment, campus of study. How might you obtain a stratified random sample? How might you obtain a cluster sample?

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Friday, May 7, 2004 ISYS3015 Analytical Methods for IS Professionals School of IT, The University of Sydney 16 Non-probability sampling Convenience sampling –Select a sample from units that are conveniently available Judgment sampling (purposive sampling) –Chose units because of certain characteristics Quota sampling –Make sure that certain subgroups of units are represented in the sample in approximately the same proportions as they are represented in the population.

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Friday, May 7, 2004 ISYS3015 Analytical Methods for IS Professionals School of IT, The University of Sydney 17 Determine the sample size Make assumptions about the population and use statistical equations to compute the sample size Rule of thumb –For small populations (N<100) survey the entire population –N is around 500, 50% of the population should be sampled –N is around 1500, 20% should be sampled –Beyond a certain point (N >= 5000), the population size is almost irrelevant, set sample size to 400.

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