Business Research Methods William G. Zikmund

1. Business Research MethodsWilliam G. Zikmund Chapter 16: Sample Designs and Sampling Procedures

2. Sampling Terminology • Sample • Population or universe • Population element • Census

3. Sample • The process of sampling involves using a small number of items or parts of the population to make conclusions regarding the whole population. • The purpose of sampling is to estimate some unknown characteristic of the population, e.g. mean, max, min, mode etc. • A sample is a subset or some part of a larger population.

4. Population • A population (finite/countable group, e.g. UPM students) or universe (infinite/uncountable group e.g. air) is any complete group sharing some common set of characteristics. • The term population element refers to and individual member of the population. • Any complete group • People • Sales territories • Stores

5. Census • Investigation of all individual elements that make up a population

6. WHY SAMPLE? • A. Pragmatic reasons: Sampling cuts costs, reduces manpower requirements, and gathers vital information quickly. • B. Accurate and reliable results: Another major reason for sampling is that properly selected samples are sufficiently accurate in most cases. A sample may be more accurate than a census. In a field survey a small, well-trained, closely supervised group may do a more careful and accurate job of collecting information than a large group of nonprofessional interviewers trying to contact everyone. • C. Destruction of test units: Many research projects, especially those in quality control testing, require the destruction of the items being tested. For example, if the manufacturer of firecrackers wished to find out whether each unit met a specific production standard, there would be no product left after testing.

7. Stages in the Selection of a Sample Define the target population Select a sampling frame Determine if a probability or nonprobability sampling method will be chosen Plan procedure for selecting sampling units Determine sample size Select actual sampling units Conduct fieldwork

8. Target Population • Relevant population (e.g. UPM CIMB customer database) • Operationally define – definition of reader – those who read at least one comic book a week. • Comic book reader? – include six year old children who do no actually read the words? Just look at the pictures.

9. Sampling Frame • A list of elements from which the sample may be drawn • The sampling frame is also called the working population, because it provides the list that can be operationally worked with. E.g. UPM CIMB customers’ telephone list. If a complete lists is not available, can use maps as sampling frame. • Sampling frame error - Sampling frame error occurs when certain elements are excluded (e.g. those without telephone was excluded) or when the entire population is not accurately represented in the sample frame (e.g. oversea travel survey – include those can’t afford to travel overseas – wrong sampling frame).

10. Sampling Units • Group or element selected for the sample e.g. every 25th customer in a complete customer database list.

11. Random Sampling Error • The difference between the sample results and the result of a census (population) conducted using identical procedures • Statistical fluctuation due to chance variations (although taken every 25th as respondent, 60% with CGPA 3.5 above in Student satisfaction survey, so produce different result from population data)

12. Systematic Errors • Nonsampling errors • Unrepresentative sample results • Not due to chance • Due to study design or imperfections in execution (e.g. interviewer bias)

13. Errors Associated with Sampling • Sampling frame error • Random sampling error • Nonresponse error

14. Two Major Categories of Sampling • Probability sampling • Known, nonzero probability for every element, each element has same prob of being selected • Nonprobability sampling • Probability of selecting any particular member is unknown, some sample elements have zero prob of being selected

15. Nonprobability Sampling • Convenience • Judgment • Quota • Snowball

16. Probability Sampling • Simple random sample • Systematic sample • Stratified sample • Cluster sample • Multistage area sample

17. Convenience Sampling • Also called haphazard or accidental sampling • The sampling procedure of obtaining the people or units that are most conveniently available • E.g. mall intercept – only include those happen to be at the mall at the time of data collection.

18. Judgment Sampling • Also called purposive sampling • An experienced individual selects the sample based on his or her judgment about some appropriate characteristics required of the sample member • For example, a fashion manufacturer regularly selects a sample of key accounts (big customers) that it believes are capable of providing the information to predict what will sell in the fall • E.g. only include those who have travelled overseas for the past two years.

19. Quota Sampling • Ensures that the various subgroups in a population are represented on pertinent/relevant sample characteristics, e.g. limit to 50 male and 50 female, one male respondents achieve 50, stop approaching males, to ensure representative sample. • To the exact extent that the investigators desire • It should not be confused with stratified sampling which is a probability sampling procedure.

20. Snowball Sampling • This technique is used to locate members of rare populations by referrals. E.g. breast cancer patients • May use a variety of procedures • Initial respondents are selected by probability methods • Additional respondents are obtained from information provided by the initial respondents

21. Simple Random Sampling • A sampling procedure that ensures that each element in the population will have an equal chance of being included in the sample • E.g. pick up names based on random number generated by computer • E.g. lucky draw procedure

22. Systematic Sampling • A simple process • Every nth name from the list will be drawn

23. Stratified Sampling • Probability sample • Subsamples are drawn within different strata • Each stratum is more or less equal on some characteristic (e.g. mgrs in an org is broken into 3 strata; top, middle and lower mgrs) • More similarity within a strata than between • Do not confuse with quota sample

24. Cluster Sampling • The purpose of cluster sampling is to sample economically while retaining the characteristics of a probability sample. • The primary sampling unit is no longer the individual element in the population • The primary sampling unit is a larger cluster of elements located in proximity to one another e.g. Northern, middle and Southern region • Similarity between cluster (all regions have three desired industries like food, automobile and chemical) is higher than within cluster

25. Examples of Clusters Population Element Possible Clusters in the United States U.S. adult population States Counties Metropolitan Statistical Area Census tracts Blocks Households

26. Examples of Clusters Population Element Possible Clusters in the United States College seniors Colleges Manufacturing firms Counties Metropolitan Statistical Areas Localities Plants

27. Examples of Clusters Population Element Possible Clusters in the United States Airline travelers Airports Planes Sports fans Football stadiums Basketball arenas Baseball parks

28. What is the Appropriate Sample Design? • Degree of accuracy - The degree of accuracy required may vary from project to project, cost savings may be a trade-off for a reduction in accuracy. • Resources - Managers concerned with the cost of the research versus the value of the information often opt for a cost savings from a nonprobability sample design. • Time - need to complete a project quickly, select simple, less time-consuming sample designs • Advanced knowledge of the population – when list of population elements is not available to the researcher, automatically rule out systematic sampling, stratified sampling, or another sampling design • National versus local - When population elements are unequally distributed geographically, a cluster sampling design may become much more attractive • Need for statistical analysis - Nonprobability sampling techniques do not allow utilizing statistical analysis to project/estimate the data beyond the sample (low generalizability)

29. Internet Sampling is Unique • Internet surveys allow researchers to rapidly reach a large sample. • Speed is both an advantage and a disadvantage. • Sample size requirements can be met overnight or almost instantaneously. • Survey should be kept open long enough so all sample units can participate.

30. Internet Sampling • Major disadvantage • lack of computer ownership and Internet access among certain segments of the population • Yet Internet samples may be representative of a target populations. • target population - visitors to a particular Web site. • Hard to reach subjects may participate

31. Web Site Visitors • Unrestricted samples are clearly convenience samples • Randomly selecting visitors • Questionnaire request randomly "pops up" • Over- representing the more frequent visitors

32. Panel Samples • Typically yield a high response rate • Members may be compensated for their time with a sweepstake or a small, cash incentive. • Database on members • Demographic and other information from previous questionnaires • Select quota samples based on product ownership, lifestyle, or other characteristics. • Probability Samples from Large Panels

33. Internet Samples • Recruited Ad Hoc Samples • Opt-in Lists