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Lecture 9

Lecture 9. SAMPLING DESIGN AND PROCEDURE. Population and Sample. Population The entire group that the researcher wishes to investigate Element A single member of the population. Population and Sample. Population (Sampling) Frame

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Lecture 9

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  1. Lecture 9 SAMPLING DESIGNAND PROCEDURE

  2. Population and Sample • Population • The entire group that the researcher wishes to investigate • Element • A single member of the population

  3. Population and Sample • Population (Sampling) Frame • A listing of all the elements in the population from which the sample is drawn • Sample • A subset of the population • Subject • A single member of the sample

  4. CENSUS • INVESTIGATION OF ALL INDIVIDUAL ELEMENTS THAT MAKE UP A POPULATION

  5. When Is A Census Appropriate? Necessary Feasible

  6. TARGET POPULATION • RELEVANT POPULATION • OPERATIONALLY DEFINE • COMIC BOOK READER?

  7. Greater speed Why Sample? Availabilityof elements Lowercost Sampling provides Greater accuracy

  8. SAMPLING FRAME • A LIST OF ELEMENTS FROM WHICH THE SAMPLE MAY BE DRAWN • WORKING POPULATION • MAILING LISTS - DATA BASE MARKETERS • SAMPLING FRAME ERROR

  9. Sampling • Process of selecting a sufficient number of elements from the population • Reasons for Sampling: practicality (time and resources), destructive sampling • Need for a representative sample

  10. SAMPLING UNITS • GROUP SELECTED FOR THE SAMPLE • PRIMARY SAMPLING UNITS (PSU) • SECONDARY SAMPLING UNITS • TERTIARY SAMPLING UNITS

  11. TWO MAJOR CATEGORIES OF SAMPLING • PROBABILITY SAMPLING • KNOWN, NONZERO PROBABLITY FOR EVERY ELEMENT • NONPROBABLITY SAMPLING • PROBABLITY OF SELECTING ANY PARTICULAR MEMBER IS UNKNOWN

  12. Probability and NonprobabilitySampling • Probability Sampling • Elements in the population have known chance of being chosen • Used when the representativeness of the sample is of importance • Nonprobability Sampling • The elements do not have a known or predetermined chance of being selected as subjects

  13. Probability Sampling Unrestricted/Simple Random Sampling • Every element in the population has a known and equal chance of being selected as a subject • Has the least bias and offers the most generalizability Restricted/Complex Probability Sampling • Systematic Sampling • Stratified Random Sampling • Cluster Sampling (USM, UM, etc) • Area Sampling • Double Sampling (USM and then grad students)

  14. PROBABLITY SAMPLING • SIMPLE RANDOM SAMPLE • SYSTEMATIC SAMPLE • STRATIFIED SAMPLE • CLUSTER SAMPLE • MULTISTAGE AREA SAMPLE

  15. 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

  16. Advantages Easy to implement with random dialing Disadvantages Requires list of population elements Time consuming Uses larger sample sizes Produces larger errors High cost Simple Random

  17. SYSTEMATIC SAMPLING • A simple process • every nth name from the list will be drawn

  18. Advantages Simple to design Easier than simple random Easy to determine sampling distribution of mean or proportion Disadvantages Periodicity within population may skew sample and results Trends in list may bias results Moderate cost Systematic

  19. STRATIFIED SAMPLING • Probability sample • Subsamples are drawn within different strata • Each stratum is more or less equal on some characteristic • Do not confuse with quota sample

  20. Advantages Control of sample size in strata Increased statistical efficiency Provides data to represent and analyze subgroups Enables use of different methods in strata Disadvantages Increased error will result if subgroups are selected at different rates Especially expensive if strata on population must be created High cost Stratified

  21. CLUSTERSAMPLING • 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.

  22. EXAMPLES OF CLUSTERS Population Element Possible Clusters in Malaysia Malaysian adult population States Districts Metropolitan Statistical Area Census tracts Blocks Households

  23. EXAMPLES OF CLUSTERS Population Element Possible Clusters in Malaysia College seniors Colleges Manufacturing firms Districts Metropolitan Statistical Areas Localities Plants

  24. EXAMPLES OF CLUSTERS Population Element Possible Clusters in Malaysia Airline travelers Airports Planes Sports fans Football stadia Basketball arenas Baseball parks

  25. Advantages Provides an unbiased estimate of population parameters if properly done Economically more efficient than simple random Lowest cost per sample Easy to do without list Disadvantages Often lower statistical efficiency due to subgroups being homogeneous rather than heterogeneous Moderate cost Cluster

  26. Stratified Population divided into few subgroups Homogeneity within subgroups Heterogeneity between subgroups Choice of elements from within each subgroup Cluster Population divided into many subgroups Heterogeneity within subgroups Homogeneity between subgroups Random choice of subgroups Stratified and Cluster Sampling

  27. Advantages May reduce costs if first stage results in enough data to stratify or cluster the population Disadvantages Increased costs if discriminately used Double

  28. Time Nonprobability Samples No need to generalize Feasibility Limited objectives Issues Cost

  29. Nonprobability Sampling Methods Convenience Judgment Quota Snowball

  30. NONPROBABLITY SAMPLING • CONVENIENCE • JUDGMENT • QUOTA • SNOWBALL

  31. Nonprobability Sampling • Convenience Sampling • Based on availability, e.g. students in a classroom • Purposive Sampling • Specific targets, because they posses the desired info • Judgement sampling • Quota sampling

  32. CONVENIENCE SAMPLING • also called haphazard or accidental sampling • the sampling procedure of obtaining the people or units that are most conveniently available

  33. QUOTA SAMPLING • ensures that the various subgroups in a population are represented on pertinent sample characteristics • to the exact extent that the investigators desire • it should not be confused with stratified sampling

  34. 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

  35. SNOWBALL SAMPLING • a variety of procedures • initial respondents are selected by probability methods • additional respondents are obtained from information provided by the initial respondents

  36. Area Sampling

  37. Sample Size • Factors Determining Sample Size • Homogeneity of population • Level of confidence • Precision • Cost, Time and Resources

  38. Confidence level Larger Sample Sizes Population variance Number of subgroups Desired precision When Small error range

  39. Roscoe’s Rule of Thumb • >30 and <500 appropriate for most research • Not less than 30 for each sub-sample • In multivariate analysis, 10 times or more the number of variables • Simple experiment with tight controls, 10-20 quite sufficient

  40. WHAT IS THE APPROPRIATE SAMPLE DESIGN • DEGREE OF ACCURACY • RESOURCES • TIME • ADVANCED KNOWLEDGE OF THE POPULATION • NATIONAL VERSUS LOCAL • NEED FOR STATISTICAL ANALYSIS

  41. What Is A Good Sample? Accurate Precise

  42. AFTER THE SAMPLE DESIGN IS SELECTED • DETERMINE SAMPLE SIZE • SELECT ACTUAL SAMPLE UNITS • CONDUCT FIELDWORK

  43. SYSTEMATIC ERRORS • NONSAMPLING ERRORS • UNREPRESENTATIVE SAMPLE RESULTS • NOT DUE TO CHANCE • DUE TO STUDY DESIGN OR IMPERFECTIONS IN EXECUTION

  44. ERRORS ASSOCIATED WITH SAMPLING • SAMPLING FRAME ERROR • RANDOM SAMPLING ERROR • NONRESPONSE ERROR

  45. RANDOM SAMPLING ERROR • THE DIFFERENCE BETWEEN THE SAMPLE RESULTS AND THE RESULT OF A CENSUS CONDUCTED USING IDENTICAL PROCEDURES • STATISTICAL FLUCTUATION DUE TO CHANCE VARIATIONS

  46. 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

  47. End of lesson

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