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Sampling Methods

Sampling Methods. Tasks. Population vs. Sample Sources of sampling error Sample size & response rates Probability sampling & its methods Non-probability sampling & its methods. What is sampling?. Process of choosing subjects for inclusion in the study

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Sampling Methods

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  1. Sampling Methods

  2. Tasks • Population vs. Sample • Sources of sampling error • Sample size & response rates • Probability sampling & its methods • Non-probability sampling & its methods

  3. What is sampling? Process of choosing subjects for inclusion in the study Individuals, teams, groups, agencies, sports Called - subjects/participants

  4. Choosing a Sample Population Sampling Frame All the people who compose a particular group AFC sales staff MVC tennis players 5th grade girls in 2 districts Male intramural players in schools 20-40,000 pop n=2,554 • Total group to which results can be generalized • NFL sales staff • DI Tennis Players • 5th grade girls • Male intramural players • N=2,554

  5. Choosing a Sample • Delimiting variables: • Demographic variables that narrow population • Age, gender, geography • Other variables • Group, division, sport, sector, grade

  6. Choosing a Sample • How does alcohol sales within a collegiate stadium impact ticket sales? • What motivates runners to participate in “fun” runs such as color runs, zombie runs, etc?

  7. Sources Error • 2 Sources of Error • Sampling error • Non-sampling error

  8. Sampling Error • The difference between characteristics of a sample & the characteristics of the population • Get a representative sample • The smaller the error, the more reliable the data • As sample size increases, error rates decrease

  9. Sampling Error • Error rates • Calculated statistically • 50% with + 4 points = 46% - 54% • Political polls…

  10. Sampling Error LV=Likely voters RV=Registered voters 56.8% of registered voters vote in presidential election

  11. Sampling Error • Who responds… • Better educated • Higher socioeconomically • Higher need for social approval • More sociable • Somewhat less conventional • Less conforming • Female

  12. Non-Sampling Error • Biases that exist due to who answers a survey • Question confusion…validity • Accessibility questions • Confusion on terms • Lack of knowledge by respondent • Don’t answer vs. Neutral response • Concealment of the truth

  13. Non-Sampling Error • Biases that exist due to who answers a survey • Loaded questions • Don’t you agree that social workers should earn more money than they currently earn? ___ Yes, they should earn more ___ No, they should not earn more ___ Don’t know/no opinion • Do you believe social worker salaries are a little lower than they should be, a little higher than they should be, or about right?

  14. Non-Sampling Error • Biases that exist due to who answers a survey • Weighted scales • Examples…

  15. Non-Sampling Error • Examples • Asking college age students about family finances • Surveying those completing class & not those registered & dropped it • Pontiac Parks & Rec - Surveying those who are members

  16. Sample Size • Goal • Collect a sample that is large enough to be representative of the population, but not so large as to waste resources • Determining sample size • Use population if it is small • Literature • Statistics to run… X number needed for analysis • Table…

  17. Sample Size • Notes: • 5% = 5% chance the sample differs from the population • point of diminishing returns

  18. Response Rates • # within your sample who complete the survey • 70% special interest groups • Parents, fans • 60% professional groups • Staff & Volunteers • 55% general interest

  19. Response Rates • Sample size vs response rates • Estimate # needed based on sample size chart • Estimate response rate • Inflate sample size to accommodate for nonresponses • Will get some unusable surveys & addresses

  20. What increases your response rates?

  21. Increase Response Rates • Pre-notification • Contact respondents in advance • Give opt out option • Interest in topic • Survey design • Short & concise

  22. Increase Response Rates • Timing & delivery • Holidays, pool openings, summer vacation, NCAA tournament • Incentives • Younger audience – electronics • Older audience – gift cards, free conference reg. • Send reminders

  23. Increase Response Rates • E-mail invites • Professionals • avoid Friday-Monday • Students • Monday afternoon, Thursday morning, Saturday afternoon • Avoid spam language • Personalize the e-mail with respondents name • Use clean, updated list

  24. Sampling Methods • Probability Sampling • Simple Random Sampling • Stratified Random Sampling • Systematic Sampling • Cluster Sampling • Non-probability Sampling • Purposive Sampling • Convenience Sampling • Quota Sampling • Snowball Sampling All members of the population have a chance of being selected Sample is not drawn by chance

  25. Simple Random Sampling • Equal probability of being selected • Results in the most reliable data • Will most represent the population • How to do it: • Draw names, teams, leagues, classes • Assign numbers • Random numbers table… • Software

  26. 1. Needs: 5 random numbers between 0-20 2. Randomly select a row. 3. Read 2 #’s at a time, select those that are between 00-20.

  27. Simple Random Sampling Software http://www.randomizer.org/lesson4.htm

  28. Simple Random Sampling • Strengths • No subject classification error • Easy to understand • Weaknesses • Have to number each person • Larger sampling error than stratified random sampling

  29. Stratified Random Sample • Randomly selected from within a stata (subpopulation) • Need to be able to assign everyone to onestrata • Age, race, gender, income, geography • Allows researcher to compare groups

  30. Stratified Random Sample • Non-proportional sampling • # selected from each strata • Gold Medal finalist/winning agency directors • Proportional sampling • Class make-up = 60% boys; 40% girls • Sample = 60% boys; 40% girls

  31. Example • Male vs. female • Female: 22/41 = 54% • Male: 19/41 = 46% • Sample size = • 54% x 36 = 19 Females • 46% x 36 = 17 Males • Kettering phone survey Population Sample

  32. Stratified Random Sample • Strengths • Can compare subgroups • More representative than simple random sampling • Results represent population • Weaknesses • Requires subgroup classification • Need to know the proportion of each group • Costly

  33. Systematic Sampling • Determine a rationale for a sampling routine • Select every “nth ” person • 5,000 population, 370 sample • Every 10th person starting with 5th person • Roll the dice; draw a number • ISU Women’s b-ball attendees • Every 4th person to pass by

  34. Systematic Sampling • Strengths • Simple process • Don’t have to classify or number people • Weaknesses • Larger sampling error than Stratified RS

  35. Cluster Sampling • Divides population into naturally occurring groups or units rather than individuals • Neighborhoods • Conferences • Grades • Use specific units to randomly select or stratify • NASPD Regions – randomly select 4 of the 8 regions • ROE – 1 of 3 counties, schools within

  36. Cluster Sampling • Strengths • Low cost • Can analyze individual clusters • Weaknesses • Higher error than simple random & stratified random • Requires everyone assigned to 1 cluster

  37. Probability Sampling Overview • Least Error…. • Stratified random sampling • Simple random / Systematic • Cluster

  38. Non-probability Sampling • Used when population is unknown • Fans • People with a specific disability • Runners, bikers, hikers, backpackers • Sample isn’t drawn by chance • Purposive Sampling • Convenience Sampling • Quota Sampling • Snowball Sampling

  39. Purposeful/Purposive Sampling • Select certain individuals because you feel they represent the entire population • Groups, classes/programs, time of day • MVC Campus Rec Departments • Qualitative • Select “info rich” cases • Key informants • Few will give in depth knowledge • Generalization isn’t the purpose

  40. Purposeful/Purposive Sampling • Strengths • Easy to administer • Less costly & time consuming • Generalization possible to similar subjects • Weaknesses • Difficult to generalize to other subjects • Experimenter subject bias

  41. Convenience Sampling • Chosen because they are accessible • Ie. Survey my classes, ISU students, IWU S-A’s • Higher error rates, less genralizability • ISU Volleyball non-attendees • Survey classmates • Dorm dwellers

  42. Convenience Sampling • Strengths • High participation rates • Cheap, easy • Generalization to similar subjects • Weaknesses • Difficult to generalize to other subjects • Experimenter subject bias

  43. Quota Sampling • Divide population into sub-groups • Survey equal number of each group • Stratified is a % • Cluster is a section • Quota is = numbers • May not be representative of the population

  44. Quota Sampling • Strengths • High participation rates • Cheap, easy • Generalization to similar subjects • More representative sample • Weaknesses • Same as others • More time consuming than others

  45. Comparison • * Probability • ** Non-Probability

  46. Snowball Sampling • Based on recommendations • Stay at home dads • Women motorcyclists • Athletes recruited by but not attending ISU/IWU

  47. Weaknesses of NPS • Generalizability • Used most if purpose is to understand & not to generalize • Show how the sample matches the population • Indicate that results will be same for the sample population • Biased sample • Bias by whom you select

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