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
Individuals, teams, groups, agencies, sports
Called - subjects/participants
Choosing a Sample
Population Total group to which results
can be generalized
NFL sales staff DI Tennis Players 5th grade girls Male intramural players
N=2,554
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
Choosing a Sample
Population Delimiting variables Sample
Delimiting variables:• Demographic variables that narrow population
• Age, gender, geography
• Other variables• Group, division, sport, sector, grade
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?
Sources Error
2 Sources of Error
1. Sampling error
2. Non-sampling error
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
Sampling Error Error rates
Calculated statistically
50% with + 4 points = 46% - 54%
Political polls…
Sampling Error
Pop. Obama Romney Undecided Margin Error
Rasmussen1,500 LV 48 46 - Obama +2 ±3.0
Ipsos/Reuters
855 LV 47 42 8 Obama +5
Gallup
3,050 RV 50 44 - Obama +6 ±2.0
LV=Likely votersRV=Registered voters
56.8% of registered voters vote in presidential election
Sampling Error
Who responds… Better educated Higher socioeconomically Higher need for social approval More sociable Somewhat less conventional Less conforming Female
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
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?
Non-Sampling Error
Biases that exist due to who answers a survey Weighted scales
Examples…
Excellent Good Fair Poor
Strongly agree Agree Disagree Strongly disagree
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
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…
Sample Size
Notes:
5% = 5% chance the sample differs from the population
point of diminishing returns
Response Rates
# within your sample who complete the survey 70% special interest groups
Parents, fans 60% professional groups
Staff & Volunteers 55% general interest
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
WHAT INCREASES YOUR RESPONSE RATES?
Increase Response Rates
Pre-notification Contact respondents in advance Give opt out option
Interest in topic
Survey design Short & concise
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
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
Sampling Methods
1. Probability Sampling Simple Random Sampling Stratified Random Sampling Systematic Sampling Cluster Sampling
2. 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
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
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.
Simple Random Sampling
Software
http://www.randomizer.org/lesson4.htm
Simple Random Sampling
Strengths No subject classification error Easy to understand
Weaknesses Have to number each person Larger sampling error than stratified random
sampling
Stratified Random Sample Randomly selected from within a stata
(subpopulation)
Need to be able to assign everyone to one strata
Age, race, gender, income, geography
Allows researcher to compare groups
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
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
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
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
Systematic Sampling Strengths
Simple process Don’t have to classify or number people
Weaknesses Larger sampling error than Stratified RS
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
Cluster Sampling Strengths
Low cost Can analyze individual clusters
Weaknesses Higher error than simple random & stratified random Requires everyone assigned to 1 cluster
Probability Sampling Overview Least Error….
Stratified random sampling Simple random / Systematic Cluster
Non-probability Sampling
Used when population is unknown Fans People with a specific disability Runners, bikers, hikers, backpackers
Sample isn’t drawn by chancePurposive SamplingConvenience SamplingQuota SamplingSnowball Sampling
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
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
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
Convenience Sampling Strengths
High participation rates Cheap, easy Generalization to similar subjects
Weaknesses Difficult to generalize to other subjects Experimenter subject bias
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
Quota Sampling Strengths
High participation rates Cheap, easy Generalization to similar subjects More representative sample
Weaknesses Same as others More time consuming than others
ComparisonN=500 alums n= 217
Sport Mgt Recreation PETE
250 (50%) 150 (30%) 100 (20%)
* Stratified Random (proportional)
109 65 43
* Cluster Sampling Select this group only
- -
** Quota Sampling* Stratified Random (non-proportional)
73 73 73
• * Probability• ** Non-Probability
Snowball Sampling
Based on recommendations
Stay at home dads
Women motorcyclists
Athletes recruited by but not attending ISU/IWU
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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