1. Sampling Fundamentals

2. Basic Concepts
Population: the entire group under study (or of interest)
Exercise: Define population for a study seeking to assess SUU student attitudes towards a) program quality and delivery, b) program content, and c) social environment.
Sample: subset of the population
Used to represent the population
Sample unit (elements): basic unit investigated (choose sampling units/elements when sampling)
Individuals, households, etc.
Census: data collected from EVERYONE in population

3. Basic Concepts (continued)
AGAIN: total error = sampling error + nonsampling error
Sampling error: error due to taking a sample (+/-zs)
Nonsampling error: everything else (measurement, data analysis, etc.)
Sample frame: list from which the sample is selected
Sample frame error: Pop’n members not in frame, and members in frame not in pop’n of interest

4. Reasons for Sampling Cost Too much information to handle
Sampling can be more accurate
Nonsampling errors can overwhelm reduction in sampling errors
Sampling work behaviors example
Census Bureau
Time problem

5. Developing a sampling plana
1. Define the population of interest.
2. Choose a data-collection method (mail, telephone, Internet, intercept, etc.).
3. Identify a sampling frame.
4. Select sampling method
5. Determine sample size.
6. Develop operational procedures for selecting sampling elements/units.
7. Execute the operational sampling plan.

6. PROBABILITY SAMPLING METHODS
Each member of population has a ‘known’ probability of being selected
Simple Random Sampling: Each member has an equal probability of being selected
Blind Draw Method
Table of Random Numbers
Useful for small samples, when Random Digit Dialing (or +1) is appropriate, and computerized lists

7. PROBABILITY METHODS (Cont’d)
Stratified Sampling: Population is segmented (stratified), and then samples are chosen from each strata using some other method
Can be more efficient (smaller sampling error)
Homogeneous within, heterogeneous without
Useful when interested in different strata (e.g., small numbers, etc)
Disproportionate versus proportionate

8. PROBABILITY METHODS (Cont’d)
Cluster Sampling: Population is divided into groups, or clusters, and then clusters are randomly chosen.
Homogenous without, heterogeneous within
Every unit in cluster examined, OR
A Random (or systematic) sample is taken from chosen cluster (2-stage or 2-step approach)
Careful with the probabilities!

9. PROBABILITY METHODS (Cont’d)
Systematic Sampling: Randomly choosing a starting point and then choosing every nth member.
Example: Need 52 data points (daily sales) for a year
Skip interval = 365/52=7.01
Randomly choose 1 day out of first 7, then choose every 7th one after that.
Variation: Choose every nth visitor

10. NONPROBABILITY SAMPLING METHODS
Probability of selection not known, and hence representativeness cannot be assessed
Technically, confidence intervals, H0 tests, etc. not appropriate
Convenience Samples:
Shopping mall intercepts, classes asked to fill out questionnaires, etc.
Judgment Samples: Someone puts together what is believed to be a relatively representative sample
Ex.: Test markets

11. Nonprobability Sampling (Cont’d)
Referral (or Snowball) Samples
Quota Samples
EXAMPLE: Choose sampling units so their representation equals their frequency in the pop’n (e.g., 52% females, 48% males)

12. The Sampling Process Identifying the Target Population
Reconciling the Population, Sampling Frame Differences
Determining the Sampling Frame
Selecting a Sampling Frame
Probability Sampling
Non-Probability Sampling
The Sampling Process
Determining the Relevant Sample Size
Execute Sampling
Data Collection From Respondents
Handling the Non-Response Problem
Information for Decision-Making

13. Nonresponse Bias Reason for nonresponse: Handling nonresponse Refusal
Lack of ability to respond
Not at home
Inaccessible
Handling nonresponse
Improve research design
Call-backs
Estimate effects
Sample nonrespondents; trends