"Time is the coin of your life. It is the only coin you have, and only you can determine how it will be spent. Be careful lest you let other people spend it for you." -Carl Sandburg

Population

Sample

Sampling Frame The list of units from which a sample is chosen

Built in Bias Convenience Sample Voluntary Response

Randomness Simple Random Sample (SRS): A sample of N units from the sampling frame chosen in such a way that every possible group of N units has the same chance of being chosen. Random - Fair - Representative - Unbiased

Random Sample Designs Simple Multi - Stage Systematic Stratified Cluster Hybrid

Simple Random Sample Every unit in sampling frame has an equal chance of being selected. Possible shortcomings - Bias due to poor sampling frame - Cost of sampling

Random Digits Table Row

Stratified Random Sample Useful with populations with known dissimilarities Why Bother? - Extra work to identify strata - Extra work to sample strata - Extra work to combine results

Stratified Random Sample Useful for populations with known dissimilarities e.g., race, age, education level Sample approach - Divide sampling frame into similar strata - Randomly select a sample from each strata - Combine individual results in weighted manner

Stratified Random Sample Strata# Students%# in Sample Freshman Sophomore Junior Senior

Systematic Random Sample Useful with large geographic regions of time dependent data Sample approach - Randomly select initial sample points e.g., 4001 Balcones Woods Drive - Sample every K-th unit from starting point e.g., 4004, 4007, 4010, 4013 B.W.D.

Multi-Stage Random Sample Useful with large geographic regions or time dependent data Reduce travel time and cost Reduce interruptions to ongoing manufacturing processes

Multi-Stage Random Sample Sample Approach: - Divide sampling frame into regions, e.g. counties - Randomly select regions - Divide selected regions into subregions e.g., city blocks - Randomly select subregions - Continue subdivision process

Cluster Sample Cluster sampling: A common form of sampling based on dividing a group into sub - units -This reduces the cost of sampling a population over a large geographic area.

Hybrid Sample Designs Hybrid: Combines features of “pure” random sample designs Meet objective of data collector - Cost - Time - Comparison

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Potential Problems in Sampling Poor Sampling Frame Cost of Sampling Built -In Bias

Cost of Sampling Money Time Wide Geographic Region

Major Errors in Sampling Bias: Consistent, repeated divergence in the same direction of a sample statistic from its associated population parameter. Lack of Precision: Large theoretical variation in a sample statistic

Sampling Error The difference between the sample statistic and its corresponding population parameter. Population: 97, 103, 96, 99, 105 (Mean = 100)

Non-Sampling Errors Survey Timing Survey Mode Interviewer – Subject Relationship Survey Topic Question Wording Question Sequence

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Statistical Significance An observed effect so large that it would rarely occur by chance.