1. 1 Sampling Partially Adapted from The Research Methods Knowledge Base, William Trochim (2006). & Methods for Social Researchers in Developing Counries, The Ahfad University for Women & Online Statistics Education: A Multimedia Course of Study, David M. Lane, Rice University.

2. 2 Agenda Introduction Sampling Terminology Types of Sampling Sampling Biases 2

3. 3 Introduction The purpose of research is to learn something about a larger group – the population It is often impractical, however, to enumerate an entire population The alternative is to obtain a representative sample

4. 4 Sampling Terminology Population: abstract vs target Sampling element: A single member or unit of target population Sample frame: list of all sampling elements in target population Statistic: finding based on a sample; used to estimate a parameter Parameter: finding based on measuring entire population Sampling Error/Sampling Bias: how well a statistic estimates a parameter

5. 5 Types of Sampling Probability Sampling: relies on random selection –Simple random sampling –Stratified random sampling –Systematic random sampling –Cluster sampling Nonprobability Sampling: no random selection –Convenience Sampling –Quota Sampling –Expert Sampling –Network Sampling

6. 6 Types of Probability Sampling Simple random sampling –Simplest form of random sampling –Select n units out of N (target population) such that each unit has equal chance of being selected Stratified random sampling –Population is divided into homogeneous subgroups and a random sample is taken from each subgroup Systematic random sampling –Sampling interval is calculated and used to select sampling elements Cluster Sampling –Population is divided into clusters –Clusters are randomly sampled –All units within clusters are measured

7. 7 Types of Nonprobability Sampling Convenience Sampling –Sample selected because of easy accessibility Quota Sampling –A quota for subgroup is set in advance –Persons with right characteristics are selected nonrandomly until number is met Expert Sampling –Sample of persons with experience or expertise is assembled Network Sampling –Begin by identifying someone who meets the criteria for inclusion in you study –Next ask them to recommend others whom they know who meet the criteria. So on, so forth –Useful when trying to reach inaccessible populations

8. 8 Sampling Biases Sampling biases are errors that arise due to method of sampling used There are 3 common types of sampling biases –Self-Selection Bias –Survivorship (Attrition) Bias –Sampling Selection Bias

9. 9 Self-Selection Bias 1.People who “self-select” themselves for an experiment might not be representative of the target population –respondents who signup for an online survey about computer use 2.When people sign-up for an experiment without knowing what it is about and leave subsequently upon finding out 3.Non-response bias when certain subjects are more likely to respond than others

10. 10 Survivorship (Attrition) Bias When observations recorded at the end of an investigation are a non-random set of those present at the beginning of the investigation –Measuring gains in stock funds when poorly performing funds are often eliminated –Measuring effects of school feeding programs when underweight students often drop-out of school

11. 11 Sampling Selection Bias Arises through researcher’s choice of sampling method or units sampled –Nonprobability sampling Under-coverage bias –Researcher samples too few observations from certain segments of the population –U.S Elections Landon-Roosevelt telephone poll: Roosevelt won despite polls in favor of Landon  Poorer people who tend to vote for Roosevelt did not have a telephone

12. 12 Effects of Biases Resulting sample becomes less representative of target population Results of study not as generalizable to the target population