1. SAMPLING PRINCIPLES Research Methods University of Massachusetts at Boston ©2011William Holmes 1

2. Part of a whole. The larger whole is a population. The subgroup is the sample. Some selected by scientific procedures Some selected by haphazard procedures Some selected with deliberate bias WHAT IS A SAMPLE? 2

3. To make generalizations about a population. Populations are expensive to get. Populations are difficult to obtain. A good sample is better than a poor population WHY DO YOU NEED A SAMPLE? 3

4. HOW DO YOU GET A GOOD SAMPLE? Fit the sampling procedure to the population, the resources, and the moral and legal constraints. Choose the most scientific procedure feasible. Choose the largest sample possible. Choose probability samples over non- probability. 4

5. TYPES OF SAMPLES Non-probability Sample— haphazard, convenient Probability Sample— systematic Fraudulent Sample— deliberately biased 5

6. WHAT ARE PROBABILITY SAMPLES? Follows standard procedure for everyone in population Chance of selection using procedure is known Unintended, random bias is possible 6

7. TYPES OF PROBABILITY SAMPLES Simple Random Sample Systematic Sample Cluster Sample Stratified Sample 7

8. WHAT ARE NONPROBABILITY SAMPLES? Uses Non-standardized (Variable) procedures Chance of selection is unknown Unintended, systematic bias may creep in 8

9. TYPES OF NON-PROBABILITY SAMPLES Convenience Sample—not deliberately biased Purposive Sample—chosen to be similar to a population, according to the chooser Quota Sample—chosen to be similar to a population, according to known characteristics Snowball Sample—using referrals from known members of a population 9

10. FRAUDULENT SAMPLES Artificially constructed to show a characteristic or a relationship Violates norms of science and research Selects cases to prove a point Concerned with non-scientific ends—money, promotion, ideology. 10

11. HOW DO YOU TELL IF YOU’VE GOT A GOOD SAMPLE? Check for scientific procedures Check for ethical and legal requirements Compare with known population characteristics Look for weirdness 11

12. SELECTING A RANDOM SAMPLE 1. Define population 2. Get list of random numbers or choose a random process 3. Make a decision rule to select cases 4. Assign random numbers 5. Select persons who meet criteria 12

13. SELECTING A SYSTEMATIC SAMPLE 1. Define population. 2. Decide on sample size. 3. Divide population into groups where the number of groups equals the sample size. 4. For first group, select one by simple random sampling. 5. Count down on list a number equal to the group size. 6. Select each person at end of count. Repeat. 13

14. SAMPLING EXAMPLE PersonAgeGenderRdn Nbr*Grp 11814#1 225131^ 321272 434252^ 522113 61912#3^ 733274 820174^ 921255 102426#5^ *from random number table. #Selected for random sample. ^Selected for systematic sample. Random Number Criteria: select persons with even random numbers Systematic Sample start: person number 2 Rdm mean age=20.3 Rdm mean sex=0.67 Syst mean age=24.4 Syst mean sex=0.60 14