1. Sampling; Experiments

2. Sampling  Logic: representative sampling  Sample should have the same variations existing in the larger population  Biased samples  Interviewing individuals who “show up” at a particular place  Call in pools

3. Sampling  Biases  Writing in responses  To eliminate biases, the best techniques involve equal probability of selection methods (EPSEM)  Need for an inclusive sampling frame  Without one, there will be some bias

4. Why EPSEM works (with a complete sampling frame)  Sampling distributions  Sampling distributions form normal distributions  We can use this finding (the Central Limit Theorem) to estimate sampling error and confidence levels and intervals

5. Sampling error  Affect by:  Sample size  Homogeneity/diversity of the population—a homogeneous population will have less sampling errors and require a smaller sample size  Adequacy of the sampling frame

6. Types of probability sampling  Concept of probability  Simple random, systematic  Stratified random sample  Stratification: dividing population into more homogeneous samples  Then random sampling from these sample proportionate to their % in the population

7. Other variations  Disproportionate stratified sampling  Sampling from specific groups of interest  Multistage cluster sampling: useful when there is not an exhaustive list  Examples: sampling police departments as a unit, and then sampling in the departments

8. Multistage clusters  No single list of a city’s population  Sample of blocks  Create a list of persons who live on each of the selected blocks and then sample from that list  Series of listing and sampling, in stages  Sampling error at each stage

9. Multistage  Sampling error can be reduced by stratifying  For example, with police departments we might stratify by size, regions, etc  For cities, by density, type of area (for example city zones)

10. NCVS  Those living in households are sampled  Does not easily include the homeless, people living in institutions (dormitories, hospitals, etc), business crimes  First level: metropolitan areas, counties

11. NCVS  Then housing units and group quarters from census records, new construction from local governments (remember, the census is done only every 10 years), and census blocks  Other countries with more centralized records are able to sample more easily (although not developing countries)

12. Nonprobability sampling  Convenience,  Purposive  Quota  Snowball

13. Experiments  IVs and DVs  Experimental and control groups  Pre and post testing  See p. 178 for basic experimental design  Issue of double blind studies: when research staff are unaware of the exp. or control group status of subjects

14. Subjects  How is the population to be selected, and how is the sample to be obtained?  Desireability of random assignment of subjects to experimental and control groups, to obtain statistically equivalent groups  Obstacles to randomization

15. Threats to validity  Other variables might affect the DV other than the IV  History  Maturation  Testing  Instrumentation—changes in measurement over time, i.e., changes in record-keeping procedures

16. Threats  Regression: statistical regression to the mean of extreme scores  Selection—biases in the comparison groups  Mortality  Time order—which came first, the chicken or the egg?

17. Threats  Diffusion of IV, elements of the IV might be passed along to the control group  Compensatory treatment: if subjects in the control group are not getting something, and staff know it, they may offer compensation (KC patrol experiment)

18. Threats  Compensatory rivalry: we’re in the control group, we try harder  Demoralization: we’re in the control group, we are discouraged.

19. External validity  The IV affected the DV: will it generalize?  Construct validity: is our IV a good measure of the concept behind it?  For example, how intensive is intensive probation? If it can have an effect, how intensive does it have to be? Need to try out different levels

20. Generalizability  Experiments may be less generalizable if done under very controlled conditions, because other settings might not have those controls  Internal validity enhanced with controls, but may decrease external validity