Sampling; Experiments
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
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
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
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
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
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
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
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)
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
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)
Nonprobability sampling Convenience, Purposive Quota Snowball
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
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
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
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?
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)
Threats Compensatory rivalry: we’re in the control group, we try harder Demoralization: we’re in the control group, we are discouraged.
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
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