Chapter 6 Sampling Introduction o A critical part of the CJ research is deciding what will be observed and what wont o o Used for two reasons Sampling Process of selecting observations 1 It is often not possible to collect info from all persons in a population 2 Often not necessary to collect data from all persons Probability sampling allows us to make relatively few observations and then generalize from those observations Probability sampling cannot be used in many situations so we use non probability sampling One important goal of all sampling o To reduce or at least understand the potential biases that may be at work in selecting The Logic of Probability Sampling subjects Sampling selecting part of a population Sample A subset of a population selected according to one or more criteria In selecting samples we want to do two related things 1 We select samples to represent some larger population 2 We may want to generalize from a sample to an unobserved population the sample is intended to represent Probability Sampling The general term for a sample selected in accord with probability theory typically involving some random selection mechanism A method of selection in which each member of a population has a known chance or probability of being selected Specific types of probability sampling o Equal probability of selection method EPSEM o o Simple random sample Systematic sample o Conscious and Unconscious Sampling Bias Makes it possible for us to make predictions that our sample accurately represents the larger population If all members of a population are identical in all respects there is no need for careful sampling procedures In this case of extreme homogeneity a single case will be sufficient as a sample Human beings who make up any real population are heterogeneous A sample of individuals from a population must contain essentially the same variations that exist in the population Since we are all different e g race age attitudes beliefs etc our sample must reflect that and be representative of the variations that exist among us Bias Those selected are not typical or representative of the larger populations This kind of bias is virtually inevitable when a researcher picks subjects casually or select subjects who are convenient Researchers own personal leanings or biases may affect the sample selected Ex a researcher is a little intimidated by prosperous lawyers so he either consciously or unconsciously avoids them Or he might believe that the attitudes of established lawyers are irrelevant to his research purpose Even if he interviews every 10th lawyer who enters the court house he wont have a representative sample o Different types of lawyers visit courthouse with different frequencies and some never go at all o Will over represent lawyers who visit the courthouse Call in polls Radio stations ask people to call specified telephone numbers to register their opinions Not everyone in the population is even aware of the poll Different stations reach different audiences Blogs Blogs tend to be selective People regularly visit blogs that present views on personal and political issues they endorse The more self selection involved the more bias o Representativeness and Probability of Selection A sample is a representative of the population from which it is selected if the aggregate characteristics of the sample closely approximate those same aggregate characteristics in the population Samples need not be representative in all aspects Representativeness is limited to those characteristics that are relevant to the substantive interests of the study Basic principle in probability sampling is that a sample will be representative of the pop from which it is selected if all members of the pop have an equal chance of being selected Equal Probability of Selection Model EPSEM a sample design in which each member of a population has the same chance of being selected in the sample Seldom perfectly representative of the pop Probability sampling offers 2 special advantages 1 More representative than other types of samples because they avoid the biases o Greater likelihood that a probability sample will be representative 2 Permits us to estimate the accuracy or representativeness of a sample Probability sample can provide an accurate estimate of success or failure because probability samples enable us to draw on probability theory Probability Theory and Sampling Distribution 4 important concepts 1 Sample Element Sample Element The unit about which info is collected and that provides the basis of analysis o Typically in survey research elements are people o Other kinds of units can be the elements for CJ research o Correctional facilities gangs police beats court cases o Elements and units of analysis are often the same in a study The former refers to sample selection and latter to data analysis 2 Population Population All people things or other elements we wish to represent o Researchers often study only a subset or sample of a pop then generalize from people things or other elements actually observed to the larger pop o The theoretically specified grouping of study elements 3 Population Parameter Population Parameter The summary description of a particular variable in the pop o Ex if the mean age of all professors at your college is 43 7 then 43 7 is the population parameter for the professors mean age The value for a given variable in a pop o Ex average income of all families in a city and age distribution of a city s pop 4 Sample Statistic Sample Statistic The summary description of a given variable in the sample o Used to make estimates of population parameters o Ex The average income computed from a sample and the age distribution of that sample are statistics Those statistics are used to estimate income and age parameters in a population The ultimate purpose of sampling is to select a set of elements from a pop in such a way that descriptions of those elements sample statistics accurately portray the parameters of the total pop Probability sampling enhances the likelihood of accomplishing this aim and provides methods for estimating the degree of probable success Key to this process is random selection o Random Selection each element has an equal chance of being selected Ex Flipping a coin o Two reasons for using random selection 1 Serves as a check on conscious or unconscious bias on the part of the researcher Researcher who selects cases on an intuitive basis might choose cases that will
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