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# Chapter 6 and 7 CJ 302-202

Marshall

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This 9 page Bundle was uploaded by Jason DeHaven on Friday March 11, 2016. The Bundle belongs to CJ 302-202 at Marshall University taught by Dr. Flesher in Spring 2016. Since its upload, it has received 67 views. For similar materials see Research Method in Science at Marshall University.

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Date Created: 03/11/16

CHAPTER 6 SAMPLING AND ELECTION POLES Introduction Much of the value of research depends on how data is collected You have to decide what will be observed and what wont for example which sub group of a certain subject will you study Sampling is the process of selecting observations and is ordinarily used to select observations for two reasons 0 1 Because it is often not possible to collect information from all people on what you ae studying or wish to study 0 2 It is not always necessary to collect data from all persons or other units One more important goal of sampling is to reduce or at least understand the potential biases that may be in work in selecting subjects The Logic of Probability Sampling In selecting a group of subjects for study social science researchers often use some type of sampling andor part of a population selected Samples individual or group we plan to study 0 1 Select samples to represent a larger population 0 2 May want to generalize from a sample to an unobserved population the sample is intended to represent Probabilitv Sampling a type of sampling that enables us to generalize a larger population by method of selection in with each member of a population has a known chance or probability of being selected Conscious and Unconscious Sampling Bias There is bias when a researcher selects subjects to represent a large population casually like selecting the rst 100 prisoners who are in cells next to each other without knowing their background information for the research to take place Our own personal leanings or biases may affect the sample selected in this manner hence the sample will not truly represent the population of lawyers Can39t trust polls linked to weblogs text messages or email because it can39 represent the whole general population Representatives and Probability of Selection Although the term representativeness had no precise scienti c meaning it carries a common sense meaning that makes it useful in the discussion of sampling A sample is 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 Equal Probability of Selection Method EPSEM a sample that is representative of the population from which it is selected if all members of the population have an equal chance of being selected in the sample Probability Samples offer two special advantages 0 1 Typically more represented then other types of samples because they avoid biases like casual selection 0 2 Probability sampling permits us to estimate the accuracy or representatives of the sample Probability Theory and Sampling Distribution We need to understand 4 concepts to examine how probability sampling works in practice 0 Sample element a unit about which information is collected and that provides the basis of analysis 0 Population the theoretically speci ed group of study element Example needs the group of study de nition the time efferent to study and a particular area Delinquent a person charged with a delinquent offense charged with a delinquent offense in the previous six months in the city if Los Angeles 0 Pooulation Parameter the value for a given variable in a population Example The average income of all families in a city and the age distribution of the city s population are parameters Sample statistic the summary description of a given variable in the sample used to make estimates of a populations parameters Two reasons for random selection methods are 0 1 The procedure preserves as a check on conscious or unconscious bias on the part of the researcher o 2 With random selection we can draw on probability theory which allows us to estimate population parameters and to estimate how accurate our statistics are likely to be The Sampling Distribution of 10 Cases Sampling distribution the dots on a graph that are used in research to estimate the mean without actually observing every subject the range of sample statistics we will obtain if we select many samples From Sampling Distribution to Parameter Estimate In order to draw an actual sample we need some sort of list of elements in our population called a sampling frame Binomial Variable the variable that has two different values such as approval or disapproval a proposed research sample An example of this is a law has been proposed and you would like to research out of 20000 voters about their approval and disapproval for a certain area Estimated Sampling Error Probability theory can help resolve our dilemma with some basic statistical concepts 0 1 If many independent random samples are selected from a population then the statistic provided by those samples will be distributed around the population parameter in a known way 0 2 Probability theory gives us a formula for estimating how closely the sample statistics are clustered around the true value The equation for this is Squot2 PxQn The 5 equals the standard error a measure of sampling error The n equals the number of cases in each sample P and Q are the population parameters for the binomial variable The standard error indicates how closely the sample estimates will be distributed around the population parameter and is also an inverse function of the sample size which means as the sample size increases the several samples will be clustered nearer to the true value Con dence Levels and Con dence Intervals Probability theory speci es that 68 percent of that ctitious large number of samples will produce estimates that fall within one standard error of the parameter As researchers we can turn the logic around and infer that any single random sample has an 8 percent chance of falling into that range 0 In this we speak of con dence levels we are 68 percent con dent that our sample estimate is within one standard error of the parameter 0 Our con dence level increases as the margin for error is extended The con dence interval at a 68 con dence level is 45 to 55 percent Probability Sampling A sampling frame is the list or quasilist of elements from which a probability is elected or of our target population We can say quotquasiquot because we can draw samples from a list that may not actually exist as if there was a list If we want to study the attitudes of correction administrators toward determinate sentencing policies we might select a sample by consulting the membership roster of the American Correctional Association For this our sampling frame is the membership roster and the correction administrators are the population we wish to describe Sampling frames serve as a real world version of an abstract study population If the sampling frame is a computerized database or another form of electronic data a simple random sample can be selected by computer 0 The computer program numbers the elements in the sampling frame and generates its own series of random numbers and prints out the list of elements selected Systematic Sampling Used when a list of elements is required and is available In this the researcher chooses all elements in the list for inclusion in the sample If the list contains 10000 elements you39d select every tenth element for the sample if you wanted a sample of 1000 However to be unbiased you choose a number between one and ten to begin with at random Virtually identical to simple random sampling The one danger to Systematic Sampling is o Periodicity a periodic arrangement of elements in the list that can make the sampling unwise o If the list of elements is arranged in a cyclical pattern that follows the sampling interval then you may draw a biased sample Strati ed Sampling Like the other two sampling methods random and systematic strati ed is not an alternative to these methods Strati cation represents a possible modi cation in their use and this method is used to obtain a greater degree of representativeness or decreasing the probable sampling error Remember how the sampling error is reduced by two degree factors in the sample design 0 1 A large sample produces a smaller sampling error then a small sample does 0 2 A homogeneous population produces samples with smaller sampling errors then a heterogeneous population does This method is based off the second factor in the sampling theory rather than selecting the whole population at large we select an appropriate number of elements from homogeneous subsets of that population The ultimate function is to organize the population into homogenous subsets with heterogeneity between subsets and to select the appropriate number of elements from each Disproportionate Strati ed Sampling Another use of strati cation is to purposely produce samples that are not representative of a population of some variable A way of obtaining sufficient numbers of these rare cases by selecting a number disproportionate to their representation in their population An example in criminal justice is a national crime survey in which one goal is to obtain some minimum number of crime victims in the sample MultiCluster Sampling Research involving the initial sampling of groups of elements clusters followed by the selection of elements within each of the selected clusters May be used when it is either impossible or impractical to compile an exhaustive list of elements that compose the target population Sampling Units population elements or aggressions of those elements Nonprobability Sampling The likelihood of any element to be selected it unknown 4 types of nonprobability sampling 0 Purposive Sampling Occasionally may be appropriate to select a sample on the basis of our own knowledge of the population its elements and the nature of our research aims Based on the judgement and purpose of the study In certain cases study elements may be selected because they exhibit some particular attribute Used to represent patterns or complex variations 0 Quota Sampling Addresses the issue of representativeness although the two methods approach the issue quite differently Begin with a matrix or table describing the characteristics of the target population we wish to represent The quota frame has to be accurate and the biases may exist in the selection of sampling elements within a given cell even though in proportion of the population is accurately corrected 0 Reliance on Available Subjects 0 Snowball Sampling Sampling that closely resembles the available subjects What I don39t know What is the difference between snowball sampling and reliance on available subjects What does it mean by value of a given population parameter What is Con dence Level in percent What I do know I understand that standard error is the level of how biased the study could be Systematic Sampling is done in a variable like every 10th A binomial variable is the variable that is before the elements like a binomial variable is a proposed law CHAPTER 7 SURVEY RESEARCH Introduction Survey research is the most frequently used mode of observation in sociology and political science and surveys are often used in criminal justice research as well This chapter begins by discussing the criminal justice topics that are the most appropriate for survey methods Then we will cover the basic principles of how to ask people questions for research purposes including some of the details of questionnaire construction Topics Appropriate to Survey Research Surveys are used for descriptive explanatory exploratory and applied research which are the best studies suited for individual people as the units of analysis However one or more individual people act the respondent or informant Countino Crime Asking people about victimization is a measure of crime that adjusts for some of the limitations of data collected by the police Survey measures have the own errors such as recall error and reluctance and the three types of surveys are selfreport perceptions and attitudes and targeted victim surveys Guidelines for Asking Questions In asking questions researchers have two different options which are openended questions and closed ended questions Openended questions are used so the respondent can answer with their own answers Closed ended questions are used so the respondent can select and answer from something like a multiple choice list When constructing close ended questions we are guided by two requirements for operationalizing variables The response categories provided should be exhaustive The answer categories must be mutually exclusive Questionnaires and Statements Questionnaire suggests a collections of questions but a typical one has many statements and questions Be clear Be short Avoid negativities Avoid bias means any property of a question that encourages respondent to answer in a particular way Designing Self Reports Items Social desirability when a person or subject is trying to make themselves appear good This social desirability is one of the problems that plagues selfreport crime surveys To reduce this one tried introducing a group of selfreport items with a disclaimer and to sanitize the presentation of offences Three techniques that guard against social desirable responses of not admitting a crime are To reassure the respondent that many people don39t think of certain acts as crimes To ask how many people they think commit each offence before being asked whether they have done so themselves Asking whether they have quotever cheated on an expense accountquot Interview people several times Questionnaire construction General questionnaire format The questionnaire shouldn39t be cluttered such as it being too long don39t39 abbreviate questions or squeeze several questions into a single space Continoencv Questions Quite often certain questions are clearly relevant to only some of the respondents and irrelevant to others These questions are when you ask a series of questions about a certain topic Matrix Questions Questions that have the same set of answer categories This format is space ef cient Easier for respondents to complete a set of questions presented in this fashion Easier for respondents to compare responses given to certain questions The danger is that it might get a respondent into a habit of agreeing with something they truly didn39t understand Ordering Items in a Questionnaire The order can affect how one answers questions due to the content Ask open ended questions if you need to avoid content complexity The BEST solution is to be sensitive to the problem You can39t truly avoid question order but you can estimate the effect it can have with certain orders In a more former questionnaire ask the interesting questions making the respondent want to answer them Finally it is common for less experienced researchers to assume that questionnaires must be newly constructed for each application It is usually preferable to use an existing questionnaire as a point of departure SelfAdministered Questionnaires Mail distribution and Return To collect data through the mail with a questionnaire and explanation of the research with a return stamp Warning Mailings and Cover Letters Can be used to verify who lives at sampled addressed and to increase response rates After the sample is generated a postcard is sent to each respondent with the notation quotAddress Correction Requestedquot the address can leave a forwarding address Warning letters are better at increasing response rate than postcards The cover letter needs to have important content The cover letter should identify the institutional af liation or sponsorship of the survey Follow Up mailings Such as being sent reminders that the survey research is still going on so the respondent can participate In Person Interview Surveys interview survev an alternative method of collecting data by asking respondents questions oray The interviewer is their so the no and I don39t know answers will be decreased The respondent is less likely to turn down the interviewer The interviewer can observe and ask questions The interviewer must know his own questionnaire

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