Methods of Sociological Inquiry
Methods of Sociological Inquiry SOC 357
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Date Created: 09/17/15
Sociology 357 Summer 2002 Study Guide Part II A Sampling Theory Will be at least 20 of exam 1 De nition of a probability sample every element of the population has a known non zero probability of selection a Recognize obvious examples of probability and nonprobability samples b NOTE Probability samples are also known as random samples Understand difference between population of theoretical interest and the actual population to which generalizations may be made the population listed by the sampling frame Sampling frame a The list or rule defining the population b Recognize examples c Understand the crucial role in sampling generalizations from probability samples can be made only to the population listed by the sampling frame d Understand difference between the sample and the sampling frame e Recognize that the sampling frame itself is not randomly chosen and that it must be evaluated for its fit to the purposes of research and ability to tap the population of theoretical interest Recognize and distinguish among examples of the types of random probability samples a Simple b Systematic c Stratified d Cluster e Stratified Cluster Idea of sampling error as the error in an estimate you get from a sample Not the technical definition but the heuristic idea of a wider or narrower range within which the estimate falls and the risk of a sample whose average is markedly different from the population Systematic Sample a Know that it is comparable to simple random if the list is random b Know that there is implicit stratification if the list is ordered first one group then another etc c Know that there is a risk of high error if the list has a periodicity that coincides with the sampling interval d Recognize examples ofthese Cluster vs stratified vs stratified cluster a Recognize examples of each b Know that stratification samples some from each group while cluster samples some of the groups c Know that compared to simple random sample cluster samples have higher errors while stratified samples have lower errors Have some basic understanding of why this is true d Recognize the difference between a random cluster sample and a nonprobability sample e Know that strati cation is worthwhile when the groups are different from each other and it is not too costly to perform the strati cation f Know that cluster sampling is worthwhile when the clusters are comparable to each other and there are substantial cost savings from clustering so that a larger sample is possible g Recognize examples of reasonable and unreasonable strati cation or clustering h Understand the idea of creating strata of clusters and then sampling clusters from each stratum of clusters i Recognize examples of strati ed cluster sampling and distinguish it from other types of samples j Complex multistage probability samples often involve several nested steps of strati ed cluster sampling 8 Response rates and nonresponse bias a Need for callbacks to bring up response rate b Samples without callbacks may be in practice indistinguishable from controlled quota samples 9 Convenience vs purposive sampling a Neither is probability sample b Convenience is pure convenience not related to purposes of research 10 Justi cations for purposive sampling a Small samples lt30 too small for random sampling to be adequate choose subjects appropriately for purposes of research b Hardtoget populations that cannot be found through screening general population 1 l Quota sampling a Recognize examples when described 39 this is quot quotJ 39 sampling c Controlled quota samples use probability sampling down to the last level then quota 12 Factors affecting needed sample size Paired examples may ask you which one would need larger sample size or you may be asked to pick these out of a list including other irrelevant factors a Recognize that accuracy of sample depends upon sample size not ratio of sample to population b Heterogeneity need larger sample to study more diverse population c Desired precision need larger sample to get smaller error d Sampling design smaller if strati ed larger if cluster e Nature of analysis complex multivariate statistics need larger samples B Other 13 Understand the idea that different measures of the same concept will be correlated with have bivariate association with each other 14 Possibly have simple examples of reliability analysis and discuss what they imply about the measurement of the concept 15 General idea of statistical control for extraneous variables in isolating causeeffect relations Sociology 357 Summer 2002 Study guide part I In general the exam will focus on material that has been discussed in class although you will nd that the textbook deepens your understanding of major concepts Test questions will be objective multiple choice matching truefalse short answer I will be less interested in rote memory of de nitions or details and more in testing whether you really understand the major concepts 1 Distinguish empirical statements or questions from other kinds of statements Especially contrasted with a De nitions b Value statements things that are good or bad c Abstract statements with no empirical referent ie with unconcretized abstractions 7 these will be obvious examples not borderline cases Recognize the difference between casual observation and controlled scienti c observation in clear examples not borderline cases Recognize examples of observation errors and be able to distinguish among them a Inaccurate observation b Overgeneralization c Selective observation Scienti c attitude a Knowledge as an end in itself b Not introspection interested in things outside yourself in knowing how it works not projecting your own ideas c Respect the facts respect the evidence d Tell the truth about your research e Recognize the boundaries of expertise know what you know and what you do not know Role oftheory in science Variables a De nition exhaustive mutually exclusive set of categories b Recognize examples determine whether something is or is not a variable c Be able to distinguish independent and dependent variables in short research descriptions in which implied causal direction is unambiguous d Understand difference between precision and accuracy in categories of a variable e Understand tradeolT between precision and accuracy in measuring a variable Level of measurement nominal ordinal interval ratio a Recognize examples b Know which kinds of statistics can be done with each percentages means correlations Units of analysis a Recognize examples b Recognize variables appropriate to different units of analysis c Distinguish among variables and units of analysis in example propositions Propositions 13 14 a Distinguish propositions from nonpropositions b Distinguish among univariate bivariate and multivariate propositions be able to correctly classify examples c Recognize examples of hypotheses de ned as the proposition being tested in a particular project d Recognize examples of assumptions de ned as propositions being taken as givens in a particular project e General form of a proposition at abstract and concrete operational level f Role of measurement assumptions in relation between abstract and concrete propositions Relations a Recognize examples of bivariate or multivariate relations b Recognize examples of positive negative curvilinear relations c Recognize examples of causal relations as opposed to merely statistical associations Operationalization measurement a Recognize clear examples of unconcretized abstractions versus operational variables b Know steps of operationalization procedures for collecting data which are concrete and speci c clear de nition of the different categories c Operationalization in survey research with closedended questions is the exact question used and the possible response categories plus what you did with non response or other problematic answers d Operationalization in surveys with openended questions is the exact question used plus the information about how the responses were coded into categories e Operationalization in observation is the exact and speci c observational procedures used plus the detailed rules and procedures for classifying behaviors into different categories both in the eld and later in revising the data for statistical computation f Operationalization in content analysis is exact description of how documents are read examined plus detailed procedures for classifying documents into different categories based on their content g Idea of an indicator an indirect measure of what you are interested in Reliability as intersubjective a Different observers agree on what is observed b Relation between procedures in your observation assignment and concept of reliability c Examples of possible results of observation and what they imply for reliability d Difference between reliability of measure and validity whether it measures what you think it measures Logic of induction as linked to sampling theory more in sampling notes Deductive logic of falsi cation a Understand how hypotheses are falsi ed and how this re ects upon the theory and measurement assumptions b Recognize illogical conclusions proving the theory rejecting the result because you reject the theory c Understand how accumulation of failure to falsify increases support for the theory Read simple statistical tables differences of means percentages correlations and recognize whether they con rm or discon rm a theory Con rm in direction predicted and large statistically significant Discon rm no association or opposite prediction Indeterminate in direction predicted but not statistically significant If hypothesis is zero association only zero statistical association confirms indeterminate is small nonsignificant association any significant association disconfrrms I will test for confirmation or disconfrrmation of zero association but not for indeterminate as we have not studied the correct way to identify indeterminate results in this case Criteria for identifying causation a Statistical association 993 b Causes come before effects c Understand the mechanism d Eliminate alternate explanations for the statistical association e Be able to recognize whether claims of causation are wellfounded in particular examples Experiments a Recognize whether something is a true experiment a quasiexperiment or not an experiment b Recognize whether a variable is manipulable c Recognize whether something is an example of true randomization d Recognize examples of experimental control through holding factors constant e Distinguish randomization from random sampling f Given a description of an experiment be able to tell whether a particular extraneous variable is controlled by randomization controlled by holding constant or uncontrolled as an alternate explanation for the result g Identify the kinds of research that can and cannot be studied through experiments Surveys a Identify the kinds of things that can and cannot be researched by asking people questions b Recognize obvious examples of errors in questionwriting ambiguous or unclear doublebarreled biased responses do not match question responses not in ordinal order responses not exhaustive and mutually exclusive Articles we have read for class a Given author s name article title and short description of the research be able to recognize major features of the article independent and dependent variables type of research measures of variables hypothesis being tested major results main purpose of research This will be tested at the level at which we went over the homework in class Questions will be designed to jog your memory enough that they should be testing your understanding of concepts and whether you read the article at all rather than detailed rote memory b Research from these articles may be used as examples for other questions on the test
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