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Danilo Yanich School of Public Policy & Administration Center for Community Research & Service

UAPP 702: Research Design for Urban & Public Policy Class Notes Babbie, The Practice of Social Research , Chaps.4&5. Danilo Yanich School of Public Policy & Administration Center for Community Research & Service University of Delaware. Ch. 4: Research Design Purposes of Research.

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Danilo Yanich School of Public Policy & Administration Center for Community Research & Service

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  1. UAPP 702: Research Design for Urban & Public PolicyClass NotesBabbie, The Practice of Social Research, Chaps.4&5 Danilo Yanich School of Public Policy & Administration Center for Community Research & Service University of Delaware

  2. Ch. 4: Research DesignPurposes of Research • Exploration:typically done for three purposes: • to satisfy the researcher’s curiosity and desire for better understanding • to test the feasibility of undertaking a more extensive study • to develop the methods to be employed in a subsequent study • Description:describe situations and events • Census is good example of descriptive research • Explanation:the “why?” of events, situations, behavior, attitudes, etc.

  3. Logic of Nomothetic Explanation • Nomothetic explanation refers to the accounting of many variations in a given phenomenon • In contrast to… • Idiographic explanation that seeks an in-depth understanding of a single case

  4. Criteria for Nomothetic Causality • Correlation: the variables must be correlated • Time order: the cause takes place before the effect • Non-spurious: the variables are non-spurious • Spurious relationship: a coincidental statistical correlation between two variables, shown to be caused by some third variable

  5. Correlation • Some relationship---or correlation—between the variables must exist before we can consider causality • Correlation: empirical relationship between two variables such that… • Changes in one are associated with changes in the other • Particular attributes of one variable are associated with particular attributes of the other

  6. False Criteria for Nomothetic Causality • Complete causation • Causation is incomplete and probabalistic • Exceptional cases • Exceptional cases do not disprove general overall pattern of causation • Majority of cases • Causal relationship may be true even if they don’t apply to the majority of cases • Example: lack of supervision & delinquency… • as long as unsupervised juveniles are more likely to be become delinquent, social science can say there is a causal relationship

  7. Necessary and Sufficient Causes • Necessary cause represents a condition that must be present for the effect to follow • Ex: must be female to become pregnant • Ex: must take college courses to get a degree…but… • Simply taking courses is not a sufficient cause • Must take the right ones

  8. Necessary and Sufficient Causes • Sufficient cause represents a condition that, if it is present, guarantees the effect in question • Not saying that sufficient cause is only possible cause for effect • Ex: skipping exam in course would be sufficient cause for failing, but students could fail in other ways, too • So, cause can be sufficient but not necessary

  9. Units of Analysis • No limit to what or whom can be studied • Common social science units of analysis: • Individuals • Groups • Organizations • Social artifacts. • Important:what you “call” a given unit of analysis is almost irrelevant—but you must be clear what that unit “is” • Are you studying marriages or marriage partners? • Crimes or criminals? • Historic buildings or the process for selecting them? • Efficiency of the hotel or the satisfaction of customers?

  10. Ecological Fallacy • Ecological in this context refers to groups or sets or systems, something larger than individuals. • Fallacy is to assume that something learned about such a unit says something about the individuals comprising that unit. • Babbie uses example of data that shows which precincts supported a female candidate… • Some census data for each precinct that shows that precincts with relatively young voters gave her more support • Could not assume that young voters were most likely to support a female candidate... • That is…we cannot assume that age affects support • The unit of analysis was the precinct, NOT the individuals in the precinct

  11. Reductionism • Tendency to explain everything in terms of a particular, narrow set of concepts • Remember paradigms that predispose researcher to a particular explanation • Definition of order by coercion, shared values, exchange

  12. Ch. 5: Conceptualization, Operationalization & Measurement • Conceptualization • The refinement and specification of abstract concepts • A specific agreed-upon meaning of the concept under study • Ex. “compassion” does not exist in any sense that we can measure in an objective sense • Operationalization • The development of specific research procedures (operations) that will result in empirical observations representing those concepts in the real world

  13. What social scientists measureTable 5.1, p. 129

  14. Indicators and Dimensions • Indicator • An observation that we consider as a reflection of the variable under study • Ex: attending church as an indicator or religiosity • Dimension • A specific aspect of a concept • Ex:action aspects of religiosity (attending church, giving money) and contemplative aspects (prayer, etc)

  15. Operational definition • Specifies precisely how a concept will be measured • Operationalization • The development of specific research procedures (operations) that will result in empirical observations representing those concepts in the real world

  16. Progression of measurement steps • Conceptualization ↓ • Nominal definition ↓ • Operational definition ↓ • Measurements in the real world “conceptual funnel”

  17. Progression of measurementTable 5.2, p. 136

  18. Operationalization Choices • Range of variation: Must be clear about the range of variation in any concept that interests you. • Babbie uses as an example studying certain ranges of income, i.e., using $100,000 as the floor for the highest income group rather than a higher amount • Attitudes toward nuclear power...might use a range of “favor it very much” to don’t favor it at all”... • But, that would leave out the people who are opposed to it. • Variations between extremes: Get as much detail in the measurement as possible. • Can always aggregate data (that is, combine precise attributes) into more general categories... • But can never separate out any variations that were lumped together during observation and measurement.

  19. Two important qualities of variables:Exhaustive & Mutually Exclusive • Exhaustive:For the variable to have any utility in research, must be able to classify every observation in terms of one of the attributes composing the variable • Babbie uses example of political party affiliation that specifies just Democrat or Republican… • When that would leave out others who do not identify with either • Use “other” or “no affiliation” to make it exhaustive. • Mutually exclusive:Must be able to classify every observation in terms of one and only one attribute. • Babbie uses defining employed and unemployed in such a way that nobody can be both at the same time • Refer to Graber “social type” variable...farmer, n’er-do-well, etc. & Family Court gender variable.

  20. Levels of measurement (NOIR) • Nominal:variables whose attribute have only the characteristics of exhaustiveness and mutual exclusivity • Examples: gender, religious affiliation, birthplace, etc • Ordinal: variables with attributes that can logically rank-order; the different attributes represent relatively more or less of a variable. • Examples: social class, conservatism, alienation, prejudice, “coolness” • Interval: variables in which the actual distance separating them can be expressed in meaningful standard variables • Examples: temperature, intelligence tests • Ratio: variables that have all of the characteristics of the previous levels of measurement AND are based on a true zero point • Examples: age, length of residence in a home, duration of news story, etc.

  21. Implications of levels of measurement • Requirements of analytical techniques: • Certain analytical techniques require variables that meet certain minimum levels of measurement • Must plan analytical techniques according to the level of measurement at which you will gather your data. • Should anticipate drawing research conclusions appropriate to the levels of measurement used in your variables. • Caution: Seek highest level of measurement possible because... • Although you can reduce a ratio measure to ordinal... • You cannot convert an ordinal measure into a ratio measure... • It is a one-way street

  22. Criteria of measurement quality • Precision and accuracy • Precision=fineness of the distinction made between the attributes that compose a variable • Saying that a woman is “43 years old” is more precise than saying that she is “in her forties” • Degree of precision is dictated by your research requirements • If your research question does not require her precise age, then additional effort to gather it precisely is wasted • However, if your needs are unclear, be more precise rather than less • Do not confuse precision with accuracy • Saying that someone was born in “Stowe, VT” is more precise than born in “New England” • But…suppose the person in question was born in Boston • The more general description of “New England” is less precise, but accurate

  23. Criteria of measurement quality, p.2 • Reliability • Whether a particular technique, applied repeatedly to the same object, yields the same result every time • Example: Measuring weight using two different persons’ estimates versus a scale • Reliability does NOT ensure accuracy • Suppose the scale is set five pounds too light • Measurement would be reliable each time, but it would also be wrong each time • Ways to cross-check the reliability of measures • Test-retest method • Split-half method • Using established measures (Miller book is useful here) • Reliability of research workers

  24. Criteria of measurement quality, p.3 • Validity • Refers to the extent to which an empirical measure adequately reflects the real meaning of the concept under consideration • Social research does operate on agreements about the terms we use and the concepts they represent

  25. Criteria of measurement quality, p.4 • Testing validity • Face validity — empirical measures that jibe with our common understanding of a concept • Ex. Grievances & worker morale • Criterion-based validity — based on external criterion • Ex. College board scores & student success in college

  26. Criteria of measurement quality, p.5 • Testing validity • Construct validity — based on logical relationships among variables • Ex. Marital fidelity & marital satisfaction • Content validity — refers to how much a measure covers the range of meanings in a concept • Ex: test of math ability can’t be limited to addition alone

  27. Criteria of measurement quality, p.6 • Tension between reliability & validity • Often a trade-off between the two because resources limit the research • Ex. Measuring morale by spending days on assembly line talking w/ workers seems a more valid measure of morale than counting grievances • If there is no clear agreement on how to measure a concept…measure it several ways • Ex. Recidivism, court success, hotel efficiency, etc. • Concept does not have any meaning other than what we give it. • Only justification to give concept a particular meaning is utility

  28. Basic Research Outline* • The Social Problem • Present a clear, brief statement of the problem, with concepts defined where necessary • Show that the problem is limited to bounds amenable to treatment or test • Describe the significance of the problem with reference to specific criteria Source:Miller, Delbert C. 1991. Handbook of Research Design and Social Measurement, 5th Edition. Newbury Park: Sage Publications, pp. 15-16.

  29. Basic Research Outline, p.2 • The Theoretical Framework • Describe the relationship of the problem to a theoretical framework • Demonstrate the relationship of the problem to previous research • Present alternate hypotheses considered feasible within the framework of the theory.

  30. Basic Research Outline, p.3 • The Research Question/Hypotheses • Clearly statethe research questions or the hypotheses selected for test. (Null and alternate) • Indicate the significance of test hypotheses to the advancement of research and theory. • For policy research state how research might inform policy. • Define concepts or variables (preferably in operational terms). • Describe possible mistakes and their consequences. • Note seriousness of possible mistakes.

  31. Basic Research Outline, p.4 • Design of the Experiment or Inquiry • Describe ideal design or designs with particular attention to the control of interfering variables • Describe selected operational design • Specifystatistical tests including dummy variables

  32. Basic Research Outline, p.5 • Sampling Procedures • Describe experimental and control samples • Specify method of drawing or selecting sample

  33. Basic Research Outline, p.6 • Methods of Gathering Data • Describemeasures of quantitative variables showing reliability and validity when these are known. Describe means of identifying qualitative variables • Includedescriptions of questionnaires or schedules • Describe interview procedure • Describe use made of pilot study, pretest, trial run.

  34. Basic Research Outline, p.7 • Working Guide • Prepare working guide with time and budget estimates • Estimate total person-hours and cost

  35. Basic Research Outline, p.8 • Analysis of Results • Specify methods of analysis

  36. Basic Research Outline, p.9 • Interpretation of Results • Discuss how conclusions will be fed back into theory…OR… • Inform policy/practice.

  37. Basic Research Outline, p.10 • Publication or Reporting Plans...Communication Plans • Monograph, Executive summary • Testimony to policy makers. • Presentations to institutions, non-governmental agencies, media, public. • Journal publication

  38. The Policy Research Process* *D. Yanich example using model in: Miller, Delbert C. (1991). Handbook of Research Design and Social Measurement. Fifth Edition. Newbury Park, CA: Sage Publications, pp15-16

  39. The Policy Research Process, p.2

  40. The Policy Research Process, p.3

  41. The Policy Research Process, p.4

  42. The Policy Research Process, p.5

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