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Measurement and Observation

Measurement and Observation. Choices During Operationalization. Researchers make a number of key decisions when deciding how to measure a concept Dimensions and sub-dimensions Range of variation within dimensions Categories to represent range Levels of measurement

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Measurement and Observation

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  1. Measurement and Observation

  2. Choices During Operationalization • Researchers make a number of key decisions when deciding how to measure a concept • Dimensions and sub-dimensions • Range of variation within dimensions • Categories to represent range • Levels of measurement • Nominal, ordinal, interval, ratio

  3. Operationalization: A deliberative process • Not a simple, linear process • Complicated and fraught with trade-offs • Iterative process with cycles of consideration • Debate over proper measurement is key

  4. Dimensions of the Concept • Creating operational measures forces realization about lack of conceptual clarity • List of possible dimensions may be long • Need to decide which ones are most relevant • Ask which ones are central to the inquiry • Reflect on research hypotheses or theories

  5. Range of Variation • Sense of the upper and lower limits • How much are you willing to combine different people into the same category? • Extremely high and Extremely low may be collapsed • Eg. Income, age, height, etc. • Opposition and support for attitudes • Agreement and disagreement

  6. Variation Between Extremes • Degree of precision • How detailed you need to be in measurement • Eg. Age breaks or Exact age? • Related to purpose of study • Eg. Political Party ID: • Dichotomy: Democrat or Republican • Continuum: 7-point scale w/ “independent-leaner”

  7. Levels of Measurement • Nominal Measures • Ordinal Measures • Interval Measures • Ratio Measures

  8. Nominal Measures • Names for characteristics • Do not Exist along an Explicit continuum • Exhaustive • Mutually Exclusive • Eg. Religious Affiliation • Eg. Place of Birth

  9. Ordinal Measures • Can be logically rank-ordered • Represent relatively more of less of variable • No consistent distance between points of measurement • Not just different from one another • More of less of some attribute • Eg. “Not very important,” “fairly important,” “very important” “Extremely important”

  10. Interval Measures • Consistent distance separating attribute • We can say how much more of an attribute • Logical distance between attributes can be Expressed in meaningful standard intervals • Eg. Temperature • 90 degrees vs. 80 degrees = 10 degree difference • 50 degrees vs. 40 degrees = 10 degree difference • Zero-point is arbitrary

  11. Ratio Measures • In addition to all the properties of nominal, ordinal, and interval measures, ratio measures have a true zero point • Eg. Length of time • Eg. Number of times • Eg. Number of affiliations • Can actually state ratio of one to another • X has twice as many affiliations as Y

  12. What’s that scale? • Style of music in a music video • Number of violent acts in a music video • Whether a music video has violence or not? • High, Medium or Low violence in a music video • Hair color • Number of hairs on your head • Sat scores • Social Security Number

  13. What’s that scale? • A baseball player's batting average • A baseball player's field position • A baseball player's position in the batting order • A baseball player's uniform number • College football rankings • IQ

  14. Types of questions • Multiple choice questions • Agree/disagree questions • Likert questions • Frequency scales • Semantic differential scales • Forced-choice statement pairs • Thermometer feeling scales • Nominal checklists • Ordinal categories • Rank-order questions • Filter questions • Open-ended

  15. Multiple Choice Question

  16. Multiple Choice with Range Options

  17. Agree/Disagree Questions

  18. Likert Scale

  19. Frequency Scale

  20. Semantic Differential Scales

  21. Forced-choice Statement Pairs

  22. Thermometer Feeling Scales

  23. Nominal Checklist

  24. Ordinal Categories

  25. Rank-order Preference Questions

  26. Rank-order Evaluation Questions

  27. Filter Questions

  28. Open-ended Questions

  29. Tips on Question Construction • 1. Make questions clear using simple language • 2. Keep questions concise • 3. Provide instructions for answering questions • Don’t assume respondent knows question style • 4. Keep research purpose in mind • Make sure items can answer research question • 5. Don’t ask double-barreled questions • E.g., “How well do you think the current Presidential Administration is handling foreign policy and the war on terrorism?”

  30. More Tips • 6. Avoid leading questions • E.g., “Like most Americans, do you read a newspaper every day?” • 7. Avoid negative questions • E.g., “The U.S. should not invade Iraq” Agree or disagree? • 8. Do not ask questions that require complicated mental calculus • E.g., “In the past 30 days, how many hours have you spent watching television with your family?” • 9. Keep ordering of questions in mind

  31. Using Pre-Existing Measures • It is okay to borrow measures • Cite source of questions to give credit • Benefits of using Existing measures: • Saves work • Pre-tested for reliability/validity • Research becomes cumulative

  32. Pretesting • Clarity in question wording • Are categories: • Exhaustive? • Mutually Exclusive? • Realistic time estimate • Preliminary empirical analysis

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