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Seven possibly controversial but hopefully useful rules

Seven possibly controversial but hopefully useful rules. Paul De Boeck K.U.Leuven. Examples. Political openness and economic openness Life satisfaction and cost of living Research performance and innovation intensity Socioeconomic status and intolerance. Multi-level governance structure.

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Seven possibly controversial but hopefully useful rules

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  1. Seven possibly controversial but hopefully useful rules Paul De Boeck K.U.Leuven

  2. Examples • Political openness and economic openness • Life satisfaction and cost of living • Research performance and innovation intensity • Socioeconomic status and intolerance

  3. Multi-level governance structure

  4. Link concept-operationalisation Political openness Participation Competition Transparance Accountability Rule of law Interfaces various aspects combine subindicies

  5. Implicit theory X1 numerical obs X2 numerical obs X3numerical obs X4numerical obs the thing to be measured test scoreglobal index

  6. “Measurement” X1 numerical obs X2 numerical obs X3numerical obs X4numerical obs the thing to be measured assignment rulee.g., sum, first component score

  7. Heavy vs light on meaning

  8. Alternative X1 numerical obs X2 numerical obs X3numerical obs X4numerical obs Can I explain?Which model can explain?

  9. The data are not meant as a measurement of something, but as to be explained. For example, responses to inventory items,how can they be explained?Do the correlations between items stem from overlap in the information used to respond?Which information is it?Why not extract the information directly?What is the origin of the information? When explained with a quantitative theory, then measurement is a by-product

  10. 1. Not everything is worth being measured or can be measured, often the data are more interesting than the concept

  11. Assignment of numbers • number finding: counts, percentages • number asking: ratings • number construction: apply a rule on original numbers in order to obtain a derived number

  12. “Measurement” X1 numerical obs X2 numerical obs X3numerical obs X4numerical obs the thing to be measured assignment rulee.g., sum, first component score

  13. Measurement • A quantity • Increasing or decreasing doesn’t change the nature • Addition from two sources is possible • Splitting is possible, e.g. in halves

  14. Questions • Why are you interested in the link between the two concepts?Why do you want to measure?Because I want to test a theorydata for the theoryWhy aren’t you interested in the data?and try to explain the data?theory for the data • Aren’t your numerical variables of sufficient interest to keep them as they are?

  15. Examples • Woodworth Personal Schedule 1917 to measure psychological adaptation Before, lists of questions were used and one would listen to the responses • Hirsch index: the maximum obtained by selecting a number of publications with each at least the same number of citations, e.g., 15 articles with 15 or more citations

  16. A strong dimension does not mean the conceptual component is important.It shows there are large individual differences in the component.

  17. 2. Psychometric criteria such as reliability and validity are not theory-independent

  18. The underlying theory is the simple implicit theory • Alternatives- canalization: one behavior has developed into a the dominant one and excludes the other behaviors- behavior competition: the strongest takes it all- negative feedback: showing a behavior makes it less likely to occur next- drop-out: only occasionally it is affected by

  19. Dynamic theories

  20. Dynamic theories

  21. Dynamic theories

  22. Reliability Repetition over • Situations • Behaviors • Time

  23. Questions • Do you have the simple theory for your data that they are a direct and linear reflection of the concept? • What is your theory of stability?Stability over?

  24. 3. Always reflect on which type of covariation is meant when speaking about the link between two concepts

  25. The case of shame and guilt • Covariation over situationsguilt vs shame is one of two dimensions • Covariation over personsguilt & shame define a dimension together with fear and anger • Covariation over culturesguilt and shame define their own common dimension

  26. Negative emotions • Fear and anger are positively correlated over persons • Fear and anger cannot co-occur because they rely on opposite action tendencies (flight and fight)

  27. Guilt • Experienced norm violation • Self-reproach • Tendency to restitute Unidimensional in the sense of individual-differences, and they each contribute separately to the probability of feeling guilty

  28. Questions • Are you interested in individual differences?Are you ready to find traits? • Components of?Meaning – semanticIndividual differencesSituational differencesTime differencesProbability of occurence

  29. 4. Measurement, reliability, validity, hypothesis testing don’t need to be sequential steps

  30. Hypothesis: link between concept A and B • Step 1: construct a measurement for A, B • Step 2: test reliability measurements • Step 3: test validity measurements • Step 4: test hypothesis

  31. measurement

  32. measurement reliability

  33. validity measurement

  34. measurement hypothesis testing

  35. validity measurement reliability hypothesis testing

  36. Questions • Do you want to construct a test? • ?Meaning – semanticIndividual differencesSituational differencesTime differencesProbability of occurence

  37. 5. Always do a PCA

  38. PCA tells you about the sources of differences between the row elements • PCA tells you whether there is interaction and where it is

  39. PCA is a quite robust way to check multidimensionality • PCA shows the main interactions in a repeated measures data matrix- unidimensional & equal loadings- unidimensional & unequal positive loadings- unidimensional bipolar- multidimensional

  40. Questions • Show me your PCA before we continue, especially when complex methods are going to be used, such as SEMs

  41. 6. One does not necessarily have to care about the scale of the data

  42. Common concern:“what is the scale level?”“are parametric statistics permissible?” • Scale level only matters when - numbers are taken for an index of something else, how does the index relate to the “something else”? • Transformations are interesting when a simpler and better structure can be found

  43. Representations of relations Example P(Xpi=1)/(1-P(Xpi=1)) = p / i • p and i are on a ratio scale,as far as they represent odds ratios

  44. Questions • Suppose you forget about the scale level and you find an interesting relationship • Do you want to generalize over other number assignment procedures? • How meaningful are the numerical variables as they are?

  45. 7. Don’t construct indices of concepts, unless for descriptive summaries

  46. Problems • The global index depends on the components, and hence, on the definition. • Often definitions are arbitrary or they are mainly semantic • Perhaps the relationships of the index follow from the relationships of the components

  47. Questions • What is the definition? • What do others say? • Aren’t you interested in the components?

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