1. Research Questions & the “Language” of Variables & Hypotheses Baxter & Babbie, 2003, Chapters 3 & 4 (Mostly)

2. Recall: Research Questions n Questions researchers ask themselves, not the questions they ask their informants n Must be empirically testable n Not u too vague u too general u untestable (with implicit, untested assumed outcomes)

3. Relationship of Theory & Empirical Observation (Wheel of Science)

4. Conceptualization & Operationalization  Conceptualization  Conceptual, abstract (or theoretical) definition - a careful, systemic definition of a construct that is explicitly written to clarify one’s thinking  Operationalization  linking a conceptual definition to specific measurement technique(s) or procedure(s)  operational definition - the definition of a variable in terms of the specific activities to measure or indicators in the empirical world

5. Matching Theoretical Concepts & Empirical (Operational) Measures n Example: (Which county had “worst” damage from bad weather?)

6. Conceptualization Issues: Distinguishing between Theory & Ideology n Similarities u Set of assumptions or starting point u System of concepts/ideas u Specifies relationships between concepts (usually “causes”) n But social scientific theories u Recognize uncertainly u Process oriented u Based on evidence u Seek logical consistency etc..

7. Elements of Theory n Concepts n Assumptions n Propositions/Hypotheses u about relationships, association

8. Which Theory is Best? n Fewest assumptions (parsimony) n Covers widest range of phenomena n More accurate predictions # 1

9. Measurement?  systematic observation  can be replicated (by someone else)  Measures:  Concepts (constructs), theories  measurement instrument/tools  Need to recognize concept in observations (measures) ??(# of library holdings as a measure of quality of university?) MacLeans Magazine survey results, 2000.

10. Concepts n Symbol (image, words, practices…) n definition n must be shared to have social meaning n Some only have one value (homelessness) n Concepts with more than one possible value or attribute sometimes called variables n Concept clusters (ex. ethnic minorities) n Constructs (in operational stage-- use multiple measures or indicators)

11. Assumptions n not necessarily explicit (may be implied-- implicit) n not tested through observation in the context used n concepts and theories build on assumptions Example: Some communication research “deconstructs” assumptions in everyday life– can do the same with scholarly research

12. Classification as conceptualization n typology u intersection of simple concepts forms new concepts u broader, abstract concepts that bring together narrower, more concrete concepts ex. Emile Durkheim’s 4 types of suicide u Varies by degree of integration to and regulation by society u Altruistic (+I), Anomic (-I), Egotistical (- R), Fatalistic (+R) Photo: R. Drew, AP

13. Propositions n logical statement about a (usually causal) relationship between two variables n i.e. “Increased television watching leads to more shared family time and better communication between children & their parents”

14. From Concept to Measure Neuman (2000: 162)

15. Examples of Developing Conceptual & Operational Definitions  Construct = alienation  if you theorize 4 components (family, work, community, friends) then operational definition must take all into account & measures  Construct= green consumer?

16. Rules for Creating Measures  Measures must be:  mutually exclusive  possible observations must only fit in one category  exhaustive  categories must cover all possibilities  composite measures must also be:  uni-dimensional

17. Operationalization Issue: Choices in Level of Measurement Based on n purposes of the study & conceptual definitions u What is range in variation of “attributes” is necessary for measuring your concept? n Practical constraints

18. Variable n Must have more than one possible “value” or “attribute” n context important, ex. u Religion (variable) F Possible Attributes: protestant, catholic, muslim, jewish, etc… u Protestant (variable) F Possible attributes: baptist, united, presbyterian, anglican etc...

19. *Types of variables* n dependent variable (effect) n independent variable (cause) n intervening variable n control variable

20. Causal Relationships n proposed for testing (NOT like assumptions) n 5 characteristics of causal hypothesis u at least 2 variables u cause-effect relationship u can be expressed as prediction u logically link to research question+ a theory u falsifiable

21. Examples of 2 possible Relationships between Two Variables (p.52)

22. Types of Hypotheses (note plural form) n null hypothesis u predicts there is no relationship u if evidence support null hypothesis then???? n Direct relationship (positive correlation) n Indirect relationships (negative correlation)

23. Ways of stating causal relationships n causes, n leads to, n is related to, n influences, n is associated with, n if…then…, the higher….the lower n etc…

24. Hypothesis Testing

25. Possible outcomes in Testing Hypotheses (using empirical research) n support (confirm) hypothesis n reject (not support) hypothesis n partially confirm or fail to support n avoid use of PROVE

26. Causal diagrams X Y Direct relationship (positive correlation) Indirect relationship (negative correlation)

27. Types of Errors in Causal Explanation n ecological fallacy n reductionism n tautology n teleology n Spuriousness

28. Double-Barrelled Hypothesis & Interaction Effect OR Means one of THREE things 1 2

29. Interaction effect

30. Ecological Fallacy & Reductionism ecological fallacy--wrong unit of analysis (too high) reductionism--wrong unit of analysis (too low)

31. Teleology & Tautology tautology--circular reasoning (true by definition) teleology--too vague for testing Neuman (2000: 140)

32. Spurious Relationship spuriousness--false relationship (unseen third variable or simply not connected) Neuman (2000: 140)

33. Examples n Storks and babies u Lots of storks seen around an apartment building u An increase in number of pregnancies u ??? ?

34. But... n The relationship is spurious. u The storks liked the heat coming from the smokestacks on the roof of the building, and so were more likely to be attracted to that building. u The tenants of the building were mostly young newlyweds starting families. u So…the storks didn’t bring the babies after all.

35. Causal Diagram for Storks n Stork = S n Baby = B S B + n Newlywed = N n Chimneys on Building = C NB + CS +

36. Examples (cont’d) n The larger the number of firefighters, the greater the damage

37. But... n A larger number of firefighters is necessary for a larger fire. Of course, a larger fire will cause more damage than a small one.

38. Causal Diagram n Firefighter = F n Damage = D n Size of Fire = S FD + F S + + D

39. Examples from research (cont’d) n tall 15 yr. olds like shopping more than basketball

40. But... n Fifteen year old girls are likely to be taller, since they are having a growth spurt at that age. n Fifteen year old girls are more likely to prefer shopping to sports. n Thus, it is gender, not height, that is the deciding factor.