Research Questions & the “Language” of Variables & Hypotheses

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

2. Recall: Research Questions • Questions researchers ask themselves, not the questions they ask their informants • Must be empirically testable • Not • too vague • too general • 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 • Example: (Which county had “worst” damage from bad weather?)

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

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

8. Which Theory is Best? Fewest assumptions (parsimony) Covers widest range of phenomena 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 Symbol (image, words, practices…) definition must be shared to have social meaning Some only have one value (homelessness) Concepts with more than one possible value or attribute sometimes called variables Concept clusters (ex. ethnic minorities) Constructs (in operational stage-- use multiple measures or indicators)

11. Assumptions not necessarily explicit (may be implied-- implicit) not tested through observation in the context used 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 typology intersection of simple concepts forms new concepts broader, abstract concepts that bring together narrower, more concrete concepts ex. Emile Durkheim’s 4 types of suicide Varies by degree of integration to and regulation by society Altruistic (+I), Anomic (-I), Egotistical (-R), Fatalistic (+R) Photo: R. Drew, AP

13. Propositions logical statement about a (usually causal) relationship between two variables 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 forCreating 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 • purposes of the study & conceptual definitions • What is range in variation of “attributes” is necessary for measuring your concept? • Practical constraints

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

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

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

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

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

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

24. Hypothesis Testing

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

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

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

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

29. Interaction effect

30. Ecological Fallacy & Reductionism ecological fallacy--wrong unit of analysis (too high) reductionism--wrong unit of analysis (too low) 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 • Storks and babies • Lots of storks seen around an apartment building • An increase in number of pregnancies • ??? ?

34. But... • The relationship is spurious. • 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. • The tenants of the building were mostly young newlyweds starting families. • So…the storks didn’t bring the babies after all.

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

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

37. But... • 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. Firefighter = F Damage = D Size of Fire = S F + + F D S + D Causal Diagram

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

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