Theory and research II (3/26)

1. Theory and research II (3/26) The input of theory to research Knowing how: the e.g. of COP Macro: regressions in States The conceptual scheme of One World

2. 5 Inputs of Theory to the Research process • Conceptualization – operationalization • Model Specification • Domain Specification (When?) • Generalization • Explanation (How? Why?) • In a research course you are mainly interested in whether a particular association exists. • In a theory course, you are interested in what it means and when, how and why?

3. Knowing How v. Knowing That • These issues are relevant to the kinds of disagreement that people have analyzing the data on the effects of poverty (e.g. INCOME @16) and culture of poverty (e.g. FAMILY @16) on opportunity (e.g. $ RANK). • That is, there are issues of conceptualization and measurement. • And, there are issues of interpretation of the coefficients and partial coefficients.

4. Levels Re Culture of Poverty We have discussed the relation of poverty and culture of poverty several times. It is useful to think how these “levels” might relate to the data that we analyzed last Monday. (See Below)

5. I Theory and Operationalization • In order to see whether X Y , you want to get an index or measure of X and one of Y and see whether and when they are associated such at when there is more X there is more Y. • An index or measure is an observable in some data set that you have reason to suppose is associated with the underlying variable of interest. • E.g. is FAMILY @16 and index of “Culture of Poverty”? Aren’t some rich families not intact? • Note: you often only operationalize a part of a theory .

6. What is the effect of measurement error? • Suppose that your index has a lot of noise in it so that it is not a very good measure of the underlying variable. • This will usually just “attenuate” the relationship, making it appear weaker than it really is, • So that the data is a conservative test of the hypothesis. • Noise is different from bias.

7. The effect of INCOME @16 • INCOME @16 by $ RANK • BELOW AVG AVERAGE ABOVE AVG TOTAL • BELOW AVER 3653 4309 1324 9286 • 39.3% 46.4% 14.3% 100.0% • AVERAGE 3699 9154 2658 15511 • 23.8% 59.0% 17.1% 100.0% • ABOVE AVER 963 1954 1895 4812 • 20.0% 40.6% 39.4% 100.0% • Missing 2988 4920 2288 10375 • TOTAL 8315 15417 5877 29609 • 8.1% 52.1% 19.8% • Gamma = .305 • What is the size of the effect of growing up poor on opportunities? • What does this prove, what does it imply, and what does it suggest • about the complex of cumulative poverty?

8. The effect of FAMILY @16 • FAMILY @16 by $ RANK • BELOW AVG AVERAGE ABOVE AVG TOTAL • YES 7638 15072 6469 29179 • 26.2% 51.7% 22.2% 100.0% • NO 3662 5256 1694 10612 • 34.5% 49.5% 16.0% 100.0% • TOTAL11300 20328 8163 39791 • 28.4% 51.1% 20.5% • Gamma = -.179 • What is the size of the effect of growing up in a non-intact family • on opportunities? • What does this prove, what does it imply, and what does it suggest • about the complex of cumulative poverty?

9. What can we conclude from the data? • What does it prove that gamma is .305? • What does it imply? • What does it suggest? • What does it prove that gamma is -.179 • What does it imply? • What does it suggest? • What does it prove that .305 > .179 • What does it imply? • What does it suggest?

10. II Theory and “model specification” • Whenever one looks at any causal relation empirically, there are always an indefinitely large number of ‘other forces’ going on. • The overall assumptions about the forces that are operating are established and justified by theory. • A crucial element of model specification is causal order. Does INCOME @16 $RANK or does $RANK INCOME @16 ?

11. Why do we care which way? • The observable data often do not prove which way the causal arrow goes. • Some people in the 1950’s then said, “Let’s stick with what can be proved from the data (associations) rather than causal inferences.” • Almost no one believes that; what is important and interesting is the underlying causal forces that brought about the associations.

12. Why Systems and Feedbacks are Inconvenient • Often there are a lot of specific causal influences that have been demonstrated. • But it is not clear how they fit together; what is their dynamic; under what conditions the effects obtain, etc. • Whenever there are feedbacks, the problems become intricate. • E.g. Myrdal.

13. Systems and feedbacks about the culture of poverty • Virtually all sociologists would agree the poverty and the culture of poverty are mutually reinforcing. • Most would also agree that INCOME @16 is a reasonable measure of the effect of poverty and that broken families (e.g. FAMILY @16) are a reasonable measure of culture of poverty. + Poverty Culture of Poverty +

14. Why Systems and Feedbacks are Inconvenient • Often there are a lot of specific causal influences that have been demonstrated. • But it is not clear how they fit together; what is their dynamic; under what conditions the effects obtain, etc. • Whenever there are feedbacks, the problems become intricate. • E.g. Myrdal.

15. Clues about Causal order and systems dynamics • The size and the relative size of the empirical associations and partial associations gives one indications of the system dynamics. • But one will always have to make model specification assumptions. • These must be theoretically motivated.

16. Controls • Some people believe that giving poor children’s parents money (e.g. AFDC) will largely or entirely fix the problems of those poor children who also have broken homes (which is many of them.) • Partly they believe that this will cause fewer homes to break up. • Some people believe that fixing children’s broken homes (e.g. faith based programs) will largely or entirely fix the problems of poor children. • Partly they believe that this will pull most of the homes out of poverty. • The size and the relative size of INCOME@16 effects and FAMILY @16 effects can be suggestive. • The effect of one, controlling the other is even more sugestive.

17. The effect of INCOME @16 controlling FAMILY @16 • INCOME @16 by $ RANK • Controls: FAMILY @16: NO • BELOW AVG AVERAGE ABOVE AVG TOTAL • BELOW AVER 1446 1522 427 3395 • 42.6% 44.8% 12.6% 100.0% • AVERAGE 907 1803 430 3140 • 8.9% 57.4% 13.7% 100.0% • ABOVE AVER 208 392 277 877 • 23.7% 44.7% 31.6% 100.0% • TOTAL 2561 3717 134 7412 • 34.6% 50.1% 15.3% • Partial Gamma = .301 (conditional gamma .260) • What is the size of the effect of growing up poor on opportunities • controlling culture of poverty? • What does this prove, what does it imply, and what does it suggest • about the complex of cumulative poverty?

18. Effect of FAMILY @16 controlling INCOME @16 (showing only 1st conditional table.) • FAMILY @16 by $ RANK • Controls: INCOME @16: BELOW AVER • BELOW AVG AVERAGE ABOVE AVG TOTAL • YES 2207 2786 896 5889 • 37.5% 47.3% 15.2% 100.0% • NO 1446 1522 427 3395 • 42.6% 44.8% 12.6% 100.0% • TOTAL3653 4308 1323 9284 • 39.3% 46.4% 14.3% • Partial Gamma = -.133 (conditional gamma -.098) • What is the size of the effect of culture of poverty on opportunities • controlling growing up poor? • What does this prove, what does it imply, and what does it suggest • about the complex of cumulative poverty?

19. Controls as an answer to “because” • Ordinarily if there is a relations between X and Y and you control T, and the original relationship goes away, that means that the original relationship is “due to” or “because of” the controlled variable. • And if there is a relations between X and Y and you control T, and the original relationship does not go away, that means that the original relationship is not “due to” or “because of” the controlled variable. • And if there is a relations between X and Y and you control T, and 1/3 the original relationship goes away, that means that 1/3 the original relationship is “due to” or “because of” the controlled variable.

20. What can we conclude from the data? • What does it prove that the partial gamma of INCOME @16 controlling FAMILY @16 is about the same as the bivariate? • What does it imply? • What does it suggest? • What does it prove that the partial gamma of FAMILY @16 controlling INCOME @16 is a little smaller than the bivariate? • What does it prove that gamma is -.179 • What does it imply? • What does it suggest?

21. Two different cases of because(why causal order makes a diff.) urbanism storks Birth rate spuriousness bleeding Shot in heart death Intervening variable

22. III) Domain Specification • A theory is a claim. • Usually it applies to some set of cases more limited than all social structures in all of recorded history, • but much more general than the cases on which the claim is based. • Theory involves establishing the domain of the theory. • Statistical interactions are the main clues about domains. • A mechanism (a “WHY?”) establishes a general domain. • If the coins in my pocket are quarters because of the hole in the bottom of my pocket, then the coins will be quarters whenever such a hole exists.

23. IV) Generalization • Particular findings, empirical generalizations, and hypotheses (e.g. Protestants have higher suicide rates) need to be related to more general processes. • Conceptualization (e.g. “deviance” rather than “crime” or “suicide”) is partly a matter of generalizing.

24. Theory simplifies to the “essential” • It is trivially true that both: • functional and conflict processes operate • and that: • Culture influences social structure, • and social structure influences culture. • and also that: • individuals create social structures and social structures shape individuals. • However, it is also trivially obvious that any theory must simplify, and that models that include everything are usually too complex to use or test.

25. V) Explanation • The conceptualization, and the establishment of the conditions and size of the effects is basic to establishing what is the mechanism that brings it about. • The main paradigms propose mechanisms.

26. Summary:Theory and research • Research establishes that there is an association. • Theoretical questions involve Why? How? and When? • I.e. what direction does the causal arrow run in, under what circumstances, why and how? • Often it is only the cumulative result of the scientific process over generations

27. The main paradigms in sociology • P. 267-276 of OW shows that the different maps of the main theoretical positions in sociology can be translated into each other. • They boil down to two dimensions: functional v conflict and micro v macro. However: • The 20-odd different sections of sociology such as medical sociology contain importantly different theoretical positions. • Any way of dividing the 20,000 or so practicing sociologists into a small number of “schools” is bound to simplify

28. The main map

29. Organization theories as a Mix • The interactionist/organization theories stemming from Weber, Mead, and others, can be viewed as an ambivalent synthesis of elements of conflict and functional theory. • Often the elements that distinguish them from functional or conflict theory appear at the micro level.

30. Micro-theory v Macro-theory • Micro-theories mainly treat social structure as the outcome of individual choices and actions. • Parsons took Weber’s action theory as the main model. • Other American sociologists took George Herbert Mead’s interactionism as a model. • The main difference between rational-action theories stemming from Weber and symbolic interaction theories, stemming from Mead is the nature of the tinker-toy, but they are both tinker-toy models.

31. Macro-theory • Macro-theories focus on the fact that humans and human behavior is shaped by the social structure. • This leads to concentrating on how social structures influence their members and other social structures.