Issues in Scientific Explanations

1. Issues in Scientific Explanations • The phenomenon • What is "explained" by the theory? • What is the "contrast space"? • Is the theory itself tested/expanded, or used to describe some phenomenon? • What are the structural presuppositions?; what is “given” in the explanation? • Psychological processes the theory invokes to explain the phenomenon • What are the core social and/or psychological concept(s) or process? • What "level of explanation" is appropriate? • When does a different level of explanation alter the nature of the phenomenon? • Pragmatic criteria for explanation • necessary & sufficient cause; • articulation with accepted principles

2. …explanations… • Descriptions of causal relations among the terms of the theory • What does "cause" mean? [ material, efficient, formal, final] • Mediators, moderators, interactions.... • Measurement v. experimental v. intervention designs • Linear v. non-linear relation between cause and effect. • Prerequisites and/or boundary conditions around the causal model/theory • Presuppositions or untestable assumptions implicit in the theory or explanation • Boundaries on the phenomenon; groups, settings, time (moderators). • How much does the theory permit or encourage enlargement to incorporate additional explanatory concepts and/or a broader range of phenomenon: • Open v. closed system. • Comparison of theory with competing or alternative explanation • Mutually exclusive? • Complementary?

3. The main tasks [in my idiosyncratic view] 1. What is the phenomenon? What exactly will your research “do”? • Will it simply describe something? What? • Will it explain something? • Will it “test” an existing or new theory? • The adequacy of the theory itself? • A derivation of the theory? • The power of theory-based variables relative to variables derived from another theory? • Will it use a theory to explain some concrete events? • Will it use a theory to construct and test an intervention? “Convergent” research / use of theory “Divergent” research

4. Some Key terms 2. Contrast space: • What is being compared to what -- what are we actually trying to explain? • At what level are we “explaining” it? • Products: • Direct effect and Mediator analyses • Explanatory theory of how the phenomenon “works” How do alcohol & drugs increase sexual risk among gay men? • drugs v. other causes? • gay v. non gay? • sexual v. other risks • increase v. decrease?

5. Some Key terms 3. Boundary conditions: • What are the conditions under which this theory applies / this hypothesis is supported? • Under what conditions might the hypothesis be reversed? • McGuire: • Counterfactuals as hypothesis generators • No hypothesis is “false” • Products: • Moderator analysis • Explanatory theory of larger variables that control when the phenomenon occurs.

6. Explanatory frame: The object to be explained + the form of explanation. What does it mean to “explain” something?; What, exactly, is being explained? • Constant v. variable terms: what needs explaining? • Why is BSB so weird? • BSB is this way because the architect wanted a dramatic building to win awards • why are there weird buildings? • why is BSB one of them? • why this form of weirdness • Why did the rabbit get eaten by the fox? • Micro question (this rabbit by this fox); proximity to tree, degree of light, time of day; the rabbit was in the wrong place, the fox was hungry... • mid-macro question; fluctuation in relative fox/rabbit populations, explain current rabbit predation rate (or fox population!) • Macro question; co-evolution  rabbit predation by foxes generally • Why do people take drugs? • ...to be more happy, to be popular, etc. (All are K, but drug taking is not) • ...why do people take crack cocaine? (Don’t ask me why I smoke, ask me why I smoke Winstons...)

7. Explanatory frame (cont.); • At what level are we explaining the phenomenon? • Are we explaining its occurrence? • …how it “works”? • …how to “fix” it”? • When is a different level of explanation actually explaining a different phenomenon? • When are two explanations consistent with each other? • When are two explanations irrelevant to each other • …”Complimentary”: wave & particle explanations of light • …“better” than another? • … actually explaining something different?

8. Explanatory frame (cont.); • How do we know what causes something? • Material causes: • Efficient cause: • Formal cause: • Final cause: • How do we decide if something is “really” the cause? The concrete “stuff” something is made of. Simple co-occurrence / correlation The larger structure or system the outcome is embedded in… The purpose or larger meaning of a process.

9. Causality; the case of the biting dog I kicked the dog and he bit me. Why? • Simple covariate: • Every time I kick a mean looking 4-legged animal I get bit. • Material (“reductionism”): • Dogs are equipped with teeth and a defensive biting reflex. • Functional or “efficient”: • I kicked him hard enough to get him mad at me; the kick directly “caused” the bite. • Formal or structural: • The dog and I have a really sick relationship where I keep kicking him and he keeps biting me. • Dogs have evolved over millennia to protect their territory, and I aggressively invaded his. • “Final”: • Dogs bite to mold human behavior (make us not kick). Eternal question: Are these “causes” all explaining the same thing?

10. Material or efficient causes Formal or structural causes “Final” causes pertaining to purposes. What caused the Challenger space shuttle to crash? • Highly vulnerable tile design • Falling foam from the booster • Damaged insulation tiles • Hot plasma in the wheel well • Loss of control of the vehicle at reentry • Habituation to tile falling (and other “anomalies”) & loss of recognition of debris as a problem • Poor communication between engineers and management • Poor decisions under powerful political pressure to show success for expensive manned space program

11. Causality: the Challenger space shuttle crash "Challenger, like Columbia, was an institutional failure. That is, it wasn't just a matter of the decision- making structure. It had to do with the entire organization and its culture, and the critical parts of that really didn't get changed [after the Columbia failure]." -- Diane Vaughan, author of The Challenger Launch Decision

12. 10% of the U.S. population is depressed. Why? • Simple covariate / descriptive (e.g., epidemiology): • Lower socio-economic status women and upper class youth get depressed. • Material: • Serotonin depletion or high re-uptake rates underlie depression. • Genetic / brain -based “negative affectivity”. • Functional or “efficient”: • After adverse events or stress people tend to get depressed. • Negative thoughts make people depressed. • Depression occurs when a variant on the 5-HTT gene gets “switched on” by stress. • Formal or structural: • Depression reflects the mismatch between human evolution and the evolution of our physical / technological / economic environment. • “[Depression] is the expression of a person’s social class position.” • “Final”: • The purpose of (transient) depression is to help us transition from one stable array of reinforcers to another. Each of these: • Are subtly different questions, that dictate different contrast spaces • …different theories • …different hypotheses • …different research strategies • Are these different / competing / complimentary explanations of the same thing? • Suggest different prevention / treatment strategies

13. Kicked… Showed my neck to Invaded the territory of Induced stress in Dog… Cat Lab rat Undergraduate Bit Barked at Ran from Ignored Me The nearest weak looking animal Whatever was closest Explanatory relativity and contrast spaces: Dog bites man When I kicked the dog he bit me. Why? • How many possible contrast spaces are there in this empirical question? • Each contrast space defines an “explanatory space” • An independent / dependent variable • A term to hold constant / boundary condition.

14. Explanatory relativity: What are we explaining? What is the “contrast space”? • Why do people get depressed[instead ofcalm, affectless, violent…] • Material & structural theories of brain function, affect and social learning. • Functional / efficient theories of social structure / resources, stress and coping. • Why / how do these people[instead of others…] get depressed? • Descriptive / “covariance” epidemiological questions. • Functional / efficient individual difference theories of “cognitive vulnerability”, social support… • Structural theories: experience and genetic expression, “temperament”. • When / where / how does depression occur [instead of then]? • More structural developmental or gender-based theories. • Functional theories: within-person differences in psychosocial variables. • Specification of possible boundary conditions. • Why is she not depressed now [whereas others still are]? • Application of functional or structural theories to developing and testing interventions.

15. Some random terms • Explanatory frame: The object to be explained + the form of explanation. • Structural presuppositions. • Nature versus nurture; what % of major depression is (alcoholism, smoking…) genetic? • Reductionism and Reducability • When is a reductionist explanation actually addressing a different phenomenon? • What is love? • Merging of soul mates • Search for meaning and intimacy • Economic / reproductive contract • Displacement of arousal • Evolutionary response to similarity • Neuro-chemical “trigger” event (…my love is chemical…) What is being explained? human attraction at all attraction to this person intensity, duration, action potential of attraction pair bonding cultural differences economic forces on social behavior… Different explanations or different phenomena?

16. Illness Stress Immune function Stress Illness Some useful (?) distinctions Three steps in convergent research; taking a phenomenon or empirical relation and developing or applying a theory to explain it, or testing the relative adequacy of diverse theories. • Find / specify a phenomenon; stressed people get sick a lot. Then specify a basic explanatory theory: Stress  Illness effects are “caused” by immune suppression.

17. Convergent research, 2 • Consider other theories that may explain the phenomenon or data pattern.(That may also lead to different “levels” of explanation). • What other mediating variable(s) (beside or in addition to immune function) may account for the effect of stress on illness? • What other psychosocial variables may lead to physical illness? • What (more exogenous) variables may control both your predictor and your mediator?

18. Immune function Negative health behavior Stress Illness Exposure to pathogens • “Convergent” use of theory application: test diverse possible mediators

19. “Convergent” theory application: testing several possible theories Ψ: Stress, Learned helplessness & negative affect Immune function Illness “Resilience”: Heartiness, optimism High (physiological) “arousability”

20. Conceptualizing / testing a structural exogenous variable Socio-economic status Stress, negative affect Immune function Illness Of course each link is a core research question

21. Adding two levels of endogenous mediating variables to a structural exogenous variable Immune function Stress, negative affect Negative health behavior Illness Socio-economic status Exposure to pathogens Structural & cultural barriers to health care Typical psychosocial conceptual frame More public health approach

22. Convergent research. 3. Turn the relation on its head: Under what conditions might stress  health? What moderatorscreate “boundary conditions” to the theory or even reverse a common effect? Stress & negative affect Interaction of stress by ψ resources Health status Immune function “Resilience”: heartiness, social support

23. Divergent research • Take an established theory and apply it to new and novel contexts. • Simple extension to new domains: • Does motivational enhancement work for “non-problem” behaviors? • Can cybernetic models of information – behavior relations be applied to self-regulation of health? • Can a physical fatigue –like model explain failure of self-regulation over time? • Shifts in levels of explanation; • Can genetic theories explain individual, contextual, or cultural differences on mood? • Can political events or socio-cultural –based stress explain risky decision making?

24. McGuire; Types of theories • Categorical • Clustering of phenomena • Types of social support (emotional, practical, etc.) • Affective clusters; NA • Clustering of people • Diagnostic categories • Personality; big 5, introversion – extroversion • Process • Flow-chart –like perspectives • Steps in persuasion • ‘Stages of Change’ • Developmental theories

25. …types of theories • Axiomatic • Write predictions from a highly plausible or tautological axiom • protection motivation theory • Axiomatic that people want protection from threat • Write predictions about… • Origins of threat perception • Mediators of responses to threat • Health belief / “Illness cognition” models • Axiom: peoples’ thoughts / understanding of illness (or illness threat)  key behaviors • Predictions: • Relevance of specific “cues” to action • Short v. long -term thoughts, etc. • Affective motivation (Ψ analysis)

26. …types of theories • “Guiding Light” or heuristic theories; larger model of man • Clear, “top-down” principles or axioms • Self-Efficacy / Learned helplessness • Cognitive-social information processing models • Basic learning theories • Deci; autonomous control as core motive • “Sense of coherence”; • Controllable, • coherent, • comprehensible • Reductionist / physiological (?) • Behavioral constraints of neuro-anatomy • Transmitter mediated models of depression, etc. • Theory itself not open to test, only to application or test of derivation

27. McGuire; Heuristics for developing hypotheses or empirical questions • I. Heuristics Simply Calling for Sensitivity to Provocative Natural Occurrences • A. Recognizing and Accounting for the Oddity of Occurrences • 1. Accounting for deviations from the general trend • 2. Accounting for the oddity of the general trend itself • B. Introspective Self-Analysis • 3. Analyzing one’s own behavior in similar situations • 4. Role playing one’s own behavior in the situation • C. Retrospective Comparison • 5. Extrapolating from similar problems already solved • 6. Juxtaposing opposite problems to suggest reciprocal solutions • D. Sustained, Deliberate Observation • 7. Intensive case studies • 8. Participant observation • 9. Assembling propositional inventories

28. II. Heuristics Involving Simple Conceptual Analysis (Direct Inference) E. Simple Conversions of a Banal Proposition 10. Accounting for the contrary of a trite hypothesis 11. Reversing the plausible direction of causality 12. Pushing an obvious hypothesis to an implausible extreme 13. Imagining the effects of reducing a variable to zero 14. Conjecturing interaction variables that qualify a relation F. Multiplying Insights by Conceptual Division 15. Linguistic explorations 16. Alternative manipulations of the independent variable 17. Dividing the dependent variable into subscales 18. Arranging output subcomponents into a sequence G. Jolting One’s Conceptualizing Out of its Usual Ruts 19. Shifting attention to an opposite pole of the problem 20. Alternating preferred with non-preferred research styles 21. Expressing one’s hypothesis in multiple modalities 22. Disrupting ordinary states of consciousness

29. III. Heuristics Calling for Complex Conceptual Analysis (Mediated Inference) • H. Deductive Reasoning Procedures • 23. Generating multiple explanations for a given relation • 24. Alternating induction and deduction • 25. Identifying counterforces obscuring an obvious relation • 26. Hypothetico-deductive sets of postulates • I. Using Thought-Diversifying Structures • 27. Using an idea-stimulating checklist • 28. Constructing provocative complex generating structures • 29. Formalizing explanatory accounts • J. Using Metatheories as Thought Evokers • 30. The evolutionary functionalism (adaptivity) paradigm • 31. Transferring conceptualizations analogously • 32. Quixotic defense of a theory

30. IV. Heuristics Demanding Reinterpretations of Past Research K. Delving into Single Past Studies 33. Accounting for irregularities in an obtained relation 34. Decomposing non-monotonic into simpler relations 35. Deviant-case analysis 36. Interpreting serendipitous interaction effects L. Discovery by Integrating Multiple Past Studies 37. Reconciling conflicting outcomes or non-replications 38. Bringing together complementary past experiments 39. Reviewing and organizing current knowledge in an area

31. V. Heuristics Necessitating Collecting New or Reanalyzing Old Data M. Qualitative Analyses 40. Allowing open-ended responses for content analysis 41. Participating actively in the research routine 42. Exploring a glamorous technique 43. Including low-cost interaction variables in the design 44. Pitting confounded factors against one another 45. Strategic planning of programmatic research N. Quantitative Analyses 46. Multivariate fishing expeditions 47. Subtracting out the effect of a known mediator 48. Computer simulation 49. Mathematical modeling