Good Theories & Basic Methodologies

1. Good Theories &Basic Methodologies Psych 231: Research Methods in Psychology

2. Properties of a good theory

3. Properties of a good theory “My theory by A. Elk. Brackets Miss, brackets. This theory goes as follows and begins now. All brontosauruses are thin at one end, much thicker in the middle and then thin again at the far end. That is my theory, it is mine, and belongs to me and I own it, and what it is too.” Link to entire Monty Python’s “My theory” transcript

4. Properties of a good theory • Organizes, Explains, & Accounts for the data • If there are data relevant to your theory, that your theory can’t account for, then your theory is wrong • Either adapt the theory to account for the new data • Develop a new theory that incorporates the new data

5. Properties of a good theory • Organizes, Explains, & Accounts for the data • Testable/Falsifiable – can’t prove a theory, can only reject it “No amount of experimentation can ever prove me right; a single experiment can prove me wrong.”

6. Omnipotent Theory

7. Omnipotent Theory • Beware theories that are so powerful/ general/ flexible that they can account for everything. These are not testable • Karl Popper claimed that Freudian theory isn’t falsifiable • If display behavior that clearly has sexual or aggressive motivation, then it is taken as proof of the presence of the Id • If such behavior isn’t displayed, then you have a “reaction formation” against it. So the Id is there, you just can’t see evidence of it. • So, as stated, the theory is too powerful and can’t be tested and so it isn’t useful

8. Properties of a good theory • Organizes, Explains, & Accounts for the data • Testable/Falsifiable • Generalizable – not too restrictive • The theory should be broad enough to be of use, the more data that it can account for the better • The line between generalizability and falsifiability is a fuzzy one.

9. Properties of a good theory • Organizes, Explains, & Accounts for the data • Testable/Falsifiable • Generalizable • Parsimony (Occam’s razor) • For two or more theories that can account for the same data, the simplest theory is the favored one “Everything should be made as simple as possible, but not any simpler.”

10. Properties of a good theory • Organizes, Explains, & Accounts for the data • Testable/Falsifiable • Generalizable • Parsimony • Makes predictions, generates new knowledge • A good theory will account for the data, but also make predictions about things that the theory wasn’t explicitly designed to account for

11. Properties of a good theory • Organizes, Explains, & Accounts for the data • Testable/Falsifiable • Generalizable • Parsimony • Makes predictions, generates new knowledge • Precision • Makes quantifiable predictions

12. Using theories in research • Induction – reasoning from the data to the general theory (data driven) • In complete practice this approach probably needs a new theory (or an adapted one) for every new data set • Deduction – reasoning from a general theory to the data (theory driven) • Here the theory (if it is a “good” one) is sometimes viewed as more critical than the data. • It also will guide the choice of what experiments get done

13. The chicken or the egg? • Typically good research programs use both Theory Induction Deduction Data

14. Research Approaches • Basic (pure) research - tries to answer fundamental questions about the nature of behavior • e.g., McBride & Dosher (1999). Forgetting rates are comparable in conscious and automatic memory: A process-dissociation study. • Applied research – Theory sometimes takes a backseat. This is research designed to solve a particular problem • e.g., Jin (2001). Advertising and the news: Does advertising campaign information in news stories improve the memory of subsequent advertisements?

15. Basic research Applied research Research Approaches • Probably the best way to think of this is as a continuum rather as two separate categories. • Often applied work may bring up some interesting basic theoretical questions, and basic theory often informs applied work.

16. Conducting Research: An example • Claim:People perform best with 8 hours of sleep a night. • How might we go about trying to test this claim? • How should we test it (what methods)? • What are the things (variables) of interest? • What is the hypothesized relationship between these variables?

17. General research approaches • Descriptive: • Observational • Survey • Case studies • Correlational • Experimental

18. Observational methods • The researcher observes and systematically records the behavior of individuals • Naturalistic observation • Participant observation • Contrived observation

19. Naturalistic Observation • Observation and description of behaviors within a natural setting • Can be difficult to do well • Good for behaviors that don’t occur (as well) in more controlled settings • Often a first step in the research project

20. Participant Observation • The researcher engages in the same behaviors as those being observed • May allow observation of behaviors not normally accessible to outside observation • Internal perspective from direct participation • But could lead to loss of objectivity • Potential for contamination by observer

21. Contrived observation • The observer sets up the situation that is observed • Observations of one or more specific variables made in a precisely defined setting • Much less global than naturalistic observations • Often takes less time • However, since it isn’t a natural setting, the behavior may be changed

22. Observational methods • Advantages • may see patterns of behaviors that are very complex and realized on in particular settings • often very useful when little is known about the subject of study • may learn about something that never would have thought of looking at in an experiment

23. Observational methods • Disadvantages • Causality is a problem • Threats to internal validity because of lack of control • Every confound is a threat • Lots of alternative explanations • Directionality of the relationship isn’t known • Sometimes the results are not reproducible

24. Survey methods • Widely used methodology • Can collect a lot of data • Done correctly, can be a very difficult method • Doesn’t provide clear cause-effect patterns

25. Case Histories • Intensive study of a single person, a very traditional method • Typically an interesting (and often rare) case • This view has a number of disadvantages • There may be poor generalizabilty • There are typically a number of possible confounds and alternative explanations

26. Correlational Methods • Measure two (or more) variables for each individual to see if the variables are related • Used for: • Predictions • Reliability and Validity • Evaluating theories • Problems: Can’t make casual claims

27. Causal claims • We’d like to say: • (variable X) causes (variable Y) • To be able to do this: • The causal variable must come first • There must be co-variation between the two variables • Need to eliminate plausible alternative explanations

28. One might argue that turbulents cause coffee spills One might argue that spilling coffee causes turbulents Causal claims • Directionality Problem: • Airplanes and coffee spills

29. Causal claims • Happy people sleep well • Or is it that sleeping well when you’re happy? • Third variable problem: • Do Storks bring babies? • A study reported a strong positive correlation between number of babies and stork sightings • Directionality Problem: • Airplanes and coffee spills

30. Theory 1: Storks deliver babies

31. Theory 2: underlying third variable

32. The experimental method • Manipulating and controlling variables in laboratory experiments • Must have a comparison • At least two groups (often more) that get compared • One groups serves as a control for the other group • Variables • Independent variable - the variable that is manipulated • Dependent variable - the variable that is measured • Control variables - held constant for all participants in the experiment

33. The experimental method • Advantages • Precise control possible • Precise measurement possible • Theory testing possible • Can make causal claims

34. The experimental method • Disadvantages • Artificial situations may restrict generalization to “real world” • Complex behaviors may be difficult to measure

35. Next time • Ethics in research • Read chapter 3