Basic Methodologies Psych 231: Research Methods in Psychology

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

The chicken or the egg? Exclusive usage of one or the other can be problematic Typically good research programs use both Theory Data Induction Deduction “Data driven research” reasoning from the data to the general theory “Theory driven research” reasoning from a general theory to the data

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.”

Omnipotent Theory Beware theories that are so powerful/general/flexible that they can account for everything. These are not testable EXTINCTION OF THE DINOSAURS FULLY EXPLAINED

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

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.

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.”

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

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

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? Today’s focus

General research approaches Descriptive: Observational Survey Case studies Correlational Experimental

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

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

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

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

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

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

Survey methods Widely used methodology More detail in Week 11 Can collect a lot of data Done correctly, can be a very difficult method Doesn’t provide clear cause-effect patterns

Case Histories This view has a number of disadvantages There may be poor generalizabilty There are typically a number of possible confounds and alternative explanations Intensive study of a single person, a very traditional method Typically an interesting (and often rare) case Phineas Gage Sept 13, 1848 Explosion propelled a railroad tamping rod through his brain

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

Causal claims We’d like to say: (variable X) causes (variable Y) To be able to do this: 1. The causal variable must come first 2. There must be co-variation between the two variables 3. Need to eliminate plausible alternative explanations

Causal claims Directionality Problem: Airplanes and coffee spills One might argue that turbulence cause coffee spills One might argue that spilling coffee causes turbulence

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

Theory 1: Storks deliver babies

Theory 2: underlying third variable

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

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

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

Next time Ethics in research Read GF chapter 4 (note syllabus said chpt 3)