Asking Questions
•	Where does the question come from
•	Formalization
•	Approaches to answering
•	Do we have the answer?
•	Validity
•	Statistics
Where Do We Get Questions?
•	Opening Our Eyes – observation
•	Real life problems
•	“What if” and other thought experiments
•	Formal theory
Observation
•	Do people who sit near the front of class make higher grades?
•	Why are Texans more conservative than New Englanders?
•	Why is there more violence in the South than in the Midwest?
Real life (applied) problems
•	Design of computer programs and equipment
•	How can you get gay men to adopt safe sex practices
•	How can you get your political comrades to vote in November?
•	How can you get students to study more effectively?
Problems with applied research
•	Often problem needs to be “decomposed”
•	Students studying more effectively? Find more basic principles
•	Have to know what cognitive processes are involved
•	How to communicate effective processes
•	How to motivate people to use them
•	Adapting them to individuals
•	Wrong to criticize basic research for not being tied to everyday issues
Thought experiments
•	What would happen if no one wore clothes at Rice?
•	What might happen if we treated the mentally ill like regular people?
•	What might happen if we legalized gay marriage? 
Formal theory
•	Classically follow the science tradition of deduction from theory
•	But not always done
•	Some of the best research is more informally derived
Formalization of questions – bad questions
•	Have to put questions into tractable form
•	Some perfectly reasonable questions are effectively unanswerable
•	Are people happy?  -- too broad and vague
•	Are Rice students happier at 10 a.m or 11 a.m.? Too specific and uninteresting
•	Does Rice culture make students have two majors? – loaded and vague and imprecise
Some questions can’t be answered easily
•	What would happen if we legalized gay marriage?
•	Hard to know how to do experiments or observe without doing what many people think is abhorrent. 
•	Also the question of “what might happen” is vague
•	Psychological consequences – and to whom
•	Societal, economic, etc. questions
Formalization of questions
•	Often stated as formal hypothesis
•	But not necessary
•	What is necessary is that the question be specific and answerable in terms of current technologies.
Approaches to answering
•	Observation
•	Surveys
•	Case studies
•	Correlational studies
•	Experiments
Observation
•	Needs to be systematic
•	Often counting instances
•	Or observing relationships
Surveys
•	Used extensively by social scientists
•	Sometimes just designed to get at attitudes and values
•	But sometimes set up to look for relationships
•	Often relationships among variables measured
•	Sometimes relationships with demographic variables
•	Technology is complex
•	Sampling
•	Question wording
Case studies
•	Can be powerful if used cautiously
•	But limited
•	Used extensively with disorders
•	Mental and personality
•	Cognitive – cognitive neuropsychology
•	Often useful in generating ideas and hypotheses
•	And confirming more basic “laws”
Problems with case studies
•	Usually almost impossible to infer causality
•	A patient with a particular mental disorder may have had brutal childrearing
•	But you can’t tell what part of the child-rearing may have been a problem
•	Or assess interactions with genetics
•	Stroke patients may exhibit cognitive impairments
•	But what parts of the brain are affected?
•	And may be multiple sites
•	People who get better after treatment may have got better anyway – placebo effects
Correlational studies
•	Always involve relationships between 2 or more variables
•	Typically interested in one that produces or causes the other
•	The cause is the independent variable
•	And the effect is the dependent variable (dependent on the independent variable)
Correlational studies are not always obvious
•	The correlations may or may not be expressed in terms of an actual correlation coefficient
•	Sometimes one simply states that 2 groups differ
•	Saying that Rice students have higher SATs than SMU students is a correlation
•	As is stating that males have more traffic accidents than females
•	Or that faculty are older than students
Problems
•	Safe to say that a huge amount of our reasoning about many things is done in terms of such informal correlations
•	Main problem is that one cannot show causality clearly – usually. 
The two sources of the causality problem
•	Direction of effects
•	Third variables
Directionality issues
•	A correlation between A and B
•	May mean A causes B
•	Or that B causes A.
•	Or both
Examples
•	Punitive parents and child behavior problems
•	Playing violent video games and violent behavior
•	Grades and SAT scores
•	Self-esteem and life success
•	Homosexuality and protective mothers/distant fathers
Directionality issues not always important
•	Logical issues
•	Height & weight
•	SES and education
•	Temporal issues
•	Smoking and cancer
•	Mammogram signs and breast cancer
•	Winning the lottery and happiness
•	Gender and car accidents
•	Schizophrenia and child rearing  
Third variables
•	Almost always a potential problem
•	A third variable (a confounding variable) causes both of the variables of interest
Examples
•	TV watching and violence
•	Smoking and lung cancer
•	Obesity and increased risk of death
Experiments cure these problems
•	Directionality is taken care of with manipulation of cause or independent variable
•	Manipulation of IV at time t cannot be caused by DV at t+1.
•	Third variable issues are a little more complex
•	Control
•	Randomization
Interactions among independent variables
•	Occur when the influence of one IV on the DV is affected by other IV
•	Main effects – the IV reliably produces a change in the DV
•	Can have both
Examples of interactions – main effect & interaction
•	Often found with multiple necessary causes
•	Gender and violence
•	Men are more violent than women
•	But only for some circumstances
Graph of main effects and interaction
Examples of interactions – no main effects
•	Quite common in everyday especially with personality
•	So an extroverted person might do well at one kind of job but be terrible at another
•	Whereas an introvert might show the reverse pattern
Graph of “cross-over” interaction
The issue of multiple causality
•	All important human behavior has multiple causes
•	Therefore because we isolate one cause of something does not preclude other causes
Also causal chains
•	When we say A causes B
•	Usually do not specify intermediate causes
•	Heat causes people to be aggressive
•	Specify underlying motivation and feelings
•	But at another level the underlying biology
Other problems with experiments
•	Some variables can’t be manipulated
•	Practical issues
•	Ethical issues
•	Sampling and generalization
•	Can rarely study more than 2 or 3 potential causal variables at a time – the interaction issue
Validity issues
•	Sometimes manipulations of IV are “too pure” and divorced from real life
•	Threat and aggression
•	Status and power in experimental settings
•	And dependent variables may also be “too pure”
•	Shocking and aggression
•	Memory for word lists