Research Design 8/30

Overview Components of scientific studies Types of scientific studies Inferring causation Independent and dependent variables Confounds, random assignment Quasi-independent variables

Components of Scientific Studies Scientific study: Basic unit of empirical research Variables Anything that can take on multiple values Height, IQ, reaction time, extraversion, favorite color Measured in scientific studies Hypothesis Conjecture about how the world works Prediction about how variables relate Taller people are smarter This drug improves memory Blue is more popular than red Data (singular: datum) Results of measurements Values of variables IQ of Subject 4 Reaction time of Subject 12 on Trial 23

Types of Scientific Studies Experiment Involves some sort of intervention or manipulation Researcher sets some variable(s) and assesses effect on other variable(s) Vary number of items in a memory list Different drugs to different rats Allows inference of causation List length affects memory Drugs differentially affect lever pressing Non-experimental study Purely observational Measure naturally occurring variables and examine relationships Row of classroom, exam grade Can't be sure about causation

Non-experimental Studies Measure variables without influencing Row of classroom, exam grade Time spent outside, depression Bicycles currently owned, lifetime head injuries (6, 4) Apples per week, colds per year Correlation Relationship between variables, in terms of what values co-occur More apples, fewer colds Smarter people tend to like the color red All that can be inferred from non-experimental studies Does not say what causes what Problems with inferring causation Reverse causation Third variable problem Self-selection

Reverse Causation Researcher expects X causes Y, but actually Y causes X Depression and time outdoors Might predict outdoors alleviates depression Might find such a correlation But, depression might reduce desire for activity XY or YX both mean X and Y co-occur If you only measure co-occurrence, can’t tell difference Solution: Intervention Manipulate X Any resulting effect on Y must be caused by X, not vice versa Experiment Group Time Outdoors Depression Depr(Outdoor Group) < Depr(Indoor Group)

Third-variable Problem X and Y might co-vary because they’re both caused by Z Apples and colds Overall health-consciousness could increase apples and reduce colds People who eat more apples would also tend to get fewer colds But, no direct causal relationship Solution: Intervention (again) Manipulate X Shouldn’t affect Z Any effect on Y must be direct Attitude Colds Apples Experiment Group Colds(Apple Group) < Colds(No-apple Group)

Self-selection Differences between groups of people can be due to who chooses to be in which group Not necessarily consequence of group membership Math GREs by major Physics majors might do better than Psych Does physics make you better at math? Kids good at math more likely to choose Physics Height by sport Playing basketball makes you taller? Effects of alternative medicine Can view as reverse causation Being tall makes you better at basketball Can view as 3rd-variable problem Math aptitude affects both major choice and GRE

Experiments X Independent variable (IV) Dependent variable (DV) Manipulated by researcher Drug/placebo, training time, priming Dependent variable (DV) Measured by researcher Pain tolerance, proficiency, reaction time Intervention assures causality Attitude Colds Apples X

Confounds and Control Importance of experimental control Confound Only manipulate the IV Hold everything else constant Confound Variable that accidentally covaries with IV Subject expectations about drug effects Familiarity with experimental context Control means not having confounds Necessary for knowing effect is due to IV

Random Assignment Values of IV must be chosen at random for each subject Only way to assure causal relationship 3rd variable again Outright cheating Time of semester Ability Performance Experiment Group

Quasi-independent Variables Some variables can’t be manipulated, but can be used to create groups Sex, age, birthplace Sometimes causal direction is obvious Height, men vs. women Hockey enjoyment, Canadians vs. Americans Allows non-experimental study to be treated like an experiment Grouping variable is quasi-independent Can treat other variables like DVs