Review

1
Review

• Bigger sample size (more data) will
• Increase reliability
• Decrease reliability

2
Review

• Greater variability among individuals will
• Increase reliability
• Decrease reliability

3
Review

• A bigger effect size in your data will
• Increase reliability
• Decrease reliability

4
Research Design

• 8/28

5
Overview

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

6
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

7
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

8
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

9
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, cant tell
  difference
• Solution Intervention
• Manipulate X
• Any resulting effect on Y must be caused by X,
  not vice versa

Depression
Depr(Outdoor Group) lt Depr(Indoor Group)
10
Third-variable Problem

• X and Y might co-vary because theyre 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
• Shouldnt affect Z
• Any effect on Y must be direct

Colds(Apple Group) lt Colds(No-apple Group)
11
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

12
Experiments

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

13
Confounds and Control

• Importance of experimental control
• 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

14
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

15
Quasi-independent Variables

• Some variables cant 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

16
Review

• Test rats in a maze, half in morning, half at
  night. Measure how long each rat takes to learn.
• Time to learn is a(n)
• Variable
• Hypothesis
• Experiment
• Datum

17
Review

• Test rats in a maze, half in morning, half at
  night. Measure how long each rat takes to learn.
• Rats are smarter in the morning is a(n)
• Variable
• Hypothesis
• Experiment
• Datum

18
Review

• Test rats in a maze, half in morning, half at
  night. Measure how long each rat takes to learn.
• The 3rd rat takes 530. This is a(n)
• Variable
• Hypothesis
• Experiment
• Datum

19
Review

• Test rats in a maze, half in morning, half at
  night. Measure how long each rat takes to learn.
• You randomly decide which group each rat is
  in.This is a(n)
• Experiment
• Non-experimental study

20
Review

• Do the same with people, and let them decide
  which time to sign up for.
• This is a(n)
• Experiment
• Non-experimental study