Chapter 2
Observation and Correlation

http://www.youtube.com/watch?v=-Xu71i89xvs
 Stack & Gundlach found that metro areas that played more country music had higher suicide rates, concluding that country music causes suicide.
 Maybe depressed people seek out sad music?
 http://www.jstor.org/stable/2580303
 Or maybe it is a spurious correlation?
 http://www.tylervigen.com/spurious-correlations

 Variable -- a characteristic that can have different values (height, weight).
 Value -- usually a single, specific number
(6 feet tall, 140 pounds).
 Measurement – the process of assigning numbers to entities in the world.
 In non-experimental research, variables may be measured, but nothing is being manipulated by the experimenter.
 No independent variable, just DVs.

 Methods for observing behavior in its natural environment.
 Behavior is complex and humans have limited attention span, so we delimit, or narrow, the range of behaviors we plan to observe.
 Reactivity -- subjects may behave differently than usual when they know they are being observed.

 Unobtrusive observation -- subject is unaware of being observed in presence of observer
 Example: chivalry study, bathroom study.
 Unobtrusive measurement -- observer collects evidence in absence of subject and infers behavior of subject.
 Example: graffiti study, collecting scat
 There is a danger in anthropomorphizing or incorrectly interpreting what is observed.
 Participant observation – “going native”

 Gives a picture of people’s attitudes, beliefs, behaviors, and feelings about a topic.
 Sample from a population, then infer based on sample -- only as good as the sample.
 Return rate may produce sampling problem.
 Collect large amounts of data from large number of people quickly.
 Does not show causality among variables.
 Can also be used to provide data for the correlational method.

 Contingency Research
 Variables are presented in a contingency table .
 A Chi Square statistic is computed to determine whether relationships among variables exist.

Values in the tables are “counts” or frequencies for categories, not measurements.
 Data is ex post facto

 Correlation – a statistical technique that expresses the degree of linear relationship between 2 variables.
 If the correlation is high, a strong linear relationship exists.
 If the correlation is low, a weak relationship exists.
 If the correlation is zero, there is no relationship.

 r is a numerical index of the degree of linear relationship between 2 variables.
 r is computed by taking into account pairs of scores – one score from one variable and the other score from another variable.
 Correlation coefficient (r) has a strength
(0-1) and a direction (+ or -).
 r allows us to more precisely compare different sets of variables:
 SAT & GPA vs IQ & GPA

 Does income level predict reading level?
 Measure income level at grade 1
 Measure reading level at grade 1
 Compute correlation between reading level
& income:
 What if r = +1.0? What if r = -1.0?
 What if r = +.88? What if r = -.88?
 What if r = +.15? What if r = -.19?
 What if r = 0.0?

Variable r
High school GPA
Number of times read chapter
SAT - verbal score
Number of classes missed
Number of credit hours enrolled
SAT - quantitative
Undergraduate classification
Ave number of hrs/wk watching TV
Ave number of beers weekly
Ave number of hrs/wk extracurric. activ’s -.06
Gender (1=m,2=f)
Shoe size
.04
-.04
Ave number of hrs/wk working
Ave number of hrs/wk studying psych
-.03
.01
.31
.22
.20
.20
.16
.14
.13
-.13
-.11

 The directionality problem -- for any correlation between X and Y:
 X may cause Y
 Y may cause X
 Z may cause both X and Y
 Classic examples:
 Hours watching violent TV & violent behaviors (+)
 Grades in physics & grades in statistics (+)

 Spurious correlation -- a correlation exists but no causal relationship exists.
 Spurious correlations sometimes occur because both variables are mediated by a third variable.
 Classic examples:
 Number of churches in a town and number of murders.
 Number of toasters owned in a household and number of teen pregnancies.
 Kids in private schools get higher test scores.

 Selection bias – a kind of spurious correlation
 Students in Mississippi had a higher average
SAT than in California even though California spent more per pupil than Mississippi.

In Mississippi only top students took the SAT whereas in California nearly all took it.
 Restriction of range -- a correlation may underestimate the relationship between two variables if the range of either variable is restricted.