Chapter 8

Question 1

Two variables are said to be associated when

Answer 1

they vary together
when one changes the other changes

Question 2

2 things to do when the independent variable is it the columns

Answer 2

calculate percentages for each group

compare the percentages horizontally

Question 3

3 characteristics of a bivariate association

Answer 3

1.) does an association exist?
2.)If an association exists: How strong is the association?
3.)What is the pattern or direction of the association?

Question 4

the stronger the relationship…

Answer 4

the greater the change in conditional distributions

Question 5

example of no association

Answer 5

Question 6

example of a perfect association

Answer 6

Question 7

How to calculate maximum difference

Answer 7

largest # - smallest # in a row

Question 8

maximum difference for weak moderate and strong (used for Phi and V)

Answer 8

weak= 0-10
moderate=11-30
strong= more than 30

Question 9

In positive relationships and EX

Answer 9

the variables vary in the same direction
as job satisfaction increases so does productivity

Question 10

in negative relationships and EX

Answer 10

the variables vary in opposite directions
as one increases the other decreases
education decreases TV viewing increases

Question 11

To examine associations in bivariate tables, follow the rule:

Answer 11

percentage DOWN
compare ACROSS

Question 12

measures of association characterize the

Answer 12

strength of bivariate relationships

Question 13

for nominal level variables there are 2 common measures of association (3)

Answer 13

chi square-base (phi or cramers V)
PRE: measure Lambda

Question 14

What does phi do and what does it use

Answer 14

judges the strength of the relationship
2x2 tables only

Question 15

What does cramers V do and what does it use

Answer 15

fixes the denominator problem by adjusting for tables size
uses anything over 2x2

Question 16

Phi and V will or will not be equal

Answer 16

will be equal

Question 17

range association for Phi and V

Answer 17

0= no association
1= perfect association

Question 18

PRE

Answer 18

proportional reduction in error

Question 19

PRE prediction 1

Answer 19

predicting the core of the dependent with no information from the independent

Question 20

PRE prediction 2

Answer 20

predicting the score of the dependent with information from the independent

Question 21

Lambda tells us the

Answer 21

improvement in predicting Y while taking X into account

Question 22

What is E1

Answer 22

Prediction 1

Question 23

What is E2

Answer 23

Prediction 2

Question 24

What is lambda

Answer 24

Difference between E1 and E2

Question 25

If the variables are associated we should make fewer errors using which prediction

Answer 25

prediction 2 should have less errors

Question 26

Lambda gives the indication of the

Answer 26

strength of the relationship

Question 26

lambda is asymmetrical meaning

Answer 26

the value will vary depending on which variable is the independent

Question 27

What does lambda do that Phi and V don't

Answer 27

predicts the proportional reduction in error