Post on 31-Dec-2015
description
Week 10: Chapter 16
Controlling for a Third Variable
Multivariate Analyses
Introduction Social science research projects are
multivariate, virtually by definition. One way to conduct multivariate
analysis is to observe the effect of 3rd variables, one at a time, on a bivariate relationship.
The elaboration technique extends the analysis of bivariate tables presented in Chapters 12-15 and 17.
Elaboration To “elaborate”, we observe how a
control variable (Z) affects the relationship between X and Y.
To control for a third variable, the bivariate relationship is reconstructed for each value of the control variable.
Problem 16.1 (see Healey p. 452) will be used to illustrate these procedures.
Problem 16.1: Bivariate Table
• Sample - 50 immigrants• X = length of residence• Y = Fluency in English• G = .71
Problem 16.1: Bivariate Table
< 5 5+
Lo 80% 40%
Hi 20% 60%
100%
(N=25)
100%
(N=25)
• The column %s and G show a strong, positive relationship: fluency increases with length of residence.
Problem 16.1 Will the relationship between fluency (Y)
and length of residence (X) be affected by gender (Z)?
To investigate, the bivariate relationship is reconstructed for each value of Z.
One partial table shows the relationship between X and Y for men (Z1) and the other shows the relationship for women (Z2).
Problem 16.1: Partial Tables Partial table for males. G = .78
< 5 5 +
Lo 83% 39%
Hi 17% 61%
Problem 16.1: Partial Tables
Partial table for females. G = .65
< 5 5 +
Lo 77% 42%
Hi 23% 58%
Problem 16.1: A Direct Relationship The percentage patterns and G’s for all
three tables are essentially the same. Sex (Z) has little effect on the relationship
between fluency (Y) and length of residence (X).
For both sexes, Y increases with X in about the same way.
There seems to be a direct relationship between X and Y.
A. Direct Relationships In a direct relationship, the control variable has little
effect on the relationship between X and Y. The column %s and gammas in the partial tables
are about the same as the bivariate table. This outcome supports the argument that X causes
Y.
X Y
Other Possible Relationships
Between X, Y, and Z: B. Spurious relationships:
X and Y are not related, both are caused by Z. C. Intervening relationships:
X and Y are not directly related but are linked by Z.
D. Interaction The relationship between X and Y changes for
each value of Z. We will extend problem 16.1 beyond the
text to illustrate these outcomes.
B. Spurious Relationships X and Y are not related, both are caused
by Z.
XZ
Y
B. Spurious Relationships Immigrants with relatives who feel at
home in the UK (Z) are more fluent (Y) and more likely to stay (X).
Length of StayRelatives
Fluency
B. Spurious Relationships With Relatives G = 0.00 < 5 5+
Low 30% 30%
High 70% 70%
B. Spurious Relationships No relatives G = 0.00 < 5 5 +
Low 65% 65%
High 35% 35%
B. Spurious Relationships
In a spurious relationship, the gammas in the partial tables are dramatically lower than the gamma in the bivariate table, perhaps even falling to zero.
C. Intervening Relationships X and Y and not
directly related but are linked by Z.
Longer term residents may be more likely to find jobs that require English and be motivated to become fluent.
ZX Y
Jobs
Length
Fluency
C. Intervening Relationships Intervening and
spurious relationships look the same in the partial tables.
Intervening and spurious relationships must be distinguished on logical or theoretical grounds.
< 5 5+
Low 30% 30%
High 70% 70%
< 5 5 +
Low 65% 65%
High 35% 35%
D. Interaction• X and Y could only
be related for some categories of Z.
• X and Y could have a positive relationship for one category of Z and a negative one for others.
Z1
X Y Z2
0
Z1 +
X YZ2 -
D. Interaction
Interaction occurs when the relationship between X and Y changes across the categories of Z.
Perhaps the relationship between fluency and residence is affected by the level of education residents bring with them.
D. Interaction Well educated
immigrants are more fluent regardless of residence.
Less educated immigrants are less fluent regardless of residence.
< 5 5+
Low 20% 20%
High 80% 80%
< 5 5 +
Low 60% 60%
High 40% 40%
Summary: Table 16.5 (see Healey, p. 441)
Partials compared
with bivariate
Pattern Implication Next Step
Theory that
X Y is
Same A. Direct Disregard ZSelect
another ZSupported
WeakerB.
SpuriousIncorporate
Z
Focus on relationship between Z
and Y
Not supported
Summary: Table 16.5 (see Healey, p. 441)
Partials compared
with bivariate
Pattern Implication Next Step
Theory that
X Y is
WeakerC.
InterveningIncorporate
Z
Focus on relationship between X,
Y, and Z
Partially supported
MixedD.
InteractionIncorporate
Z
Analyze categories of
Z
Partially supported