# MRA Part 3-Applications (2007)

```Multiple Regression Analysis:
Part 3
Use of categorical variables in MRA
1
Changing Gears

What if we wish to
include categorical
variables into our
regression equation?

For instance, we have
two categorical variables
(say gender and ethnic
group) and two
continuous variables (say
and visual processing
speed) to predict
performance
2
Regression and Mean Comparisons



Independent samples ttest: comparing two
means
Tests the null
hypothesis of…
Accomplished via the
usual t-statistic formula:
x1  x2
t
s 2p s 2p

n1 n2
mean
std. dev.
group 1
5
5
4
8
6
2
5
6
5
4
5.00
1.56
group 2
8
9
4
7
7
8
10
9
9
9
8.00
1.70
3
Calculating t…
Pooled variance estimate
2
2
9(1.56
)

9(1.70
)
2
sp 
 2.667
10  10  2
t-ratio
t
58
3

 4.108
2.667 2.667 0.7303

10
10
Effect size estimate (variance accounted for)
2
2
t

4.108
r2  2

 0.484
2
t  df 4.108  18
4
Our usual approach in SPSS
gpid
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
score
5
5
4
8
6
2
5
6
5
4
8
9
4
7
7
8
10
9
9
9


Code groups & enter
associated score
Run an independent
samples t-test to get
the following…
t-test for Equality of Means
Sig. (2Mean Std. Error
tailed) Difference Difference 95% CI of difference
t df
Lower
Upper
-4.108 18
0.001
-3
0.730
-4.534
-1.466
5
What if we coded groups as 0’s & 1’s?

Could construct a
Point-Biserial
correlation
rpb 
( M Y 1  M Y 0 ) pq
y
(3).5
rpb 
 0.696
2.156
Can you guess what 0.6962 equals?
gpid
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
score
5
5
4
8
6
2
5
6
5
4
8
9
4
7
7
8
10
9
9
9
dc
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
6
Pearson, r pb and regression

Taking advantage of the fact that rpb is merely a
Pearson-product-moment correlation in disguise…

Let’s regress y onto our binary variable to get the following
Model Summary
Model
1
R
R Square
a
.696
.484
R Square
.455
Std. Error of
the Es timate
1.63299
a. Predictors : (Constant), dummyc
Coefficientsa
Model
1
(Cons tant)
dummyc
Uns tandardized
Coefficients
B
Std. Error
5.000
.516
3.000
.730
Standardized
Coefficients
Beta
.696
t
9.682
4.108
Sig.
.000
.001
a. Dependent Variable: score
Do these look familiar?
7
This can be greatly expanded…

ANOVA can be run using…







Dummy Coding
Effect Coding
Orthogonal Coding
Multiple categories can be modeled
N-Way designs can be accommodated
ANCOVA
Repeated Measures
8
rd
3
group…
Method 1
Method 2
Method 3
3
5
2
4
8
4
3
9
4
4
3
8
7
4
2
5
6
7
8
6
7
9
10
9
Means: 4.75
4.625
7.75
9
Dummy Coding




Series of binary
variables
zeros
Other two groups are
differentiated by 1’s
Characteristics…
Method
1
1
…
1
2
2
…
2
3
3
…
3
Score
3
5
…
9
4
4
…
5
6
7
…
9
d1
0
0
0
0
0
0
0
0
1
1
1
1
d2
0
0
0
0
1
1
1
1
0
0
0
0
10
Submitting this to an MRA…
ANOVAb
Model
1
Regress ion
Res idual
Total
Sum of
Squares
50.083
86.875
136.958
df
2
21
23
Mean Square
25.042
4.137
F
6.053
Sig.
.008 a
a. Predictors : (Constant), Dummy Code s eparating 3 from 1 & 2, Dummy Code
s eparating 2 from 1 & 3
b. Dependent Variable: Exam Score
Coefficientsa
Model
1
(Cons tant)
Dummy Code
s eparating 2 from 1 & 3
Dummy Code
s eparating 3 from 1 & 2
Uns tandardized
Coefficients
B
Std. Error
4.750
.719
Standardized
Coefficients
Beta
t
6.605
Sig.
.000
-.125
1.017
-.025
-.123
.903
3.000
1.017
.592
2.950
.008
a. Dependent Variable: Exam Score
11
Comparing group means


All groups explicitly compared to “uncoded” group.
To make other comparisons, either


1.) re-run the analysis with a different coding scheme
2.) use the following equation:
t
bi  b j
1 1 
SE y  y '   
 ni n j 


Concerns over type 1 error in such comparisons remain
12
Effect Coding




Series of variables
(vectors) having values
of -1, 0, 1
-1’s
Other two groups
differentiated by 0’s &
1’s
Characteristics…
Method
1
1
…
1
2
2
…
2
3
3
…
3
Score
3
5
…
9
4
4
…
5
6
7
…
9
e1
1
1
1
1
0
0
0
0
-1
-1
-1
-1
e2
0
0
0
0
1
1
1
1
-1
-1
-1
-1
13
Solution
Coeffi cientsa
Model
1
(Const ant)
e1
e2
Unstandardized
Coeffic ients
B
St d. Error
5.708
.415
-.958
.587
-1. 083
.587
St andardiz ed
Coeffic ients
Beta
-.328
-.370
t
13.749
-1. 632
-1. 845
Sig.
.000
.118
.079
a. Dependent Variable: Ex am Score

Characteristics:



Intercept = __________
-0.958 represents ___________
-1.083 represents ____________
14
Recovering cell means
Y’ = 5.708 + e1(-0.958) + e2(-1.083)
Thus, someone in group 1…
Y’ = 5.708 + 1(-0.958) + 0(-1.083) = 4.75
For group 2…
Y’ = 5.708 + 0(-0.958) + 1(-1.083) = 4.625
For group 3…
Y’ = 5.708 + (-1)(-0.958) + (-1)(-1.083) = 7.749
15
Recovering the missing coefficients



If e1 gives us the effect for being in group 1, and…
e2 gives us the effect for being in group 2…
What is the effect for being in group 3?




How do we get it?
Method 1: recode the cells so that a different cell
gets all -1’s.
Method 2: take advantage of the fact that all b’s
must sum to zero*.
Thus…


e3 + (-0.958) + (-1.083) = 0
e3 = 0.958 + 1.083 = 2.041
16
Unequal group sizes due to population
differences

Unequal group sizes



May represent attrition in study, or other problems
May reflect existing group size differences in
population
If we wish to preserve information about
unequal population sizes…


Use weighted effect coding
Instead of one group getting all -1’s…

Group gets weighted code of –n2/n1 where n1 is the
‘baseline group and n2 is the group identified by vector
17
Two-Way ANOVA Revisited
Factor B: Anxiety Level
Low
Medium
Factor A:
Difficulty
3
2
1
5
1
9
Easy
6
7
4
7
Difficult
0
2
0
0
3
3
8
3
3
3
High
9
9
13
6
8
0
0
0
5
0
18
Recall the cell means
Factor B: Arousal
Low
Medium
High
Total
Easy
3.00
6.00
9.00
6.00
Difficult
1.00
4.00
1.00
2.00
Total
2.00
3.00
5.00
4.00
19
Dummy Coding / Effect Coding




Easy = 0
Difficult = 1
Anxiety

Vector 1











Vector 1




Low = 0
Medium = 0
High = 1
Vector 3 = TD x Vector 1
Vector 4 = TD x Vector 2
Easy = 1
Difficult = -1
Anxiety
Low = 0
Medium = 1
High = 0
Vector 2



Vector 2





Low = 1
Medium = 0
High = -1
Low = 0
Medium = 1
High = -1
Vector 3 = TD x Vector 1
Vector 4 = TD x Vector 2
20
Results using dummy coding…
Coefficientsa
Model
1
(Constant)
Dummy Code for difficulty
Dummy Code 1 for
anxiety
Dummy Code 2 for
anxiety
Dummy Code Interaction
1
Dummy Code Interaction
2
Unstandardized
Coefficients
B
Std. Error
3.000
1.000
-2.000
1.414
Standardized
Coefficients
Beta
-.289
t
3.000
-1.414
Sig.
.006
.170
3.000
1.414
.408
2.121
.044
6.000
1.414
.816
4.243
.000
-2.4E-015
2.000
.000
.000
1.000
-6.000
2.000
-.645
-3.000
.006
a. Dependent Variable: Performance
21
Results of Effect Coding
ANOVAb
Model
1
Regres sion
Residual
Total
Sum of
Squares
240.000
120.000
360.000
df
5
24
29
Mean Square
48.000
5.000
F
9.600
Sig.
.000a
a. Predic tors: (Constant), int2, ecanx2, ecdiff, int1, ecanx1
b. Dependent Variable: Performanc e
Coeffi cientsa
Model
1
(Const ant)
ec diff
ec anx1
ec anx2
int 1
int 2
Unstandardized
Coeffic ients
B
St d. Error
4.000
.408
-2. 000
.408
-2. 000
.577
1.000
.577
1.000
.577
1.000
.577
St andardiz ed
Coeffic ients
Beta
-.577
-.471
.236
.236
.236
t
9.798
-4. 899
-3. 464
1.732
1.732
1.732
Sig.
.000
.000
.002
.096
.096
.096
a. Dependent Variable: Performanc e
22
Combining Categorical and
Continuous Variables



Type of Treatment by Pre-treatment
functioning to predict Outcome
Race/Ethnicity by Attitudes toward health
care to predict wellness visits
Recent vs. non-recent hire by openness to
new experience to predict likelihood of
change.
23
Example: Workplace Deviance & Moral
Reasoning



Research Question: will scores on a moral
reasoning measure, that reflect “maintaining
norms” (~Kohlberg’s conventional level)
interact with organizational injustice to
produce workplace deviance?
Continuous Measures: Maintaining Norms
Categorical Measure: High vs. Low
Organizational Injustice
24
Results
Coefficientsa
Model
1
(Constant)
Condition
MNxCon
MNCENT
Unstandardized
Coefficients
B
Std. Error
2.569
.144
.391
.205
-.100
.014
-.019
.010
Standardized
Coefficients
Beta
.186
-.007
-.266
t
17.828
1.909
-7.143
-1.886
Sig.
.000
.059
.001
.062
Collinearity Statistics
Tolerance
VIF
.998
.475
.475
1.002
2.107
2.105
a. Dependent Variable: Average Workplace Deviance Score
ANOVAb
Model
1
Regres sion
Residual
Total
Sum of
Squares
11.491
98.641
110.132
df
3
95
98
Mean Square
3.830
1.038
F
3.689
Sig.
.015a
a. Predic tors: (Constant), MNCENT, Condit ion, MNx Con
b. Dependent Variable: Average W orkplace Devianc e Sc ore
R2 = .104
Note: data based on an actual study, interaction effect is manufactured for purpose of illustration.
25
Graph of Interaction
Interaction Chart for Dummy x Continuous Variable Interaction
6
5
Predicted Value
4
dummy code=0
dummy code=1
3
2
1
0
Low
High
Two Sample Points
26
And then there’s contrast coding

Recall our one-way teaching example

Two orthogonal contrasts:


M1 & M2 vs. M3
M1 vs. M2
Method 1
Method 2
Method 3
3
5
2
4
8
4
3
9
4
4
3
8
7
4
2
5
6
7
8
6
7
9
10
9
Means: 4.75
4.625
7.75
27
Accomplished by Contrast Coding


C1: -0.5M1 + -0.5M2 + 1M3
C2: 1M1 + -1M2 + 0M3
ANOVAb
Model
1
Regres sion
Residual
Total
Sum of
Squares
50.083
86.875
136.958
df
2
21
23
Mean Square
25.042
4.137
F
6.053
Sig.
.008a
a. Predictors: (Constant), Orthogonal comparison 2 (g1=g2), Orthogonal comparison
1 .5(1)+.5(2)=3
b. Dependent Variable: Exam Score
Coefficientsa
Model
1
(Constant)
Orthogonal comparison
1 .5(1)+.5(2)=3
Orthogonal comparison
2 (g1=g2)
Unstandardized
Standardized
Coefficients
Coefficients
B
Std. Error
Beta
5.708
.415
t
13.749
95% Confidence Interval for B
Correlations
Sig. Lower Bound Upper Bound Zero-order Partial
.000
4.845
6.572
Part
Collinearity Statistics
Tolerance
VIF
2.042
.587
.604
3.477
.002
.821
3.263
.604
.604
.604
1.000
1.000
.063
.508
.021
.123
.903
-.995
1.120
.021
.027
.021
1.000
1.000
a. Dependent Variable: Exam Score
28
```
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