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Here is some background on this research.
It involves the study of the impact of fallacious arguments on
attitudes. The dependent variable is attitude toward the topic.
The study now has two independent variables. The first independent
variable is a true experimental variable: the use or non-use of the non
sequitur fallacy in a persuasive message. The second
independent variable is a moderator variable: sex of the respondent.
This second independent variable is a moderator variable and, of course,
it is not experimentally manipulated. Thus, each independent variable
has two levels. The experimental variable dealing with the use of
the non sequitur fallacy is identified as "falexper"
(1= with the fallacy presented to subjects; 2=without the fallacy
presented to subjects). The remaining (moderator) independent
variable is "sex" and is identified with 1 equal to males and
2 equal to females.
The data is in SPSS format and named as ATTITUD1.SAV
and stored in C:\datasets.
In R, type in the following commands:
>library(foreign)
>atti<-read.spss("c:\\datasets\\attitud1.sav",
use.value.labels=TRUE, to.data.frame=TRUE, max.value.labels=Inf)
(to import the dataset into R)
If only one factor, execute the followings:
> res<-aov(ATTITUDE ~ FALEXPER, atti)
> summary(res)
Df Sum Sq Mean Sq F value
Pr(>F)
FALEXPER 1 80.79
80.79 3.3019
0.0752 .
Residuals 50 1223.44
24.47
If work on two factors, execute the followings:
> res1<-aov(ATTITUDE ~ FALEXPER + SEX +
FALEXPER *SEX, atti)
> summary(res1)
Df
Sum Sq Mean Sq F
value Pr(>F)
FALEXPER
1 80.79
80.79
3.1845 0.08066
SEX
1
5.02
5.02
0.1979 0.65841
FALEXPER:SEX 1
0.62
0.62
0.0243 0.87674
Residuals
48 1217.80 25.37
> res2<-aov(ATTITUDE ~ SEX + FALEXPER + SEX*
FALEXPER, atti)
> summary(res2)
Df Sum Sq
Mean Sq F
value Pr(>F)
SEX
1
1.73
1.73
0.0682 0.79511
FALEXPER
1
84.09
84.09
3.3143 0.07492
SEX:FALEXPER 1
0.62
0.62
0.0243 0.87674
Residuals
48 1217.80
25.37
As
we see, the order of entering the factors does make a difference.
(Alex
Liu - May 7, 2004)
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