Hello,
I have a problem with selecting the right type of sums of squares for
an ANCOVA for my specific experimental data and hypotheses. I do have
a basic understanding of the differences between Type-I, II, and III
SSs, have read about the principle of marginality, and read Venable's
"Exegeses on Linear Models"
(http://www.stats.ox.ac.uk/pub/MASS3/Exegeses.pdf). I am pretty new to R
and a search of the R-help archive did not
answer my question (although I found some good pointers).
In brief, leaving my covariates aside, I hypothesize that women (a)
generally perform lower then men in a specific task (microworld
performance, MWP) and that they (b) perform especially poor if a
certain situational condition exists ("stereotype threat"). N = 160,
80 female & 80 male participants, 82 under stereotype threat and 78 not.
I realize that it makes no sense to report/interpret a main effect of
stereotype threat in the confirmed presence of the interaction effect
GENDER:STTHREAT, because a main effect of stereotype threat would
actually be caused by the interaction (an error-bar plot illustrating
this can be found here if one scrolls a little downwars:
http://myowelt.blogspot.com/2008/05/obtaining-same-anova-results-in-r-as-in.html)
. I thus tend to use Type-II SSs and calculate my ANOVA with
library(car)
Anova(lm(MWP ~ GENDER * STTHREAT), type="II")
Anova Table (Type II tests)
Response: MWP
Sum Sq Df F value Pr(>F)
GENDER 23.939 1 32.3672 6.139e-08 ***
STTHREAT 12.684 1 17.1489 5.644e-05 ***
GENDER:STTHREAT 4.997 1 6.7557 0.01024 *
Residuals 115.380 156
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
However, it would make sense to report the main effect of GENDER in
the presence of the interaction and thus violate the marginality
principle, because of hypothesis (a) above. Would that mean that Type-
III SSs are desirable for the analysis of the main effect of GENDER,
and Type-II SSs are desirable for the main effect of STTHREAT and the
interaction? Or would it be better to specify a model that only
includes the interaction term and the main effect of gender with Type-
III SSs? Like this:
options(contrasts=c("contr.sum", "contr.poly"))
fit <- aov(MWP ~ GENDER:STTHREAT + GENDER)
drop1(fit,~.,test="F")
Single term deletions
Model:
MWP ~ GENDER:STTHREAT + GENDER
Df Sum of Sq RSS AIC F value Pr(F)
<none> 115.380 -44.310
GENDER 1 23.381 138.761 -16.787 31.612 8.475e-08 ***
GENDER:STTHREAT 2 17.680 133.061 -25.499 11.952 1.481e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
If this has been answered before and I was just too blind to find it,
I apologize and would appreciate a link to the post.
Another question that arises from the use of Type-III SSs: If every
factor is corrected for the other factors, the SSs of all factors plus
the RSS do not sum up to the total SS of the model. But doesn't that
lead to a situation where the standard way of calculating eta-square
for a factor by dividing its SS by the total SS cannot be applied?
Regards,
Bertolt
--
Bertolt Meyer
Senior Assistant
Psychological Institute, University of Zurich
Social Psychology
Binzmuehlestr. 14, Box 15
CH-8050 Zurich
Switzerland
[EMAIL PROTECTED]
tel: +41446357282
fax: +41446357279
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