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I am suggesting that you "control for" all of the sources of variance
that you have measures for. That is a 4 way ANOVA. Type * Age * Gender
* time.
It is inefficient not to control for all of the known sources of
variance. Those sources that are not in the design are therefore in the
residual (error).
All of the effects in the ANCOVA are in the ANOVA. The ANOVA is more
complete.
The ANCOVA controls for only some of the effects of age and gender.
The interaction effects are taken care of by the software when you
specify the design.
Below there is a simulation of such an analysis, Open a new instance of
SPSS. copy and paste the syntax. Run it.
You can generate the syntax via the GUI.
Art Kendall
Social Research Consultants
* make up some data.
new file.
set seed 20101219.
input program.
loop id = 1 to 391.
compute pre = rv.normal(1,1).
compute mid = pre + rv.normal(1,1).
compute post = mid + rv.normal(1,1).
end case.
end loop.
end file.
end input program.
compute type =1+ mod(id,5).
compute gender =1+ mod(id,2).
compute age = rnd(rv.uniform(1,5)).
formats id (f3) type gender age (f1).
value labels
type 1 'a' 2 'b' 3 'c' 4 'd' 5 'e'/
gender 1 'male' 2 'female'/
age 1 'first' 2 'second' 3 'third' 4 'fourth' 5 'fifth'.
*check that there are no empty cells.
crosstabs tables = type by gender by age.
* -----------------.
*show the kind of analysis.
* Custom Tables.
CTABLES
/VLABELS VARIABLES=type gender age pre mid post DISPLAY=LABEL
/TABLE type > gender > age + gender BY pre [MEAN] + mid [MEAN] + post
[MEAN]
/CATEGORIES VARIABLES=type gender age ORDER=A KEY=VALUE EMPTY=INCLUDE.
GLM pre mid post BY type gender age
/WSFACTOR=time 3 Polynomial
/CONTRAST(age)=Polynomial
/METHOD=SSTYPE(3)
/PLOT=PROFILE(time time*type time*gender time*age time*age*gender)
/EMMEANS=TABLES(OVERALL)
/EMMEANS=TABLES(type) COMPARE ADJ(BONFERRONI)
/EMMEANS=TABLES(gender) COMPARE ADJ(BONFERRONI)
/EMMEANS=TABLES(age) COMPARE ADJ(BONFERRONI)
/EMMEANS=TABLES(time) COMPARE ADJ(BONFERRONI)
/EMMEANS=TABLES(type*gender)
/EMMEANS=TABLES(type*age)
/EMMEANS=TABLES(type*time)
/EMMEANS=TABLES(gender*age)
/EMMEANS=TABLES(gender*time)
/EMMEANS=TABLES(age*time)
/EMMEANS=TABLES(type*gender*age)
/EMMEANS=TABLES(type*gender*time)
/EMMEANS=TABLES(type*age*time)
/EMMEANS=TABLES(gender*age*time)
/EMMEANS=TABLES(type*gender*age*time)
/PRINT=ETASQ OPOWER
/CRITERIA=ALPHA(.05)
/WSDESIGN=time
/DESIGN=type gender age type*gender type*age gender*age type*gender*age.
On 12/18/2010 10:45 AM, PetterBB wrote:
> Art Kendall, thanks, but I am not sure if I understand you.
>
> Do you mean it is wrong to use ANCOVA in with my dataset? My main goal is to
> check wether or not different user styles in social networking sites imply
> different types of social capital, by using gender and age as covariates.
>
> However, if I do the ANOVA the way you suggest, is this done by 2way ANOVA
> or is it 1way? I am not sure how I can control for interaction effects in
> ANOVA the way you are suggesting it? Do you have any good instructions in
> how I can do this?
>
> Thanks.
> Petter
> --
> View this message in context: http://spssx-discussion.1045642.n5.nabble.com/Can-I-use-time-and-gender-as-covariates-in-Ancova-tp3306510p3310522.html
> Sent from the SPSSX Discussion mailing list archive at Nabble.com.
>
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