pl. help a novice

S

shants

New Member
#1
#1
Hello.
I need help with determining whether there is sig diff. between enco 1 and enco 2 on three measures data is collected on RCMAS (coded as pa, wo, ca,lie and tot), FES (coh,exp,con,ind,ao,ico,aro,mre,org,ctl) and Harter(cog,soc,ath,physapp,job,rom,beh,frnd)
Is the power for this sample sufficient?
Pl. see attachment.
I have run this in SPSS using one-way Anova.
Anyone have any other ideas or can verify the results?
thanks for yr time.
 
JohnM

JohnM

TS Contributor
#2
#2
Are the variables RCMAS, FES, and Harter supposed to be composites of those columns in parentheses?

If so, how should they be combined?

Until I know how those three variables are constructed, it is really difficult to answer your questions...
 
S

shants

New Member
#3
#3
Hello.
The RCMAS is the first measure that consists of 5 subscales that include PA, WO, CA,LIE and TOT.
FES is the second measure and consists of 10 subscales including: coh, exp, con, ind, ao,ico,aro,mre,org,ctl
HARTER is the third measure in my project and includes the cog,soc,ath,physapp,job,rom,beh,and frnd subscales.
I am looking at three measures and one sample that is divided by primary and secondary encopretic children.
The three measures should not be combined but looked at separately.
I want to know if there are sig. diff. b/w primary encopretic group(coded as 1 on enc) and secondar (coded as 2) and which group has higher or lower scores on these three measures.
If specific subscales are significant and not others, I would like to qualitatively explain that in my writings later.
Age of children in months and their sex (coded as 1-male or 2-female may also be in the data columns.
Hope that clarifies things
Thanks for yr help.
 
JohnM

JohnM

TS Contributor
#4
#4
OK. If you do a set of 1-way ANOVAs on each subscale, you should be able to tell which ones are significantly different between enco1 and enco2 - the pattern of significant/insignificant differences then should be related back to your hypotheses and the underlying theory...

Although your sample sizes are small, I don't think power is an issue here...
 
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