You have all been very helpful, thank you. You are right that I have only 6
levels of convariate (a
possible problem), but I took your advice on several fronts and I'm still not
fully comprehending the
solution. Here's what I did: (I used my data with 32 subjects, which is
included at the end).
First I ran the model positioning g after the id|g random error term, and the
specifying if t>1. I got a
sig. interaction, but according a recent addition to the listserv, I learned
that this interaction may not be
as important as I once thought. Therefore, I dropped the g*t term from the
model.
anova y g / id|g x g*x t g*t if t>1, rep(t) cont(x)
Number of obs = 64 R-squared = 0.8051
Root MSE = 1.25373 Adj R-squared = 0.5767
Source | Partial SS df MS F Prob > F
-----------+----------------------------------------------------
Model | 188.354167 34 5.53982843 3.52 0.0004
|
g | .321850082 2 .160925041 0.07 0.9337
id|g | 60.8061356 26 2.33869752
-----------+----------------------------------------------------
x | 5.33333333 1 5.33333333 3.39 0.0757
g*x | 10.6666667 2 5.33333333 3.39 0.0474
t | 22.0119048 1 22.0119048 14.00 0.0008
g*t | 2.16666667 2 1.08333333 0.69 0.5100
|
Residual | 45.5833333 29 1.57183908
-----------+----------------------------------------------------
Total | 233.9375 63 3.71329365
g*x dropped from the model
anova y g / id|g x t g*t if t>1, rep(t) cont(x)
Number of obs = 64 R-squared = 0.8051
Root MSE = 1.25373 Adj R-squared = 0.5767
Source | Partial SS df MS F Prob > F
-----------+----------------------------------------------------
Model | 188.354167 34 5.53982843 3.52 0.0004
|
g | 12.0364118 2 6.01820591 2.53 0.0977
id|g | 66.5887517 28 2.3781697
-----------+----------------------------------------------------
x | 2.7111e-28 1 2.7111e-28 0.00 1.0000
t | 22.0119048 1 22.0119048 14.00 0.0008
g*t | 2.16666667 2 1.08333333 0.69 0.5100
|
Residual | 45.5833333 29 1.57183908
-----------+----------------------------------------------------
Total | 233.9375 63 3.71329365
These results seemed weird, based on the previous F value for x being much
higher. So I dropped t>1
from the model
anova y g / id|g x t g*t, rep(t) cont(x)
Number of obs = 96 R-squared = 0.7789
Root MSE = 1.18014 Adj R-squared = 0.6379
Source | Partial SS df MS F Prob > F
-----------+----------------------------------------------------
Model | 284.628472 37 7.69266141 5.52 0.0000
|
g | 8.02427455 2 4.01213727 2.53 0.0977
id|g | 44.3925011 28 1.58544647
-----------+----------------------------------------------------
x | .166666667 1 .166666667 0.12 0.7306
t | 34.015873 2 17.0079365 12.21 0.0000
g*t | 5.63888889 4 1.40972222 1.01 0.4087
|
Residual | 80.7777778 58 1.39272031
-----------+----------------------------------------------------
Total | 365.40625 95 3.84638158
I'm not sure which model it correct? Based on recent addition by Joseph
Coveney, the last model
(without t>1) would be correct.
Here is the data, sorry it is long, there are 32 subjects, 3 levels of g, 3
levels of t and 1 level of x
(remeber x is the first level of t (time)) I'm trying to covary for the
pre-test level (time==1). One more
thing, I successfully implemented the adjust command by included id in the
"by" statement. However, I
only received adjustments for those subjects I specify (ie. id<=4 gives me
subjects 1 through 3), which
makes sense. However, I would like to report the adjusted mean for each group
over each time period.
I guess I can request all id's be shown on the output by using "adjust x, by(g
t id)" and then taking the
mean of the id's for each group, but that seems cumbersome. Is there a better
way? By the way thank
you again for all your help.
id g t y x
1 1 1 1 1
1 1 2 1 1
1 1 3 1 1
2 2 1 1 1
2 2 2 1 1
2 2 3 1 1
3 3 1 5 5
3 3 2 5 5
3 3 3 1 5
4 2 1 5 5
4 2 2 4 5
4 2 3 1 5
5 3 1 6 6
5 3 3 6 6
5 3 2 6 6
6 1 1 3 3
6 1 2 3 3
6 1 3 3 3
7 1 1 6 6
7 1 2 6 6
7 1 3 6 6
8 1 1 1 1
8 1 2 1 1
8 1 3 1 1
9 2 1 5 5
9 2 2 6 5
9 2 3 1 5
10 1 1 1 1
10 1 2 1 1
10 1 3 1 1
11 1 1 4 4
11 1 2 4 4
11 1 3 1 4
12 2 1 2 2
12 2 2 2 2
12 2 3 2 2
13 2 1 5 5
13 2 2 1 5
13 2 3 1 5
14 1 1 1 1
14 1 2 1 1
14 1 3 1 1
15 1 1 5 5
15 1 2 5 5
15 1 3 1 5
16 3 1 1 1
16 3 2 1 1
16 3 3 1 1
17 1 1 6 6
17 1 2 6 6
17 1 3 6 6
18 2 1 2 2
18 2 2 2 2
18 2 3 1 2
19 3 1 4 4
19 3 2 4 4
19 3 3 2 4
20 2 1 1 1
20 2 2 1 1
20 2 3 1 1
21 1 1 6 6
21 1 2 6 6
21 1 3 1 6
22 3 1 6 6
22 3 2 6 6
22 3 3 1 6
23 2 1 1 1
23 2 2 1 1
23 2 3 1 1
24 2 1 5 5
24 2 2 5 5
24 2 3 4 5
25 2 1 5 5
25 2 2 1 5
25 2 3 1 5
26 2 1 2 2
26 2 2 1 2
26 2 3 1 2
27 3 1 2 2
27 3 2 2 2
27 3 3 1 2
28 2 1 1 1
28 2 2 1 1
28 2 3 1 1
29 1 1 1 1
29 1 2 1 1
29 1 3 1 1
30 3 1 3 3
30 3 2 3 3
30 3 3 3 3
31 1 1 2 2
31 1 2 5 2
31 1 3 5 2
32 3 1 4 4
32 3 2 4 4
32 3 3 2 4
>===== Original Message From Joseph Coveney <[email protected]> =====
>An example of a time-invariant repeated measures ANCOVA is shown below. It
>is from B. J. Winer, D. R. Brown and K. M. Michels, _Statistical Principles
>in Experimental Design_ Third Edition (New York: McGraw-Hill, 1991),
>pp.828-832. The do-file reproduces the results in the text within rounding
>error (and after correcting a typographical error in the text in
>Table 10.34).
>
>As I mentioned yesterday in a manner that was incomprehensibly articulated
>("there is no variation of x within id, so there won't be any within the
>id|g error term, either, and it should be put to the right of the id|g
>random error term"?), the continuous covariate shouldn't share the
>within-subjects error term with the between-subjects factor, and should be
>moved to the right of the id|g term. Winer's example does not include a
>term for the covariate-by-between-groups-factor interaction. For most
>purposes, between-group homogeneity of the slope of the continuous covariate
>is assumed, since its violation probably couldn't be powerfully detected by
>the statistical significance of the interaction term for most datasets not
>specifically powered to examine the interaction.
>
>Joseph Coveney
>
>clear
>set more off
>input byte subj byte a byte x1 byte y1 byte x2 byte y2
>1 1 3 10 3 8 // Note typographical error in text's Table 10.34
>2 1 5 15 5 12
>3 1 8 20 8 14
>4 1 2 12 2 6
>5 2 1 15 1 10
>6 2 8 25 8 20
>7 2 10 20 10 15
>8 2 2 15 2 10
>end
>reshape long x y, i(subj) j(b)
>/* Unadjusted repeated-measures ANOVA--Part (i) of Table 10.35
> on Page 830 */
>anova y a / a|subj b a*b, repeated(b)
>/* Repeated-measures ANCOVA--Part (ii) of Table 10.35 */
>anova y a / a|subj x b a*b, repeated(b) continuous(x)
>adjust x if subj==1 | subj==5, by(a b subj)
>exit
>
>
>
>
>
>*
>* For searches and help try:
>* http://www.stata.com/support/faqs/res/findit.html
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>* http://www.ats.ucla.edu/stat/stata/
*
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