Posted by
Maguin, Eugene on
Jun 28, 2011; 2:55pm
URL: http://spssx-discussion.165.s1.nabble.com/Re-mixed-models-time-as-DV-SEC-UNOFFICIAL-tp4515228p4532101.html
Carol,
I think a better initial model for your data is school kids who get two
different tests, english and music, at multiple times in a year. Why? Your
folks are tested on multiple tasks and are tested on a given task multiple
times. Using the schoolkids example, I think you'd have a three level model.
Level 1: y(ijk)=B1-0(ij) + B1-1(jk)*Time + e1(ijk).
Level 2a: B1-0(ij)=B2-0(i) + B2-1*TestType + e2(ij).
Level 2b: I'm going to assume that slope term, B1-1, is constant across kids
and test type.
Level 3a: B2-0(i)=B3-0 + B3-1*Gender + e3(i).
Level 3b: I'm going to assume that test type term, B2-1, is constant across
kids.
My coefficient nomenclature is very odd but is B(level)-(term number: 0 =
intercept, 1 = slope).
But here's what makes your data more difficult. Again in terms of
schoolkids. Think about a whole school system, K-12, but really small, one
class per grade. Lots of different subjects. Kids come and go. Others are
there for the whole 12 years. Some are tested one or two times in a subject
or several subject. Others are tested repeatedly.
You have some selection criteria for number of tasks per person but you have
28 tasks, I think. I don' have any experience with anything this complex.
Mixed assumes a normal distribution. It kind of sounds like you've got your
data converted to decimal minutes. How normal do they look?
>>When you suggest an offset age as a polynomial are you thinking:
1) compute agediff = age at each task - age at first task
2) compute agediff squared
3) enter age at first task
4) enter agediff squared
So your level 1 equation is
Score(ijk) = B1-0 + B1-1*Time_offset + e1(ijk).
Time_offset is the test time points relative to first task at time=0. These
are the time offset for task_type = i. Each task_type has its own time
offsets.
Gene Maguin
-----Original Message-----
From: Parise, Carol A. [mailto:
[hidden email]]
Sent: Monday, June 27, 2011 6:55 PM
To: 'Gene Maguin';
[hidden email]
Subject: RE: mixed models - time as DV [SEC: UNOFFICIAL]
Gene,
I'm not quite sure whether I have a two level or three level structure. In
the examples I am reading, it seems obvious. For example, groups of students
have the same teacher within a school. My thought is that i''ve got three
tiers: task, year, person.
I have 28 tasks. I can think of this as task being the top tier since some
tasks are known to be more difficult than others. Where things get muddled
is that year is inherently tied to the task because weather plays a huge
role within a task.
When you suggest an offset age as a polynomial are you thinking:
1) compute agediff = age at each task - age at first task
2) compute agediff squared
3) enter age at first task
4) enter agediff squared
Thanks. Everyone's comments have been very helpful.
Carol
-----Original Message-----
From: SPSSX(r) Discussion [mailto:
[hidden email]] On Behalf Of
Gene Maguin
Sent: Wednesday, June 22, 2011 1:28 PM
To:
[hidden email]
Subject: Re: mixed models - time as DV [SEC: UNOFFICIAL]
Carol,
I was thinking that if you computed a new age variable that was the offset
from the firat age at any task you could treat age at first task as a level
two (person) variable and the age offset as a level 1 variable. Offset age
could be modeled as a polynomial.
Year at first test would be a second person variable. Categorizing age makes
results display easier, for sure. The empirical question is whether
categorizing is more informative in a variance modeled sense.
Are you thinking of this dataset as having a two level or a three level
structure?
Gene Maguin
-----Original Message-----
From: SPSSX(r) Discussion [mailto:
[hidden email]] On Behalf Of
Parise, Carol A.
Sent: Wednesday, June 22, 2011 3:57 PM
To:
[hidden email]
Subject: Re: mixed models - time as DV [SEC: UNOFFICIAL]
My thought was that person, year, and task would be considered random
variables and quintile of age and 'task time' i.e. 1st, 2nd, 3rd, etc would
be fixed.
I know someone is going to say "you shouldn't group data if you don't have
to". I have read these references.
My descriptive data show that there is an improvement between task 1 and
task 2 (regardless of the task) and that improvement diminishes after around
3 tries. However, younger age leads to higher improvement than older age. So
I need to include an interaction.
Stratfiying into quintiles makes this easy to see graphically but i'm not
sure if this is the best way to handle this in the model. I've been reading
a bit about entering splines into these models but i only know enough to be
dangerous at this point.
________________________________________
From: Gosse, Michelle [
[hidden email]]
Sent: Wednesday, June 22, 2011 12:44 PM
To: Parise, Carol A.;
[hidden email]
Subject: RE: mixed models - time as DV [SEC: UNOFFICIAL]
Hi Carol,
How are you handling the "year" variable, I apologise if I have missed this
information from previous postings?
Cheers
Michelle
-----Original Message-----
From: SPSSX(r) Discussion [mailto:
[hidden email]] On Behalf Of
Parise, Carol A.
Sent: Thursday, 23 June 2011 7:41 AM
To:
[hidden email]
Subject: Re: mixed models - time as DV
great! hard to believe but these are actual times. it's kind of a crazy
'task'.
________________________________
From: Art Kendall [
[hidden email]]
Sent: Wednesday, June 22, 2011 12:39 PM
To: Parise, Carol A.; SPSSX-L post
Subject: Re: [SPSSX-L] mixed models - time as DV
This worked okay on my machine.
I guess then that these are durations (intervals) rather specific times?
data list list/thetime(time12.2).
begin data
31:22:33
24:00:00
23:59:55
42:10:10
end data.
compute secs = thetime.
formats secs (f16).
compute back = secs.
formats back (time12.2).
execute.
list.
Art
On 6/22/2011 3:29 PM, Parise, Carol A. wrote:
Thanks Art. I don't have the data in front of me today but i will check this
out.
Do you know if are differences when time is over 24:00:00 versus under
24:00:00? when working with hh:mm:ss, in the past, i recall having some
issues with this. some of these "tasks" took more than a day.
________________________________
From: Art Kendall [
[hidden email]<mailto:
[hidden email]>]
Sent: Wednesday, June 22, 2011 5:39 AM
To: Parise, Carol A.
Cc:
[hidden email]<mailto:
[hidden email]>
Subject: Re: [SPSSX-L] mixed models - time as DV
What format did you use to put the DV in?
try this syntax. It appears that time12.2 format works on my machine.
data list list/thetime(time12.2).
begin data
1:22:33
24:00:00
23:59:55
12:10:10
end data.
compute secs = thetime.
formats secs (f16).
compute back = secs.
formats back (time12.2).
execute.
list.
I then saved that file and opened a new data file.
I copied secs into the new data file.
I changed the type to date and used the drop down list to get a time format.
Also, the only time I would expect the intercept to be exactly equal to the
grand mean would be when all predictors were zero like with z-scores.
Art Kendall
Social Research Consultants
On 6/21/2011 6:37 PM, Parise, Carol A. wrote:
Art,
This appears to work if I use the format: dd-mm-yyyy hh:mm:ss. Any other
format and the numbers are not logical. But, the date that showis up is kind
of crazy: 16-MAR-1590. I'm a bit concerned that this may not be accurate.
Although the time is in the ballpark, I would think that the overall mean i
get using descriptives should be just about the same as what i get from this
model and it's off by a good amount and it's not.
Thank you!
Carol
________________________________
From: Art Kendall [mailto:
[hidden email]]
Sent: Tuesday, June 21, 2011 2:58 PM
To: Parise, Carol A.
Cc:
[hidden email]<mailto:
[hidden email]><mailto:SPSSX-L@LIS
TSERV.UGA.EDU><mailto:
[hidden email]>
Subject: Re: [SPSSX-L] mixed models - time as DV
try this as a workaround.
Time is in seconds since the start of the Gregorian calendar.
While you have the .spv file open, open a new data file <file><new><data>
Switch to the the output file. highlight and copy the intercept.
Switch to the data file. Paste the intercept.
Click the variables view tab.
change the format to time.
Art Kendall
Social Research Consultants
On 6/21/2011 5:40 PM, Parise, Carol A. wrote:
Hi all,
This one of probably many questions i will likely be posting on using linear
mixed models over the next few months. It's my first crack at using this and
i'm slowly working through the lingo by reading as much as I can. I read the
SPSS technical report on this and i found an example from nice little
article that i am using to mess with my data.
http://www.indiana.edu/~statmath/stat/all/hlm/hlm.pdf<
http://www.indiana.edu/%7Estatmath/stat/all/hlm/hlm.pdf><
http://www.indiana.edu/%7Estatmath/stat/all/hlm/hlm.pdf>
The article explains the interpretation of the intercept term in the empty
model is equivalent to the overall math achievement score.
My data have time in hh:mm:ss as the DV.
In my sample analysis, the scale of the intercept is not in hh:mm:ss and i
can't seem to adjust to to be in this format with the "cell properties" when
i click on the output.
It would be really helpful to have the correct scale versus just the p-value
for interpretation purposes. Anyone have thoughts on how I can do this?
I am running version 14.0 and we won't be upgrading anytime soon.
Thanks much.
Carol
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