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Amos 7.0 performs Bayesian model fitting
for censored data
Censored data occurs when you know that
a measurement exceeds some threshold,
but you don’t know by how much. (There
is a less common kind of censored data
where you know that a measurement falls
below some threshold, but do not know by
how much.) As an example of censored
data, suppose you watch people as they
try to solve a problem and record how
long each person takes to solve it.
Suppose that you don’t want to spend
more than 10 minutes waiting for a
person to reach a solution, so that if a
person has not solved the problem in 10
minutes, you call a halt and record the
fact that “time to solve” was greater
than 10 minutes. If five people solve
the problem and two don’t, the data from
seven people might look like this:
|
Case |
Time to solve |
|
1 |
6 |
|
2 |
2 |
|
3 |
9 |
|
4 |
>10 |
|
5 |
4 |
|
6 |
9 |
|
7 |
>10 |
In Amos 6.0, you could either treat the
observation for cases 4 and 7 as
missing, or substitute an arbitrary
number like 10 or 11 or 12 for cases 4
and 7. Treating cases 4 and 7 as missing
has the effect of biasing the sample by
excluding poor problem solvers.
Substituting an arbitrary number for a
censored value is also undesirable,
although the exact effect of
substituting an arbitrary number is
impossible to know.
In Amos 7.0 you can take advantage of
all the information you have about cases
4 and 7 without making assumptions other
than the assumption of normality.
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