IBM® SPSS® Amos™ 28
Prior to Amos 7, each measurement in a dataset consisted of a number (such as a age or income) or was missing entirely. In Amos 7 and later versions there is a third possibility: a measurement can provide inequality constraints on an age, income or other numeric quantity. This will be referred to as ordinal data. Two common examples of such ordinal data are ordered-categorical data and censored data.
Ordered-categorical data
As an example of ordered-categorical data, consider the response scale
A. Disagree
B. No opinion
C. Agree
Prior to version 7, Amos required assigning numerical scores to the three responses, for example Disagree=1, No opinion=2, Agree=3.
Amos 7 can employ a model in which there is a continuous "agreement" scale that is broken up into three contiguous intervals. If a respondent's level of agreement is in the lowest interval, he/she responds Disagree. In the middle interval the response is No opinion. In the highest interval the response is Agree.
Censored data
Censored data occurs when you know that a measurement exceeds some threshold, but you don't know by how much. (There is another kind of censored data where you know that a measurement falls below some threshold, but you don't 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 the problem. 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 solution" was greater than 10 minutes. If five people solve the problem and two don't, the data from seven people might look something like this:
Case |
Time to solve |
1 |
6 |
2 |
2 |
3 |
9 |
4 |
>10 |
5 |
4 |
6 |
9 |
7 |
>10 |
In Amos 6, you could either treat the observation for cases 4 and 7 as missing, or substitute an arbitrary number like 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. Substitution an arbitrary number is also undesirable, although the exact effect of doing that is impossible to know.
In Amos 7 and later you can use the information that cases 4 and 7 have scores above 10, but without assuming a specific value for the either person's score.
