Observation Oriented Modeling
Version 2 includes several major additions or changes:
Output for all major analyses has been streamlined so that it appears as similar as possible across analyses. This change makes it easier for the neophyte to examine the output and find and interpret the PCC index. Output has been color-coded as well such that all options chosen by the user are printed in blue font. Important messages and errors are printed in red font.
A new ordinal analysis routine has been added. This analysis allows the user to examine ordinal patterns across orderings. Much like a researcher might use repeated measures ANOVA to examine mean patterns across dependent variables, in OOM ordinal patterns of observations can be examined. The recent paper by Craig, et al., 2012 employed the ordinal analysis method. As with all analyses in OOM, the ordinal analysis permits the user to examine the data at the level of the person as well as at the aggregate level.
A new efficient cause analysis routine has been added. This analysis allows the user to examine time-lagged models of observations to test efficient cause. Patterns between two sets of observations not lagged in time can also be examined. As with all analyses in OOM, the efficient cause routine permits the user to examine the data at the level of the person as well as at the aggregate level. This video demonstrates the efficient cause algorithm.
Additional changes made to the program:
Cases labels can now be edited, and cases can be sorted by their case labels.
Ordinal compute statements can now be constructed and executed to create new orderings; for example, "if A > B and C < D, then X = 1."
Data sets can be merged according to case labels or a particular ordering of observations.
Additional options/features have been added to the various major analysis routines.
Numerous bugs and glitches have been corrected.