Data are called ipsative if they am subject to a constant-sum constrai
nt for each observation. Usually, ipsative data are the consequence of
transformation of their corresponding preipsative data. In this artic
le, two kinds of ipsative data are defined. They are the additive ipsa
tive data (AID) and the multiplicative ipsative data (MID). Jackson an
d Alwin proposed a method to analyze AID in exploratory factor analysi
s. However they failed to provide the estimates of the original factor
loadings. In this study, first, their method is modified in the conte
xt of covariance structure analysts. It is discovered that the origina
l parameter estimates can usually be recovered provided the model of t
he preipsative data is well defined. An artificial example is used to
demonstrate the suggested method. Second, the method is extended to th
e case of MID. A real example is considered also. Finally, some relate
d issues, problems, and generalizations am addressed and discussed.