That seems to me to be what you're doing in SEM when you use mean structures.
Paul R. Swank, Ph.D. Professor, Developmental Pediatrics Medical School UT Health Science Center at Houston -----Original Message----- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]]On Behalf Of nothanks Sent: Tuesday, June 25, 2002 12:37 PM To: [EMAIL PROTECTED] Subject: SEM and measures of fit. Hello sci.stat* readers, I have been working with SEMs (actually, the models I've been working with are just Mixed models) and am curious why SEM practitioners seem to assess model fit by only comparing the observed and predicted covariance matrices. As opposed to (also) using statistics based on observed vs. predicted outcomes. One can have an SEM with a covariance matrix that is fit very well (or perfectly). Yet, if you compared the predicted and observed outcomes, it's terrible! So, two things: 1. What is the rationale for ignoring this type of poorly fitting model (seems, to me, to be a fundamental flaw - even if you are just using if for "exploration")? 2. Is there software that gives you both kinds of fit statistics? LISREL 8.3 and PROC CALIS seems to only give those based on predicted and observed covariance matrix. And PROC MIXED only allows certain paramaterizations. Thanks! (I am aware of SEMNET, but thought I would ask statisticians first.) ####################################################################### . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . ================================================================= . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
