**Date:** Mon, 23 Jun 2008 10:12:54 -0500
**Reply-To:** "Swank, Paul R" <Paul.R.Swank@uth.tmc.edu>
**Sender:** "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
**From:** "Swank, Paul R" <Paul.R.Swank@uth.tmc.edu>
**Subject:** Re: Non-Positive Definite Matrices in Item Analysis and Factor
Analysis
**In-Reply-To:** <200806230858.m5MAqoVN025527@mailgw.cc.uga.edu>
**Content-Type:** text/plain; charset="us-ascii"
It is certainly possible to get a dependency in any correlation matrix,
particularly large ones, especially when the number of variables
approaches the number of subjects.

Paul R. Swank, Ph.D.
Professor and Director of Research
Children's Learning Institute
University of Texas Health Science Center - Houston

-----Original Message-----
From: SPSSX(r) Discussion [mailto:SPSSX-L@LISTSERV.UGA.EDU] On Behalf Of
Hannah State-Davey
Sent: Monday, June 23, 2008 3:58 AM
To: SPSSX-L@LISTSERV.UGA.EDU
Subject: Non-Positive Definite Matrices in Item Analysis and Factor
Analysis

All,

I am running an item/reliability analysis and factor analysis on 129
questionnaire items using SPSS 14. I have 204 respondents. However, I am
getting a warning in reliability analysis saying 'the determinant of the
covariance matrix is zero or approximately zero. Statistics based on its
inverse matrix cannot be computed and they are displayed as system
missing
values'. What effect does this have on results in reliability analysis?

I know that if using principal components analysis a non-positive
definite
covariance matrix is not an issue as no matrices are inverted.

I also thought that if using Pearson's r then you shouldn't obtain a
non-
positive definite matrix?

Can anyone shed any light on this for me?

Many thanks in advance

Hannah State-Davey

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