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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
Comments: To: Hannah State-Davey <hmclarke@HOTMAIL.CO.UK>
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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