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Re: st: defining a covariance matrix

From   John Antonakis <>
Subject   Re: st: defining a covariance matrix
Date   Wed, 01 May 2013 22:32:19 +0200

Hi Ariel:


matrix C = (2,1,-1\1,1,-.5\-1,-.5,1)
corr2data x1 x2 x3, cov(C) n(1000)

You can also do this with -ssd-, but only if you estimate the model with -sem-:

ssd init x1 x2 x3
ssd set obs 1000
ssd set cov 2 \ 1 1 \ -1 -.5 1



John Antonakis
Professor of Organizational Behavior
Director, Ph.D. Program in Management

Faculty of Business and Economics
University of Lausanne
Internef #618
CH-1015 Lausanne-Dorigny
Tel ++41 (0)21 692-3438
Fax ++41 (0)21 692-3305

Associate Editor
The Leadership Quarterly

On 01.05.2013 20:21, Ariel Linden, DrPH wrote:
Hi All,

I am trying to replicate some analyses from a paper, and I came across the following sentence:

"X1, X2, and X3 are multivariate normal with means zero, variances of (2, 1, 1) and covariances of (1,−1,−0.5) respectively."

Can someone tell me how to define the covariance matrix  as described above? This rest is easy enough:

*** code****
matrix m = (0,0,0)
matrix sd = (sqrt(2),1,1)
matrix C = ?
drawnorm X1 X2 X3, n(1000) means(m) sds(sd) cov(C)

Thanks in advance


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