1. 1.可否提供 correlation matrix來判斷！ 2. 2.以下是德州大學的統計諮詢中心的FAQ存檔資料，提供您參考！http://ssc.utexas.edu/consulting/answers/lisrel/lisrel3.htmlLISREL FAQ #3: Covariance matrix not positive definite Question:When I run my data I get an error message that states that my covariance matrix is not positive definite. I have searched the LISREL book that I have and it doesn't provide any explanation for this error message. Answer:This message generally means one or more of the following things is happening: 1) There are redundancies among the correlation matrices- in other words, some of the correlations may be a linear function of some of the other correlations. You can fix this by removing the redundant variables or collecting more data. 2) Your model may be estimating more parameters than you have degrees of freedom to use. You can check this by examining how many degrees of freedom you have and the number of parameters you are estimating. The formula for calculating the number of degrees of freedom available to you is q(q+1)/2, where q is the number of measured variables. If you have 9 measured variables, then you must estimate, (9(9+1)/2 = 45), less than 45 parameters. As part of its standard output, LISREL will count the number of parameters it estimates. The difference between the number of estimable parameters and the number of estimated parameters is the number of degrees of freedom used in the chi-square test by LISREL (the first chi-square test to appear on the printout, not the Independence Model chi-square test). 3) LISREL is not correctly reading the raw data, correlation matrix, or covariance matrix. Alternatively, you may be inputting a correlation or covariance matrix which is based on incorrectly read raw data values via PRELIS, SPSS, or another program which has the capability to convert raw data into correlation or covariance matrix form. 4) You computed a covariance or correlation matrix using pairwise deletion of missing data. The solution here is to use a different method of handling missing data. Be sure to check the accuracy of the raw data, correlation, or covariance matrices before you proceed further with your analyses.
