DATE: 8/ 8/2001 TIME: 14:41 L I S R E L 8.30 BY Karl G. Jöreskog & Dag Sörbom This program is published exclusively by Scientific Software International, Inc. 7383 N. Lincoln Avenue, Suite 100 Lincolnwood, IL 60712, U.S.A. Phone: (800)247-6113, (847)675-0720, Fax: (847)675-2140 Copyright by Scientific Software International, Inc., 1981-2000 Use of this program is subject to the terms specified in the Universal Copyright Convention. Website: www.ssicentral.com The following lines were read from file E:\ANTI\TRASH.SPL: title: linear unconditional antisocial Observed Variables anti1 anti2 anti3 anti4 read1 read2 read3 read4 gen homecog subjid Raw Data from file e:\anti\antiread.dat 3 6 4 5 2.10 2.90 4.50 4.50 1 9 1 Sample size = 221 Latent variables intcept slope Relationships anti1 = 1*intcept 0*slope anti2 = 1*intcept 1*slope anti3 = 1*intcept 2*slope anti4 = 1*intcept 3*slope Equal error variances: anti1 anti2 anti3 anti4 Equation: intcept=const Equation: slope=const Let the errors of intcept and slope correlate End of Problem Sample Size = 221 linear unconditional antisocial Covariance Matrix to be Analyzed anti1 anti2 anti3 anti4 -------- -------- -------- -------- anti1 2.37 anti2 1.16 3.21 anti3 1.22 1.63 3.24 anti4 1.35 2.00 2.24 4.35 Means anti1 anti2 anti3 anti4 -------- -------- -------- -------- 1.49 1.84 1.88 2.07 linear unconditional antisocial Number of Iterations = 1 LISREL Estimates (Maximum Likelihood) Measurement Equations anti1 = 1.00*intcept, Errorvar.= 1.54 , R² = 0.39 (0.10) 14.83 anti2 = 1.00*intcept + 1.00*slope, Errorvar.= 1.54 , R² = 0.47 (0.10) 14.83 anti3 = 1.00*intcept + 2.00*slope, Errorvar.= 1.54 , R² = 0.56 (0.10) 14.83 anti4 = 1.00*intcept + 3.00*slope, Errorvar.= 1.54 , R² = 0.64 (0.10) 14.83 Covariance Matrix of Independent Variables intcept slope -------- -------- intcept 0.97 (0.21) 4.66 slope 0.15 0.10 (0.07) (0.04) 2.10 2.21 Mean Vector of Independent Variables intcept slope -------- -------- 1.55 0.18 (0.10) (0.04) 16.13 4.12 Goodness of Fit Statistics Degrees of Freedom = 8 Minimum Fit Function Chi-Square = 5.55 (P = 0.70) Normal Theory Weighted Least Squares Chi-Square = 5.73 (P = 0.68) Estimated Non-centrality Parameter (NCP) = 0.0 90 Percent Confidence Interval for NCP = (0.0 ; 6.86) Minimum Fit Function Value = 0.025 Population Discrepancy Function Value (F0) = 0.0 90 Percent Confidence Interval for F0 = (0.0 ; 0.031) Root Mean Square Error of Approximation (RMSEA) = 0.0 90 Percent Confidence Interval for RMSEA = (0.0 ; 0.062) P-Value for Test of Close Fit (RMSEA < 0.05) = 0.90 Expected Cross-Validation Index (ECVI) = 0.073 90 Percent Confidence Interval for ECVI = (0.073 ; 0.10) ECVI for Saturated Model = 0.091 ECVI for Independence Model = 1.20 Chi-Square for Independence Model with 6 Degrees of Freedom = 255.51 Independence AIC = 263.51 Model AIC = 17.73 Saturated AIC = 20.00 Independence CAIC = 281.10 Model CAIC = 44.12 Saturated CAIC = 63.98 Normed Fit Index (NFI) = 0.98 Non-Normed Fit Index (NNFI) = 1.01 Parsimony Normed Fit Index (PNFI) = 1.30 Comparative Fit Index (CFI) = 1.00 Incremental Fit Index (IFI) = 1.01 Relative Fit Index (RFI) = 0.98 Critical N (CN) = 797.59 Root Mean Square Residual (RMR) = 0.15 Standardized RMR = 0.048 Goodness of Fit Index (GFI) = 0.99 Adjusted Goodness of Fit Index (AGFI) = 0.99 Parsimony Goodness of Fit Index (PGFI) = 0.79 The Problem used 3496 Bytes (= 0.0% of Available Workspace) Time used: 0.020 Seconds