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Table 2 Effects in Linear Equations and Estimates of the Variances – MIMIC model

From: Comparing multiple statistical software for multiple-indicator, multiple-cause modeling: an application of gender disparity in adult cognitive functioning using MIDUS II dataset

Parameter Indicators Latent Variable Parameter SAS Mplus R t-value (SAS)
Factor Loading SGST (Y1) Executive Functioning λ11 0.13 (0.004) 0.14 (0.004) 0.14 (0.004) 33.53
NmCorr (Y2) Executive Functioning λ21 7.60 (0.159) 7.60 (0.159) 7.60 (0.159) 47.8
NmSr (Y3) Executive Functioning λ31 0.83 (0.021) 0.83 (0.022) 0.83 (0.021) 38.49
UniItemF (Y4) Executive Functioning λ41 2.97 (0.087) 2.97 (0.087) 2.97 (0.087) 34.24
DgtSpan (Y5) Executive Functioning λ51 0.57 (0.022) 0.57 (0.022) 0.57 (0.022) 26.01
UniItemD (Y6) Episodic Memory λ62 2.06 (0.036) 2.06 (0.035) 2.06 (0.036) 57.84
UniItemI (Y7) Episodic Memory λ72 1.85 (0.031) 1.85 (0.032) 1.85 (0.031) 59.15
Regression Coefficient Age Executive Functioning γ11 −0.05 (0.002) −0.05 (0.002) −0.05 (0.002) −26.8
Gender Executive Functioning γ12 −0.38 (0.038) −0.38 (0.038) − 0.38 (0.038) −10
Age Episodic Memory γ21 −0.03 (0.001) −0.03 (0.001) − 0.03 (0.001) −20.73
Gender Episodic Memory γ22 0.53 (0.035) 0.53 (0.035) 0.53 (0.034) 15.27
Residual Variance SGST (Y1)   \( {\sigma}_{\epsilon_1}^2 \) 0.06 (0.001) 0.06 (0.001) 0.06 (0.001) 39.96
NmCorr (Y2)   \( {\sigma}_{\epsilon_2}^2 \) 53.95 (1.944) 53.95 (1.982) 53.95 (1.943) 27.75
NmSr (Y3)   \( {\sigma}_{\epsilon_3}^2 \) 1.42 (0.038) 1.42 (0.038) 1.42 (0.038) 37.4
UniItemF (Y4)   \( {\sigma}_{\epsilon_4}^2 \) 25.38 (0.640) 25.38 (0.642) 25.38 (0.640) 39.65
DgtSpan (Y5)   \( {\sigma}_{\epsilon_5}^2 \) 1.83 (0.043) 1.83 (0.043) 1.83 (0.043) 42.45
UniItemD (Y6)   \( {\sigma}_{\epsilon_6}^2 \) 1.73 (0.106) 1.73 (0.106) 1.73 (0.106) 16.39
UniItemI (Y7)   \( {\sigma}_{\epsilon_7}^2 \) 0.93 (0.082) 0.93 (0.082) 0.93 (0.082) 11.39
Covariance   Executive Functioning vs. Episodic Memory cov(ζ1, ζ2) 0.44 (0.017) 0.44 (0.018) 0.44 (0.017) 25.3