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Structural Equation Modeling

Structural Equation Modeling. Mgmt 290 Lecture 7 E xplanation of LISREL Results & E valuation of Models Nov. 9, 2009. Normal Distributions Assumptions Used. More on LISREL ML Estimation. # endogenous. To minimize Log| Σ(Θ)|+tr(S Σ -1 (Θ)) – log|S| -(p+q). F ml. # exogenous.

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Structural Equation Modeling

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  1. Structural Equation Modeling Mgmt 290 Lecture 7 Explanation of LISREL Results & Evaluation of Models Nov. 9, 2009

  2. Normal Distributions Assumptions Used More on LISRELML Estimation # endogenous To minimize Log|Σ(Θ)|+tr(S Σ-1(Θ)) – log|S| -(p+q) Fml # exogenous tr --- sum of diagonal elements Σ(Θ)| -- model implied matrix S – observed matrix Fml =0 if Σ = S

  3. Partial derivatives Algebraic solution Iterative • Starting values • - prior research, OLS results, LISREL automatic (by IV & 2SLS) • Methods to proceed • partial derivative of Fml of Θ • Final conversion Θ1, Θ2, Θ3 … , Θl

  4. RC The ridge constant. This constant will be multiplied repeatedly by 10 until the matrix becomes positive-definite. Default: RC=0.001 More on toSpecify Outputs (1) • OU ME = IV/TS/UL/GL/ML/WL/DW  RC=c     NS   NS If this option appears, the program will not compute starting values. The user must supply starting values with ST or MA commands. IV Instrumental variables TS Two-stage least squares UL Unweighted least squares GL Generalized least squares ML Maximum likelihoodWL Generally weighted least squares DW Diagonally weighted least squares

  5. More on toSpecify Outputs (2) St Dev T Values Residuals Modification Indices Total Indirect • OU • SE  TV   RS  EF   MI  ALL   • Get more outputs • OU matrix1 =   matrix2 =   ... • save matrix • OU TM=t  IT=n   LY, LX, BE, GA, PH, PS, TE, TD TM The maximum number of CPU seconds for the current problem. Default: PC version, TM=172800 (2 days); IT Maximum number of iterations for the current problem. Default: IT=ten times the number of free parameters

  6. Explanation of LISREL Results (1) Outputs for Path Analysis • Path coefficients • Hypothesis testing results

  7. Equation Coefficients LISREL Estimates (Maximum Likelihood) • Structural Equations • Y1 = - 0.087*X2, Errorvar.= 12.96, R² = 0.11 • (0.019) (1.41) • -4.65 9.22 • Y2 = - 0.28*Y1 + 0.058*X2, Errorvar.= 8.49 , R² = 0.23 • (0.062) (0.016) (0.92) • -4.58 3.59 9.22 • Y3 = - 0.22*Y1 + 0.85*Y2 + 0.86*X1, Errorvar.= 19.45, R² = 0.39 • (0.098) (0.11) (0.34) (2.11) • -2.23 7.53 2.52 9.22 Ex3a.spl Sample size < 120 Use T table Otherwise, use Z table

  8. Total & Indirect Effects Use OU EF See Ex3aef.ls8 • Total Effects of Y on Y • Y1 Y2 Y3 • -------- -------- -------- • Y1 - - - - - - • Y2 -0.28 - - - - • (0.06) • -4.58 • Y3 -0.46 0.85 - - • (0.10) (0.11) • -4.41 7.53 OR LISREL OUTPUT: EF In SIMPLIS

  9. Explanation of LISREL Results (2) Output for CFA • Loadings • Error variances • R 2

  10. Ex5a.spl Loadings and R2 • VIS PERC = 0.672*Visual, Errorvar.= 0.548 , R² = 0.452 • (0.0910) (0.0971) • 7.388 5.645 • CUBES = 0.513*Visual, Errorvar.= 0.737 , R² = 0.263 • (0.0924) (0.101) • 5.551 7.300 • LOZENGES = 0.684*Visual, Errorvar.= 0.532 , R² = 0.468 • (0.0910) (0.0974) • 7.516 5.461 • PAR COMP = 0.867*Verbal, Errorvar.= 0.248 , R² = 0.752 • (0.0702) (0.0515) • 12.348 4.819 • ……

  11. Explanation of LISREL Results (3) Hybrid Model Outputs • A combination of • Path coefficients • Loadings

  12. Path Coefficients & Loadings LISREL Estimates (Maximum Likelihood) • Measurement Equations • ANOMIA67 = 2.66*Alien67, Errorvar.= 4.74 , R² = 0.60 • (0.45) • 10.43 • … • Error Covariance for ANOMIA71 and ANOMIA67 = 1.62 • (0.31) • 5.17 • … • Structural Equations • Alien67 = - 0.56*Ses, Errorvar.= 0.68 , R² = 0.32 • (0.047) (0.066) • -12.09 10.35 • Alien71 = 0.57*Alien67 - 0.21*Ses, Errorvar.= 0.50 , R² = 0.50 • (0.048) (0.046) (0.050) • 11.89 -4.52 10.10

  13. Path Diagram

  14. Explanation of LISREL Results (4) Fit Indices • Chi-square • Chi-square / df < 3 Compare to just-identified (N-1)Fml Σ vs. Σ(θ) Increases as N Not good for large sample

  15. Relative amount of the variance In S that are predicted by Σ Fit Indices • GFI • AGFI • NFI= 1-F/Fi • CFI • NNFI GFI = 1 – tr[(Σ-1 S- I)2] / tr[(Σ-1 S) 2 AGFI = 1 – [q(q+1)/2 df] [ 1-GFI] Close to 1 Compare to a baseline model

  16. Fit Indices • RMR • Root Mean squared Residual • SRMR – Standardized Root Mean Squared Residual • RMSEA – Root Mean Square Error of Approximation RMR=[2 ΣΣ(sij-δij)2 / q(q+1)] 1/2 Close to 0

  17. Parsimonious Fit • AIC • CAIC • PNFI=dfmodel/dfindep X NFI • PGFI=1-(P/N)GFI Chi-square adjusted for df - 2df

  18. Relative Fit • NFI, NNFI – compare to indep model • Compare nested model • use Chi-square difference

  19. Common Practice in Reporting- 3 must-report items • Chi-square, DF, Significance • GFI or NFI or CFI • NNFI or AGFI • SRMR

  20. What is considered as a good fit • Chi-square/df <3 • GFI >.95 • SRMR <.08 • And check residuals

  21. Modification Indexes • Each such modification index measures how much chi-square is expected to decrease if this particular parameter is set free and the model is re-estimated. • (Comparative fit for ONE parameter)

  22. 3 Foundation for Model Re-specification • Theory-based revisions • Modification Indices • Residual Matrix

  23. ExampleSIMPLIS The Modification Indices Suggest to Add the Path to from Decrease in Chi-Square New Estimate x1 dem 13.7 -5.83 x4 dem 26.3 7.93 x5 lib 119.9 41.83 x8 lib 9.4 -5.88 The Modification Indices Suggest to Add an Error Covariance Between and Decrease in Chi-Square New Estimate x2 x1 19.8 1.42 x4 x3 11.1 -0.63 x5 x3 18.0 0.51 x6 x3 13.4 -0.82 x6 x4 11.2 1.28 x6 x5 9.7 -0.74 x7 x3 8.1 -0.85 x7 x6 42.5 4.41 x8 x4 14.8 1.11 x8 x6 78.9 3.27 x8 x7 16.2 2.01 • Bollen80.spl • Bollen80n.spl

  24. Example for LISREL Expected Change for LAMBDA-X pollib demo -------- -------- x1 - - -5.83 x2 - - -4.20 x3 - - 1.27 x4 - - 7.93 x5 41.83 - - x6 -5.24 - - x7 -4.61 - - x8 -5.88 - - • Bollen80.ls8 • Use MI in OU

  25. Two Roads to Parsimonious Model (1) Model Trimming • Delete links to achieve a parsimonious model • Start from the most complicated model To get a parsimonious model

  26. (2) Model Building • Add links to build a better model • You may start from the simplest model • and move up To get a model of good fit

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