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Measurement Models: Identification and Estimation

Measurement Models: Identification and Estimation. James G. Anderson, Ph.D. Purdue University. Identification and Estimation. Identification is concerned with whether the parameters of the model are uniquely determined.

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Measurement Models: Identification and Estimation

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  1. Measurement Models:Identification and Estimation James G. Anderson, Ph.D. Purdue University

  2. Identification and Estimation • Identification is concerned with whether the parameters of the model are uniquely determined. • Estimation involves using sample data to make estimates of population parameters.

  3. A Confirmatory Factor Analysis Model of Psychological Disorders

  4. Mathematical Specification

  5. Mathematical Specification

  6. Assumptions • Each observed variable (X) loads on only one latent variable • Each observed variable (X) is also affected by a single residual or unique factor • Curved arrows correspond to correlations among latent variables • Variances and covariances of the residual factors are contained in the Theta matrix

  7. Conditions for Identification • Necessary • Sufficient • Necessary and Sufficient

  8. Degrees of Freedom

  9. Degrees of Freedom

  10. Models

  11. Model A

  12. Model B

  13. Model C

  14. Model D

  15. Model E

  16. Model F

  17. Model G

  18. Model H

  19. Model Identification

  20. Estimation • Unweighted Least Squares (ULS): Minimizes the trace or the sum of the diagonal elements of tr[(S-Sigma)2] ULS makes no distributional assumption so there are no tests of significance. Also ULS is scale dependent.

  21. Estimation • Generalized Least Squares (GLS): In the fitting function, differences between S and Sigma are weighted by elements of S-1 The fitting function is tr[(S-Sigma)S-1]2 • Maximum Likelihood(ML)minimizes the fitting function tr(SSigma-1) +log lSigmal - log lSl] – q If X has a multivariate normal distribution, both GLS and ML have desirable asymptotic properties.

  22. Estimates with Different Constraints

  23. Estimation of Model Md with ULS, GLS and ML

  24. Model Building • Models are nested when one model can be obtained from the other by imposing one or more free parameters. Therefore, model Mg is nested in Md and Mg is nested in Mh. • When models are nested, the difference in Chi Square value is also distributed as Chi Square so the models can be compared statistically.

  25. Comparing the Fit of Nested Models

  26. Standardization • The observed variables (X) can be standardized so S is a correlation matrix. • The latent variables can be standardized by constraining the diagonal elements of the phi matrix to be 1.0.

  27. Estimates of Md and Mg with Standardized Observed Variables and/or Standardized Latent Variables

  28. Effects of Standardization • For Md the decision to analyze the covariance or correlation matrix or to set the metric by fixing loadings or variances makes no difference when scale free estimators such as GLS or ML are used. • For Mg which involves equality constraints, analyzing the correlation matrix versus the covariance matrix can have substantive effects on the results obtained.

  29. Improper Solutions • Nonpositive definite matrices • Nonconvergence • Heywood cases • Improper sign in nonrecursive models • Binary variables

  30. Information to Report on CFA Models • Model specification • List the indicators for each factor • Indicate how the metric of each factor was defined • Describe all fixed and constrained parameters • Demonstrate that the model is identified • Input data • Description of sample characteristics and size • Description of the type of data (e.g., nominal, interval, and scale range of indicators) • Tests of assumptions • Extent and method of missing data management • Provide correlations, means, and SDs

  31. Information to Report on CFA Models • Model estimation • Indicate software and version • Indicate type of data matrix analyzed • Indicate estimation method used • Model evaluation • Report chi square with df and p value • Report multiple fit indices (e.g., RMSEA, CFI, and confidence intervals if applicable) • Report strategies used to assess strains in the solution (e.g., MIs, standardized residuals) • If model is specified, provide a substantive rational for added or removed parameters

  32. Information to Report on CFA Models • Parameter estimates • Provide all parameter estimates (e.g., factor loadings, error variances, factor variances) • Include the standard errors of the parameter estimates • Consider the clinical as well as the statistical significance of the parameter estimates • Substantive conclusions • Discuss the CFA results in regard to their substantive implications • Interpret the findings in the context of the study limitations

  33. Reference • J.S. Long, Confirmatory Factor Analysis, Series: Quantitative Applications in the Social Sciences, No. 33, Sage publications, 1983.

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