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Intro to SEM

Intro to SEM. P. Soukup. Literature. Kline.2005. Principles and practice of structural equation modeling . New York : Guilford Press Byrne. 2001. Structural equation modeling with AMOS :basic concepts, applications, and programming . New Jersey: Lawrence Erlbaum

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Intro to SEM

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  1. Intro to SEM P. Soukup

  2. Literature • Kline.2005. Principles and practice of structural equation modeling. New York : Guilford Press • Byrne. 2001. Structural equation modeling with AMOS :basic concepts, applications, and programming. New Jersey: Lawrence Erlbaum • Maruyama.1998. Basics of structural equation modeling. Sage Publications • Raykov and Marcoulides.2006. A first course in structural equation modeling. Mahwah : Lawrence Erlbaum Associates • Schumacker and Lomax.2004.A beginner’s guide to structural equation modeling. Mahwah : Lawrence Erlbaum Associates • Articles: Journal Structural Equation Modeling

  3. ExploratoryFA Confirmatory FA

  4. Why SEM (CFA)? • Testing of hypothesis(es) • Complex model of relashionships between latent and manifest vars • Also possible: compare groups, longitudinal analysis etc.

  5. Before CFA Correlation, regression and path analysis

  6. Correlation and simple regression • Both sided relationship=correlation • One sided relationship = regression (simple). E

  7. Multiple Regression analysis • More ind. vars Y' = a + b1X1 +b2X2 +b3X3

  8. Some statistical notes • Nr. of estimated parameters (what are these) • Correlation • Regression • Nr. of individual pieces of info from data • Degrees of freedom • Test of model: chi-square • Examples in regression and correlation

  9. E E Path analysis=more regressions • Two or more regressions at once • Dependent and independent vars – necessary to exchange by exogenous and endogenous • Direct and indirect effects • Measurement error (E)

  10. Path analysis-example • Duncan’s model • Evaluation of different models • Constraining of parameters • The best model? (AIC or BIC for selection)

  11. Intro to CFA

  12. ExploratoryFA Confirmatory FA

  13. CFA – equations (I) • Equations for vars:

  14. CFA – equations (II) • Equations for covariances: • We have: covariance matrix for manifest vars in our data (Σ) • We estimatecovariance matrix of latent vars (Ψ), measurement errors (Θ) and factor weights (Λ)

  15. CFA – estimates • Many techniques • unweighted LS generalized LS • max. likelihood

  16. CFA – evaluation • Overall evaluation – test and criterias • Individual parameters – statistical and substantive significance • Change in model – modification indeces

  17. Software for SEM • AMOS • EQS • LISREL • MPlus • SAS – CALIS • Statistica - SEPATH

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