1 / 17

Intro to SEM

This introductory guide explores the principles and practices of structural equation modeling (SEM) in social sciences. Covering topics ranging from correlation and regression to path analysis and confirmatory factor analysis (CFA), it delves into complex relationships between latent and manifest variables. The text also discusses hypothesis testing, model evaluation, software options for SEM analysis, and statistical notes essential for understanding SEM. Whether you are a beginner or seeking to enhance your SEM knowledge, this resource provides valuable insights and examples to navigate the complexities of SEM in research.

knappa
Download Presentation

Intro to SEM

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related