Good fit is relative to progress in the field Should fit measures be used at all? Reporting goodness of fit Data example CFA input in Amos CFA output in Amos CFA in Stata CFA input in Stata CFA output in Stata Further aspects of measurement models Handling correlated error Assigning metrics Reflective vs. Measurement error terms Simple variables and single indicator latent variables Error variance when reliability is known Measurement error terms vs. Measurement validity using reliability coefficients Data Example Models leading to the final structural model Structural modeling in Amos SEM input in Amos SEM output in Amos Structural modeling in Stata SEM input in Stata SEM output in Stata Specification Search all possible subsets SEM Specification search in Amos Specification search user interface The Options button Perform Specification Search button Other tools for specification search Specification search in SAS Specification search in Stata Multi-group Analysis To standardize or not to standardize?

Model invariance Examining non-invariance across groups Critical ratios of differences test Example and data for multi-group analysis Baseline multi-group testing Data setup for multi-group analysis in Amos Viewing group models Testing for measurement invariance across groups multi-group modeling error! Goodness of fit Path and covariance significance Modification indexes No negative variances Critical ratios of differences tests Multi-group analysis in SAS Data setup for multi-group analysis in SAS The unconstrained multigroup model The measurement weights model Other models Multi-group analysis in Stata Multi-group models in Stata Failure to converge Latent Growth Curve Modeling Example data The LGC model in Amos Amos input for the LGC model Amos output for the LGC model Amos linear growth model with a time-invariant predictor AMOS summary SAS summary The LGC model in Stata Stata input for the LGC model Stata LGC output Stata LGC model with a time-invariant predictor Stata summary Multiple linear growth models Ordinal data in SEM Treating ordinal variables as interval in data level Conversion to dummy variables Using an appropriate correlation matrix as input Bayesian estimation Generalized structural equation modeling Statistical packages treatment of ordinal data SPSS Amos Bayesian SEM Key concepts and terms Prior distributions Posterior predictive p DIC deviance information criterion Effective number of parameters Combining Bayesian and ML methods Residual analysis Data levels Nominal-level data in Amos with Bayesian estimation Ordinal-level data in Amos with Bayesian estimation Entering ordinal data Censored data Data imputation Warning regarding mixture modeling Warning regarding binning numerical variables Warning regarding variable names To estimate means and intercepts The Bayesian estimation window Prior parameter distributions Posterior parameter distributions Diagnostic graphs Fit measures Additional estimates Amos input Amos output Latent structure analysis Mixture regression modeling Mean Structure Analysis Obtaining output Model fit criteria Upholding the baseline model Analysis of mean structure Estimates of latent means Other output tables Mean structure analysis in SAS SAS input SAS syntax for mean structure analysis SAS output Mean structure analysis in Stata Stata input Putting the example dataset in use Running the measurement intercepts model Running the structural means model A likelihood ratio test of model differences Stata output Why generalized?

Data distributions and link functions GSEM postestimation commands GSEM limitations in Stata Default GSEM output Postestimation GSEM output When multilevel modeling is needed Multilevel SEM software Structural GSEM Estimation options in SEM Maximum likelihood estimation ML Full information maximum likelihood FIML Weighted least squares estimation WLS Generalized least squares estimation GLS Ordinary least squares estimation OLS Unweighted least squares estimation ULS Two-stage least squares estimation 2SLS Asymptotically distribution-free estimation ADF Elliptical distribution theory estimation EDT Bootstrapped vs.

Bayesian estimates A helpful spreadsheet Goodness-of-fit measures and tests based on predicted vs. Hoelter's critical N Minimum fit function FMIN The standardized residual matrix Goodness-of-fit index GFI Adjusted goodness-of-fit index, AGFI Goodness of fit tests involving comparison with the null model Error!

Likelihood ratio test Wald tests Comparative fit index CFI Normed fit index NFI Relative fit index RFI Incremental fit index IFI Bentler-Bonett index BBI Goodness-of-fit tests penalizing for lack of parsimony Parsimony comparative fit index PCFI Parsimony normed fit index PNFI Parsimony normed fit index 2 PNFI Parsimony goodness of fit index PGFI Parsimony index PI Noncentrality-based goodness of fit Noncentrality parameter NCP McDonald noncentrality index NCI Information theory goodness of fit measures Browne-Cudeck criterion BCC Expected cross-validation index ECVI Cross-validation index CVI Data level Dichotomous data Ordinal data Nominal data Multinomial data Sample size Multiple indicators One-indictor regression models Low measurement error Complete data or appropriate data imputation Multivariate normal distribution of the indicators Multivariate normal distribution of the latent dependent variableserror!

Correlated indicators Not theoretically under-identified or just identified Not empirically identified due to high multicollinearity High precision Small, random residuals Uncorrelated error terms Non-zero covariances Frequently Asked Questions SEM analysis What are common guidelines for conduction SEM research and reporting it? How do I write up a SEM analysis? What is a "structural equation model" and how is it diagrammed?

How do I save latent variable factor scores for use in other procedures?

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What is four-step SEM modeling? How can I use SEM to test for the unidimensionality of a construct?

## glm_multivariate_cc_p.pdf

How do you test for interaction effects and use crossproduct interaction terms in SEM? Can one use simple variables in lieu of latent variables in SEM models? How is the model-implied covariance matrix computed to compare with the sample one in model fit measures in SEM? What are "replacing rules" for equivalent models?

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Are these the same? What is the relation of goodness of fit measures to the null model? Abstract Views. Anderson, N.

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Fann Thomas, G. The central role of communication in developing trust and its effect on employee involvement. Journal of Business Communication, 46 3 , Creativity in complex military systems. Garson, G. Testing statistical assumptions.

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