**Structural Equation Modeling** (SEM) is a statistical modeling technique to assess hypothesis of relationships among variables. It provides an overview of the statistical theory underlying SEMs and will introduce participants to practical examples involving some of the commonly used SEM software packages (AMOS, LISREL and MPlus). Relationship among variables is postulated into the form of a model. Model fit and goodness of fit testing exposes substantive theory to empirical disconfirmation.

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- Categorical and ordinal data
- Categorical data modeling
- Conditional process modeling
- Covariance’s vs. Pearson correlations
- Crossed lagged models
- Causal mediation analysis
- Dealing with missing data
- Discrepancy (or fit) functions
- Equivalent models
- Estimation, testing and modification of models
- Evaluating measurement invariance in multiple-samples CFA
- Important continuous distributions
- Important SEM books and overview articles and chapters
- Important SEM methodology journals
- Latent growth models and nonlinear curve fitting
- Local model fit evaluation and sensitivity analysis
- Measurement, exploratory, and confirmatory factor analysis
- Methodological alternatives to SEM
- Missing data techniques
- Model comparison strategies
- Modeling latent variable interactions
- Moderation and mediation in the same model
- Multiple group analysis
- Multiple group models
- Not positive definite matrices-causes and cures
- Other on-line SEM information sites--see my home page
- Path analysis with observed variables
- Power analysis at the model level
- Quasi simplex models
- Regression refresher, basic concepts, use of software
- Reporting standards for published SEM studies
- Sample size planning, power analysis, and simulation studies
- SEM and experiments/quasi-experiments
- SEM Software Packages
- SEMNET Citizenship
- SEMNET LISTSERV documentation, fully updated
- Statistical power in SEM
- Structural models with latent variables
- The Form of Structural Equation Models

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