应该是大体内容吧.
COURSE OUTLINE
PART I: FUNDAMENTALS OF STRUCTURAL EQUATION MODELING
1. Review of Factor Analysis and Linear Regression Analysis
Exploratory and confirmatory factor analysis (EFA and CFA)
Example 1.1: An Exploratory Factor Analysis using SPSS
Example 1.2: A Confirmatory Factor Analysis using Mplus
Linear regression analysis
Example 1.3: A simple regression using SPSS
Example 1.4: A multiple regression using Mplus
2. Introduction to Structural Equation Modeling (SEM)
Fundamental principles underlying structural equation modeling
3. Model Conceptualisation, Path Diagram Construction and Model Specification
SEM Step 1. Model conceptualisation
SEM Steps 2 & 3. Path diagram construction and Model specification
4. Model Identification and Parameter Estimation
SEM Step 4. Model Identification
SEM Step 5. Parameter Estimation
Using Mplus to estimate parameters
5. Assessing Model Fit, Model Re-specification and Model Cross Validation
SEM Step 6. Assessing model fit
SEM Step 7. Model re-specification
SEM Step 8. Model cross-validation
PART II: BASIC MODELS
6. Models with Observed Variables only
Regression, recursive and non-recursive path models with continuous variables,
Probit, Logistic, and Multinomial logistic regression for categorical dependent variables; Poisson regression for count dependent variables;
Regression for censored variables.
7. One Factor Measurement Models
Confirmatory factor analysis: Parallel vs. congeneric measurement models
Modeling one factor congeneric measurement models
Correlated error variances in one factor congeneric measurement models
Factor score regression weights
Reliability and validity
8. Confirmatory and Second Order Factor Analysis
Confirmatory factor analysis
Discriminant Validity
Multitrait-Multimethod models
Second order factor analysis
CFA with categorical, censored and count variables
9. Full Structural Equation Models for Latent Variables
Path analysis with latent variables
Multiple Indicator and Multiple Causes (MIMIC) models
Longitudinal (or panel) studies: Two-wave models
Simplex models
Totally endogenous models
Mediating in Full Models
PART III: PROBLEMS IN STRUCTURAL EQUATION MODELING
10. Dealing with Data Problems
Missing Data
Outliers
Ordinal and/or dichotomous data and the WLSMV estimator
11. Dealing with Model Problems
Unidentified models: Constraining Parameters
Non-positive definite matrices
Constraining error variances to be non-negative
12. Constructing Composite Variables for use in Structural Equation Models
Using composite scale reliabilities to fix composite variable regression coefficients and measurement error variances in subsequent structural equation models
A worked example: The LoUQ Instrument
PART IV: ADVANCED STRUCTURAL EQUATION MODELS
13. Multi-group analysis
Testing model invariance across groups
Testing parameter invariance across groups
14. Interaction and Non-linear Effects in Structural Equation Modeling
Analyses of interactions with categorical moderator variables
Analyses with interactions amongst continuous variables
Non-linear Effects
15. Mean structure analysis
Regression models with intercepts
Estimation of factor means
An SEM alternative to analysis of covariance
16. Two approaches to Longitudinal or Repeated Measure Designs
Linear Growth Modeling (LGM) for a continuous outcome
LGM for a categorical outcome
Quadratic growth modeling; and
LGM for a continuous outcome with time invariant and time-varying covariates.
17. Multilevel structural equation modeling
Two-level regression analysis;
Two-level CFA;
Two-level SEM;
Two-level growth models; and
Multi-level mixture modeling.
18. Mixture Modeling (including Latent Class Analysis)
Mixture regression analysis;
Latent Class Analysis (LCA) with binary, ordinal, nominal or continuous latent class indicators;
CFA and Structural equation mixture modeling.
Longitudinal models include Growth Mixture Modeling (GMM) for a continuous or categorical outcome;
GMM with known classes (multi-group analysis); and
Latent Class Growth Analysis (LCGA) for a binary, ordinal or count outcome.
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