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Structural Equation Modeling Reference Manual

Publisher:  Stata Press
Copyright:  2023
ISBN-13:  978-1-59718-397-0
Pages:  664
Suggested citation

StataCorp. 2023. Stata 18 Structural Equation Modeling Reference Manual. College Station, TX: Stata Press.

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Structural Equation Modeling Reference Manual for Stata
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Table of contents

Intro 1 Introduction
Intro 2 Learning the language: Path diagrams and command language
Intro 3 Learning the language: Factor-variable notation (gsem only)
Intro 4 Substantive concepts
Intro 5 Tour of models
Intro 6 Comparing groups
Intro 7 Postestimation tests and predictions
Intro 8 Robust and clustered standard errors
Intro 9 Standard errors, the full story
Intro 10 Fitting models with survey data
Intro 11 Fitting models with summary statistics data (sem only)
Intro 12 Convergence problems and how to solve them
Builder SEM Builder
Builder, generalized SEM Builder for generalized models
estat eform Display exponentiated coefficients
estat eqgof Equation-level goodness-of-fit statistics
estat eqtest Equation-level test that all coefficients are zero
estat framework Display estimation results in modeling framework
estat ggof Group-level goodness-of-fit statistics
estat ginvariant Tests for invariance of parameters across groups
estat gof Goodness-of-fit statistics
estat lcgof Latent class goodness-of-fit statistics
estat lcmean Latent class marginal means
estat lcprob Latent class marginal probabilities
estat mindices Modification indices
estat residuals Display mean and covariance residuals
estat scoretests Score tests
estat sd Display variance components as standard deviations and correlations
estat stable Check stability of nonrecursive system
estat stdize Test standardized parameters
estat summarize Report summary statistics for estimation sample
estat teffects Decomposition of effects into total, direct, and indirect
Example 1 Single-factor measurement model
Example 2 Creating a dataset from published covariances
Example 3 Two-factor measurement model
Example 4 Goodness-of-fit statistics
Example 5 Modification indices
Example 6 Linear regression
Example 7 Nonrecursive structural model
Example 8 Testing that coefficients are equal, and constraining them
Example 9 Structural model with measurement component
Example 10 MIMIC model
Example 11 estat framework
Example 12 Seemingly unrelated regression
Example 13 Equation-level Wald test
Example 14 Predicted values
Example 15 Higher-order CFA
Example 16 Correlation
Example 17 Correlated uniqueness model
Example 18 Latent growth model
Example 19 Creating multiple-group summary statistics data
Example 20 Two-factor measurement model by group
Example 21 Group-level goodness of fit
Example 22 Testing parameter equality across groups
Example 23 Specifying parameter constraints across groups
Example 24 Reliability
Example 25 Creating summary statistics data from raw data
Example 26 Fitting a model with data missing at random
Example 27g Single-factor measurement model (generalized response)
Example 28g One-parameter logistic IRT (Rasch) model
Example 29g Two-parameter logistic IRT model
Example 30g Two-level measurement model (multilevel, generalized response)
Example 31g Two-factor measurement model (generalized response)
Example 32g Full structural equation model (generalized response)
Example 33g Logistic regression
Example 34g Combined models (generalized responses)
Example 35g Ordered probit and ordered logit
Example 36g MIMIC model (generalized response)
Example 37g Multinomial logistic regression
Example 38g Random-intercept and random-slope models (multilevel)
Example 39g Three-level model (multilevel, generalized response)
Example 40g Crossed models (multilevel)
Example 41g Two-level multinomial logistic regression (multilevel)
Example 42g One- and two-level mediation models (multilevel)
Example 43g Tobit regression
Example 44g Interval regression
Example 45g Heckman selection model
Example 46g Endogenous treatment-effects model
Example 47g Exponential survival model
Example 48g Loglogistic survival model with censored and truncated data
Example 49g Multiple-group Weibull survival model
Example 50g Latent class model
Example 51g Latent class goodness-of-fit statistics
Example 52g Latent profile model
Example 53g Finite mixture Poisson regression
Example 54g Finite mixture Poisson regression, multiple responses
gsem Generalized structural equation model estimation command
gsem estimation options Options affecting estimation
gsem family-and-link options Family-and-link options
gsem group options Fitting models on different groups
gsem lclass options Fitting models with latent classes
gsem model description options Model description options
gsem path notation extensions Command syntax for path diagrams
gsem postestimation Postestimation tools for gsem
gsem reporting options Options affecting reporting of results
lincom Linear combinations of parameters
lrtest Likelihood-ratio test of linear hypothesis
Methods and formulas for gsem Methods and formulas for gsem
Methods and formulas for sem Methods and formulas for sem
nlcom Nonlinear combinations of parameters
predict after gsem Generalized linear predictions, etc.
predict after sem Factor scores, linear predictions, etc.
sem Structural equation model estimation command
sem and gsem option constraints( ) Specifying constraints
sem and gsem option covstructure( ) Specifying covariance restrictions
sem and gsem option from( ) Specifying starting values
sem and gsem option reliability( ) Fraction of variance not due to measurement error
sem and gsem path notation Command syntax for path diagrams
sem and gsem syntax options Options affecting interpretation of syntax
sem estimation options Options affecting estimation
sem group options Fitting models on different groups
sem model description options Model description options
sem option method( ) Specifying method and calculation of VCE
sem option noxconditional Computing means, etc. of observed exogenous variables
sem option select( ) Using sem with summary statistics data
sem path notation extensions Command syntax for path diagrams
sem postestimation Postestimation tools for sem
sem reporting options Options affecting reporting of results
sem ssd options Options for use with summary statistics data
ssd Making summary statistics data (sem only)
test Wald test of linear hypotheses
testnl Wald test of nonlinear hypotheses
Combined author index
Combined subject index