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Structural Equation Modeling

Member Training: Goodness of Fit Statistics

by TAF Support


What are goodness of fit statistics? Is the definition the same for all types of statistical model? Do we run the same tests for all types of statistic model?

[Read more…] about Member Training: Goodness of Fit Statistics

Tagged With: count models, goodness of fit, linear regression, logistic regression, mixed model, Structural Equation Modeling

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Member Training: A Guide to Latent Variable Models

by Jeff Meyer

An extremely useful area of statistics is a set of models that use latent variables: variables whole values we can’t measure directly, but instead have to infer from others. These latent variables can be unknown groups, unknown numerical values, or unknown patterns in trajectories.

[Read more…] about Member Training: A Guide to Latent Variable Models

Tagged With: Confirmatory Factor Analysis, Growth Mixture Model, latent class analysis, Latent Growth Curve Model, Latent Profile Analysis, Latent Transition Analysis, latent variable, Structural Equation Modeling

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  • One of the Many Advantages to Running Confirmatory Factor Analysis with a Structural Equation Model
  • First Steps in Structural Equation Modeling: Confirmatory Factor Analysis
  • Member Training: Introduction to Structural Equation Modeling
  • Member Training: Reporting Structural Equation Modeling Results

One of the Many Advantages to Running Confirmatory Factor Analysis with a Structural Equation Model

by Jeff Meyer 9 Comments

Based on questions I’ve been asked by clients, most analysts prefer using the factor analysis procedures in their general statistical software to run a confirmatory factor analysis.

While this can work in some situations, you’re losing out on some key information you’d get from a structural equation model. This article highlights one of these.

[Read more…] about One of the Many Advantages to Running Confirmatory Factor Analysis with a Structural Equation Model

Tagged With: CFA, Confirmatory Factor Analysis, Cronbach's alpha, eigenvalue, Factor Analysis, factor loadings, latent construct, Latent Growth Curve Model, latent variable, Model Fit, residuals, SEM, Structural Equation Modeling

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First Steps in Structural Equation Modeling: Confirmatory Factor Analysis

by Jeff Meyer 4 Comments

Confirmatory factor analysis (CFA) is the fundamental first step in running most types of SEM models. You want to do this first to verify the measurement quality of any and all latent constructs you’re using in your structural equation model.

[Read more…] about First Steps in Structural Equation Modeling: Confirmatory Factor Analysis

Tagged With: CFA, Confirmatory Factor Analysis, latent construct, Latent Growth Curve Model, latent variable, SEM, Structural Equation Modeling

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  • One of the Many Advantages to Running Confirmatory Factor Analysis with a Structural Equation Model
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  • The Four Models You Meet in Structural Equation Modeling
  • Three Myths and Truths About Model Fit in Confirmatory Factor Analysis

Member Training: Reporting Structural Equation Modeling Results

by Jeff Meyer

The last, and sometimes hardest, step for running any statistical model is writing up results.

As with most other steps, this one is a bit more complicated for structural equation models than it is for simpler models like linear regression.

Any good statistical report includes enough information that someone else could replicate your results with your data.

[Read more…] about Member Training: Reporting Structural Equation Modeling Results

Tagged With: CFA, discriminant analysis, error term, factor loadings, Intercept, Latent Growth Curve Model, mean, mediation, parameter estimates, principal component analysis, reliability, reporting, SEM, Structural Equation Modeling

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  • One of the Many Advantages to Running Confirmatory Factor Analysis with a Structural Equation Model
  • First Steps in Structural Equation Modeling: Confirmatory Factor Analysis
  • Member Training: Latent Growth Curve Models
  • Member Training: Confirmatory Factor Analysis

Member Training: Latent Growth Curve Models

by Jeff Meyer 2 Comments

What statistical model would you use for longitudinal data to analyze between-subject differences with within-subject change?

Most analysts would respond, “a mixed model,” but have you ever heard of latent growth curves? How about latent trajectories, latent curves, growth curves, or time paths, which are other names for the same approach?

[Read more…] about Member Training: Latent Growth Curve Models

Tagged With: between-subject, Latent Growth Curve Model, Longitudinal Data, Mixed, model, SEM, Structural Equation Modeling, within-subject

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  • Member Training: Reporting Structural Equation Modeling Results
  • One of the Many Advantages to Running Confirmatory Factor Analysis with a Structural Equation Model
  • First Steps in Structural Equation Modeling: Confirmatory Factor Analysis
  • Member Training: Introduction to Structural Equation Modeling

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