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SEM

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

Related Posts

  • First Steps in Structural Equation Modeling: Confirmatory Factor Analysis
  • Member Training: Reporting Structural Equation Modeling Results
  • Three Myths and Truths About Model Fit in Confirmatory Factor Analysis
  • The Four Models You Meet in Structural Equation Modeling

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

Related Posts

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

Related Posts

  • 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

Related Posts

  • 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

Three Myths and Truths About Model Fit in Confirmatory Factor Analysis

by guest contributer 1 Comment

by Christos Giannoulis, PhD

We mentioned before that we use Confirmatory Factor Analysis to evaluate whether the relationships among the variables are adequately represented by the hypothesized factor structure. The factor structure (relationships between factors and variables) can be based on theoretical justification or previous findings.

Once we estimate the relationship indicators of those factors, the next task is to determine the extent to which these structure specifications are consistent with the data. The main question we are trying to answer is:

[Read more…] about Three Myths and Truths About Model Fit in Confirmatory Factor Analysis

Tagged With: CFA, Confirmatory Factor Analysis, factor structure, fit statistics, Model Fit, SEM, Structural Equation Modeling

Related Posts

  • One of the Many Advantages to Running Confirmatory Factor Analysis with a Structural Equation Model
  • First Steps in Structural Equation Modeling: Confirmatory Factor Analysis
  • The Four Models You Meet in Structural Equation Modeling
  • Measurement Invariance and Multiple Group Analysis

Member Training: Model Fit Statistics in Structural Equation Modeling

by Karen Grace-Martin Leave a Comment

Structural Equation Modelling (SEM) increasingly is a ‘must’ for researchers in the social sciences and business analytics. However, the issue of how consistent the theoretical model is with the data, known as model fit, is by no means agreed upon: There is an abundance of fit indices available – and wide disparity in agreement on which indices to report and what the cut-offs for various indices actually are. [Read more…] about Member Training: Model Fit Statistics in Structural Equation Modeling

Tagged With: indices, Model Fit, SEM, Structural Equation Modeling, theoretical model

Related Posts

  • One of the Many Advantages to Running Confirmatory Factor Analysis with a Structural Equation Model
  • Member Training: Reporting Structural Equation Modeling Results
  • Member Training: Latent Growth Curve Models
  • Member Training: Goodness of Fit Statistics

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