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Model Fit

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

Correlated Errors in Confirmatory Factor Analysis

by Jeff Meyer 2 Comments

Latent constructs, such as liberalism or conservatism, are theoretical and cannot be measured directly.

But we can use a set of questions on a scale, called indicators, to represent the construct together by combining them into a latent factor.

Often prior research has determined which indicators represent the latent construct. Prudent researchers will run a confirmatory factor analysis (CFA) to ensure the same indicators work in their sample.

[Read more…] about Correlated Errors in Confirmatory Factor Analysis

Tagged With: Confirmatory Factor Analysis, error term, Factor Analysis, latent variable, Model Fit

Related Posts

  • One of the Many Advantages to Running Confirmatory Factor Analysis with a Structural Equation Model
  • Measurement Invariance and Multiple Group Analysis
  • Why Adding Values on a Scale Can Lead to Measurement Error
  • First Steps in Structural Equation Modeling: Confirmatory Factor Analysis

Member Training: Model Building Approaches

by TAF Support

There is a bit of art and experience to model building. You need to build a model to answer your research question but how do you build a statistical model when there are no instructions in the box? 

Should you start with all your predictors or look at each one separately? Do you always take out non-significant variables and do you always leave in significant ones?

[Read more…] about Member Training: Model Building Approaches

Tagged With: centering, interaction, lasso, Missing Data, Model Building, Model Fit, Multicollinearity, overfitting, Research Question, sample size, specification error, statistical model, Stepwise

Related Posts

  • What Is Specification Error in Statistical Models?
  • Member Training: The LASSO Regression Model
  • Steps to Take When Your Regression (or Other Statistical) Results Just Look…Wrong
  • December Member Training: Missing Data

Three Myths and Truths About Model Fit in Confirmatory Factor Analysis

by guest 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

Poisson or Negative Binomial? Using Count Model Diagnostics to Select a Model

by Jeff Meyer 7 Comments

How do you choose between Poisson and negative binomial models for discrete count outcomes?

One key criterion is the relative value of the variance to the mean after accounting for the effect of the predictors. A previous article discussed the concept of a variance that is larger than the model assumes: overdispersion.

(Underdispersion is also possible, but much less common).

There are two ways to check for overdispersion: [Read more…] about Poisson or Negative Binomial? Using Count Model Diagnostics to Select a Model

Tagged With: count model, dispersion statistic, Model Fit, negative binomial, overdispersion, poisson, predicted count, residual plot

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  • Overdispersion in Count Models: Fit the Model to the Data, Don’t Fit the Data to the Model
  • The Problem with Linear Regression for Count Data
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  • Analyzing Zero-Truncated Count Data: Length of Stay in the ICU for Flu Victims

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: A Guide to Latent Variable Models

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