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Model Fit

Correlated Errors in Confirmatory Factor Analysis

by Jeff Meyer  3 Comments

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

But we can represent the latent construct by combining a set of questions on a scale, called indicators. We do this via factor analysis.

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
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Measures of Model Fit for Linear Regression Models

by Karen Grace-Martin  38 Comments

A well-fitting regression model results in predicted values close to the observed data values. Stage 2

The mean model, which uses the mean for every predicted value, generally would be used if there were no useful predictor variables. The fit of a proposed
regression model should therefore be better than the fit of the mean model. [Read more…] about Measures of Model Fit for Linear Regression Models

Tagged With: F test, Model Fit, R-squared, regression models, RMSE

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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
  • The Four Models You Meet in Structural Equation Modeling
  • Three Myths and Truths About Model Fit in 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

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  • What Is Specification Error in Statistical Models?
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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
  • Correlated Errors in Confirmatory Factor Analysis

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

by Jeff Meyer  10 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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  • Analyzing Zero-Truncated Count Data: Length of Stay in the ICU for Flu Victims

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