The Analysis Factor
Statistical Consulting, Resources, and Statistics Workshops for Researchers
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.
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
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?
[Read more…] about Member Training: Model Building Approaches
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
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
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