In April and May, we’re doing something new: including in membership the workshop Interpreting (Even Tricky) Regression Coefficients with Karen Grace-Martin.
We’ll be releasing the first 3 of 6 modules in April and modules 4-6 in May and holding a special Q&A with Karen at the end of each month.
If you’ve ever wanted to know how to interpret your results or set up your model to get the information you needed, you’ll love this workshop.
Although it’s at Stage 2 and focuses entirely on linear models, everything applies to all sorts of regression models — logistic, multilevel, count models. All of them.
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There are two main types of factor analysis: exploratory and confirmatory. 
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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.
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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.
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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.
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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.
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