OptinMon

In a previous post we explored bounded variables and the difference between truncated and censored. Can we ignore the fact that a variable is bounded and just run our analysis as if the data wasn’t bounded? (more…)

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Proportion and percentage data are tricky to analyze.

Much like count data, they look like they should work in a linear model.

They’re numeric. They’re often continuous.

And sometimes they do work. Some proportion data do look normally distributed so estimates and p-values are reasonable.

But more often they don’t. So estimates and p-values are a mess. Luckily, there are other options. (more…)

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By Manolo Romero Escobar

If you already know the principles of general linear modeling (GLM) you are on the right path to understand Structural Equation Modeling (SEM).

As you could see from my previous post, SEM offers the flexibility of adding paths between predictors in a way that would take you several GLM models and still leave you with unanswered questions.

It also helps you use latent variables (as you will see in future posts).

GLM is just one of the pieces of the puzzle to fit SEM to your data. You also need to have an understanding of:
(more…)

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Like the chicken and the egg, there’s a question about which comes first: run a model or test assumptions? Unlike the chicken’s, the model’s question has an easy answer.

There are two types of assumptions in a statistical model.  Some are distributional assumptions about the errors.  Examples include independence, normality, and constant variance in a linear model.

Others are about the form of the model.  They include linearity and (more…)

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By Manolo Romero Escobar

What is a latent variable?

“The many, as we say, are seen but not known, and the ideas are known but not seen” (Plato, The Republic)

My favourite image to explain the relationship between latent and observed variables comes from the “Myth of the Cave” from Plato’s The Republic.  In this myth a group of people are constrained to face a wall.  The only things they see are shadows of objects that pass in front of a fire (more…)

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One common reason for running Principal Component Analysis (PCA) or Factor Analysis (FA) is variable reduction.

In other words, you may start with a 10-item scale meant to measure something like Anxiety, which is difficult to accurately measure with a single question.

You could use all 10 items as individual variables in an analysis–perhaps as predictors in a regression model.

But you’d end up with a mess.

Not only would you have trouble interpreting all those coefficients, but you’re likely to have multicollinearity problems.

And most importantly, you’re not interested in the effect of each of those individual 10 items on your (more…)

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