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by Jeff Meyer

You put a lot of work into preparing and cleaning your data. Running the model is the moment of excitement.

You look at your tables and interpret the results. But first you remember that one or more variables had a few outliers. Did these outliers impact your results? [click to continue…]

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Free May Craft of Statistical Analysis Webinar: Unlocking the Power of Stata’s Macros and Loops

There are many steps to analyzing a dataset. One of the first steps is to create tables and graphs of your variables in order to understand what is behind the thousands of numbers on your screen. But the type of table and graph you create depends upon the type of variable you are looking at…

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Linear Regression in Stata: Missing Data and the Stories it Might Tell

In a previous blog post we examined how to use the same sample when comparing the differences among regression models. Using different samples in our models could lead to erroneous conclusions when interpreting our models. But excluding observations can also result in inaccurate results…

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Issues with Truncated Data

Can we ignore the fact that a variable is bounded and just run our analysis as if the data wasn’t bounded?

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May 2016 Topic Webinar: Communicating Statistical Results: When to use tables vs graphs to tell the data’s story

In this webinar, we will discuss when tables and graphs are (and are not) appropriate and how people tend to engage with each of these media…

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April 2016 Topic Webinar: An Introduction to Kaplan-Meier Curves

In this talk, you will see a simple example of this using fruit fly data, and learn how to interpret the Kaplan-Meier curve to estimate survival probabilities and survival percentiles..

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The four models you meet in Structural Equation Modeling

On a previous post (Why do I need to have knowledge of multiple regression to understand SEM?) we showed how a multiple regression model could be conceptualized using Structural Equation Model path diagrams. That’s the simplest SEM you can create, but its real power lies in expanding on that regression model. Here I will discuss 4 ways to do that..

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Zero One Inflated Beta Models for Proportion Data

Like logistic and Poisson regression, beta regression is a type of generalized linear model. It works nicely for proportion data because the values of a variable with a beta distribution must fall between 0 and 1. It’s a bit of a funky distribution in that it’s shape can change a lot depending on the values of the mean and dispersion parameters. Here are a few examples of the possible shapes of a beta distribution, with different means and variances…

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Five things you need to know before learning Structural Equation Modeling

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 […]

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When to Check Model Assumptions

If any of these fail, it’s nearly impossible to get normally distributed residuals, even with remedial transformations.

Types of variables that will generally fail these criteria include:

Categorical Variables, both nominal and ordinal.
Count Variables, which are often distributed as Poisson or Negative Binomial.

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