Truncated

Member Training: Working with Truncated and Censored Data

July 1st, 2016 by

Statistically speaking, when we see a continuous outcome variable we often worry about outliers and how these extreme observations can impact our model.

But have you ever had an outcome variable with no outliers because there was a boundary value at which accurate measurements couldn’t be or weren’t recorded?

Examples include:

  • Income data where all values above $100,000 are recorded as $100k or greater
  • Soil toxicity ratings where the device cannot measure values below 1 ppm
  • Number of arrests where there are no zeros because the data set came from police records where all participants had at least one arrest

These are all examples of data that are truncated or censored.  Failing to incorporate the truncation or censoring will result in biased results.

This webinar will discuss what truncated and censored data are and how to identify them.

There are several different models that are used with this type of data. We will go over each model and discuss which type of data is appropriate for each model.

We will then compare the results of models that account for truncated or censored data to those that do not. From this you will see what possible impact the wrong model choice has on the results.

Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.

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Issues with Truncated Data

May 12th, 2016 by

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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6 Types of Dependent Variables that will Never Meet the Linear Model Normality Assumption

September 17th, 2009 by

The assumptions of normality and constant variance in a linear model (both OLS regression and ANOVA) are quite robust to departures.  That means that even if the assumptions aren’t met perfectly, the resulting p-values will still be reasonable estimates.

But you need to check the assumptions anyway, because some departures are so far off that the p-values become inaccurate.  And in many cases there are remedial measures you can take to turn non-normal residuals into normal ones.

But sometimes you can’t.

Sometimes it’s because the dependent variable just isn’t appropriate for a linear model.  The (more…)

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