The first real data set I ever analyzed was from my senior honors thesis as an undergraduate psychology major. I had taken both intro stats and an ANOVA class, and I applied all my new skills with gusto, analyzing every which way.
It wasn’t too many years into graduate school that I realized that these data analyses were a bit haphazard. (Okay, a LOT). And honestly, not at all well thought out.
A few decades of data analysis experience later, I realized that’s just a symptom of being an inexperienced data analyst.
But even experienced data analysts can get off track. It’s especially easy with large data sets with many variables. It’s just so tempting to try one thing, then another, and pretty soon you’ve spent weeks getting nowhere. (more…)
Data Cleaning is a critically important part of any data analysis. Without properly prepared data, the analysis will yield inaccurate results. Correcting errors later in the analysis adds to the time, effort, and cost of the project.
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Outliers are one of those realities of data analysis that no one can avoid.
Those pesky extreme values cause biased parameter estimates, non-normality in otherwise beautifully normal variables, and inflated variances.
Everyone agrees that outliers cause trouble with parametric analyses. But not everyone agrees that they’re always a problem, or what to do about them even if they are.
Sometimes a nonparametric or robust alternative is available — and sometimes not.
There are a number of approaches in statistical analysis for dealing with outliers and the problems they create. It’s common for committee members or Reviewer #2 to have very strong opinions that there is one and only one good approach.
Two approaches that I’ve commonly seen are: 1) delete outliers from the sample, or 2) winsorize them (i.e., replace the outlier value with one that is less extreme).
The problem with both of these “solutions” is that they also cause problems — biased parameter estimates and underweighted or eliminated valid values. (more…)
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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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? (more…)
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:
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