Learn how to Implement Maximum Likelihood Approaches for Missing Data that Eliminate Bias, Retain Power, and Produce Accurate P-Values

The effects of missing data on statistical results range from slight loss of power to devastating bias that renders results compeletely inaccurate.

And many of the traditional solutions to missing data create more problems than they solve.

But two modern approaches to missing data give unbiased results with no loss of power in many circumstances.

This home study mini-workshop explains one of these approaches--Maximum Likelihood.

Maximum Likelihood is quite easy to implement--it's much easier than Multiple Imputation, which can take weeks to do well. You may already be using it for your analysis.

But unfortunately, it's only possible for certain kinds of regression models and sometimes requires specialized software.

This mini-workshop gives you everything you need to get started.

It includes:

1. A Downloadable Video: Maximum Likelihood Approaches to Missing Data Maximum Likelihood Approaches to Missing Data

This 23-minute video will give you an overview of Maximum Likelihood (ML) approaches to Missing Data. You will learn:

• What maximum likelihood estimation is
  
  
• The specific kinds of models that can use it
  
  
• When you can use the different maximum likelihood appraches to missing data, such as the E-M algorithm, REML, and Full Information Maximum Likelihood
  
  
• Its advantages, disadvantages, and assumptions
  
  
• The statistical methods and corresponding software that can use each type of ML

This video will give you the background understanding you need to use Maximum Likelihood approaches to missing data.

2. A Downloadable Video: Using AMOS 16.0 to Run Regression Models with Missing Data

One great, and easy-to-use software package that implements Full Information Maximum Likelihood is AMOS 16.0. AMOS is actually a Structural Equation modeling software package, but can be used to run regression models.

One really nice thing about AMOS is that it reads SPSS files and comes bundled with many versions of SPSS, including the GradPak.

If you have never used AMOS and need to run a regression model on a data set with missing data, this ebook and video will tell you exactly what steps to take. AMOS is not difficult to use to run regressions. But if you don't set it up just right, it won't run.

The video will show you step-by-step how to implement Full Information Maximum Likelihood (ML) approaches to Missing Data for Linear Regression models in AMOS 16.

The video is great for seeing the steps actually implemented--seeing where each button is, exactly which boxes to check, and an annotated explanation of why you need to do various steps.

Along with the video, you also get:

3. The 12-page ebook: How to Use Full Information Maximum Likelihood in AMOS to Analyze Regression Models with Missing Data

The ebook cuts it down to the essentials. Once you've gotten your bearings watching the video, the ebook will take you through each of the steps with screenshots. Use it as a checklist to make sure you've done each step correctly.

The information in both the video and ebook also apply to AMOS version 4.0. There are slight differences in the menus, which are discussed in the video.

And a bonus video:

Free Bonus: Approaches to Missing Data: The Good, the Bad, and the Unthinkable

You’ve probably heard about many different approaches to dealing with missing data, and you’ve probably gotten different opinions about which one you should use.  In this video, you’ll get an overview of:

• the three types of missing data, and how they affect the approach to take
• the common approach that is generally worse than any other
• the easy, common, seemingly bad approach that often isn’t so bad, and the situations when it doesn’t work
• the two approaches that give unbiased results, one that is very easy to implement, but only works in limited situations and one that is harder to implement well, but works with any statistical analysis

 

the mini-workshop for only $27.00