maximum likelihood

Member Training: Generalized Linear Models

In this webinar, we will provide an overview of generalized linear models. You may already be using them (perhaps without knowing it!).

For example, logistic regression is a type of generalized linear model that many people are already familiar with. Alternatively, maybe you’re not using them yet and you are just beginning to understand when they might be useful to you.

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How to Diagnose the Missing Data Mechanism

by Karen Grace-Martin 4 Comments

One important consideration in choosing a missing data approach is the missing data mechanism—different approaches have different assumptions about the mechanism.

Each of the three mechanisms describes one possible relationship between the propensity of data to be missing and values of the data, both missing and observed.

The Missing Data Mechanisms

Missing Completely at Random, MCAR, means there is no relationship between [Read more…] about How to Diagnose the Missing Data Mechanism

Two Recommended Solutions for Missing Data: Multiple Imputation and Maximum Likelihood

by Karen Grace-Martin 17 Comments

Two methods for dealing with missing data, vast improvements over traditional approaches, have become available in mainstream statistical software in the last few years.

Both of the methods discussed here require that the data are missing at random–not related to the missing values. If this assumption holds, resulting estimates (i.e., regression coefficients and standard errors) will be unbiased with no loss of power.

The first method is Multiple Imputation (MI). Just like the old-fashioned imputation [Read more…] about Two Recommended Solutions for Missing Data: Multiple Imputation and Maximum Likelihood

Quiz Yourself about Missing Data

by Karen Grace-Martin 3 Comments

Do you find quizzes irresistible? I do.

Here’s a little quiz about working with missing data:

True or False?

1. Imputation is really just making up data to artificially inflate results. It’s better to just drop cases with missing data than to impute.

2. I can just impute the mean for any missing data. It won’t affect results, and improves power.

3. Multiple Imputation is fine for the predictor variables in a statistical model, but not for the response variable.

4. Multiple Imputation is always the best way to deal with missing data.

5. When imputing, it’s important that the imputations be plausible data points.

6. Missing data isn’t really a problem if I’m just doing simple statistics, like chi-squares and t-tests.

7. The worst thing that missing data does is lower sample size and reduce power.

Answers to the Missing Data Quiz

by Karen Grace-Martin 3 Comments

In my last post, I gave a little quiz about missing data. This post has the answers.

If you want to try it yourself before you see the answers, go here. (It’s a short quiz, but if you’re like me, you find testing yourself irresistible).

True or False?

1. Imputation is really just making up data to artificially inflate results. It’s better to just drop cases with missing data than to impute. [Read more…] about Answers to the Missing Data Quiz

Missing Data: Criteria for Choosing an Effective Approach

by Karen Grace-Martin 3 Comments

In choosing an approach to missing data, there are a number of things to consider. But you need to keep in mind what you’re aiming for before you can even consider which approach to take.

There are three criteria we’re aiming for with any missing data technique:

1. Unbiased parameter estimates: Whether you’re estimating means, regressions, or odds ratios, you want your parameter estimates to be accurate representations of the actual population parameters. In statistical terms, that means the estimates should be unbiased. If all the [Read more…] about Missing Data: Criteria for Choosing an Effective Approach