Imputation as an approach to missing data has been around for decades. You probably learned about mean imputation in methods classes, only to be ... Continue Reading
Imputation as an approach to missing data has been around for decades. You probably learned about mean imputation in methods classes, only to be ... Continue Reading
There are a number of simplistic methods available for tackling the problem of missing data. Unfortunately there is a very high likelihood that each ... Continue Reading
A data set can contain indicator (dummy) variables, categorical variables and/or both. Initially, it all depends upon how the data is coded as to ... Continue Reading
Two methods for dealing with missing data, vast improvements over traditional approaches, have become available in mainstream statistical software in ... Continue Reading
Sure. One of the big advantages of multiple imputation is that you can use it for any analysis. It's one of the reasons big data libraries use ... Continue Reading
Missing Data, and multiple imputation specifically, is one area of statistics that is changing rapidly. Research is still ongoing, and each year new ... Continue Reading
Most Multiple Imputation methods assume multivariate normality, so a common question is how to impute missing values from categorical ... Continue Reading
Two excellent resources about multiple imputation and missing data: Joe Schafer’s Multiple Imputation FAQ Page gives more detail about multiple ... Continue Reading
Mean imputation: So simple. And yet, so dangerous. Perhaps that's a bit dramatic, but mean imputation (also called mean substitution) really ought ... Continue Reading
I’m sure I don’t need to explain to you all the problems that occur as a result of missing data. Anyone who has dealt with missing data—that means ... Continue Reading
A new version of Amelia II, a free package for multiple imputation, has just been released today. Amelia II is available in two versions. One is ... Continue Reading
You may have never heard of listwise deletion for missing data, but you’ve probably used it. Listwise deletion means that any individual in a data ... Continue Reading
One important consideration in choosing a missing data approach is the missing data mechanism—different approaches have different assumptions about ... Continue Reading
Standardized regression coefficients remove the unit of measurement of predictor and outcome variables. They are sometimes called betas, but I don't ... Continue Reading
I recently received this question: I have scale which I want to run Chronbach's alpha on. One response category for all items is 'not applicable'. I ... Continue Reading
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 ... Continue Reading
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 ... Continue Reading
One of the most common situations in which researchers get stuck with statistics is choosing which statistical methodology is appropriate to analyze ... Continue Reading
There are many ways to approach missing data. The most common, I believe, is to ignore it. But making no choice means that your statistical software ... Continue Reading
It’s easy to think that if you just knew statistics better, data analysis wouldn’t be so hard. It’s true that more statistical knowledge is ... Continue Reading
statistical consulting Sometimes you need one-on-one intensive guidance. With ... Continue Reading
Hourly Statistical Consulting For when you need guidance and advice for a statistical plan or analysis. Perfect if you have: a need for help to ... Continue Reading
Introduction to Data Analysis using Stata 9 Module Tutorial — Currently Available Enroll Now! As a researcher, it’s critical for you to know ... Continue Reading
survival analysis and event history analysis resources Listed below are workshops, trainings, and articles to help you learn survival analysis, ... Continue Reading
missing data resources I don't need to tell you, missing data stinks. And frankly, it's in just about every data set. These days, though, there are ... Continue Reading