Influence.ME: Tools for Detecting Influential Data in Multilevel Regression Models

December 20, 2012
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(This article was first published on Curving Normality » R-Project, and kindly contributed to R-bloggers)

Despite the increasing popularity of multilevel regression models, the development of diagnostic tools lagged behind. Typically, in the social sciences multilevel regression models are used to account for the nesting structure of the data, such as students in classes, migrants from origin-countries, and individuals in countries. The strength of multilevel models lies in analyzing data on a large number of groups with only a couple of observations within each group, such as for instance students in classes.

Nevertheless, in the social sciences multilevel models are often used to analyze data on a limited number of groups with per group a large number of observations. A typical example would be the analysis of data on individuals nested within countries. By nature, only a limited number of countries exists. In practice, typical country-comparative analyses are based on about 25 countries. With such a small number of groups (e.g. countries), observations on a single group can easily be overly influential to the outcomes. This means that the conclusions based on the multilevel regression model could no longer hold when a single group is removed from the data.

In our recent publication in the R Journal, we introduce influence.ME, software that provides tools for detecting influential data in multilevel regression models (or: in mixed effects models, as these are commonly referred to in statistics). influence.ME is a publically available R package that evaluates multilevel regression models that were estimated with the lme4.0 package. It calculates standardized measures of influential data for the point estimates of generalized mixed effects models, such as DFBETAS, Cook’s distance, as well as percentile change and a test for changing levels of significance. influence.ME calculates these measures of influence while accounting for the nesting structure of the data. The package and measures of influential data are introduced, a practical example is given, and strategies for dealing with influential data are suggested.

With this publication, and of course with the software that was available for quite some time, we hope to contribute to a better usage of multilevel regression models. The provided example and guidelines were geared towards applications in the social sciences, but are applicable in all disciplines.

On a final note, the editorial of the R Journal describes how this journal is quickly ranking up in the degree of (academic) recognition it receives:

Thomson Reuters has informed us that The R Journal has been accepted for listing in the Science Citation Index-Expanded (SCIE), including the Web of Science, and the ISI Alerting Service, starting with volume 1, issue 1 (May 2009). This complements the current listings by EBSCO and the Directory of Open Access Journals (DOAJ), and completes a process started by Peter Dalgaard in 2010.

More information on our influence.ME software is available on this website.

Download the paper from the R Journal
Rense Nieuwenhuis, Manfred te Grotenhuis, & Ben Pelzer (2012). Influence.ME: tools for detecting influential data in mixed effects models R Journal, 4 (2), 38-47

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