Influential cases in multilevel modeling: A methodological comment

  • Van der Meer T
  • Te Grotenhuis M
  • Pelzer B
  • 113


    Mendeley users who have this article in their library.
  • 79


    Citations of this article.


A large number of cross-national survey data- sets have become available in recent decades. Consequently, scholars frequently apply mul- tilevel models to test hypotheses on both the individual and the country level. However, no currently available cross-national survey project covers more than 54 countries (GESIS 2009). Multilevel modeling therefore runs the risk that higher-level slope estimates (and the substantial conclusions drawn from these estimates) are unreliable due to one or more influential cases (i.e., countries). This comment emphasizes the problem of influential cases and presents ways to detect and deal with them. To detect influential cases, one may use both graphic tools (e.g., scatter plots at the aggregate level) and numeric tools (e.g., diagnostic tests such as Cook’s D and DFBETAS). To illustrate the usefulness and necessity of these tools, we apply them to a study that was recently pub- lished in this journal (Ruiter and De Graaf 2006). Finally, we provide recommendations and tools to detect and handle influential cases, specifically in cross-sectional multi- level analyses.

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document


  • Tom Van der Meer

  • Manfred Te Grotenhuis

  • Ben Pelzer

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free