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Multilevel Models Part 1: Do I Need a Multilevel Model?

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If you have data where the observations are not independent due to nesting or clustering, you may need a multilevel model. Another scenario that would require a multilevel model is if you have data where observations have been gathered multiple times on the same subject (a.k.a., longitudinal data or repeated measures).

Here are a few examples:

  • Student math scores, where students are nested within classrooms and then further nested within schools;
  • Automobile loan default (yes/no), where loans are nested within automobile dealerships, which are nested within relationship managers;
  • Measures of reforestation (such as number of species growing), taken annually for several years at specific locations after a forest fire;
  • Counts of defected silicon wafers, where the wafers are nested within manufacturing site; and
  • Patient outcomes where patients are nested within doctors and doctors are nested within clinics.

Whenever observations are clustered, they will be correlated. If your model doesn't account for these correlations, your inference may be compromised. Another advantage of multilevel models is that, in addition to accounting for the correlations during modeling, you can obtain estimates of the degree of correlation among your observations.

You can learn more about multilevel models by signing up for our course.  If you’re attending Analytics 2013 in London, we’re also offering a training session on June 17 and 18.

Tags multilevel models statistical training
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About Author

Chris Daman

Sr Analytical Training Consultant

Chris Daman is a statistical training specialist and course developer in the Education Division at SAS. She has more than 20 years of teaching experience—both nationally and internationally—in the fields of programming, statistics, and mathematics. Before joining SAS in 2005, she taught classes at N.C. State University and IBM, worked in the pharmaceutical and financial industries, and was a survey statistician at an international research organization. She currently teaches advanced statistics courses covering mixed models, generalized linear mixed models, hierarchical linear models, and design of probability surveys; in addition, she teaches design of experiments and analysis of complex data, such as longitudinal data, multilevel data, or data from complex surveys. She also teaches data mining classes, including applied analytics and advanced decision trees. She has a bachelor's degree in mathematics from the University of North Carolina at Greensboro and a master's degree in statistics from N.C. State University. Chris's favorite part of teaching is the interaction with the students. To keep them involved with the material and each other, she often uses a variety of teaching techniques (such as analogies, optical illusions, stories, object lessons, and group interactions) rather than the standard instructor-to-student lecture format. As a result, students give high ratings to her classes and typically include comments such as "I enjoyed Chris's teaching style very much. She did an excellent job of engaging the class and fostering interactions between all the students and herself" or "I love Chris's sense of humor. It definitely helps you get through complicated material". In her spare time, Chris enjoys dancing, reading, spending time with her family, and traveling.

5 Comments

  1. Pingback: When is a Multilevel Model not appropriate? - The SAS Training Post

  2. Candice Bruton on

    Hello,

    Can you tell me when mlm is not appropriate? I have data that by design is clustered but the random intercept in the null model is not significant. I have seen advice that says when this is the case all of your level 2 variation and thus the model for level 2 is over-fitted. I have heard others say that that doesn't matter use mlm anyway.

    Any response is most appreciated.

  3. Pingback: What is a multilevel model?

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