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Adjusting for Unequal Selection Probability in Multilevel Models: A Comparison of Software Packages

Author

Listed:
  • Kim Chantala

    (Carolina Population Center, UNC at Chapel Hill)

  • CM Suchindran

    (Carolina Population Center, UNC at Chapel Hill)

  • Dan Blanchette

    (Carolina Population Center, UNC at Chapel Hill)

Abstract

Most surveys collect data using complex sampling plans involving selection of both clusters and individuals with unequal probability of selection. Research in methods of using multilevel modeling (MLM) procedures to analyze such data is relatively new. Often sampling weights based on selection probabilities of individuals are used to estimate population-based models. However, sampling weights used for estimating multilevel models need to be constructed differently than weights used for single-level (population-average) models. This paper compares the capabilities of MLwiN, Mplus, LISREL, PROC MIXED (SAS), and gllamm (Stata) for estimating MLM from data collected with a complex sampling plan. We illustrate how sampling weights for estimating multilevel models with these software packages can be constructed from population average weights. Finally, we use data from the National Longitudinal Survey of Adolescent Health to contrast the results from these packages.

Suggested Citation

  • Kim Chantala & CM Suchindran & Dan Blanchette, 2005. "Adjusting for Unequal Selection Probability in Multilevel Models: A Comparison of Software Packages," North American Stata Users' Group Meetings 2005 12, Stata Users Group.
    • Handle: RePEc:boc:asug05:12
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