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Fitting multilevel models in complex survey data with design weights: Recommendations.

¹Department of Psychology, University of North Florida, Jacksonville, FL 32224, USA. adam.carle@unf.edu

BMC Medical Research Methodology

|July 16, 2009

Summary

This summary is machine-generated.

Scaling design weights in multilevel models (MLM) with complex survey data is recommended. While scaled-weighted and unweighted analyses showed minor differences, they did not alter conclusions, increasing confidence in MLM results.

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Area of Science:

Public Health
Statistics
Survey Methodology

Background:

Multilevel models (MLM) are crucial for analyzing public health data with individual and contextual factors.
Guidance on fitting MLM with complex survey data and design weights is limited.
Previous simulations suggest scaling design weights is necessary for accurate MLM analysis.

Purpose of the Study:

To examine the performance of scaled-weighted versus unweighted multilevel model analyses.
To evaluate different scaling methods for design weights in MLM.
To compare MLM results across various software programs.

Main Methods:

Utilized data from the National Survey of Children with Special Health Care Needs (NS-CSHCN) (n = 40,723).
Analyzed data with clustered children within states, considering categorical and continuous outcomes.
Compared results from Mplus, MLwiN, and GLLAMM software for multilevel modeling.

Main Results:

Scaled-weighted estimates and standard errors showed slight differences from unweighted analyses but were similar to each other.
Minimal differences were observed across different software programs.
Inferential conclusions remained consistent regardless of scaling method or software used.

Conclusions:

Scaling design weights is recommended when fitting multilevel models with complex survey data.
Analysts should use software that correctly incorporates scaled weights during estimation.
Findings support the reliability of MLM results irrespective of software choice when weights are properly scaled.