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Related Experiment Videos

Testing for conditional multiple marginal independence.

Christopher R Bilder1, Thomas M Loughin

  • 1Department of Statistics, Oklahoma State University, Stillwater 74078, USA. bilder@okstate.edu

Biometrics
|March 14, 2002
PubMed
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New statistical tests were developed for analyzing survey data with multiple response variables, also known as pick any/c variables. These methods, including bootstrap procedures and Bonferroni adjustments, accurately assess conditional multiple marginal independence.

Area of Science:

  • Statistics
  • Survey Methodology
  • Biostatistics

Background:

  • Survey data often involves 'pick any/c' variables where respondents select multiple options.
  • Analyzing contingency tables with these variables requires specialized statistical methods.
  • Existing tests like Cochran's and Mantel-Haenszel are unsuitable due to independence assumptions.

Purpose of the Study:

  • To develop and evaluate statistical tests for conditional multiple marginal independence (CMMI) in survey data.
  • To address the limitations of traditional tests when dealing with 'pick any/c' variables.
  • To provide robust methods for assessing the relationship between group and 'pick any/c' variables, conditional on a stratification variable.

Main Methods:

  • Extension of Cochran's test statistic for r x 2 x q tables.

Related Experiment Videos

  • Development of a modified statistic to test CMMI.
  • Application of bootstrapping for approximating sampling distributions.
  • Exploration of bootstrap p-value combination and Bonferroni adjustments.

    • Main Results:

    • Proposed bootstrap procedures and Bonferroni adjustments demonstrate correct statistical size.
    • These methods exhibit significant power against various alternative hypotheses.
    • Simulation findings validate the effectiveness of the developed CMMI testing approaches.

    Conclusions:

    • The novel testing methods effectively address the analysis of 'pick any/c' variables in stratified contingency tables.
    • Bootstrap and Bonferroni adjustment approaches provide reliable tools for CMMI testing.
    • These advancements enhance the statistical rigor for analyzing complex survey data structures.