Using ranked auxiliary covariate as a more efficient sampling design for ANCOVA model: Analysis of a psychological intervention to buttress resilience

Rajai Jabrah, Hani M. Samawi, Robert Vogel, Haresh D. Rochani, Daniel F Linder, Jeff Klibert

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

Drawing a sample can be costly or time consuming in some studies. However, it may be possible to rank the sampling units according to some baseline auxiliary covariates, which are easily obtainable, and/or cost efficient. Ranked set sampling (RSS) is a method to achieve this goal. In this paper, we propose a modified approach of the RSS method to allocate units into an experimental study that compares L groups. Computer simulation estimates the empirical nominal values and the empirical power values for the test procedure of comparing L different groups using modified RSS based on the regression approach in analysis of covariance (ANCOVA) models. A comparison to simple random sampling (SRS) is made to demonstrate efficiency. The results indicate that the required sample sizes for a given precision are smaller under RSS than under SRS. The modified RSS protocol was applied to an experimental study. The experimental study was designed to obtain a better understanding of the pathways by which positive experiences (i.e., goal completion) contribute to higher levels of happiness, well-being, and life satisfaction. The use of the RSS method resulted in a cost reduction associated with smaller sample size without losing the precision of the analysis.

Original languageEnglish (US)
Pages (from-to)241-254
Number of pages14
JournalCommunications for Statistical Applications and Methods
Volume24
Issue number3
DOIs
StatePublished - May 1 2017

Keywords

  • ANCOVA
  • Cost-effectiveness
  • Emotional uplifting intervention
  • Empirical power
  • Multiple regression
  • Ranked auxiliary covariate
  • Ranked set sampling
  • Reduced sample size
  • Sampling design

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Finance
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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