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Presentation
Evaluating the statistical power of goodness-of-fit tests for health and medicine survey data
18th IMACS world congress MODSIM 09: International congress on modelling and simulation: Interfacing modelling and simulation with mathematical and computational sciences
  • Michael Steele, Bond University
  • Neil Smart, Bond University
  • Cameron Hurst
  • Janet Chaseling, Bond University
Date of this Version
7-17-2009
Document Type
Conference Proceeding
Publication Details
Published Version.

Steele, M., Smart, N., Hurst, C., & Chaseling, J. (2009). Evaluating the statistical power of goodness-of-fit tests for health and medicine survey data. Paper presented at The Modelling and Simulation Society of Australia and New Zealand Inc. (MODSIM) and the International Association for Mathematics and Computers in Simulation (IMACS), Cairns, Australia.

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2009 HERDC submission. FoR Code: 0104

© Copyright The Modelling and Simulation Society of Australia and New Zealand Inc. and the International Association for Mathematics and Computers in Simulation, 2009. All rights reserved.
Abstract
Goodness-of-fit test statistics are widely used in health and medicine related surveys however little regard is usually given to their statistical power. This paper investigates the simulated power of five categorical goodness-of-fit test statistics used to analyze health and medicine survey data collected on a 5-point Likert scale. The test statistics used in this power study are Pearson’s Chi-Square, the Kolmogorov-Smirnov test statistic for discrete data, the Log-Likelihood Ratio, the Freeman-Tukey and the special case of the Power Divergence statistic defined by Cressie and Read (1984). Recommendations based on these simulations are provided on which of these goodness-of-fit test statistics is the most powerful overall and which is the most powerful for the predefined uniform null against the four general shaped alternative distributions (see Figure 1) investigated in this paper.
Citation Information
Michael Steele, Neil Smart, Cameron Hurst and Janet Chaseling. "Evaluating the statistical power of goodness-of-fit tests for health and medicine survey data" 18th IMACS world congress MODSIM 09: International congress on modelling and simulation: Interfacing modelling and simulation with mathematical and computational sciences (2009) p. 192 - 196
Available at: http://works.bepress.com/neil_smart/2/