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Related Experiment Video

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Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

Modeling and inference for an ordinal effect size measure.

Euijung Ryu¹, Alan Agresti

¹Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, U.S.A. Ryu.Euijung@mayo.edu

Statistics in Medicine

|October 9, 2007

Summary

This summary is machine-generated.

This study introduces an ordinal effect size measure for comparing ordered categories. New confidence interval methods were developed and validated through simulations, showing good performance for various data types.

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Area of Science:

Statistics
Biostatistics
Ordinal Data Analysis

Background:

Comparing ordered categorical distributions is crucial in many scientific fields.
Existing methods may lack robustness or comprehensive confidence interval approaches for ordinal effect sizes.
The need for reliable effect size measures that account for ties and dependencies is evident.

Purpose of the Study:

To develop and evaluate confidence interval methods for an ordinal measure of effect size.
To assess the performance of these methods under different sampling conditions (independent vs. dependent samples).
To explore the relationship between ordinal effect size and measures used for normal distributions, and to model its dependence on covariates.

Main Methods:

Development of confidence interval methods based on score tests and pseudo-score-type tests.
Simulation studies using independent multinomial samples and fully-ranked data.
Comparison with Wald intervals on the logit scale for dependent samples.
Exploration of the ordinal effect size's relationship with normal distribution effect measures and its modeling using logit regression.

Main Results:

Confidence intervals derived from inverting the score test and a pseudo-score-type test demonstrate strong performance with independent multinomial samples.
The score method also shows efficacy with fully-ranked data.
For dependent samples and small sample sizes, a simple Wald interval on the logit scale may offer superior performance.

Conclusions:

The developed confidence interval methods provide reliable tools for quantifying effect sizes in ordinal categorical data.
The choice of confidence interval method should consider the nature of the data (independent vs. dependent) and sample size.
The ordinal effect size measure and its associated logit model offer a flexible framework for analyzing treatment effects, as illustrated in the shoulder-tip pain study.