Paper Info
Title
Evaluation Of Procedures For Adjusting Problem-Discovery Rates Estimated From Small Samples
Abstract
There are 2 excellent reasons to compute usability problem-discovery rates. First, an estimate of the problem-discovery rate is a key component for projecting the required sample size for a usability study. Second, practitioners can use this estimate to calculate the proportion of discovered problems for a given sample size. Unfortunately, small-sample estimates of the problem-discovery rate suffer from a serious overestimation bias. This bias can lead to serious underestimation of required sample sizes and serious overestimation of the proportion of discovered problems. This article contains descriptions and evaluations of a number of methods for adjusting small-sample estimates of the problem-discovery rate to compensate for this bias. A series of Monte Carlo simulations provided evidence that the average of a normalization procedure and Good-Turing (Jelinek, 1997, Manning & Schutze, 1999) discounting produces highly accurate estimates of usability problem-discovery rates from small sample sizes.
Year
DOI
Venue
2001
10.1207/S15327590IJHC1304_06
INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION
DocType
Volume
Issue
Journal
13
4
ISSN
Citations 
PageRank 
1044-7318
18
1.78
References 
Authors
4
1
Name
Order
Citations
PageRank
James R. Lewis11109119.18