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Introduction to Sample Size Choice for Confidence Intervals Based on 't' Statistics

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Bibliographic Details
Title: Introduction to Sample Size Choice for Confidence Intervals Based on 't' Statistics
Language: English
Authors: Liu, Xiaofeng Steven, Loudermilk, Brandon, Simpson, Thomas
Source: Measurement in Physical Education and Exercise Science. 2014 18(2):91-100.
Availability: Routledge. Available from: Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 10
Publication Date: 2014
Document Type: Journal Articles
Reports - Descriptive
Descriptors: Sample Size, Statistical Analysis, Confidence Testing, Intervals, Measurement Techniques, Computation, Exercise
DOI: 10.1080/1091367X.2013.864657
ISSN: 1091-367X
Abstract: Sample size can be chosen to achieve a specified width in a confidence interval. The probability of obtaining a narrow width given that the confidence interval includes the population parameter is defined as the power of the confidence interval, a concept unfamiliar to many practitioners. This article shows how to utilize the Statistical Analysis System (SAS) proc power procedure to determine an appropriate sample size and achieve sufficient power for a specified confidence interval. Two examples in sport and exercise science are used to illustrate sample size determination for confidence intervals in the dependent and independent "t" tests. The relevant SAS code and output are provided with detailed annotations. As the use of confidence intervals becomes a more integral part of studies in sport and exercise science, the reporting and analysis of their power should be considered much like that of hypothesis tests.
Abstractor: As Provided
Number of References: 18
Entry Date: 2014
Accession Number: EJ1029741
Database: ERIC
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Description
Abstract:Sample size can be chosen to achieve a specified width in a confidence interval. The probability of obtaining a narrow width given that the confidence interval includes the population parameter is defined as the power of the confidence interval, a concept unfamiliar to many practitioners. This article shows how to utilize the Statistical Analysis System (SAS) proc power procedure to determine an appropriate sample size and achieve sufficient power for a specified confidence interval. Two examples in sport and exercise science are used to illustrate sample size determination for confidence intervals in the dependent and independent "t" tests. The relevant SAS code and output are provided with detailed annotations. As the use of confidence intervals becomes a more integral part of studies in sport and exercise science, the reporting and analysis of their power should be considered much like that of hypothesis tests.
ISSN:1091-367X
DOI:10.1080/1091367X.2013.864657