How small is big: sample size and skewness

Piovesana, Adina and Senior, Graeme (2016) How small is big: sample size and skewness. Assessment. pp. 1-8. ISSN 1073-1911

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Abstract

Sample sizes of 50 have been cited as sufficient to obtain stable means and standard deviations in normative test data. The influence of skewness on this minimum number, however, has not been evaluated. Normative test data with varying levels of skewness were compiled for 12 measures from 7 tests collected as part of ongoing normative studies in Brisbane, Australia. Means and standard deviations were computed from sample sizes of 10 to 100 drawn with replacement from larger samples of 272 to 973 cases. The minimum sample size was determined by the number at which both mean and standard deviation estimates remained within the 90% confidence intervals surrounding the population estimates. Sample sizes of greater than 85 were found to generate stable means and standard deviations regardless of the level of skewness, with smaller samples required in skewed distributions. A formula was derived to compute recommended sample size at differing levels of skewness.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Submitted version deposited in accordance with the copyright policy of the publisher.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Psychology and Counselling
Date Deposited: 13 Feb 2017 04:11
Last Modified: 12 Sep 2017 02:15
Uncontrolled Keywords: minimum sample sizes, normative data, skewness, psychometrics, psychological assessment
Fields of Research : 17 Psychology and Cognitive Sciences > 1701 Psychology > 170109 Personality, Abilities and Assessment
17 Psychology and Cognitive Sciences > 1701 Psychology > 170101 Biological Psychology (Neuropsychology, Psychopharmacology, Physiological Psychology)
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970117 Expanding Knowledge in Psychology and Cognitive Sciences
Identification Number or DOI: 10.1177/1073191116669784
URI: http://eprints.usq.edu.au/id/eprint/29806

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