A consideration of normality in importance–performance analysis

Ivan Ka Wai Lai and Michael Hitchcock

Current Issues in Tourism, 2015, vol. 18, issue 10, 979-1000

Abstract: It is widely believed that results of research are only valid when the distribution of data is relatively normal. However, most previous importance–performance analysis (IPA) studies have not considered the assessment of data normality. The aim of this study is to evaluate the importance of the normality assumption in IPA studies. Four data sets obtained from four experimental questionnaire surveys were used to demonstrate the changes in outcomes from non-normally distributed data sets to relatively normally distributed data sets by removing abnormal data. The results indicate that the distributions of attributes in four quadrants in importance–performance mapping for non-normally distributed data sets and relatively normally distributed data sets are similar when using direct IPA measurement method; however, they show differences when performing indirect IPA measurement methods. It is concluded that in order to ensure high-quality research outcomes, researchers should justify data normality in their IPA studies.

Date: 2015
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/13683500.2014.990423 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:rcitxx:v:18:y:2015:i:10:p:979-1000

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/rcit20

DOI: 10.1080/13683500.2014.990423

Access Statistics for this article

Current Issues in Tourism is currently edited by Jennifer Tunstall

More articles in Current Issues in Tourism from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().