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Sleep Disorders
Volume 2018, Article ID 9643937, 9 pages
https://doi.org/10.1155/2018/9643937
Research Article

Shortening of the Pittsburgh Sleep Quality Index Survey Using Factor Analysis

1Division of Animal and Nutritional Sciences, Davis College of Agriculture, Natural Resources, and Design, West Virginia University, Morgantown, WV, USA
2Office of Statistics, West Virginia Agriculture and Forestry Experiment Station, West Virginia University, 4100 Agricultural Sciences Building, P.O. Box 6108, Morgantown, WV 26506-6108, USA
3University of Tennessee, Knoxville, 916 Volunteer Boulevard, UT SMC 247, Knoxville, TN 37996, USA
4University of New Hampshire, Kendall Hall, Room 115, 129 Main Street, Durham, NH 03824, USA
5University of Tennessee, Knoxville, 1215 W. Cumberland Avenue, 229 Jessie Harris Building, Knoxville, TN 37996, USA

Correspondence should be addressed to Melissa D. Olfert; ude.uvw.liam@treflo.assilem

Received 16 November 2017; Accepted 6 March 2018; Published 12 April 2018

Academic Editor: Michel M. Billiard

Copyright © 2018 Oluremi A. Famodu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Objective/Introduction. Lengthy surveys have the potential to burden users and can lead to inaccuracies. Conducting analyses to shorten existing validated surveys is beneficial. The objective, therefore, was to shorten the Pittsburgh Quality Sleep Index (PSQI) for young adults. Methods. PSQI data from 1246 college students were used. An exploratory factor analysis (FA) was utilized to shorten survey after dropping select items. Nonparametric correlation analysis (Spearman’s rho) was conducted between the global sleep scores of the shortened and original surveys. Agreements tests (Kappa and McNemar’s test) measured the agreement of the surveys and sensitivity and specificity were evaluated. Results. Six factors were examined using maximum likelihood factoring method, applying squared multiple correlations with Promax rotation to allow for correlated variables. FA with six factors explained 100% of shared variance based on eigenvalues and accounted for 61% of variability based on variables. The FA resulted in 13 selected questions (“shortPSQI”), corresponding to 5 of the 7 components of the original survey. High correlation was found between the global scores of the original survey and the “shortPSQI” (rho = 0.94, ). When the global score was converted to the categorical variable of good or poor sleepers, the agreement test indicated strong agreement (Kappa 0.83, 95% CI 0.79–0.86, ). Conclusion. The validated, 19-item PSQI survey was shortened to 13 items. Tests of correlation and agreement indicate the “shortPSQI” may be an acceptable alternative to the original survey for young adults. Clinical Trial Registration. Data for this study was taken from the Get Fruved study, registered on October 21, 2016, on clinicaltrials.gov (NCT02941497).