Psychometric Analysis of the SPEED Questionnaire

Lisa Jones-Jordan

Abstract

Purpose: The Standard Patient Evaluation of Eye Dryness (SPEED) questionnaire was developed to evaluate the symptoms of dry eye in patients with the purpose to include an assessment of longer standing problems over the past three months. The original sample tested 50 subjects and only a few studies have further evaluated the questionnaire. This study reports on the performance of this questionnaire in a diverse sample of professional adults, and it is compared to the CLDEQ-8 for the subset of contact lens wearers.

 

Methods: A cross-sectional study was completed at Academy 2015 New Orleans with the SPEED questionnaire administered to the subjects along with other questions about demographics. Contact lens wearers completed the CLDEQ-8. Rasch analysis was done to evaluate the questionnaires for unidimensionality and to evaluate the performance of the individual questions. Additionally, the SPEED was correlated with the CLDEQ-8 for contact lens wearers to determine if they performed similarly.

 

Results: There were 284 subjects, of which 150 reported as contact lens wearers. The average age was 39.4 years ± 14.2 and 61% were female. Rasch analysis was performed initially using all of the questions on the SPEED, but the questions measuring long-term symptoms were found incompatible with the unidimensionality assumption of the model. The 8 core questions about symptom frequency and intensity were found to measure one latent variable, measurement precision was acceptable, and the model fit was reasonable. The response category structure functioned well. The CLDEQ-8 also functioned as one latent variable with acceptable measurement properties. The two scales were highly correlated in contact lens wearers (r = 0.73, p<0.001).

 

Conclusions: Overall, a short form of the SPEED questionnaire had good measurement properties in this large, diverse sample of adults. Both the SPEED and the CLDEQ-8 questionnaires seem to be tapping into a similar construct for dry eye symptoms.

 

Details

Year: 2016

Program Number: 160019

Resource Type: Scientific Program

Author Affiliation: The Ohio State University

Co-Authors: Andrew Pucker, Bradley Dougherty, Justin Kwan, Carolina Kunnen, Sruthi Srinivasan

Co-Author Affiliation: The Ohio State University College of Optometry, The Ohio State University College of Optometry, Southern California College of Optometry at Ketchum, University of Houston, University of Waterloo

Room: 212 AB