Development of a Microsoft Excel tool for applying a factor retention criterion of a dimension coefficient to a survey on patient safety culture
| Autor: | Yang Shao, Tsair-Wei Chien, Dong-Hui Jen |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Safety Management
Psychometrics Scale (ratio) Datasets as Topic Visual Basic for Applications lcsh:Computer applications to medicine. Medical informatics Dimension coefficient 03 medical and health sciences 0302 clinical medicine 0504 sociology Cronbach's alpha Surveys and Questionnaires Statistics Humans Computer Simulation Patient safety culture survey 030212 general & internal medicine Mathematics Visual basic for applications Rasch model Area under receiver operating characteristic curve Research 05 social sciences Public Health Environmental and Occupational Health Reproducibility of Results 050401 social sciences methods Computer module Polytomous Rasch model General Medicine ROC Curve Sample size determination Data Interpretation Statistical Quality of Life lcsh:R858-859.7 Parallel analysis Patient Safety Computer technology |
| Zdroj: | Health and Quality of Life Outcomes Health and Quality of Life Outcomes, Vol 15, Iss 1, Pp 1-8 (2017) |
| ISSN: | 1477-7525 |
| DOI: | 10.1186/s12955-017-0784-8 |
| Popis: | Background Many quality-of-life studies have been conducted in healthcare settings, but few have used Microsoft Excel to incorporate Cronbach’s α with dimension coefficient (DC) for describing a scale’s characteristics. To present a computer module that can report a scale’s validity, we manipulated datasets to verify a DC that can be used as a factor retention criterion for demonstrating its usefulness in a patient safety culture survey (PSC). Methods Microsoft Excel Visual Basic for Applications was used to design a computer module for simulating 2000 datasets fitting the Rasch rating scale model. The datasets consisted of (i) five dual correlation coefficients (correl. = 0.3, 0.5, 0.7, 0.9, and 1.0) on two latent traits (i.e., true scores) following a normal distribution and responses to their respective 1/3 and 2/3 items in length; (ii) 20 scenarios of item lengths from 5 to 100; and (iii) 20 sample sizes from 50 to 1000. Each item containing 5-point polytomous responses was uniformly distributed in difficulty across a ± 2 logit range. Three methods (i.e., dimension interrelation ≥0.7, Horn’s parallel analysis (PA) 95% confidence interval, and individual random eigenvalues) were used for determining one factor to retain. DC refers to the binary classification (1 as one factor and 0 as many factors) used for examining accuracy with the indicators sensitivity, specificity, and area under receiver operating characteristic curve (AUC). The scale’s reliability and DC were simultaneously calculated for each simulative dataset. PSC real data were demonstrated with DC to interpret reports of the unit-based construct validity using the author-made MS Excel module. Results The DC method presented accurate sensitivity (=0.96), specificity (=0.92) with a DC criterion (≥0.70), and AUC (=0.98) that were higher than those of the two PA methods. PA combined with DC yielded good sensitivity (=0.96), specificity (=1.0) with a DC criterion (≥0.70), and AUC (=0.99). Conclusions Advances in computer technology may enable healthcare users familiar with MS Excel to apply DC as a factor retention criterion for determining a scale’s unidimensionality and evaluating a scale’s quality. Electronic supplementary material The online version of this article (10.1186/s12955-017-0784-8) contains supplementary material, which is available to authorized users. |
| Databáze: | OpenAIRE |
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