Beyond factor analysis: Multidimensionality and the Parkinson's Disease Sleep Scale-Revised

Autor: Maria Pushpanathan, Romola S. Bucks, Natalie Gasson, Caitlin Timms, Meghan G. Thomas, Andrea M. Loftus, Michelle Olaithe
Rok vydání: 2017
Předmět:
Male
Parkinson's disease
Pulmonology
Physiology
Apnea
lcsh:Medicine
0302 clinical medicine
Mathematical and Statistical Techniques
Insomnia
Medicine and Health Sciences
030212 general & internal medicine
Longitudinal Studies
lcsh:Science
Aged
80 and over
Sleep disorder
Principal Component Analysis
Multidisciplinary
Movement Disorders
Depression
Sleep apnea
Cognition
Parkinson Disease
Neurodegenerative Diseases
Middle Aged
Confirmatory factor analysis
Neurology
Physical Sciences
Disease Progression
Female
medicine.symptom
Psychology
Factor Analysis
Statistics (Mathematics)
Clinical psychology
Research Article
Adult
Sleep Wake Disorders
Sleep Apnea
Research and Analysis Methods
03 medical and health sciences
Mental Health and Psychiatry
medicine
Humans
Statistical Methods
Aged
Mood Disorders
lcsh:R
Biology and Life Sciences
medicine.disease
Dyssomnias
Mood
Multivariate Analysis
lcsh:Q
Factor Analysis
Statistical
Sleep
Physiological Processes
Sleep Disorders
030217 neurology & neurosurgery
Mathematics
Zdroj: PLoS ONE
PLoS ONE, Vol 13, Iss 2, p e0192394 (2018)
ISSN: 1932-6203
Popis: Many studies have sought to describe the relationship between sleep disturbance and cognition in Parkinson's disease (PD). The Parkinson's Disease Sleep Scale (PDSS) and its variants (the Parkinson's disease Sleep Scale-Revised; PDSS-R, and the Parkinson's Disease Sleep Scale-2; PDSS-2) quantify a range of symptoms impacting sleep in only 15 items. However, data from these scales may be problematic as included items have considerable conceptual breadth, and there may be overlap in the constructs assessed. Multidimensional measurement models, accounting for the tendency for items to measure multiple constructs, may be useful more accurately to model variance than traditional confirmatory factor analysis. In the present study, we tested the hypothesis that a multidimensional model (a bifactor model) is more appropriate than traditional factor analysis for data generated by these types of scales, using data collected using the PDSS-R as an exemplar. 166 participants diagnosed with idiopathic PD participated in this study. Using PDSS-R data, we compared three models: a unidimensional model; a 3-factor model consisting of sub-factors measuring insomnia, motor symptoms and obstructive sleep apnoea (OSA) and REM sleep behaviour disorder (RBD) symptoms; and, a confirmatory bifactor model with both a general factor and the same three sub-factors. Only the confirmatory bifactor model achieved satisfactory model fit, suggesting that PDSS-R data are multidimensional. There were differential associations between factor scores and patient characteristics, suggesting that some PDSS-R items, but not others, are influenced by mood and personality in addition to sleep symptoms. Multidimensional measurement models may also be a helpful tool in the PDSS and the PDSS-2 scales and may improve the sensitivity of these instruments.
Databáze: OpenAIRE
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