Beyond factor analysis: Multidimensionality and the Parkinson’s Disease Sleep Scale-Revised
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 r...
| Main Authors: | , , , , , , |
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| Format: | Journal Article |
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Public Library of Science
2018
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| Online Access: | http://hdl.handle.net/20.500.11937/66994 |
| _version_ | 1848761446832275456 |
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| author | Pushpanathan, M. Loftus, A. Gasson, Natalie Thomas, M. Timms, C. Olaithe, M. Bucks, R. |
| author_facet | Pushpanathan, M. Loftus, A. Gasson, Natalie Thomas, M. Timms, C. Olaithe, M. Bucks, R. |
| author_sort | Pushpanathan, M. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T10:31:48Z |
| format | Journal Article |
| id | curtin-20.500.11937-66994 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:31:48Z |
| publishDate | 2018 |
| publisher | Public Library of Science |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-669942018-07-17T00:40:04Z Beyond factor analysis: Multidimensionality and the Parkinson’s Disease Sleep Scale-Revised Pushpanathan, M. Loftus, A. Gasson, Natalie Thomas, M. Timms, C. Olaithe, M. Bucks, R. 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. 2018 Journal Article http://hdl.handle.net/20.500.11937/66994 10.1371/journal.pone.0192394 http://creativecommons.org/licenses/by/4.0/ Public Library of Science fulltext |
| spellingShingle | Pushpanathan, M. Loftus, A. Gasson, Natalie Thomas, M. Timms, C. Olaithe, M. Bucks, R. Beyond factor analysis: Multidimensionality and the Parkinson’s Disease Sleep Scale-Revised |
| title | Beyond factor analysis: Multidimensionality and the Parkinson’s Disease Sleep Scale-Revised |
| title_full | Beyond factor analysis: Multidimensionality and the Parkinson’s Disease Sleep Scale-Revised |
| title_fullStr | Beyond factor analysis: Multidimensionality and the Parkinson’s Disease Sleep Scale-Revised |
| title_full_unstemmed | Beyond factor analysis: Multidimensionality and the Parkinson’s Disease Sleep Scale-Revised |
| title_short | Beyond factor analysis: Multidimensionality and the Parkinson’s Disease Sleep Scale-Revised |
| title_sort | beyond factor analysis: multidimensionality and the parkinson’s disease sleep scale-revised |
| url | http://hdl.handle.net/20.500.11937/66994 |