Resumen
Objective: Creation of normative data with regression corrections for demographic covariates reduces risk of small cell sizes compared with traditional normative approaches. We explored whether methods of correcting for demographic covariates (e.g., full regression models versus hybrid models with stratification and regression) and choice of covariates (i.e., correcting for age with or without sex and/or education correction) impacted reliability and validity of normative data. Method: Measurement invariance for sex and education was explored in a brief telephone-administered cognitive battery from the Canadian Longitudinal Study on Aging (CLSA; after excluding persons with neurological conditions N = 12,350 responded in English and N = 1,760 in French). Results: Measurement invariance was supported in hybrid normative models where different age-based regression models were created for groups based on sex and education level. Measurement invariance was not supported in full regression models where age, sex, and education were simultaneous predictors. Evidence for reliability was demonstrated by precision defined as the 95% inter-percentile range of the 5th percentile. Precision was higher for full regression models than for hybrid models but with negligible differences in precision for the larger English sample. Conclusions: We present normative data for a remotely administered brief neuropsychological battery that best mitigates measurement bias and are precise. In the smaller French speaking sample, only one model reduced measurement bias, but its estimates were less precise, underscoring the need for large sample sizes when creating normative data. The resulting normative data are appended in a syntax file.
Idioma original | English |
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Publicación | Clinical Neuropsychologist |
DOI | |
Estado | Accepted/In press - 2021 |
Nota bibliográfica
Funding Information:This research was made possible using the data collected by the Canadian Longitudinal Study on Aging (CLSA). Funding for the Canadian Longitudinal Study on Aging (CLSA) is provided by the Government of Canada through the Canadian Institutes of Health Research (CIHR) under grant reference: LSA 94473 and the Canada Foundation for Innovation. This research has been conducted using the CLSA dataset [Baseline Tracking Data set version 3.0], under Application Number [180001S]. The opinions expressed in this manuscript are the author’s own and do not reflect the views of the Canadian Longitudinal Study on Aging. The preparation of this manuscript was partially supported by funding provided by Alzheimer Society of Canada/Alzheimer Société de Canada and the Pacific Alzheimer Research Foundation. Support was provided for the second author (LG) by the McLaughlin Foundation Professorship in Population and Public Health. PR holds Tier 1 Canada Research Chair in Geroscience and Labarge Chair in Optimal Aging. We would like to acknowledge Dr. Todd Little for his StatsCamp training on MG-CFA. We would like to acknowledge one of MEO’s PhD students, Jake Ursenbach for his help creating part of the R code and Dr. Audrey J. Leroux, Ph.D., who checked this R code during R consulting at StatsCamp.
Publisher Copyright:
© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
ASJC Scopus Subject Areas
- Neuropsychology and Physiological Psychology
- Developmental and Educational Psychology
- Arts and Humanities (miscellaneous)
- Clinical Psychology
- Psychiatry and Mental health
PubMed: MeSH publication types
- Journal Article