abstract
- 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.