OBJECTIVE: The NIH Toolbox for Assessment of Neurological and Behavioral Function (NIH Toolbox) is a comprehensive battery of brief assessment tools. The purpose of this article is to describe plans to establish normative reference values for the NIH Toolbox measures. METHODS: A large sample will be obtained from the US population for the purpose of calculating normative values. The sample will be stratified by age (ages 3-85 years), sex, and language preference (English or Spanish) and have a total sample size of at least 4,205. The sample will include a minimum of 25-100 individuals in each targeted demographic and language subgroup. RESULTS: Norming methods will include poststratification adjustment calculated using iterative proportional fitting, also known as raking, so that the weighted sample will have the same distribution on key demographic variables as the US population described in the 2010 Census. CONCLUSIONS: As with any set of norms, users should be mindful of the reference population and make conclusions consistent with the limitations of normative sampling, since it is not a probability-based sample. However, the NIH Toolbox norming study has been designed to minimize bias and maximize representativeness and precision of estimates. The availability of a "toolbox" of normed measures will be an important foundation for addressing critical research questions in neurologic and behavioral health.
OBJECTIVE: The NIH Toolbox for Assessment of Neurological and Behavioral Function (NIH Toolbox) is a comprehensive battery of brief assessment tools. The purpose of this article is to describe plans to establish normative reference values for the NIH Toolbox measures. METHODS: A large sample will be obtained from the US population for the purpose of calculating normative values. The sample will be stratified by age (ages 3-85 years), sex, and language preference (English or Spanish) and have a total sample size of at least 4,205. The sample will include a minimum of 25-100 individuals in each targeted demographic and language subgroup. RESULTS: Norming methods will include poststratification adjustment calculated using iterative proportional fitting, also known as raking, so that the weighted sample will have the same distribution on key demographic variables as the US population described in the 2010 Census. CONCLUSIONS: As with any set of norms, users should be mindful of the reference population and make conclusions consistent with the limitations of normative sampling, since it is not a probability-based sample. However, the NIH Toolbox norming study has been designed to minimize bias and maximize representativeness and precision of estimates. The availability of a "toolbox" of normed measures will be an important foundation for addressing critical research questions in neurologic and behavioral health.
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