Best Practices and Methodological Strategies for Addressing Generalizability in Neuropsychological Assessment

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TL;DR

This narrative review argues that generalizability remains a critical threat to neuropsychological assessment, particularly for pediatric populations, due to over-reliance on WEIRD (Western, Educated, Industrialized, Rich, Democratic) samples. The authors synthesize historical harms (e.g., eugenics movements fueled by early intelligence testing) and contemporary evidence (e.g., White participants making up 75% or more of RCT samples) to demonstrate how a lack of generalizability leads to diagnostic errors and compounded stigmatization for underrepresented groups. They provide concrete, multipronged recommendations for researchers and clinicians across research design, analytic approaches, and clinical application to advance generalizability and cultural competency in pediatric neuropsychology.

Full summary

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**Background:** Despite increasing institutional pushes for demographic consideration (e.g., NIH 2022 requirements to disaggregate effects by race/ethnicity and gender), generalizability remains a contemporary threat to neuropsychological assessment validity. The American Academy of Clinical Neuropsychology's (AACN) Relevance 2050 Initiative estimates that 60% of the American population will be 'un-testable' by 2050 using current assessment strategies. The authors define generalizability as the extent to which research outcomes with certain samples apply to the general human population or across multiple populations and contexts. They situate this issue within historical harms—notably, early intelligence testing (Binet, Goddard) was used to support eugenics, including compulsory sterilization laws in Virginia (1924) and North Carolina (into the 1970s). This history has created lasting medical mistrust: people of color are less willing than European Americans to participate in developmental research due to mistrust.

**Methods:** This is a narrative review synthesizing literature on generalizability in psychology and neuropsychology, with a focus on pediatric assessment. The authors reviewed historical considerations, contemporary evidence of demographic reporting gaps, and methodological approaches to testing generalizability. They propose a framework organized around three domains: research design, analytic approaches, and evaluation/application of findings. The review draws on prior work including Henrich et al. (2010) on WEIRD samples, Cheon et al. (2020) on title-level demographic reporting in 5000 published articles, and AACN (2021) position statements on race norming.

**Key Results:** The authors report that recent RCTs indicate White participants make up to 75% or more of reported samples (De Jesús-Romero et al., 2022, Preprint). They note that only 7.5% of 1200 articles using the Beck Depression Inventory (BDI) reported sample-specific reliability information (Yin & Fan, 2000), and that BDI reliability was substantially lower among participants who abused substances than among non-clinical participants. The review highlights that measurement invariance testing is rarely reported: Avila et al. (2020) found full invariance across ethnicity/race and sex/gender for most neuropsychological tests in a sample of over 6000 participants, except for language domain tests. The Test of Memory Malingering (TOMM), used by 75–78% of North American neuropsychologists, was validated in samples that failed to report ethnicity-related information, though subsequent cross-cultural validation has occurred in Romanian, Singaporean, and Colombian samples. In a Colombian sample, participants lacking formal education scored significantly lower on TOMM Trial 1 than those with 12+ years of education. The authors also note that 93% of a pediatric sample (ages 4–7) reached adult-comparable TOMM performance by Trial 2.

**Clinical Implications:** The authors argue that a lack of generalizability operates as both a threat to understanding humankind (constraining knowledge of psychological phenomena) and a practical threat (depriving underrepresented populations of valid, reliable assessment). They recommend against race norming, citing the AACN (2021) position that race norming erases diverse drivers of disparities and reinforces myths of innate biological differences—exemplified by the NFL case where race norms for dementia systematically placed Black players at a disadvantage for compensation. Instead, they advocate for: (1) increasing sample and researcher diversity through team science (e.g., ManyBabies Consortium), community-based participatory research (CBPR), and remote platforms (e.g., Lookit, TheChildLab); (2) measuring and reporting minimum sample demographics following APA guidelines, including in abstracts and constraints-on-generalizability sections; (3) testing for effects across informative demographics using measurement invariance testing and reporting effect sizes alongside p-values; (4) evaluating psychometric properties (reliability) at the sample level rather than presuming from prior work; (5) incentivizing replicability studies and cross-cultural work; (6) considering historical reasons for treatment-seeking gaps and patient mistrust; and (7) evaluating sample demographics alongside psychometrics before using assessments for diagnosis. For linguistically diverse children, they recommend using translated tests (e.g., WISC-V Spanish), interpreters, and bilingual psychometrists. The authors emphasize that generalizability is not a static endpoint but a continuous process that must evolve with society.