@article {12665,
	title = {Linear linking for related traits (LLRT): A novel method for the harmonization of cognitive domains with no or few common items.},
	journal = {Methods},
	volume = {204},
	year = {2022},
	pages = {179-188},
	abstract = {<p>Harmonization means to make data comparable. Recent efforts to generate comparable data on cognitive performance of older adults from many different countries around the world have presented challenges for direct comparison. Neuropsychological instruments vary in many respects, including language, administration techniques and cultural differences, which all present important obstacles to assumptions regarding the presence of linking items. Item response theory (IRT) methods have been previously used to harmonize cross-national data on cognition, but these methods rely on linking items to establish the shared metric. We introduce an alternative approach for linking cognitive performance across two (or more) groups when the fielded assessments contain no items that can be reasonably considered linking items: Linear Linking for Related Traits (LLRT). We demonstrate this methodological approach in a sample from a single United States study split by educational attainment, and in two sets of cross-national comparisons (United States to England, and United States to India). All data were collected as part of the Harmonized Cognitive Assessment Protocol (HCAP) and are publicly available. Our method relies upon strong assumptions, and we offer suggestions for how the method can be extended to relax those assumptions in future work.</p>},
	keywords = {Cognition, HCAP, Research Design},
	issn = {1095-9130},
	doi = {10.1016/j.ymeth.2021.11.011},
	author = {Nichols, Emma L and Cadar, Dorina and Lee, Jinkook and Richard N Jones and Gross, Alden L}
}
