Differential item functioning in the cognitive screener used in the Longitudinal Aging Study in India

Goel, Ashish; Gross, Alden L.

doi:10.1017/s1041610218001746

Cited by 16 publications

(12 citation statements)

References 31 publications

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“…Three of these nine items showed DIF of greater than negligible magnitude, all items from the MMSE: orientation to year, orientation to state, and reading and following a command. We next evaluated whether these items with DIF had a meaningful impact on the factor scores by examining whether the difference between DIF-adjusted scores and non-DIF adjusted scores exceeded a threshold of 0.3 SD units (Goel & Gross, 2019). We found no evidence of meaningful (salient) DIF on the factor score generated from this confident linking item set (Figure 2, Panel A)…”

Section: Resultsmentioning

confidence: 99%

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A cultural neuropsychological approach to harmonization of cognitive data across culturally and linguistically diverse older adult populations.

Briceño¹,

Rentería²,

Gross³

et al. 2023

Neuropsychology

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Objective: To describe a cultural neuropsychological approach to prestatistical harmonization of cognitive data across the United States (U.S.) and Mexico with the Harmonized Cognitive Assessment Protocol (HCAP). Method: We performed a comprehensive review of the administration, scoring, and coding procedures for each cognitive test item administered across the English and Spanish versions of the HCAP in the Health and Retirement Study (HRS) in the U.S. and the Ancillary Study on Cognitive Aging in Mexico (Mex-Cog). For items that were potentially equivalent across studies, we compared each cognitive test item for linguistic and cultural equivalence and classified items as confident or tentative linking items, based on the degree of confidence in their comparability across cohorts and language groups. We evaluated these classifications using differential item functioning techniques. Results: We evaluated 132 test items among 21 cognitive instruments in the HCAP across the HRS and Mex-Cog. We identified 72 confident linking items, 46 tentative linking items, and 14 items that were not comparable across cohorts. Measurement invariance analysis revealed that 64% of the confident linking items and 83% of the tentative linking items showed statistical evidence of measurement differences across cohorts. Conclusions: Prestatistical harmonization of cognitive data, performed by a multidisciplinary and multilingual team including cultural neuropsychologists, can identify differences in cognitive construct measurement across languages and cultures that may not be identified by statistical procedures alone.

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Section: Resultsmentioning

confidence: 99%

“…This analysis step looks for evidence of salient DIF by computing the difference between non-DIF adjusted scores and DIF-adjusted scores. We calculate the proportion of participants with scores that differ by more than 0.3 SD units across the two sets of estimates (Goel & Gross, 2019).…”

Section: Statistical Procedures To Detect Differential Item Functioni...mentioning

confidence: 99%

A cultural neuropsychological approach to harmonization of cognitive data across culturally and linguistically diverse older adult populations.

Briceño¹,

Rentería²,

Gross³

et al. 2023

Neuropsychology

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“…LASI is an ongoing nationally representative survey on the health, economic, and social well-being of over 70,000 adults aged 45 years and over in 30 States and 6 Union Territories of India; the first wave of data collection was initiated in 2017 and completed in 2019 [4]. LASI is modeled after comparable studies in other countries, including the HRS [14,15]. LASI-DAD builds on LASI's initial cognitive assessment with a more detailed cognitive evaluation, including informant interviews.…”

Section: Plos Onementioning

confidence: 99%

Cross-national harmonization of cognitive measures across HRS HCAP (USA) and LASI-DAD (India)

et al. 2022

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Background As global populations age, cross-national comparisons of cognitive health and dementia risk are increasingly valuable. It remains unclear, however, whether country-level differences in cognitive function are attributable to population differences or bias due to incommensurate measurement. To demonstrate an effective method for cross-national comparison studies, we aimed to statistically harmonize measures of episodic memory and language function across two population-based cohorts of older adults in the United States (HRS HCAP) and India (LASI-DAD). Methods Data for 3,496 HRS HCAP (≥65 years) and 3,152 LASI-DAD (≥60 years) participants were statistically harmonized for episodic memory and language performance using confirmatory factor analysis (CFA) methods. Episodic memory and language factor variables were investigated for differential item functioning (DIF) and precision. Results CFA models estimating episodic memory and language domains based on a priori adjudication of comparable items fit the data well. DIF analyses revealed that four out of ten episodic memory items and five out of twelve language items measured the underlying construct comparably across samples. DIF-modified episodic memory and language factor scores showed comparable patterns of precision across the range of the latent trait for each sample. Conclusions Harmonization of cognitive measures will facilitate future investigation of cross-national differences in cognitive performance and differential effects of risk factors, policies, and treatments, reducing study-level measurement and administrative influences. As international aging studies become more widely available, advanced statistical methods such as those described in this study will become increasingly central to making universal generalizations and drawing valid conclusions about cognitive aging of the global population.

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“…As the IA approach is implemented in the popular Mplus software since its inclusion in Version 7.1 (May 2013; see [76]), it was already employed in a broad range of applications [40,75,[77][78][79][80][81][82][83][84][85][86][87][88][89][90][91][92][93][94][95]. The applications are most frequently found in the disciplines of education science, political science, psychology, and sociology.…”

Section: Previous Simulation Studies and Applicationsmentioning

confidence: 99%

Lp Loss Functions in Invariance Alignment and Haberman Linking with Few or Many Groups

Robitzsch

2020

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The comparison of group means in latent variable models plays a vital role in empirical research in the social sciences. The present article discusses an extension of invariance alignment and Haberman linking by choosing the robust power loss function ρ(x)=|x|p(p>0). This power loss function with power values p smaller than one is particularly suited for item responses that are generated under partial invariance. For a general class of linking functions, asymptotic normality of estimates is shown. Moreover, the theory of M-estimation is applied for obtaining linking errors (i.e., inference with respect to a population of items) for this class of linking functions. In a simulation study, it is shown that invariance alignment and Haberman linking have comparable performance, and in some conditions, the newly proposed robust Haberman linking outperforms invariance alignment. In three examples, the influence of the choice of a particular linking function on the estimation of group means is demonstrated. It is concluded that the choice of the loss function in linking is related to structural assumptions about the pattern of noninvariance in item parameters.

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Differential item functioning in the cognitive screener used in the Longitudinal Aging Study in India

Cited by 16 publications

References 31 publications

A cultural neuropsychological approach to harmonization of cognitive data across culturally and linguistically diverse older adult populations.

A cultural neuropsychological approach to harmonization of cognitive data across culturally and linguistically diverse older adult populations.

Cross-national harmonization of cognitive measures across HRS HCAP (USA) and LASI-DAD (India)

Lp Loss Functions in Invariance Alignment and Haberman Linking with Few or Many Groups

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