The effect of missing data and imputation on the detection of bias in cognitive testing using differential item functioning methods.

BACKGROUND: Item response theory (IRT) methods for addressing differential item functioning (DIF) can detect group differences in responses to individual items (e.g., bias). IRT and DIF-detection methods have been used increasingly often to identify bias in cognitive test performance by characteristics (DIF grouping variables) such as hearing impairment, race, and educational attainment. Previous analyses have not considered the effect of missing data on inferences, although levels of missing cognitive data can be substantial in epidemiologic studies. METHODS: We used data from Visit 6 (2016-2017) of the Atherosclerosis Risk in Communities Neurocognitive Study (N = 3,580) to explicate the effect of artificially imposed missing data patterns and imputation on DIF detection. RESULTS: When missing data was imposed among individuals in a specific DIF group but was unrelated to cognitive test performance, there was no systematic error. However, when missing data was related to cognitive test performance and DIF group membership, there was systematic error in DIF detection. Given this missing data pattern, the median DIF detection error associated with 10%, 30%, and 50% missingness was -0.03, -0.08, and -0.14 standard deviation (SD) units without imputation, but this decreased to -0.02, -0.04, and -0.08 SD units with multiple imputation. CONCLUSIONS: Incorrect inferences in DIF testing have downstream consequences for the use of cognitive tests in research. It is therefore crucial to consider the effect and reasons behind missing data when evaluating bias in cognitive testing.

Assessing Bias in Cognitive Testing for Older Adults with Sensory Impairment: An Analysis of Differential Item Functioning in the Baltimore Longitudinal Study on Aging (BLSA) and the Atherosclerosis Risk in Communities Neurocognitive Study (ARIC-NCS).

OBJECTIVES: Vision and hearing impairments affect 55% of people aged 60+ years and are associated with lower cognitive test performance; however, tests rely on vision, hearing, or both. We hypothesized that scores on tests that depend on vision or hearing are different among those with vision or hearing impairments, respectively, controlling for underlying cognition. METHODS: Leveraging cross-sectional data from the Baltimore Longitudinal Study of Aging (BLSA) and the Atherosclerosis Risk in Communities Neurocognitive Study (ARIC-NCS), we used item response theory to test for differential item functioning (DIF) by vision impairment (better eye presenting visual acuity worse than 20/40) and hearing impairment (better ear .5-4 kHz pure-tone average > 25 decibels). RESULTS: We identified DIF by vision impairment for tests whose administrations do not rely on vision [e.g., Delayed Word Recall both in ARIC-NCS: .50 logit difference between impaired and unimpaired (p = .04) and in BLSA: .62 logits (p = .02)] and DIF by hearing impairment for tests whose administrations do not rely on hearing [Digit Symbol Substitution test in BLSA: 1.25 logits (p = .001) and Incidental Learning test in ARIC-NCS: .35 logits (p = .001)]. However, no individuals had differences between unadjusted and DIF-adjusted measures of greater than the standard error of measurement. CONCLUSIONS: DIF by sensory impairment in cognitive tests was independent of administration characteristics, which could indicate that elevated cognitive load among persons with sensory impairment plays a larger role in test performance than previously acknowledged. While these results were unexpected, neither of these samples are nationally representative and each has unique selection factors; thus, replication is critical.