Cross-classified Random Effects Research Articles

How well do workplace-based assessments support summative entrustment decisions? A multi-institutional generalisability study.

Assessment of the Core Entrustable Professional Activities for Entering Residency requires direct observation through workplace-based assessments (WBAs). Single-institution studies have demonstrated mixed findings regarding the reliability of WBAs developed to measure student progression towards entrustment. Factors such as faculty development, rater engagement and scale selection have been suggested to improve reliability. The purpose of this investigation was to conduct a multi-institutional generalisability study to determine the influence of specific factors on reliability of WBAs. The authors analysed WBA data obtained for clerkship-level students across seven institutions from 2018 to 2020. Institutions implemented a variety of strategies including selection of designated assessors, altered scales and different EPAs. Data were aggregated by these factors. Generalisability theory was then used to examine the internal structure validity evidence of the data. An unbalanced cross-classified random-effects model was used to decompose variance components. A phi coefficient of >0.7 was used as threshold for acceptable reliability. Data from 53 565 WBAs were analysed, and a total of 77 generalisability studies were performed. Most data came from EPAs 1 (n = 17 118, 32%) 2 (n = 10 237, 19.1%), and 6 (n = 6000, 18.5%). Low variance attributed to the learner (<10%) was found for most (59/77, 76%) analyses, resulting in a relatively large number of observations required for reasonable reliability (range = 3 to >560, median = 60). Factors such as DA, scale or EPA were not consistently associated with improved reliability. The results from this study describe relatively low reliability in the WBAs obtained across seven sites. Generalisability for these instruments may be less dependent on factors such as faculty development, rater engagement or scale selection. When used for formative feedback, data from these instruments may be useful. However, such instruments do not consistently provide reasonable reliability to justify their use in high-stakes summative entrustment decisions.

Exploring Disparities for Obesity in Korea Using Hierarchical Age-Period-Cohort Analysis With Cross-Classified Random Effect Models.

This research article investigates the age, period, and birth cohort effects on prevalence of obesity in the Korean population, with the goal of identifying key factors to inform effective public health strategies. We analyzed data from the Korea National Health and Nutrition Examination Survey, spanning 2007-2021, including 35,736 men and 46,756 women. Using the hierarchical age-period-cohort (APC) analysis with cross-classified random effects modeling, we applied multivariable mixed logistic regression to estimate the marginal prevalence of obesity across age, period, and birth cohort, while assessing the interaction between APC and lifestyle and socioeconomic factors. Our findings reveal an inverted U-shaped age effect on obesity, influenced by smoking history (P for interaction = 0.020) and physical activity (I for interaction < 0.001). The period effect was positive in 2020 and 2021, while negative in 2014 (P for period effect < 0.001). A declining trend in obesity prevalence was observed in birth cohorts from 1980s onward. Notably, disparities in obesity rates among recent birth cohorts have increased in relation to smoking history (P for interaction = 0.020), physical activity (P for interaction < 0.001), and residence (P for interaction = 0.005). Particularly, those born after 1960 were more likely to be obese if they were ex-smokers, physical inactive, or lived in rural areas. These findings highlight growing disparities in obesity within birth cohorts, underscoring the need for targeted health policies that promote smoking cessation and physical activity, especially in rural areas.

Approaches to Item-Level Data with Cross-Classified Structure: An Illustration with Student Evaluation of Teaching

Student evaluation of teaching (SET) questionnaires are ubiquitously applied in higher education institutions in North America for both formative and summative purposes. Data collected from SET questionnaires are usually item-level data with cross-classified structure, which are characterized by multivariate categorical outcomes (i.e., multiple Likert-type items in the questionnaires) and cross-classified structure (i.e., non-nested students and instructors). Recently, a new approach, namely the cross-classified IRT model, was proposed for appropriately handling SET data. To inform researchers in higher education, in this article, the cross-classified IRT model, along with three existing approaches applied in SET studies, including the cross-classified random effects model (CCREM), the multilevel item response theory (MLIRT) model, and a two-step integrated strategy, was reviewed. The strengths and weaknesses of each of the four approaches were also discussed. Additionally, the new and existing approaches were compared through an empirical data analysis and a preliminary simulation study. This article concluded by providing general suggestions to researchers for analyzing SET data and discussing limitations and future research directions.

The Association of ACGME Milestones With Performance on American Board of Surgery Assessments: A National Investigation of Surgical Trainees.

To determine the relationship between, and predictive utility of, milestone ratings and subsequent American Board of Surgery (ABS) vascular surgery in-training examination (VSITE), vascular qualifying examination (VQE), and vascular certifying examination (VCE) performance in a national cohort of vascular surgery trainees. Specialty board certification is an important indicator of physician competence. However, predicting future board certification examination performance during training continues to be challenging. This is a national longitudinal cohort study examining relational and predictive associations between Accreditation Council for Graduate Medical Education (ACGME) Milestone ratings and performance on VSITE, VQE, and VCE for all vascular surgery trainees from 2015 to 2021. Predictive associations between milestone ratings and VSITE were conducted using cross-classified random-effects regression. Cross-classified random-effects logistic regression was used to identify predictive associations between milestone ratings and VQE and VCE. Milestone ratings were obtained for all residents and fellows(n=1,118) from 164 programs during the study period (from July 2015 to June 2021), including 145,959 total trainee assessments. Medical knowledge (MK) and patient care (PC) milestone ratings were strongly predictive of VSITE performance across all postgraduate years (PGYs) of training, with MK ratings demonstrating a slightly stronger predictive association overall (MK coefficient 17.26 to 35.76, β = 0.15 to 0.23). All core competency ratings were predictive of VSITE performance in PGYs 4 and 5. PGY 5 MK was highly predictive of VQE performance [OR 4.73, (95% CI, 3.87-5.78), P <0.001]. PC subcompetencies were also highly predictive of VQE performance in the final year of training [OR 4.14, (95% CI, 3.17-5.41), P <0.001]. All other competencies were also significantly predictive of first-attempt VQE pass with ORs of 1.53 and higher. PGY 4 ICS ratings [OR 4.0, (95% CI, 3.06-5.21), P <0.001] emerged as the strongest predictor of VCE first-attempt pass. Again, all subcompetency ratings remained significant predictors of first-attempt pass on CE with ORs of 1.48 and higher. ACGME Milestone ratings are highly predictive of future VSITE performance, and first-attempt pass achievement on VQE and VCE in a national cohort of surgical trainees.

Competitive peer influence on knowledge contribution behaviors in online Q&amp;A communities: a social comparison perspective

PurposeUsers' knowledge contribution behaviors are critical for online Q&amp;A communities to thrive. Well-organized question threads in online Q&amp;A communities enable users to clearly read existing answers and their evaluations before contributing. Based on the social comparison and peer influence literature, the authors examine peer influence on the informativeness of knowledge contributions in competitive settings. The authors also consider three levels of moderating factors concerning individuals' perception of competitiveness: question level, thread level and contributor level.Design/methodology/approachThe authors collected data from one of the largest online Q&amp;A communities in China. The hypotheses were validated using hierarchical linear models with cross-classified random effects. The generalized propensity score weighting method was employed for the robustness check.FindingsThe authors demonstrate the peer influence due to social comparison concerns among knowledge contribution behaviors in the same question thread. If more prior knowledge contributors choose to contribute long answers in the question thread, the subsequent contributions are more informative. This peer influence is stronger for factual questions and questions with higher popularity of answering but weaker in recommendation-type and well-answered questions and for contributors with higher social status.Originality/valueThis research provides a new cue of peer influence on online UGC contributions in competitive settings initiated by social comparison concerns. Additionally, the authors identify three levels of moderating factors (question level, thread level and contributor level) that are specific to online Q&amp;A settings and are related to a contributor's perception of competitiveness, which affect the direct effect of peer influence on knowledge contributions. Rather than focus on motivation and quality evaluation, the authors concentrate on the specific content of online knowledge contributions. Peer influence here is not based on an actual acquaintance or a following relationship but on answering the same question. The authors also illustrate the competitive peer influence in subjective and personalized behaviors in online UGC communities.

Patterns of change in the justifiability of euthanasia across OECD countries

IntroductionThe public's justifiability of euthanasia has increased as more countries have adopted laws permitting a range of end-of-life practices. Despite this trend, there is a dearth of longitudinal and comparative studies investigating attitudes toward euthanasia. Consequently, it remains unclear whether this rise in justifiability is a period-specific trend or generational change.MethodsWe analyzed data from the European and World Values Survey from 1981 to 2021 to examine period variations, between-cohort differences, and within-cohort changes across 35 affluent countries. This analysis was conducted using dynamic comparative multilevel regression and a comparative version of the cross-classified random effects regressions.ResultsOur descriptive results supported our hypotheses, indicating an increase in euthanasia's justifiability in virtually all surveyed countries, with both overall and within-cohort changes gravitating toward higher degrees of justifiability. Furthermore, newer periods and younger cohorts were found to be more permissive than their older counterparts. These consistent increases in the justifiability of euthanasia were verified by the multilevel models.DiscussionOur results were in line with modernization theory, observing a gradual change in attitudes between cohorts due to generational replacement. However, we also identified intra-cohort changes related to the processes of human development across various countries. Some robustness checks produced ambiguous results in distinguishing period and cohort effects, yet the combination of these components aligns with substantive theory.ConclusionOur findings indicate a more complex pattern of change than predicted by the impressionable years model, a leading approach in political socialization research. This study contributes significantly to our understanding of evolving attitudes toward euthanasia, bridging the gap in longitudinal and comparative studies on the subject.

Comparing random effects models, ordinary least squares, or fixed effects with cluster robust standard errors for cross-classified data.

Cross-classified random effects modeling (CCREM) is a common approach for analyzing cross-classified data in psychology, education research, and other fields. However, when the focus of a study is on the regression coefficients at Level 1 rather than on the random effects, ordinary least squares regression with cluster robust variance estimators (OLS-CRVE) or fixed effects regression with CRVE (FE-CRVE) could be appropriate approaches. These alternative methods are potentially advantageous because they rely on weaker assumptions than those required by CCREM. We conducted a Monte Carlo Simulation study to compare the performance of CCREM, OLS-CRVE, and FE-CRVE in models, including conditions where homoscedasticity assumptions and exogeneity assumptions held and conditions where they were violated, as well as conditions with unmodeled random slopes. We found that CCREM out-performed the alternative approaches when its assumptions are all met. However, when homoscedasticity assumptions are violated, OLS-CRVE and FE-CRVE provided similar or better performance than CCREM. When the exogeneity assumption is violated, only FE-CRVE provided adequate performance. Further, OLS-CRVE and FE-CRVE provided more accurate inferences than CCREM in the presence of unmodeled random slopes. Thus, we recommend two-way FE-CRVE as a good alternative to CCREM, particularly if the homoscedasticity or exogeneity assumptions of the CCREM might be in doubt. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Propensity Score Matching with Cross-Classified Data Structures: A Comparison of Methods

In the current study, we compare propensity score (PS) matching methods for data with a cross-classified structure, where each individual is clustered within more than one group, but the groups are not hierarchically organized. Through a Monte Carlo simulation study, we compared sequential cluster matching (SCM), preferential within cluster matching (PWCM), greedy matching (GM), and optimal full matching (OFM), using propensity scores from four different models. The results indicated that the four matching methods performed well when PSs were estimated with logistic regression containing both level-1 and level-2 covariates. When the level-2 covariates were omitted in the logistic regression PS model, matching methods resulted in biased treatment effect estimates. However, omission of level-2 covariates did not result in biased estimates when the PS model was a logistic cross-classified random effects model (CCREM). SCM and PWCM outperformed GM and OFM with a logistic CCREM that included level-1 and level-2 covariates.

The salience of social context, opioid antagonist use, and prior opioid exposure as determinants of fatal and non-fatal opioid overdoses.

This study examines the salience of social context for opioid overdoses in Boston from 2014 to 2019. Longitudinal negative binomial models with random effects indicated that higher levels of concentrated disadvantage, residential instability, and illicit drug activity increased annual block group counts of opioid overdoses. Logistic hierarchical and cross-classified random effects models indicated that the use of Narcan and greater exposure to drugs through previous opioid overdose and contextual lillicit drug crime activity reduced the odds of fatal opioid overdose relative to non-fatal opioid overdose. The findings suggest that the accurate tracking of both fatal and non-fatal overdoses, and a consideration of the broader social context, can facilitate effective public health resource allocation to reduce opioid overdoses.

Applying the Multilevel Approach in Estimation of Income Population Differences

Income inequality remains one of the most burning issues discussed in the world. The difficulty of the problem arises from its multiple manifestations at regional and local levels and unique patterns within countries. This paper employs a multilevel approach to identify factors that influence income and wage inequalities at regional and municipal scales in Russia. We carried out the study on data from 2017 municipalities of 75 Russian regions from 2015 to 2019. A Hierarchical Linear Model with Cross-Classified Random Effects (HLMHCM) allowed us to establish that most of the total variances in population income and average wages accounted for the regional scale. Our analysis revealed different variances of income per capita and average wage; we disclosed the reasons for these disparities. We also found a mixed relationship between income inequality and social transfers. These variables influence income growth but change the relationship between income and labour productivity. Our study underlined that the impacts of shares of employees in agriculture and manufacturing should be considered together with labour productivity in these industries.

Modern applications of cross-classified random effects models in social and behavioral research: Illustration with R package PLmixed.

Cross-classified random effects models (CCREMs) have been developed for appropriately analyzing data with a cross-classified structure. Despite its flexibility and the prevalence of cross-classified data in social and behavioral research, CCREMs have been under-utilized in applied research. In this article, we present CCREMs as a general and flexible modeling framework, and present a wide range of existing models designed for different purposes as special instances of CCREMs. We also introduce several less well-known applications of CCREMs. The flexibility of CCREMs allows these models to be easily extended to address substantive questions. We use the free R package PLmixed to illustrate the estimation of these models, and show how the general language of the CCREM framework can be translated into specific modeling contexts.

Multiple imputation approaches for handling incomplete three-level data with time-varying cluster-memberships.

Three‐level data arising from repeated measures on individuals clustered within higher‐level units are common in medical research. A complexity arises when individuals change clusters over time, resulting in a cross‐classified data structure. Missing values in these studies are commonly handled via multiple imputation (MI). If the three‐level, cross‐classified structure is modeled in the analysis, it also needs to be accommodated in the imputation model to ensure valid results. While incomplete three‐level data can be handled using various approaches within MI, the performance of these in the cross‐classified data setting remains unclear. We conducted simulations under a range of scenarios to compare these approaches in the context of an acute‐effects cross‐classified random effects substantive model, which models the time‐varying cluster membership via simple additive random effects. The simulation study was based on a case study in a longitudinal cohort of students clustered within schools. We evaluated methods that ignore the time‐varying cluster memberships by taking the first or most common cluster for each individual; pragmatic extensions of single‐ and two‐level MI approaches within the joint modeling (JM) and the fully conditional specification (FCS) frameworks, using dummy indicators (DI) and/or imputing repeated measures in wide format to account for the cross‐classified structure; and a three‐level FCS MI approach developed specifically for cross‐classified data. Results indicated that the FCS implementations performed well in terms of bias and precision while JM approaches performed poorly. Under both frameworks approaches using the DI extension should be used with caution in the presence of sparse data.

Assessing the effects of student perceptions of instructional quality: A cross-subject within-student design

This study examines associations between student-perceived instructional quality and a student’s motivation and achievement using unique data from Germany in which 9th-grade students reported on instructional quality in their English and German classes in the same academic year. Student outcomes, including self-efficacy, course grades, and standardized achievement were measured in both subjects. We utilized a cross-classified random effects model to decompose the variance in student reports of teacher support and monitoring, finding that 17% and 16%, respectively, of the variation in ratings was due to the student reporter with 26% and 27%, respectively, due to the teacher being rated. Students and teachers were further nested within classes of students who stayed together across the two subjects; however, almost no variance was attributable to class groupings. Student-reported teacher support was positively associated with achievement gains and self-efficacy at the within-student, between-student, and between-teacher levels whereas monitoring was inconsistently associated with outcomes. Standardized betas of perceived teacher support on test score gains were small (<0.10), small-to-medium on course grade gains (0.16–0.24), and medium to large on self-efficacy (0.33–0.52). Measuring the same students with different teachers allows us to disentangle dyadic student-teacher variance from student rater variance in student perceptions and outcomes and facilitates the use of powerful within-student analytical techniques to quantify the effects of educational environments on student learning and self-efficacy.

Estimating the Importance of Families in Modeling Educational Achievement Using Linked Swedish Administrative Data

While the family is a critical determinant of educational achievement, methodological difficulties and the availability of data limit estimation of the family contribution in school effectiveness models. This study uses multilevel modeling to estimate the proportion of variation in student educational achievement between families, family-level intraclass correlation coefficients, and specific family structure effects (family size, birth order, birth spacing, sibling sex ratio). We use cross-classified random effects to account for school and neighborhood variation. We analyze Swedish administrative education records linked with birth records for four academic cohorts of students, with siblings identified from a wider pool of 21 cohorts. We show that almost half of the variation in student achievement described as “between students” in traditional school effectiveness studies would be better described as variation “between families,” suggesting effectiveness research might give greater consideration to family-based interventions in tandem with existing student- and school-based approaches to raising low achievement.

Socioeconomic Status and Changes in Iranian Household Food Basket Using National Household Budget and Expenditure Survey Data, 1991-2017

Background:Socioeconomic factors are major determinants of health and food consumption. The aim of the present study was assessing the changes in energy and different food groups consumption related to the socioeconomic status of Iranian households.Methods:In this repeated cross-sectional study, 717432 households data from the Households Income and Expenditure Survey (HIES) conducted yearly by the Statistical Centre of Iran (SCI) from 1991 to 2017 was used. The food consumption outcome was modeled as a function of household head age, household head age-squared, socioeconomic variables, household size, place of residence, and household‘s head sex. Principle Component Analysis (PCA) was used to extract a socioeconomic status variable based on the educational, occupational, and income status of households. A cross-classified random effects modeling (CCREM) specifications of Hierarchical Age-Period-Cohort (HAPC) models was used in the present study.Results:In the present study, by moving to the higher quartiles of SES had higher calorie intake increased. There was a positive relationship between “Vegetables”, “Fruits”, “Dairy”, “Meat, Poultry, Fish, Eggs, Legumes, and Nuts” and “Fats, Oils, Sugars, and Sweets” with socioeconomic statues category. In the case of “Bread, Cereal, Rice, and Pasta”, there was a reverse relationship, and lower SES quartile consumed a higher amount of this food group.Conclusion:Lower income, education, and occupational status in lower SES quartile groups lead to lower calorie consumption and higher consumption of “Bread, Cereal, Rice, and Pasta” food group, which had lower energy cost compared to the other food groups.

Trends in activities of daily living disability among Chinese older adults from 1998 to 2018: an age-period-cohort analysis.

This study aims to investigate the age, period, and cohort effects on trends in activities of daily living (ADL) disability among Chinese older adults; and to explore these three temporal effects on gender and residence disparities in disability. We utilized multiple cross-sectional waves of the Chinese Longitudinal Healthy Longevity Survey data (1998-2018), including 89,511 participants aged above 65years old. Our measurement of disability is the number of ADL items (dressing, bathing, indoor transferring, toileting, eating, and continence) participants can't perform independently. Hierarchical age-period-cohort cross-classified random effects models were conducted to investigate age, period and cohort trends in ADL disability. Results showed that ADL disability increased with age at an increasing rate. A V-shaped cohort trend and a fluctuated period trend were identified. Females and urban residents were associated with more ADL limitations. When age increased, the gender and residence gaps in disability further increased. The cohort-based gender and residence inequalities in ADL limitations converged with successive cohorts. The period-based residence gap in ADL limitations diverged throughout the 20-year period, while the corresponding period-based change in gender disparity was not significant. These findings suggested that age, period, and cohort had different and independent effects on ADL disability among Chinese older adults. The age effect on trends in ADL is stronger compared to period and cohort effects. The gender and residence disparities in disability increased with age and decreased with successive cohorts. These patterns might help inform healthcare planning and the priorities for medical resource allocation accordingly.

Economic Growth of Municipalities in Russia: Assessment of Unevenness in Time and Space

The unevenness of economic development is revealed at all levels of administrative-territorial administration and is an integral feature of the economic space as a whole. The relevance of the study is determined by the unresolved issues of ensuring long-term sustainable growth, determining the optimal level of unevenness that contributes to the effective growth of the entire economic system of the country. The purpose of the work is to identify the conditions for variation in the economic growth of Russian municipalities in space and time. The hypothesis of the study is that the uneven economic growth of municipalities is determined simultaneously by the contextual characteristics of the regions in which they are included, and the characteristics of individual time periods. The emphasis is on intergroup and intragroup differences determined by the determinants of time and the hierarchical relationship of administrative territorial units of management within a hierarchical linear model with cross-classified random effects. The study was conducted on the data of 2,016 municipalities in the context of 75 constituent entities of the Russian Federation in the period 2015-2019. The results of the study show that 68.4% of the total variation of municipalities in terms of the volume of shipped goods, works, services for 2015-2019. due to differences in municipalities, 18.9% - the characteristics of the constituent entities of the Russian Federation, 0.1% - the determinants of time. The new results of the study are the concretization of the influence of well-known growth factors in relation to individual levels of the territorial hierarchy, as well as the rationale for the inter-level interaction of factors. It is shown that the relationship between investment and production at the municipal level should be considered in conjunction with the share of the extractive industry, and the relationship between wages and production, taking into account the share of agriculture in the constituent entities of the Russian Federation. The results obtained indicate the presence of regional features, manifested in intermunicipal and interregional spatio-temporal variation, and emphasize the importance of both municipal and regional factors for ensuring the growth of the economy of municipalities. They can be used in the development of recommendations for improving state policy in the field of municipal and regional government.

Age, Period, and Cohort Effects on Literacy Skills across Life Stages

Literacy skills are essential adult competencies for economic, social, political and cultural participation, which are linked to higher quality of life. Literacy skills are known to be lower for older age groups. However, relatively little is known about cohort and period effects, which provide clues to the sociohistorical impacts on literacy, in addition to the well-known age effects, over the life course. This study analyzed three nationally representative cross-sectional survey data of the U.S. adults at five time points, from the 1994 International Adult Literacy Survey (IALS), 2003 Adult Literacy and Life Skills Survey (ALL), and the 2012/2014/2017 Program for International Assessment of Adult Competencies (PIAAC). The total analytic sample was 17,450 adults age between 18 and 65 years old. The literacy measures were re-scaled (0-500 points) to be comparable across the surveys. An age-period-cohort hierarchical linear model (i.e., cross-classified random effects model) was constructed using the Bayes estimator. Individuals were cross-classified based on 14 five-year birth cohort and 5 periods (survey years) information. Results showed that literacy skills improved [95% credibility-interval (CI) for linear effect of age = (0.31, 1.07), but the rate of improvement declined over time, faster rates of decline in later life stages [95% CI for quadratic effect of age = (-0.17, -0.09)]. Additionally, the notable variability across the cohorts and periods [95% CI variances = (5.34, 52.52) and (2.30, 172.01), respectively] were identified. Possible explanations for the identified age, period and cohort effects on literacy and implications for adult competencies in later life are evaluated.

Exploring the relations between child and word characteristics and preschoolers' word-learning

Preschool vocabulary interventions have reported modest increases in learning of target words, with wide variability among participants. To design interventions that are effective for all learners, more fine-grained information is needed. In the present study, English-only and dual language learner preschoolers (n = 128) were taught new words during shared book-reading. Using cross-classified random effects models, we investigated how both child and word characteristics related to vocabulary learning. Preschoolers were assessed on general receptive vocabulary (PPVT), executive function skills, and expressive knowledge of target words at pretest and posttest. Target words were rated on their perceptual accessibility, as captured by imageability, concreteness, and consistency of shape. Controlling for other predictors, target word pretest scores, PPVT, executive function skills, and words' imageability were all positive significant predictors of posttest vocabulary. Tailoring instructional strategies to better account for these factors may help to support the learning of a range of children and word types.

Age Patterns in Self-Reported Cognitive Impairment Among Older Latino Subgroups and Non-Latino Whites in the United States, 1997–2018: Implications for Public Health Policy

Background and ObjectivesU.S. Latinos are a heterogeneous population with unique characteristics related to individual-level socioeconomic and contextual factors based on nativity status and country of origin. Population aging and greater public awareness of dementia may contribute to an increasing prevalence of self-reported cognitive impairment. However, population-level trends in self-reported cognitive impairment among Latinos are unclear and it is unknown whether there are differences among Latino subgroups. Thus, this study aims to examine heterogeneity in self-reported cognitive impairment among older U.S. Latino subgroups.Research Design and MethodsWe used data from the 1997–2018 National Health Interview Survey to document age-specific patterns in self-reported cognitive impairment among U.S.-born Mexican, foreign-born Mexican, island-born Puerto Rican, foreign-born Cuban, and U.S.-born non-Latino Whites aged 60 and older. We estimated hierarchical age–period–cohort cross-classified random effects models (HAPC-CCREM) to isolate age patterns in self-reported cognitive impairment across disaggregated Latino subgroups and U.S.-born non-Latino Whites.ResultsThe overall prevalence of self-reported cognitive impairment increased from 6.0% in 1997 to 7.1% in 2018. This increase was evident among U.S.-born non-Latino Whites and U.S.-born and foreign-born Mexicans but not other Latino subgroups. Fully adjusted HAPC-CCREM estimates indicated that Latinos were more likely to self-report cognitive impairment than U.S-born non-Latino Whites (b = 0.371, p < .001). When disaggregated by Latino subgroup, the difference in the likelihood for self-reported cognitive impairment compared to U.S.-born non-Latino Whites was greatest for island-born Puerto Ricans (b = 0.598, p < .001) and smallest for foreign-born Cubans (b = 0.131, p > .05).Discussion and ImplicationsWe found evidence of considerable heterogeneity in the age patterns of self-reported cognitive impairment among U.S. Latino subgroups. We also detected large differences in the likelihood for self-reported cognitive impairment between U.S. Latino subgroups compared to U.S.-born non-Latino Whites. These results underscore the importance of differentiating between unique Latino subpopulations when studying population-level trends in cognitive function.