The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
A Guide for Population-based Analysis of the Adolescent Brain Cognitive Development (ABCD) Study Baseline Data
Open accessAbstract
ABCD is a longitudinal, observational study of U.S. children, ages 9-10 at baseline, recruited at random from the household populations in defined catchment areas for each of 21 study sites. The 21 geographic locations that comprise the ABCD research sites are nationally distributed and generally represent the range of demographic and socio-economic diversity of the U.S. birth cohorts that comprise the ABCD study population. The clustering of participants and the potential for selection bias in study site selection and enrollment are features of the ABCD observational study design that are informative for statistical estimation and inference. Both multi-level modeling and robust survey design-based methods can be used to account for clustering of sampled ABCD children in the 21 study sites. Covariate controls in analytical models and propensity weighting methods that calibrate ABCD weighted distributions to nationally-representative controls from the American Community Survey (ACS) can be employed in analysis to account for known informative sample design features or to attenuate potential demographic and socio-economic selection bias in the national sampling and recruitment of eligible children. This guide will present results of an empirical investigation of the ABCD baseline data that compares the statistical efficiency of multi-level modeling and distribution-free design-based approaches—both weighted and unweighted--to analyses of the ABCD baseline data. Specific recommendations will be provided for researchers on robust, efficient approaches to both descriptive and multivariate analyses of the ABCD baseline data.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Data analyses were performed using Stata 18.0 (College Station, TX) and applied propensity weights based on the American Community Survey [104].”
From Screen time and mental health: a prospective analysis of the Adolescent Brain Cognitive Development (ABCD) Study (2024) · cites this paper as [104]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Austin, P. C., & Stuart, E. A. (2015) · Statistics in Medicine · doi:10.1002/sim.6607 · Cited by 4,495 · cited 2×
“…population estimation and inference, ABCD has used a second weighting method that is closely related to the inverse propensity score weighting methodology (IPSW) that is employed to reduce confounding and estimate average treatment effects (ATE) from observational data (Austin and Stuart, 2015).”
MethodMultiple imputation after 18+ years
Rubin, D. B. (1996) · Journal of the American Statistical Association · doi:10.1080/01621459.1996.10476908 · Cited by 4,072
Multiple Imputation After 18+ Years
Rubin, D. B. (1996) · Journal of the American Statistical Association · doi:10.2307/2291635 · Cited by 2,686 · cited 1×
“…of sample inclusion or differential nonresponse, some authors have suggested that the weight variable itself be included as a covariate in the model specification as further protection against sample selectivity that may not be captured in the observed covariate controls (Rubin, 1996).”
BackgroundRecruiting the ABCD sample: Design considerations and procedures
Garavan, H., Bartsch, H., Conway, K., Decastro, A., Goldstein, R. Z., Heeringa, S., Jernigan, T., Potter, A., Thompson, W., & Zahs, D. (2018) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2018.04.004 · Cited by 1,393
Self-citationComplex Surveys: A Guide to Analysis Using R
Lumley, T. (2010) · doi:10.1002/9780470580066 · Cited by 647
Multilevel Modelling of Complex Survey Data
Rabe‐Hesketh, S., & Skrondal, A. (2006) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2006.00426.x · Cited by 642 · cited 2×
“Weighted estimation of multi-level models requires a disaggregation and scaling of the individual propensity or population weights for each level of the model (Rabe-Hesketh and Skrondal, 2006).”
BackgroundGeneralizing Observational Study Results: Applying Propensity Score Methods to Complex Surveys
DuGoff, E. H., Schuler, M., & Stuart, E. A. (2014) · Health Services Research · doi:10.1111/1475-6773.12090 · Cited by 605
Complex Surveys: A Guide to Analysis Using R
Lumley, T. (2010) · Cited by 571
West, B. T., Welch, K. B., & Gałecki, A. T. (2014) · Chapman & Hall/CRC eBooks · doi:10.1201/b17198 · Cited by 485
Weighting for Unequal Selection Probabilities in Multilevel Models
Pfeffermann, D., Skinner, C. J., Holmes, D. J., Goldstein, H., & Rasbash, J. (1998) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/1467-9868.00106 · Cited by 438
Flagged first, then by citations
Research works printed but not linked: 1
- 10.1038/s411467-017-00908-7 · DOI
Cited by
Sorted by citations
Items 201–247 of 247
Dai, H. D., Puga, T. B., Zhang, J., & Benowitz, N. L. (2026) · Tobacco Induced Diseases · doi:10.18332/tid/216705 · Cited by 3
“All analyses followed the statistical guide for the population-based analysis of the ABCD study to account for the clustering of participants over 21 study sites, sample selection biases, and non-responsiveness in the observational study design 22 .”
Shchetinina, A., Haneuse, S., Chambers, C., Tiemeier, H. W., & Slopen, N. B. (2026) · Alcoholism Clinical and Experimental Research · doi:10.1111/acer.70266 · Cited by 1
Nagata, J. M., Wong, J. H., Helmer, C. K., Mehta, A. S., Gooding, H. C., Ganson, K. T., Testa, A. M., Baker, F. C., & Laraia, B. A. (2026) · Pediatric Research · doi:10.1038/s41390-026-05156-4 · Cited by 1
Moraes, A. C. D. D., Hunt, E. T., Szeszulski, J., de Mello, G. D., Nascimento-Ferreira, M., & Madden, P. (2026) · Scandinavian Journal of Medicine & Science in Sports · doi:10.1111/sms.70346
“Following other studies, participants were included in the final analysis if their device registered days with > 599 min of recorded wake wear time within each participant's 3-week study period, with a minimum requirement of 1000 steps per day [20].”
Sullivan, R. M., Wallace, A. L., Shankula, C. A., Celhay, O., Ziemer, L. R., Smith, C. J., Berman, S., Linkersdörfer, J., Martz, M. E., Courtney, K. E., et al. (2026) · Drug and Alcohol Dependence Reports · doi:10.1016/j.dadr.2026.100463 · Cited by 1
“Weights derived from ACS [for detailed comparisons between ACS sample and ABCD cohort see: (Heeringa and Berglund, 2020)] — to sociodemographically match the ABCD cohort to the ACS sample and improve generalizability — were utilized to obtain weighted prevalences of SU.”
Weighted prevalence of biochemically verified substance use in healthy adolescents across the USA
Wade, N. E., Si, Y., Tapert, S. F., Linkersdörfer, J., Lisdahl, K. M., Moore, H. I., Tally, L., Das, B., Huestis, M. A., Wallace, A. L., et al. (2026) · Addiction · doi:10.1111/add.70548 · Cited by 2
“Strengths of the present analyses are the use of a benchmark data set (ACS) for creating baseline weighting [21], broad consideration of factors potentially influencing hair testing results, focusing on metabolites (i.e. cotinine, EtG and THCCOOH) to confirm the personal ingestion of each…”
InfluentialPalmer, C., Sheth, C., Marshall, A., Adise, S., Baker, F., Chang, L., Clark, D., Dagher, R., Dowling, G., Gonzalez, M., et al. (2021) · bioRxiv · doi:10.1101/2021.06.30.450555 · Cited by 3
“All contingency tables additionally show the proportion of subjects at each level of the dependent variable using propensity-weights based on nationally-representative controls from the American Community Survey (ACS) as calculated in [27] and applied in the ABCD Study Data Exploration Analysis Portal (DEAP)(https://d…”
MethodDeep roots of admixture-related cognitive differences in the USAPreprint
Fuerst, J. G. R., & Hu, M. (2023) · Qeios · doi:10.32388/ccn648.3 · Cited by 1
Deep roots of admixture-related cognitive differences in the USA?Preprint
Fuerst, J. G. R., & Hu, M. (2023) · Qeios · doi:10.32388/ccn648.7
Deep roots of admixture-related cognitive differences in the USA?Preprint
Fuerst, J. G. R., & Hu, M. (2023) · Qeios · doi:10.32388/ccn648.6 · Cited by 1
Wade, N. E., Si, Y., Tapert, S. F., Linkersdörfer, J., Lisdahl, K. M., Moore, H. I., Tally, L., Das, B., Huestis, M. A., Wallace, A. L., et al. (2025) · medRxiv · doi:10.1101/2025.09.19.25336190 · Cited by 1
Rosario, M. A., Cardenas‐Iniguez, C., Chavez, J. V., Bottenhorn, K. L., Ahmadi, H., Herting, M. M., & Thompson, W. K. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.30.702968 · Cited by 1
Reward Processing in Children with Psychotic-like ExperiencesPreprint
Harju‐Seppänen, J., Irizar, H., Bramon, E., Blakemore, S., Mason, L., & Bell, V. (2021) · medRxiv · doi:10.1101/2021.06.01.21257966
Bignardi, G., Mareva, S., & Astle, D. E. (2022) · doi:10.31219/osf.io/kqtsd
Deep roots of admixture-related cognitive differences in the USAPreprint
Fuerst, J. G. R., & Hu, M. (2023) · Qeios · doi:10.32388/ccn648.2
Deep roots of admixture-related cognitive differences in the USA?Preprint
Fuerst, J. G. R., & Hu, M. (2023) · Qeios · doi:10.32388/ccn648.5
Sex, gender diversity, and brain structure in children ages 9 to 11 years oldPreprint
Torgerson, C. M., Ahmadi, H., Choupan, J., Fan, C. C., Blosnich, J. R., & Herting, M. M. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.28.551036
“The heterogeneity of sites captured geographic, demographic, and socioeconomic diversity of participants (Garavan et al., 2018; Heeringa & Berglund, 2020).”
Pedagogical Technologies and Cognitive Development in Secondary Education
Akhmetova, A., Карманова, Ж. А., Demisenova, S., Sadvakassova, N., & Koshkumbaev, K. (2024) · Open Education Studies · doi:10.1515/edu-2022-0214
“However, as denoted by Heeringa and Berglund (2020), during adolescence, the development processes become somewhat more complicated based on the changes that have already occurred.”
BackgroundJeong, H. J., Lahey, B. B., Reimann, G. E., Durham, E. L., Archer, C., Moore, T. M., Shah, K., & Kaczkurkin, A. N. (2024) · Frontiers in Child and Adolescent Psychiatry · doi:10.3389/frcha.2024.1410804
Discrimination and manic symptoms in early adolescence: A prospective cohort study
Nagata, J. M., Wong, J., Zamora, G., Al-Shoaibi, A. A. A., Low, P., Ganson, K. T., Testa, A. M., He, J., Lavender, J. M., & Baker, F. C. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.10.078
Cotter, D. L., Kiss, O., Ahmadi, H., de Jesus, A., Schwartz, J. D., Baker, F. C., Hackman, D. A., & Herting, M. M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.13.638133
“Lastly, the sample used here is large and regionally diverse, but not representative of the U.S. population or the larger ABCD Study cohort (79,80).”
Early Non-Coital Sexual and Romantic Behaviors in Latine Adolescents: Differences by Latine Origin
Espinosa‐Hernández, G., Vasilenko, S. A., Zhang, Y., & Halgunseth, L. C. (2025) · The Journal of Early Adolescence · doi:10.1177/02724316251326176
“Researchers used a multi-stage, strati fi ed sampling approach, and recruited from 21 geographically diverse sites (i.e., Northeast, Midwest, South, and West regions) that re fl ected the demographic and socio-economic diversity of these U.S. birth cohorts (Heeringa & Berglund, 2020).”
Social Connectedness and Neurodevelopmental Functioning in Youth: Insights from the ABCD Study®
Cosgrove, K. T., Rhudy, J. L., Morris, A. S., Thompson, W. K., Mosconi, M. W., Paulus, M. P., & Aupperle, R. L. (2025) · Advances in Neurodevelopmental Disorders · doi:10.1007/s41252-025-00448-y · Cited by 1
Silver, D., Polikoff, M. S., Berhane, K. T., Kwon, A., Herting, M. M., Sowell, E. R., Kraemer, D. J. M., & Hackman, D. A. (2025) · AERA Open · doi:10.1177/23328584251349182
“The ABCD Study also includes a set of sampling weights designed to render the full ABCD sample observ-ably similar to national benchmarks from the American Community Survey (Heeringa & Berglund, 2020).”
InfluentialPubertal mediators of early life stress and age on adolescent alcohol initiation: Analysis by sex
Donovan, A., Assari, S., Grella, C. E., Shaheen, M. A., Richter, L., & Friedman, T. C. (2025) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2025.107559
“Post-stratification weights based on the American Community Survey were utilized during descriptive analysis to account for complex sampling within the study, as recommended in Heeringa and Berglund (2020).”
Torgerson, C. M., Bottenhorn, K. L., Ahmadi, H., Choupan, J., & Herting, M. M. (2025) · Imaging Neuroscience · doi:10.1162/imag.a.127
“The study included children from diverse geographic, demographic, and socioeconomic backgrounds ( Garavan et al., 2018; Heeringa & Berglund, 2020).”
Kaiver, C. M., Thompson, E. L., Hawes, S. W., Lehman, S. M., Adams, A. R., Wing, D., Laird, A. R., & González, R. (2025) · Drug and Alcohol Dependence Reports · doi:10.1016/j.dadr.2025.100373
Carvalho, C., Koss, K. J., & Ravindran, N. (2025) · Development and Psychopathology · doi:10.1017/s0954579425100618 · Cited by 2
“…provided by the ABCD Study were also accounted for in all models to calibrate distributions to nationally representative controls based on the American Community survey to increase the generalizability of findings (see Heeringa & Berglund, 2020; Saragosa-Harris et al., 2022 for more information).”
Age-Related Trends in Self-Identification of Sexual Orientation During Early Adolescence
Nagata, J. M., Otmar, C. D., Lopez, A., Kim, A. E., Sui, S. S., Li, K., & Shao, I. Y. (2025) · Journal of Adolescent Health · doi:10.1016/j.jadohealth.2025.07.001
Weight discrimination and eating disorder symptoms in early adolescence: a prospective cohort study
Nagata, J. M., Thompson, A. S., Helmer, C. K., Ganson, K. T., Testa, A. M., Barnhart, W. R., He, J., Baker, F. C., & Lavender, J. M. (2025) · Journal of Eating Disorders · doi:10.1186/s40337-025-01404-w · Cited by 3
“Sampling weights were applied to yield representative estimates based on the American Community Survey from the U.S. Census [53].”
Overholtzer, L. N., Bottenhorn, K. L., Karalunas, S. L., Peterson, B. S., & Herting, M. M. (2026) · Scientific Reports · doi:10.1038/s41598-026-52107-4
Oshri, A., Howard, C. J., Kogan, S. M., Zhang, L., Geier, C. F., Bauer, B. W., & House, E. M. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03710-y
Social epidemiology of gender diversity in early adolescents
Nagata, J. M., Li, K., Sui, S. S., Low, P., Talebloo, J., Otmar, C. D., Shao, I. Y., Ganson, K. T., Testa, A. M., He, J., et al. (2025) · Pediatric Research · doi:10.1038/s41390-025-04576-y · Cited by 1
Perceived Discrimination Experiences Among Multiracial Children in the ABCD Study
Vora, A. S., Zhang, Y., Leu, C., & Grilo, S. A. (2025) · Pediatrics Open Science · doi:10.1542/pedsos.2024-000326
The Prevalence of Stressful Life Events among a Racially Diverse National Sample of AdolescentsPreprint
Garcia, A. R., Lovins, K., & Karcher, N. R. (2026) · Research Square · doi:10.21203/rs.3.rs-8414362/v1
“We also examined prevalence weights adjusted for American Community Survey propensity scores (Heeringa & Berglund, 2020) to improve the representativeness of the prevalence estimates for the U.S. population.”
Neurostructural Substrates of Hierarchical Dimensions of Internalizing Symptoms in Youth
Durham, E. L., Moore, T. M., Ellis, K. E., Wang, S., Jeong, H. J., Reimann, G. E., Archer, C., & Kaczkurkin, A. N. (2026) · Human Brain Mapping · doi:10.1002/hbm.70461
“…weights, which address non-representative site sampling to make the sample more representative of US demographic and socioeconomic distributions (Heeringa and Berglund 2020), and non-participation weights to account for biases due to non-random exclusion from neurostructural analyses.”
Patel, B., Tyrka, A. R., & Angeles, W. W. L.-D. L. (2026) · Journal of Attention Disorders · doi:10.1177/10870547261421667
“Of note, some studies with the ABCD data set use statistical procedures to match the data to the nationally representative American Community Survey (Heeringa & Berglund, 2020; Lewis-de Los Angeles & Liu, 2021; Raney et al., 2022), this study does not do so due to the missing data and differences…”
Allgaier, N. A., Owens, M. M., Thompson, W. K., Hahn, S., Hyatt, C., Albaugh, M. D., Jernigan, T., Yuan, D., Garavan, H., & Potter, A. (2021) · OSF Preprints (OSF Preprints)
Li, X.-X., Zhang, G.-M., Qu, G., Orlichenko, A., Ding, Z.-M., Wilson, T. W., Stephen, J., Calhoun, V., & Wang, Y.-P. (2026) · bioRxiv · doi:10.64898/2026.05.16.725673
“Results from the Adolescent Brain Cognitive Development (ABCD) study show that LMMs provide a principled framework for modeling complex neuroimaging data with multi-level dependencies such as site, family, and longitudinal measures, facilitating valid inference at the population level [21].”
Wildfire pollution exposure during childhood adversely affects cognitive and neural developmentPreprint
Judd, N., & Kievit, R. (2026) · medRxiv · doi:10.64898/2026.06.08.26355154
Almeida, M., Zekelman, L., Lan, Z., Rushmore, J., Karayumak, S. C., Zhang, F., Pieper, S., Makris, N., Golby, A. J., Yogesh Rathi, A., et al. (2026) · Brain Structure and Function · doi:10.1007/s00429-026-03148-y
“To better understand the relationship between cerebellar pathway dMRI measures and ASD symptom domains, we examined how an ASD diagnosis and FA, MD, and NoS of the cerebellar pathways were associated with measures of SCI and RRB using multiple linear regression.”
Bastien, J., Garcia, K., Wallace, A. L., Sullivan, R. M., Hoh, E., & Wade, N. E. (2026) · medRxiv · doi:10.64898/2026.08.31.26361835
Nagata, J. M., Balasubramanian, P., Lopez, A., Helmer, C. K., Wong, J. H., Shao, I. Y., Otmar, C. D., Ganson, K. T., Testa, A. M., Dooley, E. E., et al. (2026) · International Journal of Cardiology Cardiovascular Risk and Prevention · doi:10.1016/j.ijcrp.2026.200707
“ABCD Study sample weights based on the U.S. Census American Community Survey were applied to improve the representativeness of the analytic sample to the U.S. population [26].”
Yousefkhani, M. P., Antshel, K. M., Sahandi, A., & Vasilenko, S. A. (2026) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.70230
Smartphone Ownership and Eating Disorder Symptoms in Early Adolescence
Nagata, J. M., Wong, J. H., Talebloo, J., Engle, K., Ganson, K. T., Testa, A. M., He, J., Barnhart, W. R., Baker, F. C., & Lavender, J. M. (2026) · JAMA Network Open · doi:10.1001/jamanetworkopen.2026.37897
Physical Activity and Adolescent Brain Structure, Mental Health, and Academic Outcomes
Li, Q., Paluch, A. E., & Rakesh, D. (2026) · JAMA Network Open · doi:10.1001/jamanetworkopen.2026.36973
Hullenaar, K. L., Fisher, B. W., & Rivara, F. P. (2026) · Educational Researcher · doi:10.3102/0013189x261492835
Metadata
- Authors
- Heeringa, S. G., & Berglund, P. A.
- Affiliations
- University of Michigan (US)
- Corresponding author
- Heeringa, Steven G.
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2020-02-10
- OpenAlex
- W3005123580
- Cited by
- 247
- References
- 17
- Citations by year
- 2026: 23 · 2025: 50 · 2024: 54 · 2023: 44 · 2022: 38 · 2021: 30 · 2020: 8
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Survey Sampling and Estimation Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- Adolescent Brain Cognitive Development Study, population-based study, survey design, multilevel modeling, design-based estimation, survey weights, selection bias, socioeconomic diversity, American Community Survey, statistical efficiency, adolescent brain development, cognitive development
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
