{"doi":"10.1016/j.ssmph.2020.100661","title":"Cross-classified multilevel models (CCMM) in health research: A systematic review of published empirical studies and recommendations for best practices","abstract":"Recognizing that health outcomes are influenced by and occur within multiple social and physical contexts, researchers have used multilevel modeling techniques for decades to analyze hierarchical or nested data. Cross-Classified Multilevel Models (CCMM) are a statistical technique proposed in the 1990s that extend standard multilevel modeling and enable the simultaneous analysis of non-nested multilevel data. Though use of CCMM in empirical health studies has become increasingly popular, there has not yet been a review summarizing how CCMM are used in the health literature. To address this gap, we performed a scoping review of empirical health studies using CCMM to: (a) evaluate the extent to which this statistical approach has been adopted; (b) assess the rationale and procedures for using CCMM; and (c) provide concrete recommendations for the future use of CCMM. We identified 118 CCMM papers published in English-language literature between 1994 and 2018. Our results reveal a steady growth in empirical health studies using CCMM to address a wide variety of health outcomes in clustered non-hierarchical data. Health researchers use CCMM primarily for five reasons: (1) to statistically account for non-independence in clustered data structures; out of substantive interest in the variance explained by (2) concurrent contexts, (3) contexts over time, and (4) age-period-cohort effects; and (5) to apply CCMM alongside other techniques within a joint model. We conclude by proposing a set of recommendations for use of CCMM with the aim of improved clarity and standardization of reporting in future research using this statistical approach.","journal":"SSM - Population Health","year":2020,"id":93874,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":39,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9487,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":5215,"name":"Erin C. Dunn","orcid":"0000-0003-1413-3229","position":1,"is_corresponding":false},{"id":443796,"name":"Tracy K. Richmond","orcid":"0000-0001-8178-1125","position":2,"is_corresponding":false},{"id":468145,"name":"Sarah Ahmed","orcid":"0000-0001-8807-1281","position":3,"is_corresponding":false},{"id":400834,"name":"Matthew J. Hawrilenko","orcid":null,"position":4,"is_corresponding":false},{"id":468146,"name":"Clare R. Evans","orcid":"0000-0002-9862-9506","position":5,"is_corresponding":false},{"id":468144,"name":"Kathryn M. Barker","orcid":"0000-0002-6242-0217","position":0,"is_corresponding":true}],"reference_count":165,"raw_metadata":null,"created_at":"2026-07-18T22:31:34.149524Z","pmid":"32964097","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}