{"doi":"10.1002/9780470479216.corpsy0053","title":"Analysis of Covariance","abstract":"<jats:title>Abstract</jats:title>\n          <jats:p>Analysis of covariance (ANCOVA) is a statistical method that may be viewed as an extension of analysis of variance (ANOVA) when, in addition to one or more factors (discrete explanatory variables, typically group membership), it is required to account for possible differences due to a continuous variable(s), usually called covariate(s) or concomitant variable(s). The latter variable(s) co‐varies with a dependent variable under consideration (response, outcome), and it is of interest to examine whether group differences on the latter may be related to group differences on the covariate(s). Typically, a covariate is highly correlated with a response variable; that is, the covariate contains information about the response variable and therefore possibly also about group differences on the outcome measure(s). Empirical settings in which ANCOVA is appropriate usually have at least one categorical factor and one or more continuous covariates.</jats:p>","journal":"The Corsini Encyclopedia of Psychology","year":2010,"id":17361,"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":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":124503,"name":"Tenko Raykov","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"21071399","pmcid":null,"openalex_id":"https://openalex.org/W4250904219","authors":[],"funders":[],"total_grants":0,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[],"oa_status":"closed","license":"http://doi.wiley.com/10.1002/tdm_license_1.1","oa_locations":[{"url":"https://onlinelibrary.wiley.com/doi/pdf/10.1002/9780470479216.corpsy0053","host_type":"publisher"},{"url":"https://onlinelibrary.wiley.com/doi/full-xml/10.1002/9780470479216.corpsy0053","host_type":"publisher"},{"url":"https://doi.org/10.1002/9780470479216.corpsy0053","host_type":"journal"}],"fields_of_study":["Data Analysis with R"],"mesh_terms":[],"keywords":["Covariate","Analysis of covariance","Categorical variable","Statistics","Mathematics","Variable (mathematics)","Covariance","Analysis of variance","Econometrics","Outcome (game theory)","Variables"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-06-02T18:33:25.335801Z","pmid":null,"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":[]}