A comparative study of fixed effects models and random intercept/slope models as a special case of linear mixed models for repeated measurements

dc.contributor.authorİyit, Neslihan
dc.contributor.authorGenç, Aşır
dc.date.accessioned2020-03-26T17:16:50Z
dc.date.available2020-03-26T17:16:50Z
dc.date.issued2007
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractAny dataset in which subjects are measured repeatedly over time or space can be described as repeated measurements data. A linear mixed model (LMM) is a powerful method for analyzing repeated measurements data. It is made up of two components. The first component consists of a regression model for the average response over time and the effects of covariates on this average response. The second component provides a model for the pattern of covariances or correlations between the repeated measurements. In this study, a comparative evaluation of fixed effects models with random intercept models and random intercept and slope models as a special case of random effects models from linear mixed models are taken into consideration and the superiority of random intercept and slope models allow to modeling possible heterogeneity in intercepts and in slopes of the individual's own regression line for repeated measurements data is emphasizeden_US
dc.identifier.endpage74en_US
dc.identifier.issn1302-7980en_US
dc.identifier.issue1en_US
dc.identifier.startpage57en_US
dc.identifier.urihttp://www.trdizin.gov.tr/publication/paper/detail/TnpReE1ETXo=
dc.identifier.urihttps://hdl.handle.net/20.500.12395/21104
dc.identifier.volume8en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.ispartofTürkiye Klinikleri Psikiyatri Dergisien_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectMatematiken_US
dc.titleA comparative study of fixed effects models and random intercept/slope models as a special case of linear mixed models for repeated measurementsen_US
dc.typeArticleen_US

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