title: Categorical data analysis in primary care research: log-linear models.
abstract: Primary care researchers often wish to perform multiple variable analyses using variables measured at a nominal or ordinal level. This paper provides a step-by-step description of log-linear modeling, an approach uniquely well suited to explore and describe interactions among three or more nominal or ordinal variables. The method of log-linear analysis is illustrated with the use of an example from a primary care research project in which the relationships among hypertension, diet, and sodium were examined. The advantages and disadvantages of log-linear models and logistic regression are compared and available computer software programs discussed.
authors.affiliation: Department of Family Medicine, University of Connecticut/St. Francis Hospital and Medical Center.
authors.forename: B F
authors.initials: BF
authors.lastname: Crabtree
authors.forename: A E
authors.initials: AE
authors.lastname: Gelfand
authors.forename: W L
authors.initials: WL
authors.lastname: Miller
authors.forename: S
authors.initials: S
authors.lastname: Zyzanski
authors.forename: P J
authors.initials: PJ
authors.lastname: O'Connor
authors.forename: D
authors.initials: D
authors.lastname: Winsemius
mesh_terms: D006801:Humans; D006973:Hypertension / Q000453:epidemiology; D016014:Linear Models; D016015:Logistic Models; D011320:Primary Health Care / Q000706:statistics & numerical data*; D012107:Research Design; D012984:Software