title: Lower level mediation in multilevel models.
abstract: Multilevel models are increasingly used to estimate models for hierarchical and repeated measures data. The authors discuss a model in which there is mediation at the lower level and the mediational links vary randomly across upper level units. One repeated measures example is a case in which a person's daily stressors affect his or her coping efforts, which affect his or her mood, and both links vary randomly across persons. Where there is mediation at the lower level and the mediational links vary randomly across upper level units, the formulas for the indirect effect and its standard error must be modified to include the covariance between the random effects. Because no standard method can estimate such a model, the authors developed an ad hoc method that is illustrated with real and simulated data. Limitations of this method and characteristics of an ideal method are discussed.
authors.affiliation: Department of Psychology, University of Connecticut, Storrs 06269-1020, USA. kenny@uconnvm.uconn.edu
authors.forename: David A
authors.initials: DA
authors.lastname: Kenny
authors.forename: Josephine D
authors.initials: JD
authors.lastname: Korchmaros
authors.forename: Niall
authors.initials: N
authors.lastname: Bolger
mesh_terms: D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D019964:Mood Disorders / Q000523:psychology; D013223:Statistics as Topic; D013315:Stress, Psychological / Q000150:complications