title: [How to fit and interpret multilevel models using SPSS].
abstract: Hierarchic or multilevel models are used to analyse data when cases belong to known groups and sample units are selected both from the individual level and from the group level. In this work, the multilevel models most commonly discussed in the statistic literature are described, explaining how to fit these models using the SPSS program (any version as of the 11 th ) and how to interpret the outcomes of the analysis. Five particular models are described, fitted, and interpreted: (1) one-way analysis of variance with random effects, (2) regression analysis with means-as-outcomes, (3) one-way analysis of covariance with random effects, (4) regression analysis with random coefficients, and (5) regression analysis with means- and slopes-as-outcomes. All models are explained, trying to make them understandable to researchers in health and behaviour sciences.
authors.affiliation: Universidad Autónoma de Madrid. antonio.pardo@uam.es
authors.forename: Antonio
authors.initials: A
authors.lastname: Pardo
authors.forename: Miguel A
authors.initials: MA
authors.lastname: Ruiz
authors.forename: Rafael
authors.initials: R
authors.lastname: San Martín
mesh_terms: D003627:Data Interpretation, Statistical; D006801:Humans; D008960:Models, Psychological; D011584:Psychology / Q000379:methods* / Q000706:statistics & numerical data*; D011897:Random Allocation