Multilevel modelling: Beyond the basic applications.
Over the last 30 years statistical algorithms have been developed to analyse datasets that have a hierarchical/multilevel structure. Particularly within developmental and educational psychology these techniques have become common where the sample has an obvious hierarchical structure, like pupils nested within a classroom. We describe two areas beyond the basic applications of multilevel modelling that are important to psychology: modelling the covariance structure in longitudinal designs and using generalized linear multilevel modelling as an alternative to methods from signal detection theory (SDT). Detailed code for all analyses is described using packages for the freeware R.
Wright
Daniel B
DB
Department of Psychology, Florida International University, Miami, Florida, USA. danw@fiu.edu
London
Kamala
K
D000465:Algorithms; D000704:Analysis of Variance; D001008:Anxiety Disorders / Q000175:diagnosis; D005260:Female; D006801:Humans; D016018:Least-Squares Analysis; D016013:Likelihood Functions; D016014:Linear Models; D008137:Longitudinal Studies; D008432:Mathematical Computing; D011939:Mental Recall; D015233:Models, Statistical; D016011:Normal Distribution; D010555:Personality Inventory / Q000706:statistics & numerical data; D011247:Pregnancy; D011584:Psychology / Q000706:statistics & numerical data*; D011594:Psychometrics / Q000706:statistics & numerical data; D011644:Puerperal Disorders / Q000175:diagnosis; D011930:Reaction Time; D015203:Reproducibility of Results; D017603:Signal Detection, Psychological; D012984:Software; D013223:Statistics as Topic / Q000379:methods