Loglinear analysis begins with a (definitionally true but trivial) ‘saturated’ model, where all possible direct and interaction effects are specified. Simpler models are then examined which leave out some of these effects (on the basis of theory or hunch) to see whether good fits to the data can be obtained with fewer effects (that is, with a more parsimonious model), and in this way the researcher may infer what variables are most important and what the pattern of effect actually is in the data. It is a very flexible multivariate procedure, best adapted to analysing attributes (variables at the nominal level of measurement), and is only feasible using computer programs.
Nigel Gilbert's Modelling Society (1981) and David Knoke and Peter J. Burke's Log Linear Models (1980) are both excellent introductions to topic. For substantive examples, and an explanation of how to read and interpret the various models, see Gordon Mashall et al. , Against the Odds? (1997
). See also MULTIVARIATE ANALYSIS; MOBILITY, SOCIAL.
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