For example, let us imagine that we are interested in the factors which influence whether the word *for* is present or omitted from phrases of duration such as *She studied [for] three years in Munich*. We may hypothesise several factors which could have an effect on this, e.g. the text genre, the semantic category of the main verb and whether or not the verb is separated by an adverb from the phrase of duration. Any one of these factors might be solely responsible for the omission of *for*, or it might be the case that a combination of factors are culpable. Finally, *all* the factors working together could be responsible for the presence/omission of *for*. A loglinear analysis provides us with a number of models which take these points into account.

The way that we test the models in loglinear analysis is first to test the significance of associations in the most complex model - that is the model which assumes that all of the variables are working together. Then we take away each variable at a time from the model and see whether significance is maintained in each case, until we reach the model with the lowest possible dimensions. So in the above example, we would start with a model that posited three variables (e.g. genre, verb class and adverb separation) and test the significance of a three variable model. Then we would test each of the two variable models (taking away one variable in each case) and finally each of the three one-variable models. The best model would be taken to be the one with the *fewest* number of variables which still retained statistical significance.

*Read about variable rule analysis and probabilistic language modelling in Corpus Linguistics, Chapter 3, pages 83-84.*