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Using log-linear models to analyse categorical data

Mental Health Services of Salford NHS Trust, School of Nursing, Midwifery & Health visiting, University of Manchester

David Richards, PhD, BSc(Hons), RN

School of Nursing, Midwifery, & Health Visiting, University of Manchester

This paper provides a brief introduction to log-linear modelling that is designed to be accessible to readers with a cursory knowledge of elementary statistics. Log-linear models belong to the family of general linear models and they are generally regarded as the method of choice for analysing the associations between variables in large sets of categorical data. The primary objective of using log-linear modelling procedures is usually to identify the simplest model that fits the data adequately, although in some instances more than one model may be acceptable. The main drawback of using log-linear models is that the procedures require very large data sets. A case study illustrates that, even with a relatively large sample, problems may arise if the data are unevenly distributed.

Key Words: Log-linear models • Categorical data • Model-fitting • Sample size

Nursing Times Research, Vol. 6, No. 5, 867-875 (2001)
DOI: 10.1177/136140960100600511


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