Abstract

Objective: The aim of the study was to examine log-linear analysis and correspondence analysis by an application with regard to advantages and disadvantages of the two methods.

Material and Method: In this study, we used an artificial data set, which is expanded without changing its nature, from a study conducted in Medical Faculty of Baskent University, Infectious Disease and Clinical Microbiology Department. Relations and interactions between variables and among subcategories have been investigated with log linear and correspondence analysis.

Results: It has been shown that when the assumptions of log-linear analysis were not available, better understanding of the data set could be obtained by correspondence analysis (CA). However, conclusions about the data may not be generalized in a confidence interval to the population by CA. In addition, in log-linear analysis, it is possible to make inferences about the population on the basis of sample data. The other important result obtained from the study was that some of the relations between the same variable categories could be detected by CA, which is not possible with log-linear analysis.

Conclusion: As a result, we suggest the complementary use of log-linear analysis and correspondence analysis for detailed results.

Keywords : Correspondence analysis, log-linear analysis, inertia, saturated models, unsaturated model