On the analysis of discrete data finding dependencies in small sample sizes

Abstract

An analysis of survey data is a fundamental part of research concerning various aspects of human behavior. Such survey data are often discrete, and the size of the collected sample is regularly insufficient for the most potent modelling tools such as logistic regression, clustering, and other data mining techniques. In this paper, we take a closer look at the results of the stated preference survey analyzing how inhabitants of cities in Thailand, Poland, and Czechia understand and perceive “smartness” of a city. An international survey was conducted, where respondents were asked 15 questions. Since the most common data modelling tools failed to provide a useful insight into the relationship between variables, so-called lambda coefficient was used and its usefulness for such challenging data was verified. It uses the principle of conditional probability and proves to be truly useful even in data sets with relatively small sample size.