eng TQMP Tutorials in Quantitative Methods for Psychology 1913-4126 2010-03-01 6 1 1 15 article Howard B. Lee howard.lee@csun.edu 1 Gary S. Katz gary.katz@csun.edu 1 Alberto F. Restori alberto.restori@csun.edu 1 California State University, Northridge This paper re-introduces and demonstrates the use of Mickey’s (1970) canonical correlation method in analyzing large two-dimensional contingency tables. This method of analysis supplements the traditional analysis using the Pearson chi-square. Examples and a MATLAB source listing are provided. http://www.tqmp.org/Content/vol06-1/p001/p001.pdf Stastitics Canonical correlation Contingency table eng TQMP Tutorials in Quantitative Methods for Psychology 1913-4126 2010-03-01 6 1 16 30 article Fabien Mathy fabien.mathy@univ-fcomte.fr 1 Université de Franche-Comté In this paper, a few basic notions stemming from information theory are presented with the intention of modeling the abstraction of relevant information in categorization tasks. In a categorization task, a single output variable is the basis for performing a dichotomic classification of objects that can be distinguished by a set of input variables which are more or less informative about the category to which the objects belong. At the beginning of the experiment, the target classification is unknown to learners who must select the most informative variables relative to the class in order to succeed in classifying the objects efficiently. I first show how the notion of entropy can be used to characterize basic psychological processes in learning. Then, I indicate how a learner might use information gain and mutual information –both based on entropy– to efficiently induce the shortest rule for categorizing a set of objects. Several basic classification tasks are studied in succession with the aim of showing that learning can improve as long as subjects are able to compress information. Referring to recent experimental results, I indicate in the Conclusion that these notions can account for both strategies and performance in subjects trying to simplify a learning process. http://www.tqmp.org/Content/vol06-1/p016/p016.pdf Information theory Information gain and mutual information eng TQMP Tutorials in Quantitative Methods for Psychology 1913-4126 2010-03-01 6 1 31 38 article Dominic Langlois mystarel@gmail.com 1 Sylvain Chartier sylvain.chartier@uottawa.ca 1 Dominique Gosselin dgoss085@uottawa.ca 1 University of Ottawa This paper presents an introduction to independent component analysis (ICA). Unlike principal component analysis, which is based on the assumptions of uncorrelatedness and normality, ICA is rooted in the assumption of statistical independence. Foundations and basic knowledge necessary to understand the technique are provided hereafter. Also included is a short tutorial illustrating the implementation of two ICA algorithms (FastICA and InfoMax) with the use of the Mathematica software. http://www.tqmp.org/Content/vol06-1/p031/p031.pdf Statistics Independent component analysis