eng TQMP Tutorials in Quantitative Methods for Psychology 1913-4126 2011-04-01 7 1 1 4 article Denis Cousineau denis.cousineau@umontreal.ca 1 Louis Laurencelle louis.laurencelle@uqtr.ca 2 Université de Montréal Université du Québec à Trois-Rivières Non-central t distribution needed for assessing the power of the t test is described. Three approximations are compared and their merits discussed in regard to simplicity and accuracy. http://www.tqmp.org/Content/vol07-1/p001/p001.pdf Statistical power t test Noncentral t distribution eng TQMP Tutorials in Quantitative Methods for Psychology 1913-4126 2011-04-01 7 1 5 14 article Laura Doey laurah6@hotmail.com 1 Jessica Kurta jkurt060@uottawa.ca 1 University of Ottawa Correspondence analysis is an exploratory data technique used to analyze categorical data (Benzecri, 1992). It is used in many areas such as marketing and ecology. Correspondence analysis has been used less often in psychological research, although it can be suitably applied. This article discusses the benefits of using correspondence analysis in psychological research and provides a tutorial on how to perform correspondence analysis using the Statistical Package for the Social Sciences (SPSS). http://www.tqmp.org/Content/vol07-1/p005/p005.pdf Correspondence analysis statistics eng TQMP Tutorials in Quantitative Methods for Psychology 1913-4126 2011-04-01 7 1 15 18 article Denis Cousineau denis.cousineau@umontreal.ca 1 Université de Montréal With the advent of fast computers, the randomization test of mean (also called the permutation test) received some attention in the recent years. Here we show that the randomization test is possible only for two-group design; comparing three groups requires a number of permutations so vast that even three groups of ten participants is beyond the current capabilities of modern computers. Further, we show that the rate of increase in the number of permutation is so large that simply adding one more participant per group to the data results in a computation time increased by at least one order of magnitude (in the three-group design) or more. Hence, the exhaustive randomization test may never be a viable alternative to ANOVAs. http://www.tqmp.org/Content/vol07-1/p015/p015.pdf Randomization test statistics