Development of a Distribution Free Multivariate Canonical Analysis (Published)
This study illustrates the use of Median Polish analysis (MP) as a distribution free procedure that can be used to identify multivariate canonical data structures. The MP may be especially useful in situations where the sample sizes are small, or where the distributions do not meet the assumptions of conventional Canonical Correlation analysis (CC). We begin by comparing the CC and MP analyses with a sample multivariate data set. We go on to compare Type 1 error rates for each of these analyses using Monte Carlo procedures in which we manipulated sample size and skewness of the data distributions. Results indicated that Type 1 error was significantly higher for the CC relative to the MP when the distributions were skewed and/or when the sample sizes were n=20 or 30.
Keywords: Monte-Carlo Simulation, Multivariate, canonical correlation analysis, distribution free, exploratory research, hypothesis testing, median polish analysis, non-parametric
An Association Rule General Analytic System (ARGAS) for hypothesis testing in qualitative and quantitative research (Published)
This paper describes an Association Rule General Analytic System (ARGAS) as an alternative to the General Linear Model (GLM) for hypothesis testing. We illustrate how the ARGAS can be used to analyze both qualitative and quantitative research data. The advantages of the ARGAS approach derives from the fact that it is designed to analyze words or numbers that are converted into words. Unlike the GLM, it does not have any distributional assumptions. Association rule calculations are well-developed and there are a variety of computer software applications available that expedite the computations. The purpose of this study is to illustrate how the ARGAS can be applied and how to interpret the results.
Keywords: ARGAS, GLM, Pattern Recognition, association rule analysis, hypothesis testing, qualitative, quantitative
An Association Rule General Analytic System (ARGAS) for hypothesis testing in qualitative and quantitative research (Published)
This paper describes an Association Rule General Analytic System (ARGAS) as an alternative to the General Linear Model (GLM) for hypothesis testing. We illustrate how the ARGAS can be used to analyze both qualitative and quantitative research data. The advantages of the ARGAS approach derives from the fact that it is designed to analyze words or numbers that are converted into words. Unlike the GLM, it does not have any distributional assumptions. Association rule calculations are well-developed and there are a variety of computer software applications available that expedite the computations. The purpose of this study is to illustrate how the ARGAS can be applied and how to interpret the results.
Keywords: ARGAS, GLM, Pattern Recognition, association rule analysis, hypothesis testing, qualitative, quantitative