Chong Ho Yu

Exploratory data analysis in the context of data mining and resampling.

Today there are quite a few widespread misconceptions of exploratory data analysis (EDA). One of these misperceptions is that EDA is said to be opposed to statistical modeling. Actually, the essence of EDA is not about putting aside all modeling and preconceptions; rather, researchers are urged not to start the analysis with a strong preconception only, and thus modeling is still legitimate in EDA. In addition, the nature of EDA has been changing due to the emergence of new methods and convergence between EDA and other methodologies, such as data mining and resampling. Therefore, conventional conceptual frameworks of EDA might no longer be capable of coping with this trend. In this article, EDA is introduced in the context of data mining and resampling with an emphasis on three goals: cluster detection, variable selection, and pattern recognition. TwoStep clustering, classification trees, and neural networks, which are powerful techniques to accomplish the preceding goals, respectively, are illustrated with concrete examples.

Teaching Factor Analysis in Terms of Variable Space and Subject Space Using Multimedia Visualization

There are many common misconceptions regarding factor analysis. For example, students do not know that vectors representing latent factors rotate in subject space, rather than in variable space. Consequently, eigenvectors are misunderstood as regression lines, and data points representing variables are misperceived as data points depicting observations. The topic of subject space is omitted by many statistics textbooks, and indeed it is a very difficult concept to illustrate. An animated tutorial was developed in an attempt to alleviate this problem. Since the target audience is intermediate statistics students who are familiar with regression, regression in variable space is used as an analogy to lead learners into factor analysis in the subject space. At the end we apply the Gabriel biplot to combine the two spaces. Findings from a textbook review, a survey, and a “think aloud” protocol were taken into account during the program development and are discussed here.

Sas programs for generating winsteps control files and web-based presentations

Winsteps (Rasch Measurement Software and Publications, 2003), as its name implies, is a psychometric program created specifically to compute the step function (Wright & Masters, 1982) for exams carrying partial-credit items. Despite the clarity of the Rasch model (Bond & Fox, 2001) and the availability of rich features in the program, such as the item-person map, Scalogram, and fitness indices, coding the input file for Winsteps, especially the partial-credit key, and interpretation of Winsteps’s output could be challenging to novices. This challenge is partly due to the Winsteps’s text-based interface. To augment Winsteps, two SAS macros programs were written to aid users by formatting the input file for Winsteps and by adding graphical presentation of the Winsteps item parameter output.