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Profile Analysis via Multivariate Statistics for Continuous and Categorical Data
Tue, April 17, 10:35am to 12:05pm, Vancouver Convention Centre, Floor: Second Level, West Room 206Session Submission Type: Symposium
Abstract
This symposium is designed to introduce a new profile analytical approach called Profile Analysis via Multivariate Statistics (PAMS) and the application of PAMS to analyses of continuous and categorical data. PAMS was originally based on multidimensional scaling to identify the most typical profiles in a population, but it has been expanded to a multiple regression technique for identifying the predictor score patterns that account for variation in a criterion variable. Moreover, PAMS can be applied to analysis of categorical data, utilizing the correspondence analysis paradigm. The current symposium aims to represent PAMS applications for multiple regression and correspondence analysis.
Sub Unit
Chair
Papers
Investigating Group Differences in Criterion-Related Patterns - Mark L. Davison, University of Minnesota; Ernest C. Davenport, Jr., University of Minnesota
Patterns of SAT Verbal and SAT Quantitative Scores Associated With College Major and College Success: Results From a Moderated Profile Analysis Technique - Ernest C. Davenport, Jr., University of Minnesota; Mark L. Davison, University of Minnesota
Application of PAMS (Profile Analysis via Multivariate Statistics) to Interpreting Category Dimension Profiles in Correspondence Analysis - Se-Kang Kim, Fordham University
The PAMS (Profile Analysis via Multivariate Statistics) Perspective on Educational Survey Data via Correspondence Analysis - Jennifer Lord-Bessen, Fordham University; Se-Kang Kim, Fordham University