Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Ali Ünlü, Anatol Sargin
Title: DAKS: An R Package for Data Analysis Methods in Knowledge Space Theory
Abstract: Knowledge space theory is part of psychometrics and provides a theoretical framework for the modeling, assessment, and training of knowledge. It utilizes the idea that some pieces of knowledge may imply others, and is based on order and set theory. We introduce the R package DAKS for performing basic and advanced operations in knowledge space theory. This package implements three inductive item tree analysis algorithms for deriving quasi orders from binary data, the original, corrected, and minimized corrected algorithms, in sample as well as population quantities. It provides functions for computing population and estimated asymptotic variances of and one and two sample Z tests for the diff fit measures, and for switching between test item and knowledge state representations. Other features are a function for computing response pattern and knowledge state frequencies, a data (based on a finite mixture latent variable model) and quasi order simulation tool, and a Hasse diagram drawing device. We describe the functions of the package and demonstrate their usage by real and simulated data examples.

Page views:: 4047. Submitted: 2009-03-22. Published: 2010-11-16.
Paper: DAKS: An R Package for Data Analysis Methods in Knowledge Space Theory     Download PDF (Downloads: 3738)
DAKS_2.1-1.tar.gz: R source package Download (Downloads: 664; 716KB)
v37i02.R: R example code from the paper Download (Downloads: 704; 2KB)

DOI: 10.18637/jss.v037.i02

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.