Implementation of the Multiple-Measure Maximum Likelihood strategy classification method in R

Implementation of the Multiple-Measure Maximum Likelihood strategy classification method in R

One major challenge to behavioral decision research is to identify the cognitive processes underlying judgment and decision making. Gloeckner (2009) has argued that, compared to previous methods, process models can be more efficiently tested by simultaneously analyzing choices, decision times, and c...

Full description

Bibliographic Details
Main Authors: Marc Jekel, Andreas Nicklisch, Andreas Gloeckner
Format: Article
Language:English
Published: Society for Judgment and Decision Making 2010-02-01
Series:Judgment and Decision Making
Subjects:
One major challenge to behavioral decision research is to identifythe cognitive processes underlying judgment and decision making.Gloeckner (2009) has argued that
compared to previousmethods
process models can be more efficiently tested bysimultaneously analyzing choices
decision times
and confidencejudgments. The Multiple-Measure Maximum Likelihood (MM-ML) strategyclassification method was developed for this purpose and implementedas a ready-to-use routine in STATA
a commercial package forstatistical data analysis. In the present article
we describe theimplementation of MM-ML in R
a free package for dataanalysis under the GNU general public license
and we provide a practicalguide to application. We also provide MM-ML as an easy-to-use Rfunction. Thus
prior knowledge of R programming is notnecessary for those interested in using MM-ML.
Online Access:http://journal.sjdm.org/10/91015/jdm91015.pdf

Internet

http://journal.sjdm.org/10/91015/jdm91015.pdf

Similar Items