Theoretical Versus Mathematical Approach To Modeling Psychological And Physiological Data
Keywords
Brain activity measurement; Cognitive modeling; Electroencephalography; Emotion; Human performance; Interaction; Perception; Physiological measuring
Abstract
Variable selection for predictive modeling has traditionally relied on theory in the psychological domain. Given the recent advancements in computing technology and availability, researchers are able to utilize more sophisticated mathematical modeling techniques with greater ease. The challenge becomes evaluating whether theory or mathematics should be relied upon for model development. The presented analyses compared the use of hierarchical and stepwise variable selection methods during a predictive modeling task using linear regression. The results show that the stepwise variable selection method is able to obtain a more efficient model than the hierarchical variable selection method. Implications and recommendations for researchers are further discussed.
Publication Date
1-1-2016
Publication Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
9743
Number of Pages
383-393
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1007/978-3-319-39955-3_36
Copyright Status
Unknown
Socpus ID
84978821880 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/84978821880
STARS Citation
Reinerman-Jones, Lauren; Lackey, Stephanie J.; Abich, Julian; Sollins, Brandon; and Hudson, Irwin, "Theoretical Versus Mathematical Approach To Modeling Psychological And Physiological Data" (2016). Scopus Export 2015-2019. 4426.
https://stars.library.ucf.edu/scopus2015/4426