Modern Psychometrics

2020 ◽  
Author(s):  
John Rust ◽  
Michal Kosinski ◽  
David Stillwell
Keyword(s):  
Modern Psychometrics

scholarly journals Modern psychometrics applied in rheumatology–A systematic review

2012 ◽  
Vol 13 (1) ◽  
Author(s):  
Liseth Siemons ◽  
Peter M ten Klooster ◽  
Erik Taal ◽  
Cees AW Glas ◽  
Mart AFJ Van de Laar
Keyword(s):  
Systematic Review ◽  
Modern Psychometrics

scholarly journals Towards a Network Psychometrics Approach to Assessment: Simulations for Redundancy, Dimensionality, and Loadings

2020 ◽  
Author(s):  
Alexander P. Christensen
Keyword(s):  
Alternative Explanation ◽  
Network Models ◽  
Assessment Instrument ◽  
Assessment Instruments ◽  
Detection Algorithms ◽  
Psychological Attributes ◽  
Confirmatory Factor ◽  
Psychometric Tool ◽  
Modern Psychometrics

Research using network models in psychology has proliferated over the last decade. The popularity of network models has largely been driven by their alternative explanation for the emergence of psychological attributes—observed variables co-occur because they are causally coupled and dynamically reinforce each other, forming cohesive systems. Despite their rise in popularity, the growth of network models as a psychometric tool has remained relatively stagnant, mainly being used as a novel measurement perspective. In this dissertation, the goal is to expand the role of network models in modern psychometrics and to move towards using these models as a tool for the validation of assessment instruments. This paper presents three simulation studies and an empirical example that are designed to evaluate different aspects of the psychometric network approach to assessment: reducing redundancy, detecting dimensions, and estimating loadings. The first simulation evaluated two novel approaches for determining whether items are redundant, which is a key component for the accuracy and interpretation of network measures. The second simulation evaluated several different community detection algorithms, which are designed to detect dimensions in networks. The third simulation evaluated an adapted formulation of the network measure, node strength, and how it compares to factor loadings estimated by exploratory and confirmatory factor analysis. The results of the simulations demonstrate that network models can be used as an effective psychometric tool and one that is on par with more traditional methods. Finally, in the empirical example, the methods from the simulations are applied to a real-world dataset measuring personality. This example demonstrated that these methods are not only effective, but they can validate whether an assessment instrument is consistent with theoretical and empirical expectations. With these methods in hand, network models are poised to take the next step towards becoming a robust psychometric tool.

Modern Psychometrics for the Experimental Psychology of Reasoning

2010 ◽  
Vol 42 (1) ◽  
pp. 99-110 ◽  
Author(s):  
Jean-François Bonnefon ◽  
Stéphane Vautier
Keyword(s):  
Experimental Psychology ◽  
Psychology Of Reasoning ◽  
Modern Psychometrics

Modern Psychometrics With R

Technometrics ◽  
2020 ◽  
Vol 62 (1) ◽  
pp. 135-137 ◽  
Author(s):  
Stan Lipovetsky
Keyword(s):  
Modern Psychometrics

scholarly journals Towards a Network Psychometrics Approach to Assessment: Simulations for Redundancy, Dimensionality, and Loadings

2020 ◽  
Author(s):  
Alexander P. Christensen
Keyword(s):  
Alternative Explanation ◽  
Network Models ◽  
Assessment Instrument ◽  
Assessment Instruments ◽  
Detection Algorithms ◽  
Psychological Attributes ◽  
Confirmatory Factor ◽  
Psychometric Tool ◽  
Modern Psychometrics

Research using network models in psychology has proliferated over the last decade. The popularity of network models has largely been driven by their alternative explanation for the emergence of psychological attributes—observed variables co-occur because they are causally coupled and dynamically reinforce each other, forming cohesive systems. Despite their rise in popularity, the growth of network models as a psychometric tool has remained relatively stagnant, mainly being used as a novel measurement perspective. In this dissertation, the goal is to expand the role of network models in modern psychometrics and to move towards using these models as a tool for the validation of assessment instruments. This paper presents three simulation studies and an empirical example that are designed to evaluate different aspects of the psychometric network approach to assessment: reducing redundancy, detecting dimensions, and estimating loadings. The first simulation evaluated two novel approaches for determining whether items are redundant, which is a key component for the accuracy and interpretation of network measures. The second simulation evaluated several different community detection algorithms, which are designed to detect dimensions in networks. The third simulation evaluated an adapted formulation of the network measure, node strength, and how it compares to factor loadings estimated by exploratory and confirmatory factor analysis. The results of the simulations demonstrate that network models can be used as an effective psychometric tool and one that is on par with more traditional methods. Finally, in the empirical example, the methods from the simulations are applied to a real-world dataset measuring personality. This example demonstrated that these methods are not only effective, but they can validate whether an assessment instrument is consistent with theoretical and empirical expectations. With these methods in hand, network models are poised to take the next step towards becoming a robust psychometric tool.

Psycho-Informatics: Big Data shaping modern psychometrics

2014 ◽  
Vol 82 (4) ◽  
pp. 405-411 ◽  
Author(s):  
Alexander Markowetz ◽  
Konrad Błaszkiewicz ◽  
Christian Montag ◽  
Christina Switala ◽  
Thomas E. Schlaepfer
Keyword(s):  
Big Data ◽  
Modern Psychometrics
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