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

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.

Review of Modern Psychometrics with R

Well-informed psychometrics textbooks do not appear with clockwork regularity. Despite the importance of psychometric techniques to a wide variety of scientific disciplines and societal applications, many existing treatments are either too difficult for an introductory purpose or too superficial to support a full course on psychometrics.Given this situation, the appearance of Patrick Mair’s Psychometrics with R is a breath of fresh air in the landscape of psychometrics teaching. The book provides a mostly accessible treatment of a wide variety of techniques that are relevant to psychometrics, and illustrates these by means of analytic examples that are supported by freely downloadable R-code.

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

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.