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Description: Schizotypy is a multidimensional construct that provides a useful framework for understanding the etiology, development, and risk for schizophrenia-spectrum disorders. Past research has applied traditional methods, such as factor analysis, to uncover common dimensions of schizotypy. In the present study, we aim to advance the construct of schizotypy, measured by the Wisconsin Schizotypy Scales-Short Forms (WSS-SF), beyond this general scope by applying two different psychometric network filtering approaches—the state-of-the-art approach (lasso), which has been employed in previous studies, and an alternative approach (Information Filtering Networks; IFN). First, we applied both filtering approaches to two large, independent samples of WSS-SF data (n = 5,831 and n = 2,171) and assessed each approach’s representation of the WSS-SF’s schizotypy construct. Both filtering approaches produced results similar to traditional methods, with the IFN approach producing results more consistent with previous theoretical interpretations of schizotypy. Then, we evaluated how well both filtering approaches reproduced global and local network characteristics between the two samples. We found that the IFN approach produced more consistent results for both global and local network characteristics. Finally, we sought to evaluate the predictability of the network centrality measures for each filtering approach by determining core, intermediate, and peripheral items of the WSS-SF and using them to predict interview reports of schizophrenia spectrum symptoms. We found some similarities and differences in their effectiveness, with the IFN approach’s network structure providing better overall predictive distinctions. We discuss the implications of our findings for schizotypy and for psychometric network analysis more generally.