https://www.selleckchem.com/products/eribulin-mesylate-e7389.html Exploratory graph analysis (EGA) is a new technique that was recently proposed within the framework of network psychometrics to estimate the number of factors underlying multivariate data. Unlike other methods, EGA produces a visual guide-network plot-that not only indicates the number of dimensions to retain, but also which items cluster together and their level of association. Although previous studies have found EGA to be superior to traditional methods, they are limited in the conditions considered. These issues are addressed through an extensive simulation study that incorporates a wide range of plausible structures that may be found in practice, including continuous and dichotomous data, and unidimensional and multidimensional structures. Additionally, two new EGA techniques are presented one that extends EGA to also deal with unidimensional structures, and the other based on the triangulated maximally filtered graph approach (EGAtmfg). Both EGA techniques are compared with 5 widely used factor analytic techniques. Overall, EGA and EGAtmfg are found to perform as well as the most accurate traditional method, parallel analysis, and to produce the best large-sample properties of all the methods evaluated. To facilitate the use and application of EGA, we present a straightforward R tutorial on how to apply and interpret EGA, using scores from a well-known psychological instrument the Marlowe-Crowne Social Desirability Scale. (PsycINFO Database Record (c) 2020 APA, all rights reserved).OBJECTIVE The primary purpose of the study was to evaluate the psychometric properties of the Job Satisfaction of Persons with Disabilities Scale in a sample of peer support specialists. METHOD A total of 121 employed peer support specialists with lived experience of a serious mental health condition were recruited for this study from statewide peer certification training programs and the International Association of