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 here 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 five 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.

BackgroundThe WHODAS-2 is a disability assessment instrument based on the conceptual framework of the International Classification of Functioning, Disability, and Health (ICF). It provides a global measure of disability and 7 domain-specific scores. The aim of this study was to assess WHODAS-2 conceptual model and metric properties in a set of chronic and prevalent clinical conditions accounting for a wide scope of disability in Europe.Methods1,119 patients with one of 13 chronic conditions were recruited in 7 European centres. Participants were clinically evaluated and administered the WHODAS-2 and the SF-36 at baseline, 6 weeks and 3 months of follow-up. The latent structure was explored and confirmed by factor analysis (FA). Reliability was assessed in terms of internal consistency (Cronbach's alpha) and reproducibility (intra-class correlation coefficients, ICC). Construct validity was evaluated by correlating the WHODAS-2 and SF-36 domains, and comparing known groups based on the clinical-severity and work status. Effect size (ES) coefficient was used to assess responsiveness. To assess reproducibility and responsiveness, subsamples of stable (at 6 weeks) and improved (after 3 moths) patients were defined, respectively, according to changes in their clinical-severity.ResultsThe satisfactory FA goodness of fit indexes confirmed a second order factor structure with 7 dimensions, and a global score for the WHODAS-2. Cronbach's alpha ranged from 0.77 (self care) to 0.98 (life activities: work or school), and the ICC was lower, but achieved the recommended standard of 0.7 for four domains. Correlations between global WHODAS-2 score and the different domains of the SF-36 ranged from -0.29 to -0.65. Most of the WHODAS-2 scores showed statistically significant differences among clinical-severity groups for all pathologies, and between working patients and those not working due to ill health (p < 0.001). Among the subsample of patients who had improved, responsiveness coefficients were small to moderate (ES = 0.3-0.7), but higher than those of the SF-36.ConclusionsThe latent structure originally designed by WHODAS-2 developers has been confirmed for the first time, and it has shown good metric properties in clinic and rehabilitation samples. Therefore, considerable support is provided to the WHODAS-2 utilization as an international instrument to measure disability based on the ICF model.

One important problem in the measurement of non-cognitive characteristics such as personality traits and attitudes is that it has traditionally been made through Likert scales, which are susceptible to response biases such as social desirability (SDR) and acquiescent (ACQ) responding. Given the variability of these response styles in the population, ignoring their possible effects on the scores may compromise the fairness and the validity of the assessments. Also, response-style-induced errors of measurement can affect the reliability estimates and overestimate convergent validity by correlating higher with other Likert-scale-based measures. Conversely, it can attenuate the predictive power over non-Likert-based indicators, given that the scores contain more errors. This study compares the validity of the Big Five personality scores obtained: (1) ignoring the SDR and ACQ in graded-scale items (GSQ), (2) accounting for SDR and ACQ with a compensatory IRT model, and (3) using forced-choice blocks with a multi-unidimensional pairwise preference model (MUPP) variant for dominance items. The overall results suggest that ignoring SDR and ACQ offered the worst validity evidence, with a higher correlation between personality and SDR scores. The two remaining strategies have their own advantages and disadvantages. The results from the empirical reliability and the convergent validity analysis indicate that when modeling social desirability with graded-scale items, the SDR factor apparently captures part of the variance of the Agreeableness factor. On the other hand, the correlation between the corrected GSQ-based Openness to Experience scores, and the University Access Examination grades was higher than the one with the uncorrected GSQ-based scores, and considerably higher than that using the estimates from the forced-choice data. Conversely, the criterion-related validity of the Forced Choice Questionnaire (FCQ) scores was similar to the results found in meta-analytic studies, correlating higher with Conscientiousness. Nonetheless, the FCQ-scores had considerably lower reliabilities and would demand administering more blocks. Finally, the results are discussed, and some notes are provided for the treatment of SDR and ACQ in future studies.