Many analysts, one data set: making transparent how variations in analytic choices affect results

Twenty-nine teams involving 61 analysts used the same data set to address the same research question: whether soccer referees are more likely to give red cards to dark-skin-toned players than to light-skin-toned players. Analytic approaches varied widely across the teams, and the estimated effect sizes ranged from 0.89 to 2.93 (Mdn = 1.31) in odds-ratio units. Twenty teams (69%) found a statistically significant positive effect, and 9 teams (31%) did not observe a significant relationship. Overall, the 29 different analyses used 21 unique combinations of covariates. Neither analysts’ prior beliefs about the effect of interest nor their level of expertise readily explained the variation in the outcomes of the analyses. Peer ratings of the quality of the analyses also did not account for the variability. These findings suggest that significant variation in the results of analyses of complex data may be difficult to avoid, even by experts with honest intentions. Crowdsourcing data analysis, a strategy in which numerous research teams are recruited to simultaneously investigate the same research question, makes transparent how defensible, yet subjective, analytic choices influence research results

A modern network approach to revisiting the positive and negative affective schedule (PANAS) construct validity

Introduction: The factor structure of the Positive and Negative Affective Schedule (PANAS) is still a topic of debate. There are several reasons why using Exploratory Graph Analysis (EGA) for scale validation is advantageous and can help understand and resolve conflicting results in the factor analytic literature. Objective: The main objective of the present study was to advance the knowledge regarding the factor structure underlying the PANAS scores by utilizing the different functionalities of the EGA method. EGA was used to (1) estimate the dimensionality of the PANAS scores, (2) establish the stability of the dimensionality estimate and of the item assignments into the dimensions, and (3) assess the impact of potential redundancies across item pairs on the dimensionality and structure of the PANAS scores. Method: This assessment was carried out across two studies that included two large samples of participants. Results and Conclusion: In sum, the results are consistent with a two-factor oblique structure.Fil: Flores Kanter, Pablo Ezequiel. Universidad Empresarial Siglo XXI; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Garrido, Luis Eduardo. Pontificia Universidad Católica Madre y Maestra; República DominicanaFil: Moretti, Luciana Sofía. Universidad Empresarial Siglo XXI; Argentina. Pontificia Universidad Católica Madre y Maestra; República Dominicana. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Medrano, Leonardo. Universidad Empresarial Siglo XXI; Argentina. Pontificia Universidad Católica Madre y Maestra; República Dominicana. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Supply chain sustainability performance measurement of small and medium sized enterprises using structural equation modeling

Sustainability of small and medium sized enterprises (SMEs) is significant as SMEs contribute to GDP substantially in every economy. This research develops an innovative sustainable supply chain performance measurement model for SMEs. Prior researches predominantly use balanced score card (BSC) approach that presume causal relationship of criteria and Data Envelopment Analysis (DEA), which derive efficiency of units from a few input and output criteria. While DEA is effective for policymakers, BSC is more suitable for individual SME. The proposed method that uses structural equation modeling (SEM) approach to derive the relationship of criteria and criteria weights formulates regression-type models for a specific region as well as for specific SME. The SEM-based supply chain sustainability performance measurement model is beneficial to policymakers as they can determine means for improvement at a regional level. The proposed method could also facilitate managers/owners of individual SMEs with measures for improving their supply chain sustainability performance. The method has been applied to three varied geographical locations in the UK, France and India in order to demonstrate its effectiveness

An examination of the self-referent executive processing model of test anxiety: control, emotional regulation, self-handicapping, and examination performance

According to the self-referent executive processing (S-REF) model, test anxiety develops from interactions between three systems: executive self-regulation processes, self-beliefs, and maladaptive situational interactions. Studies have tended to examine one system at a time, often in conjunction with how test anxiety relates to achievement outcomes. The aim of this study was to enable a more thorough test of the S-REF model by examining one key construct from each of these systems simultaneously. These were control (a self-belief construct), emotional regulation through suppression and reappraisal (an executive process), and self-handicapping (a maladaptive situational interaction). Relations were examined from control, emotional regulation, and self-handicapping to cognitive test anxiety (worry), and subsequent examination performance on a high-stakes test. Data were collected from 273 participants in their final year of secondary education. A structural equation model showed that higher control was indirectly related to better examination performance through lower worry, higher reappraisal was indirectly related to worse examination performance through higher worry, and higher self-handicapping was related to worse examination performance through lower control and higher worry. These findings suggest that increasing control and reducing self-handicapping would be key foci for test anxiety interventions to incorporate. © 2018 The Author(s