Essentialist thinking has been implicated in producing segregation between social groups even in the absence of negative attitudes. This mode of category representation brings social group information to the fore in social information processing, suggesting that the social consequences of essentialism are associated with basic categorization processes. Drawing on recent work demonstrating that automatic approach and avoidance behaviors are directly embedded in intergroup categorization, we show that people who hold essentialist beliefs about human attributes are faster to approach their ingroup. Moreover this relationship is not accounted for by explicit prejudice towards the outgroup and essentialist beliefs were unrelated to implicit evaluation of either group. The findings demonstrate that essentialist beliefs are associated with immediate behavioral responses attached to social category exemplars, highlighting the links between these beliefs and basic categorization processes.
Researchers often combine longitudinal panel data analysis with tests of interactions (i.e., moderation). A popular example is the cross-lagged panel model (CLPM). However, interaction tests in CLPMs and related models require caution because stable (i.e., between-level, B) and dynamic (i.e., within-level, W) sources of variation are present in longitudinal data, which can conflate estimates of interaction effects. We address this by integrating literature on CLPMs, multilevel moderation, and latent interactions. Distinguishing stable B and dynamic W parts, we describe three types of interactions that are of interest to researchers: 1) purely dynamic or WxW; 2) cross-level or BxW; and 3) purely stable or BxB. We demonstrate estimating latent interaction effects in a CLPM using a Bayesian SEM in Mplus to apply relationships among work-family conflict and job satisfaction, using gender as a stable B variable. We support our approach via simulations, demonstrating that our proposed CLPM approach is superior to a traditional CLPMs that conflate B and W sources of variation. We describe higher-order nonlinearities as a possible extension, and we discuss limitations and future research directions.
This article compares a general cross-lagged model (GCLM) to other panel data methods based on their coherence with a causal logic and pragmatic concerns regarding modeled dynamics and hypothesis testing. We examine three "static" models that do not incorporate temporal dynamics: random- and fixed-effects models that estimate contemporaneous relationships; and latent curve models. We then describe "dynamic" models that incorporate temporal dynamics in the form of lagged effects: cross-lagged models estimated in a structural equation model (SEM) or multilevel model (MLM) framework; Arellano-Bond dynamic panel data methods; and autoregressive latent trajectory models. We describe the implications of overlooking temporal dynamics in static models and show how even popular cross-lagged models fail to control for stable factors over time. We also show that Arellano-Bond and autoregressive latent trajectory models have various shortcomings. By contrasting these approaches, we clarify the benefits and drawbacks of common methods for modeling panel data, including the GCLM approach we propose. We conclude with a discussion of issues regarding causal inference, including difficulties in separating different types of time-invariant and time-varying effects over time.
A review of the present status, recent enhancements, and applicability of the Siesta program is presented. Since its debut in the mid-1990s, Siesta's flexibility, efficiency, and free distribution have given advanced materials simulation capabilities to many groups worldwide. The core methodological scheme of Siesta combines finite-support pseudo-atomic orbitals as basis sets, norm-conserving pseudopotentials, and a real-space grid for the representation of charge density and potentials and the computation of their associated matrix elements. Here, we describe the more recent implementations on top of that core scheme, which include full spin-orbit interaction, non-repeated and multiple-contact ballistic electron transport, density functional theory (DFT)+U and hybrid functionals, time-dependent DFT, novel reduced-scaling solvers, density-functional perturbation theory, efficient van der Waals non-local density functionals, and enhanced molecular-dynamics options. In addition, a substantial effort has been made in enhancing interoperability and interfacing with other codes and utilities, such as wannier90 and the second-principles modeling it can be used for, an AiiDA plugin for workflow automatization, interface to Lua for steering Siesta runs, and various post-processing utilities. Siesta has also been engaged in the Electronic Structure Library effort from its inception, which has allowed the sharing of various low-level libraries, as well as data standards and support for them, particularly the PSeudopotential Markup Language definition and library for transferable pseudopotentials, and the interface to the ELectronic Structure Infrastructure library of solvers. Code sharing is made easier by the new open-source licensing model of the program. This review also presents examples of application of the capabilities of the code, as well as a view of on-going and future developments. ; SIESTA development has been historically supported by different Spanish National Plan projects: MEC-DGESPB95-0202, MCyT-BFM2000-1312, MEC-BFM2003-03372,FIS2006-12117, FIS2009-12721, FIS2012-37549, FIS2015- 64886-P, and RTC-2016-5681-7, the latter one together with Simune Atomistics Ltd. Currently, we thank financial support from the Spanish Ministry of Science, Innovation and Universities through the grant No. PGC2018-096955-B. We acknowledge the Severo Ochoa Centers of Excellence Program under Grants No. SEV-2015-0496 (ICMAB), and SEV-2017-0706 (ICN2), the GenCat Grant No.2017SGR1506, and the European Union MaX Center of Excellence (EU-H2020 Grant No. 824143). P.G.-F. acknowledges support from Ramón y Cajal Grant No. RyC-2013-12515. J.I.C acknowledges RTI2018-097895-B-C41. R.C. acknowledges to the European Union's Horizon 2020 research and innovation program under the Marie Skłodoswka–Curie grant agreement no. 665919. D.S.P, P.K, and P.B acknowledge MAT2016-78293-C6, FET-Open No. 863098, and UPV-EHU Grant IT1246-19. V. Yu was supported by a MolSSI fellowship (U.S. NSF award 1547580), and the ELSI development (V.B.,V.Yu) by NSF award 1450280. We also acknowledge Honghui Shang and Xinming Qin for giving us access to the HONPAS code, where a preliminary version of the hybrid functionals support described here was implemented. We are indebted to other contributors to the SIESTA project, whose names can be seen in the file in the Docs/Contributors.txt file of the SIESTA distribution, and we thank those, too many to list, contributing fixes, comments, clarifications, and documentation for the code. The data that support the findings of this study are available from the corresponding author upon reasonable request. ; Peer Reviewed ; Postprint (author's final draft)
