35
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Introduction to Bayesian Mindsponge Framework analytics: An innovative method for social and psychological research

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The paper introduces Bayesian Mindsponge Framework (BMF) analytics, a new analytical tool for investigating socio, psychological, and behavioral phenomena. The strengths of this method derive from the combination of the mindsponge mechanism's conceptual formulation power and Bayesian analysis's inferential advantages. The BMF-based research procedure includes six main steps, in which the mindsponge-based conceptualization and model construction is the key step that makes the method unique. Therefore, we elaborate on the fundamental components and functions of the mindsponge mechanism and summarize them into five memorable principles so that other researchers can capitalize directly. An exemplary analysis was performed using a dataset of 3071 Vietnamese entrepreneurs’ decisiveness and perceptions of the likelihood of success/continuity to validate the method.

          • The paper provides five strong points of BMF analytics, originating from the good match between the mindsponge mechanism and Bayesian inference.

          • The paper also provides a step-by-step procedure for conducting BMF-based research.

          • The mindsponge mechanism's basic components and functions are elaborated and summarized into five core principles that can be applied directly for research conceptualization and model construction.

          Graphical abstract

          Related collections

          Most cited references59

          • Record: found
          • Abstract: not found
          • Article: not found

          brms: An R Package for Bayesian Multilevel Models Using Stan

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            A theory of human motivation.

            A. MASLOW (1943)
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Stan: A Probabilistic Programming Language

              Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.
                Bookmark

                Author and article information

                Contributors
                Journal
                MethodsX
                MethodsX
                MethodsX
                Elsevier
                2215-0161
                05 August 2022
                2022
                05 August 2022
                : 9
                : 101808
                Affiliations
                [a ]Centre for Interdisciplinary Social Research, Phenikaa University, Ha Dong District, Hanoi 100803, Viet Nam
                [b ]AISDL, Vuong & Associates, Dong Da, Hanoi 100000, Viet Nam
                Author notes
                Article
                S2215-0161(22)00188-1 101808
                10.1016/j.mex.2022.101808
                9400117
                36034522
                371ada6e-5034-476e-8ac4-15eb49070107
                © 2022 The Author(s)

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 25 June 2022
                : 27 July 2022
                Categories
                Method Article

                bayesian inference,mindsponge mechanism,information process,social sciences,psychological and behavioral sciences

                Comments

                Comment on this article