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      Bottom Up Construction of a Personality Taxonomy

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          Abstract

          Abstract. In pursuit of a more systematic and comprehensive framework for personality assessment, we introduce procedures for assessing personality traits at the lowest level: nuances. We argue that constructing a personality taxonomy from the bottom up addresses some of the limitations of extant top-down assessment frameworks (e.g., the Big Five), including the opportunity to resolve confusion about the breadth and scope of traits at different levels of the organization, evaluate unique and reliable trait variance at the item level, and clarify jingle/jangle issues in personality assessment. With a focus on applications in survey methodology and transparent documentation, our procedures contain six steps: (1) identification of a highly inclusive pool of candidate items, (2) programmatic evaluation and documentation of item characteristics, (3) test-retest analyses of items with adequate qualitative and quantitative properties, (4) analysis of cross-ratings from multiple raters for items with adequate retest reliability, (5) aggregation of ratings across diverse samples to evaluate generalizability across populations, (6) evaluations of predictive utility in various contexts. We hope these recommendations are the first step in a collaborative effort to identify a comprehensive pool of personality nuances at the lowest level, enabling subsequent construction of a robust hierarchy – from the bottom up.

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          Multiple imputation by chained equations: what is it and how does it work?

          Multivariate imputation by chained equations (MICE) has emerged as a principled method of dealing with missing data. Despite properties that make MICE particularly useful for large imputation procedures and advances in software development that now make it accessible to many researchers, many psychiatric researchers have not been trained in these methods and few practical resources exist to guide researchers in the implementation of this technique. This paper provides an introduction to the MICE method with a focus on practical aspects and challenges in using this method. A brief review of software programs available to implement MICE and then analyze multiply imputed data is also provided.
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            An Introduction to the Five-Factor Model and Its Applications

            The five-factor model of personality is a hierarchical organization of personality traits in terms of five basic dimensions: Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness to Experience. Research using both natural language adjectives and theoretically based personality questionnaires supports the comprehensiveness of the model and its applicability across observers and cultures. This article summarizes the history of the model and its supporting evidence; discusses conceptions of the nature of the factors; and outlines an agenda for theorizing about the origins and operation of the factors. We argue that the model should prove useful both for individual assessment and for the elucidation of a number of topics of interest to personality psychologists.
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              Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning.

              Psychology has historically been concerned, first and foremost, with explaining the causal mechanisms that give rise to behavior. Randomized, tightly controlled experiments are enshrined as the gold standard of psychological research, and there are endless investigations of the various mediating and moderating variables that govern various behaviors. We argue that psychology's near-total focus on explaining the causes of behavior has led much of the field to be populated by research programs that provide intricate theories of psychological mechanism but that have little (or unknown) ability to predict future behaviors with any appreciable accuracy. We propose that principles and techniques from the field of machine learning can help psychology become a more predictive science. We review some of the fundamental concepts and tools of machine learning and point out examples where these concepts have been used to conduct interesting and important psychological research that focuses on predictive research questions. We suggest that an increased focus on prediction, rather than explanation, can ultimately lead us to greater understanding of behavior.
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                Author and article information

                Contributors
                Journal
                jpa
                European Journal of Psychological Assessment
                Hogrefe Publishing
                1015-5759
                2151-2426
                January 19, 2021
                November 2020
                : 36
                : 6 , Special Issue: New Approaches Toward Conceptualizing and Assessing Personality
                : 923-934
                Affiliations
                [ 1 ]Department of Psychology, University of Oregon, Eugene, OR, USA
                [ 2 ]Department of Management/Culverhouse College of Business, University of Alabama, USA
                [ 3 ]Department of Psychology, University of Edinburgh, UK
                [ 4 ]Institute of Psychology, University of Tartu, Estonia
                [ 5 ]Department of Psychology, University of Milan-Bicocca, Italy
                [ 6 ]Department of Behavioural and Cognitive Sciences, University of Luxembourg, Luxembourg
                [ 7 ]Department of Psychology, California State University, Fullerton, CA, USA
                [ 8 ]Department of Psychology, Northwestern University, USA
                [ 9 ]Department of Psychology, University of Pittsburgh, USA
                [ 10 ]Department of Psychology, Humboldt Universität zu Berlin, Germany
                [ 11 ]Department of Psychology, University of Kassel, Germany
                Author notes
                David M. Condon, University of Oregon, 1227 University St, Eugene, OR 9740, USA, E-mail dcondon@ 123456uoregon.edu
                Article
                jpa_36_6_923
                10.1027/1015-5759/a000626
                342462c7-e073-4d8a-aa03-2d1ed2d39bf6
                Copyright @ 2020
                History
                Categories
                Invited Article

                Assessment, Evaluation & Research methods,Psychology,General behavioral science
                taxonomy,personality assessment,nuances,hierarchy,survey methodology

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