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      Who Differentiates by Skin Color? Status Attributions and Skin Pigmentation in Chile

      , , ,
      Frontiers in Psychology
      Frontiers Media SA

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          Abstract

          A growing body of research has shown that phenotypes and skin pigmentation play a fundamental role in stratification dynamics in Latin American countries. However, the relevance of skin color on status attribution for different status groups has been little studied in the region. This article seeks to broaden the research on phenotypic status cues using Chile as a context for analysis – a Latin American country with a narrow although continuous spectrum of skin tones, marked status differences, and a mostly white elite. We draw on status construction theory to hypothesize that skin pigmentation in Chile has become a status cue, although its heuristic relevance could differ across status groups. Using visual stimuli and a repeated measure design, we studied this relationship and tested whether the use of skin pigmentation as a status cue is conditional upon the status of those categorizing others. The results reveal that participants attribute, on average, lower status to others of darker skin. Besides, skin pigmentation has a conditional effect on the social status of participants: whereas skin pigmentation does not work as a status cue for lower status participants, it is an important status marker for the categorizations that middle and especially higher status participants perform. The phenotypic composition of reference groups of low- and high-status individuals and system justification are discussed as potential explanations for these results.

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              The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded

              The coefficient of determination R 2 quantifies the proportion of variance explained by a statistical model and is an important summary statistic of biological interest. However, estimating R 2 for generalized linear mixed models (GLMMs) remains challenging. We have previously introduced a version of R 2 that we called for Poisson and binomial GLMMs, but not for other distributional families. Similarly, we earlier discussed how to estimate intra-class correlation coefficients (ICCs) using Poisson and binomial GLMMs. In this paper, we generalize our methods to all other non-Gaussian distributions, in particular to negative binomial and gamma distributions that are commonly used for modelling biological data. While expanding our approach, we highlight two useful concepts for biologists, Jensen's inequality and the delta method, both of which help us in understanding the properties of GLMMs. Jensen's inequality has important implications for biologically meaningful interpretation of GLMMs, whereas the delta method allows a general derivation of variance associated with non-Gaussian distributions. We also discuss some special considerations for binomial GLMMs with binary or proportion data. We illustrate the implementation of our extension by worked examples from the field of ecology and evolution in the R environment. However, our method can be used across disciplines and regardless of statistical environments.
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                Author and article information

                Journal
                Frontiers in Psychology
                Front. Psychol.
                Frontiers Media SA
                1664-1078
                July 3 2019
                July 3 2019
                : 10
                Article
                10.3389/fpsyg.2019.01516
                9480d724-d565-45ab-8816-6f7d9b82cce1
                © 2019

                Free to read

                https://creativecommons.org/licenses/by/4.0/

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