Women in Science, Technology, Engineering, and Mathematics (STEM): An Investigation of Their Implicit Gender Stereotypes and Stereotypes' Connectedness to Math Performance

In spite of many barriers facing women's enrollment in Science, Technology, Engineering, and Mathematics (STEM), some women are successful in these counter-stereotypic disciplines. The present research extended work primarily conducted in the United States by investigating implicit gender-STEM stereotypes—and their relation to performance—among female and male engineering and humanities students in Southern France. In study 1 (N = 55), we tested whether implicit gender-math stereotypes—as measured by the Implicit Association Test (IAT; Greenwald et al. 1998)—would be weaker among female engineering students as compared to female humanities, male engineering and male humanities students. In study 2 (N = 201), we tested whether this same results pattern would be observed with implicit gender-reasoning stereotypes (using a newly created IAT) and, in addition, whether implicit gender-reasoning stereotypes would be more strongly (and negatively) related to math grades for female humanities students as compared to the three other groups. Results showed that female engineering students held weaker implicit gender-math and gender-reasoning stereotypes than female humanities, male engineering and male humanities students. Moreover, implicit stereotyping was more negatively related to math grades for female humanities students than for the three other groups. Together, findings demonstrate that female engineering students hold weaker implicit gender-STEM stereotypes than other groups of students and, in addition, that these stereotypes are not necessarily negatively associated with math performance for all women. Discussion emphasizes how the present research helps refine previous findings and their importance for women's experience in STE

Individual differences in perceived social desirability of Openness to experience: A new framework for social desirability responding in personality research

The extent to which response distortion – such as social desirability responding (SDR) – is present in self–report measures is an issue of concern and debate in personality research, as it may seriously impact such measures' psychometric indices. The present research aimed at using the social value framework to shed new light on SDR in self–report personality tests. Two studies tested the moderating role of individual differences in perceived social desirability of the Openness to Experience dimension for test–retest reliability and predictive validity of a typical Openness measure. Results support the hypothesized moderating role of perceived social desirability for improving test–retest reliability, providing the testing condition guarantees full anonymity (Study 1), and for predictive validity (Study 2). Findings are discussed with regards to SDR in personality research and the social value framework

Analisis Pentingnya Penilaian Prestasi Kerja dalam Hubungannya dengan Peningkatan Motivasi Karyawan

Performance appraisal is one of many aspects that are very important in Human resource management. On the other hand, motivation is also vital for a Manager because it drives and directs all human behavior, including an employees performance. This short essay tries to analyze the relationship Between performance appraisal and motivation, and how performance appraisal system can enhance motivation of employees. The motivation itself is seen in the light of content and Process theorie

Women in Science, Technology, Engineering, and Mathematics (STEM): An Investigation of Their Implicit Gender Stereotypes and Stereotypes’ Connectedness to Math Performance

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Développer des interventions de prévention et de promotion de la santé fondées sur les données probantes : la Démarche EValuation Action (DEVA)

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Focus sur les stéréotypes de genre implicites dans les domaines académiques : différences intergroupes, apports des méthodologies mixtes et perspectives d’évolution

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Sample reconstruction from summary statistics: assumptions and novel contributions

International audienceResponse distributions in social psychology papers are usually summarized by means and standard deviations (in absence of raw data), even when they are not necessarily continuous, unbounded nor unimodal (e.g., scores on a small set of Likert-like items). Applying statistical methods assuming normality (e.g., of prediction errors for the general linear model) may lead to inflated type I error rates and broadly to incorrect inferences drawn from the data. This particularly applies to social cognition when studying biases that may heavily distort the distributions (e.g., social desirability or utility response bias).Given the need to confirm that summary statistics are correct (to prevent errors or fraud) and representative of the distributions to trust scientific results, several sample reconstruction techniques have been recently developed to infer probable distributions given a reported mean, standard deviation, and measure constraints. Nevertheless, current methods are either heuristic (e.g., iterative in SPRITE; Heathers et al., 2018) or impose strong constraints (e.g., integer measures in CORVIDS; Wilner, Wood, & Simons, 2018) yet are used to detect reporting errors based on probabilistic reasoning. They implicitly assume distributions of real-world study samples closely resemble random distributions generated through model-based simulations. Although generating some specific samples through deterministic or stochastic simulations may be highly improbable, experimental manipulation and study constraints may drastically increase this probability, which cannot be estimated with such methods.We here introduce two contributions to study and address methodological issues of existing approaches:1) Exact calculation of the number of samples matching a given approximate mean, approximate standard deviation and measure constraints. Combinatorics allows to assess with certitude the (im)possibility of given statistics being observed on a real sample satisfying the constraints, and the exhaustivity of solutions provided by sample reconstruction techniques.2) Graph-based method for exhaustive reconstruction of all possible samples given approximate mean, approximate standard deviation and measure constraints (any finite set of arbitrary values), not relying on any heuristics. The method also allows to relax assumptions from existing methods, and the resulting graph can be used to generate samples under various additional constraints, better reflecting study constraints and/or assessing the validity of empirical results.Beyond these two contributions aiming to circumvent methodological issues of existing approaches, they more broadly aim at showing that all mathematical and computational developments (including sample reconstruction techniques and most statistical methods) rely on a set of assumptions that must be satisfied for the inference to be correct

A Focus on Mastery in the Assessment Process Favors Low-Socioeconomic-Status Students’ Educational Attainment at University

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Dynamic competition and binding of concepts through time and space

International audienceModels of implicit stereotypes (e.g., association of male with math or female with language) usually explain the faster responses observed for stereotype-congruent trials in the Implicit Association Test (IAT) by requiring a fundamental opposition between the male and female concepts (or math–language), limiting the decision-making dynamics to abstract dimensions. This paper introduces alternate models exploiting the sensorimotor dimensions of the IAT, which naturally account for the opposition between concepts, because typically mapped on opposite corners of the screen space and on different response actions. In addition to the emergence of the IAT effect, dynamic characteristics of the decision-making process within these models are tested against human data, obtained with a mouse-tracking adapted IAT procedure

Focus sur les stéréotypes de genre implicites dans les domaines académiques : différences intergroupes, apports des méthodologies mixtes et perspectives d’évolution

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