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      Elucidating the Associations Between Achievement Goals and Academic Dishonesty: a Meta-analysis

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          Abstract

          Academic dishonesty is a pervasive problem undermining the effectiveness of educational institutions. From a motivational perspective, researchers have proposed achievement goals as antecedents of academic dishonesty. Empirical findings corroborate the notion that mastery goals (focus on learning and competence development) are negatively linked to academic dishonesty. However, even though theoretical considerations suggest positive links between performance goals (focus on competence demonstration) and academic dishonesty, empirical findings are mixed. To provide a better understanding of how goals matter for academic dishonesty, we conducted three-level meta-analyses encompassing 163 effect sizes from 33 studies and a total of 19,787 participants. We found a disproportional use of correlational designs (using self-report measures of academic dishonesty) and personal goal measures (opposed to surrounding goal structures). Evidence of publication bias was not found. Our results confirmed the expected negative associations between mastery goals and academic dishonesty and revealed heterogenous findings for performance goals, with indications of positive associations within behavioral and intentional dishonesty measures, but not within self-reports. To further clarify the associations between achievement goals and academic dishonesty, we call for more methodological rigor in the measurement of goals and dishonesty as well as multi-methods approaches when investigating their interplay.

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          Measuring inconsistency in meta-analyses.

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            Bias in meta-analysis detected by a simple, graphical test

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              The FAIR Guiding Principles for scientific data management and stewardship

              There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
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                Author and article information

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                Journal
                Educational Psychology Review
                Educ Psychol Rev
                Springer Science and Business Media LLC
                1040-726X
                1573-336X
                March 2023
                March 11 2023
                March 2023
                : 35
                : 1
                Article
                10.1007/s10648-023-09753-1
                299d53ac-db9b-4dc8-99b5-9043a0fec984
                © 2023

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

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

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