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Development and validation of a scale assessing achievement goals in driving

Achievement goals have been a major topic of research for more than 30 years. Achievement goals represent what and why individuals want to achieve. This literature has provided a large body of research in many domains (e.g., education, sports, work), but no study has hitherto been conducted in the d...

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Detalles Bibliográficos
Autores principales: Mascret, Nicolas, Nicolleau, Martin, Ragot-Court, Isabelle
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7067493/
https://www.ncbi.nlm.nih.gov/pubmed/32163498
http://dx.doi.org/10.1371/journal.pone.0230349
Descripción
Sumario:Achievement goals have been a major topic of research for more than 30 years. Achievement goals represent what and why individuals want to achieve. This literature has provided a large body of research in many domains (e.g., education, sports, work), but no study has hitherto been conducted in the driving domain. Moreover, no scale was available to assess achievement goals in driving even though driving is an achievement context. Indeed, drivers’ personal competence is engaged and continuously evaluated both by others and the drivers themselves. The present study seeks to fill these gaps. The aims of the study were to emphasize the interest of investigating achievement goals in car driving, to develop and validate a scale named Achievement Goal Questionnaire in Driving (AGQ-D), to compare this baseline model with five alternative models, to assess the gender invariance of the scale, and to study its concurrent validity using interest and self-efficacy in driving, accidents, at-fault accidents, emergency maneuvers, and fines. The results of the Confirmatory Factor Analysis showed the good psychometric properties of the scale completed by 420 French car drivers, in comparison with five alternative models. The scale was also invariant across gender. Finally, the results of the hierarchical regression analyses showed its concurrent validity. The most significant results highlighted that mastery-avoidance goals (i.e., to avoid being a bad driver and avoiding failing in driving task demands) negatively predicted self-reported accidents and at-fault accidents. Performance-approach goals (i.e., to outperform other drivers) also positively predicted self-reported emergency maneuvers. The AGQ-D is now a tool available to develop research in the driving domain and to extend the numerous advances already found in other domains.