August 6,6 /School Subjects Specificity of Autonomous and Controlled Motivationsperformance on a

August 6,6 /School Subjects Specificity of Autonomous and Controlled Motivationsperformance on a five-point scale ranging from 1 “Among the worst students of the class” to 5 “Among the best students of the class”. Study 2. Autonomous and controlled regulations. An adapted version of the Academic Motivation Scale (AMS) [23] was administered to the children. The same three items were used to assess each regulation at the school subject and contextual levels (mathematics, French, English and physical education). For example, the same intrinsic motivation item was used for all school subjects and for the contextual measure. The children were asked to rate how much they agreed with each item on a seven-point scale from 1 (Totally disagree) to 7 (Totally agree). Students’ self-concept. Six items were selected from the Academic Self-Description Questionnaire [21, 22] to assess self-concept in the four school subjects and at the contextual level (e.g., “I have always qhw.v5i4.5120 done well in Fruquintinib cost French (mathematics, English, physical education, school)”, “French (mathematics, English, physical education, school) is easy for me”, and “I learn things quickly in French (mathematics, English, physical education, school)”.Statistical analysesCorrelated trait-correlated method minus one model. The above-mentioned hierarchical and multidimensional aspects require statistical models to be built according to hierarchically structured constructs [24]. Among the multiple approaches proposed, the correlated trait-correlated method minus one (CTCM-1) model [25] appeared to be the most suitable model for our research purpose. This method is used in multitrait-multimethod studies to distinguish variances attributable to traits and methods. This modeling procedure has the advantage of combining and disentangling variances in measures attributable to a global (i.e., contextual) trait or to a state or method (i.e., specific) measure. As highlighted by Brunner, Keller, Dieredonck, Reichert, Ugen, Fishbach and Martin [26] regarding academic self-concepts, this model is suitable for hierarchical and multidimensional constructs “because the model is able to account for (a) the subject-specific nature of constructs, (b) the separation of subjectspecific (constructs) across domains, and (c) the hierarchical organization of (constructs), with (d) general academic (construct) at the apex” (p.968). This model therefore allows testing of the hierarchical structure of autonomous and controlled academic motivations while taking into account multiple school subjects according to the HMIEM principles HIV-1 integrase inhibitor 2 cost postulated by Vallerand [3]. More specifically, the CTCM-1 disentangles the variance in autonomous and controlled motivations attributable to contextual (school) or school subject (e.g., mathematics, science, writing, and reading) levels. Fig 2 presents an example of variance j.jebo.2013.04.005 partitioning for intrinsic motivation. More specifically, intrinsic motivation at the school level is considered as a single trait, whereas intrinsic motivations for various school subjects are considered as correlated methods or school subject deviations from the global trait. Intrinsic motivation indicators for the four school subjects are therefore caused not only by specific latent constructs but also by a latent construct for contextual intrinsic motivation. Consequently, the latent factor of the single trait for the four regulations (i.e., intrinsic, identified, introjected and external) at the contextual level compri.August 6,6 /School Subjects Specificity of Autonomous and Controlled Motivationsperformance on a five-point scale ranging from 1 “Among the worst students of the class” to 5 “Among the best students of the class”. Study 2. Autonomous and controlled regulations. An adapted version of the Academic Motivation Scale (AMS) [23] was administered to the children. The same three items were used to assess each regulation at the school subject and contextual levels (mathematics, French, English and physical education). For example, the same intrinsic motivation item was used for all school subjects and for the contextual measure. The children were asked to rate how much they agreed with each item on a seven-point scale from 1 (Totally disagree) to 7 (Totally agree). Students’ self-concept. Six items were selected from the Academic Self-Description Questionnaire [21, 22] to assess self-concept in the four school subjects and at the contextual level (e.g., “I have always qhw.v5i4.5120 done well in French (mathematics, English, physical education, school)”, “French (mathematics, English, physical education, school) is easy for me”, and “I learn things quickly in French (mathematics, English, physical education, school)”.Statistical analysesCorrelated trait-correlated method minus one model. The above-mentioned hierarchical and multidimensional aspects require statistical models to be built according to hierarchically structured constructs [24]. Among the multiple approaches proposed, the correlated trait-correlated method minus one (CTCM-1) model [25] appeared to be the most suitable model for our research purpose. This method is used in multitrait-multimethod studies to distinguish variances attributable to traits and methods. This modeling procedure has the advantage of combining and disentangling variances in measures attributable to a global (i.e., contextual) trait or to a state or method (i.e., specific) measure. As highlighted by Brunner, Keller, Dieredonck, Reichert, Ugen, Fishbach and Martin [26] regarding academic self-concepts, this model is suitable for hierarchical and multidimensional constructs “because the model is able to account for (a) the subject-specific nature of constructs, (b) the separation of subjectspecific (constructs) across domains, and (c) the hierarchical organization of (constructs), with (d) general academic (construct) at the apex” (p.968). This model therefore allows testing of the hierarchical structure of autonomous and controlled academic motivations while taking into account multiple school subjects according to the HMIEM principles postulated by Vallerand [3]. More specifically, the CTCM-1 disentangles the variance in autonomous and controlled motivations attributable to contextual (school) or school subject (e.g., mathematics, science, writing, and reading) levels. Fig 2 presents an example of variance j.jebo.2013.04.005 partitioning for intrinsic motivation. More specifically, intrinsic motivation at the school level is considered as a single trait, whereas intrinsic motivations for various school subjects are considered as correlated methods or school subject deviations from the global trait. Intrinsic motivation indicators for the four school subjects are therefore caused not only by specific latent constructs but also by a latent construct for contextual intrinsic motivation. Consequently, the latent factor of the single trait for the four regulations (i.e., intrinsic, identified, introjected and external) at the contextual level compri.