A Meta-Analysis of the Self Regulation Domain
The current research is a meta-analysis of the nomological network of the self-regulation domain. It is proposed that trainees must regulate both their affective and cognitive self-regulatory processes in order to maintain favorable learning outcomes in training. Regulating affect requires trainees to maintain emotional control and to avoid anxiety and other negative emotions (e.g., worry) during training. Failure to control one’s emotions impairs learning and performance because negative emotions direct attention away from training towards oneself (Kanfer, Ackerman, & Heggestad, 1996). Additionally, suppression of negative emotions drains cognitive resources (Muraven & Baumeister, 2000). Regulating cognition includes controlling one’s thought processes (Ford, Smith, Weissbein, Gully, & Salas, 1998), applying effort to learn the material (Yeo & Neal, 2004), and involves planning and monitoring as well as evaluating progress towards one’s goals during training (Schraw & Moshman, 1995; Sternberg, 1998). Multiple indicators of affective and cognitive self-regulation are examined in order to assess the joint effect of the two components of self-regulation on declarative and procedural knowledge. In addition, we assess if self-regulation accounts for variability in learning outcomes after controlling for the strongest predictor of training performance, cognitive ability (Colquitt, LePine, & Noe, 2000; Ree & Earles, 1991).
Preliminary results suggest both affective and cognitive self-regulation have moderate relationships with declarative and procedural knowledge and account for variability in learning after controlling for cognitive ability. Metacognition, effort, mental focus, emotional control and anxiety had mean corrected correlations of .12, .21, .22, .36, -.21, respectively, for declarative knowledge and .12, .25, .17, .38, -.27, respectively, for procedural knowledge. In addition, the self-regulation constructs tended to correlate weakly with cognitive ability, suggesting trainees high and low in cognitive ability are equally likely to engage in self-regulation processes during training. After controlling for cognitive ability, together affective and cognitive self-regulatory processes accounted for 17% of the variance in declarative knowledge and 21% of the variance in procedural knowledge. Overall the results suggest self-regulation has an essential role in predicting learning during training and accounts for variability in learning over cognitive ability.
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