Modeling of Professional Growth and Learning: Bayesian approach

The major goal of the study was to contribute both to the basic research on professional growth and learning, and to the development and use of quantitative research methodology in these research areas. The research goal was addressed with four empirical studies conducted between 2002-2007. The research questions were as follows: 1) What is the optimal number of dimensions and items in the Growth-oriented Atmosphere Questionnaire (GOAQ) to describe the theoretical model of growth-oriented atmosphere? (Study I); 2) Are the theoretical dimensions of the Self-confidence attitude attribute Scales (SaaS) questionnaire identified in the domain of three groups of mathematically gifted participants: Academic mathematics Olympians, polytechnic institute of higher education students, and elementary school students who have participated in mathematical competitions? (Study II); 3) What is the optimal number of dimensions and items in the Abilities for Professional Learning Questionnaire (APLQ) to describe the theoretical model of learning experiences and motivation? (Study III); 4) Is there a difference between substantive interpretations of the results of Bayesian dependency modeling and linear bivariate correlational analysis with professional growth data that has both linear and non-linear dependencies? (Study IV); 5) Is there a difference between substantive interpretations of the results of Bayesian dependency modeling and linear confirmatory factor analysis with professional growth data that has both linear and non-linear dependencies? (Study IV). Results of the first study showed that the theoretical four group classification of the growth-oriented atmosphere factors was supported by the empirical evidence: 1) Support and rewards from the management; 2) Incentive value of the job; 3) Operational capacity of the team; 4) Work related stress. Further, the results of categorical factor analysis showed that the 67-item and thirteen-factor solution was the most interpretable in terms of correspondence to the theoretical GOA model. Results of the second study showed that the theoretical four group classification of the self-confidence attitude attribute factors was supported by the empirical evidence: 1) Success due to ability; 2) Failure due to a lack of ability; 3) Success due to effort; 4) Failure due to a lack of effort. Further, the results of exploratory factor analysis showed that the eight item and four factor solution was the most interpretable in terms of the attribution theory. Results of the third study showed that the theoretical six group classification of the motivational factors was supported by the empirical evidence: 1) Intrinsic goal orientation; 2) Extrinsic goal orientation; 3) Meaningfulness of study; 4) Control beliefs; 5) Self-efficacy; 6) Test anxiety. Further, the results of confirmatory factor analysis showed that the 21-item solution was the most interpretable in terms of correspondence to the baseline model. Results of the fourth study showed that in general Bayesian network models were congruent with the correlation matrixes as both techniques found the same variables independent of all the other variables. However, non-linear modeling found with both linear and non-linear samples a greater number of strong dependencies between the GOA factors. Results further showed that by using linear methods with non-linear data may lead to different substantive interpretations when using non-linear methods with the same data.