Owing to the dynamic requirements of today’s teaching–learning environment, teachers’ training has emerged as an important policy question. The Pandit Madan Mohan Malaviya National Mission on Teachers and Teaching is an important step taken by the Ministry of Education, Government of India in this direction. The current study seeks to analyse the interactions among different aspects of Faculty Development Programmes and the overall outcomes. Using data ( n = 375) from programmes conducted under the mission, the study employs structure equation modelling to find that relevance has a positive effect on the change in attitude, which subsequently has a positive effect on the satisfaction of the trainees. Satisfaction further positively affects the change in confidence of the trainees. However, once this positive effect is controlled for, satisfaction and change in confidence correlate negatively. The estimation of the structural relationships is a novelty of this study, paving the way for further exploration.
Objective: The issue of affirmative action is a major point of discussion in distributive justice and allocation of scarce resources such as job-offerings or college seats. Philosophical arguments around this focus on diversity, historical justice and correction. The objective of this study is to examine the argument of correction in relation to affirmative action, which states that affirmative action helps correct biases in metrics used to measure merit. Method: We develop of a stochastic formulation of the argument of correction and analyse it using a probability-theory approach. Result: We find with the help of two counterexamples that the argument of correction is not generally valid, when we admit the stochastic nature of the problem. Conclusion: Though the argument of correction may be valid in some special cases, its general lack of validity presents a major challenge to this dominant argument widely used in the literature. Keywords: Affirmative action; distributive Justice; allocation of resources
Rational ignorance suggests that voters largely ignore a lot of information while voting due to the high cost of attaining and processing the information. It is further suggested that rational voters do not vote to affect election results but to express opinions. It is thus likely that cognitive biases shape electoral decision-making. The Halo effect, for instance, extrapolates information in one domain to another and helps voters avoid processing extra information. In this paper, we investigate the conditions under which extra information is processed or ignored, and first impressions are generalised. We find, through a Randomised Control Experiment, that new and weakly formed political beliefs also have effects like strongly held political beliefs, on information provided later. In particular, the study presented picture-information about candidates, either accompanying or not accompanying text-information. Additional text-information did not significantly change voter-choice when the text information reaffirmed picture-based preferences but did significantly change voter-choice when it contradicted picture-based preferences. These results are viewed from the perspective of both the Identity-Protective Cognition Thesis and the Halo effect, thus hinting that the two may be connected, an insight that is largely missing in the previous literature.
Despite the success of behavioral finance, the question of whether behavioral biases persist in the face of expertise is an oft-expressed concern. It becomes pertinent to explore if investor sophistication is associated with behavioral biases, as traders gain sophistication with experience and knowledge. The current study explores this relationship by proposing a new conceptualization of investors’ sophistication via the processes of learning and competition. The study empirically explores if herding and overconfidence biases are related to learning and competition, and thus, with investors’ sophistication via these aspects. Using data from equity investors from India (n = 257), the study employs ANOVA and multiple regression analysis through indicator function to form dummy variables for different categories. The results of the study conclude that diversification is significantly related to both the biases using ANOVA (F(3,253) = 3.081; p < 0.05) as well as multiple regression (p < 0.05). The other variables considered are found to be non-significant (p > 0.05) for both the biases. The study controls for all the other observed variables of the conceptual model to find out the effect of the change in the observed variables on the level of investor sophistication, making this study a novel and a distinct attempt.
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