RESUMO
Many semiparametric spatial autoregressive (SSAR) models have been used to analyze spatial data in a variety of applications; however, it is a common phenomenon that heteroscedasticity often occurs in spatial data analysis. Therefore, when considering SSAR models in this paper, it is allowed that the variance parameters of the models can depend on the explanatory variable, and these are called heterogeneous semiparametric spatial autoregressive models. In order to estimate the model parameters, a Bayesian estimation method is proposed for heterogeneous SSAR models based on B-spline approximations of the nonparametric function. Then, we develop an efficient Markov chain Monte Carlo sampling algorithm on the basis of the Gibbs sampler and Metropolis-Hastings algorithm that can be used to generate posterior samples from posterior distributions and perform posterior inference. Finally, some simulation studies and real data analysis of Boston housing data have demonstrated the excellent performance of the proposed Bayesian method.
RESUMO
This research, comprising three preregistered studies, investigated the link between self-efficacy and cheating on an academic test in 5- and 6-year-old children. Study 1 assessed children's general self-efficacy and found it to be unrelated to their cheating behavior. Study 2 assessed task-specific self-efficacy, which was not found to be associated with cheating. In Study 3, children were randomly assigned to either an experimental group, which received brief positive feedback on task-specific self-efficacy, or a control group, which received no feedback. The experimental group demonstrated significantly less cheating. These findings, for the first time, identify a specific connection between young children's self-efficacy and academic dishonesty and suggest that positive feedback on task-specific efficacy could be a simple effective strategy for fostering academic integrity early on.
