How do people know which family member is trustworthy? In this study, the authors tested the hypothesis that people use their perception of a family member's self‐control as an indicator of his or her trustworthiness. Eighty‐four Dutch families consisting of 2 parents and 2 children completed questionnaires assessing each family member's trust in and perceived self‐control of the other 3 family members. This full‐family design enabled the authors to examine their hypothesis in horizontal relationships, between family members of equal status (i.e., parent–parent and sibling–sibling relationships), and vertical relationships, in which partners have unequal status (i.e., parent–child and child–parent relationships). Consistent with the hypothesis, Social Relations Model analyses showed that being perceived as having higher self‐control is related to greater trustworthiness among adults and children in the large majority of horizontal and vertical relationships (10 out of 12). These findings highlight that perceived self‐control is an important factor by which to gauge trustworthiness in families. 相似文献
Latent class analysis (LCA) has been found to have important applications in social and behavioral sciences for modeling categorical response variables, and nonresponse is typical when collecting data. In this study, the nonresponse mainly included “contingency questions” and real “missing data.” The primary objective of this research was to evaluate the effects of some potential factors on model selection indices in LCA with nonresponse data.
We simulated missing data with contingency questions and evaluated the accuracy rates of eight information criteria for selecting the correct models. The results showed that the main factors are latent class proportions, conditional probabilities, sample size, the number of items, the missing data rate, and the contingency data rate. Interactions of the conditional probabilities with class proportions, sample size, and the number of items are also significant. From our simulation results, the impact of missing data and contingency questions can be amended by increasing the sample size or the number of items. 相似文献
In high-dimensional setting, componentwise L2boosting has been used to construct sparse model that performs well, but it tends to select many ineffective variables. Several sparse boosting methods, such as, SparseL2Boosting and Twin Boosting, have been proposed to improve the variable selection of L2boosting algorithm. In this article, we propose a new general sparse boosting method (GSBoosting). The relations are established between GSBoosting and other well known regularized variable selection methods in the orthogonal linear model, such as adaptive Lasso, hard thresholds, etc. Simulation results show that GSBoosting has good performance in both prediction and variable selection. 相似文献