Instrumental Variables Two-Stage Least Squares (2SLS) vs. Maximum Likelihood Structural Equation Modeling of Causal Effects in Linear Regression Models

In the presence of omitted variables or similar validity threats, regression estimates are biased. Unbiased estimates (the causal effects) can be obtained in large samples by fitting instead the Instrumental Variables Regression (IVR) model. The IVR model can be estimated using structural equation modeling (SEM) software or using Econometric estimators such as two-stage least squares (2SLS). We describe 2SLS using SEM terminology, and report a simulation study in which we generated data according to a regression model in the presence of omitted variables and fitted (a) a regression model using ordinary least squares, (b) an IVR model using maximum likelihood (ML) as implemented in SEM software, and (c) an IVR model using 2SLS. Coverage rates of the causal effect using regression methods are always unacceptably low (often 0). When using the IVR model, accurate coverage is obtained across all conditions when N = 500. Even when the IVR model is misspecified, better coverage than regression is generally obtained. Differences between 2SLS and ML are small and favor 2SLS in small samples (N ≤ 100).     

Positivity and quality of college life of business students: The mediating role of learning approaches

Drawing upon the causal agency theory, this study examined the direct and indirect (via learning approaches) links between positivity and quality of college life (QCL) of business students in a transitioning market, Vietnam. Two studies were conducted to test these relationships using structural equation modeling. Findings from study 1, based on a sample of 428 business students, showed that positivity was positively related to QCL. Further, deep learning approaches mediated the relationship between positivity and QCL. Findings from study 2, based on a sample of 416 business students, reconfirmed the role of positivity in QCL. However, the indirect relationship between positivity and QCL was not significant, demonstrating that surface learning approaches did not mediate the relationship between positivity and QCL. The findings contribute to the literature on positivity in higher education by confirming the overarching role that positivity plays, directly and indirectly (via deep learning approaches), in QCL.     

时空图对时钟佯谬诠释的应用

用时空图对时钟佯谬命题中的三个论断作了全面诠释.     

Effects of Diagrams on Mathematics Performance

Research on the facilitative effect of diagrams on solving mathematics problems is both inconsistent and inconclusive. This study investigates the effects of diagrams on 124 Singaporean pupils in three classes: Primary One and three, and Secondary One. Pupils' performance was measured with the use of teacher-written mathematics problems in the form of worksheets. Number of errors made on problems accompanied with and without diagrams were compared. Whether or not pupils benefit from diagrams was estimated by their patterns of errors made in the presence or absence of diagrams. Results both confirm and disconfirm previous findings and shed further light on the use of diagrams in mathematics.     

高等学校会计核算图解

根据国家教育委员会颁发的1989年1月1日起试行的“高等学校会计制度”的规定,绘制成10幅图解来介绍全国统一的高等学校现行的会计实务。本图解按资金收付记帐法绘制,左收右付、清晰地反映了帐户之间的对应关系,并且附有经济业务的简要说明。     

溶液中离子反应的接受势与优势图

根据水溶液中离子反应的特点,介绍了离子给体/受体共轭对的接受势和优势图,并结合实例阐述了接受势和优势图的应用。     

Inference making ability and its relation to comprehension failure in young children

Young children's reading comprehension skill is associated with their ability to draw inferences (Oakhill 1982, 1984). An experiment was conducted to investigate the direction of this relation and to explore possible sources of inferential failure. Three groups of children participated: Same-age skilled and less skilled comprehenders, and a comprehension-age match group. The pattern of performance indicated that the ability to make inferences was not a by-product of good reading comprehension, rather that good inference skills are a plausible cause of good reading comprehension ability. Failure to make inferences could not be attributed to lack of relevant general knowledge. Instead, the pattern of errors indicated that differences in reading strategy were the most likely source of these group differences.     

Dual disentanglement of user–item interaction for recommendation with causal embedding

To achieve personalized recommendations, the recommender system selects the items that users may like by learning the collected user–item interaction data. However, the acquisition and use of data usually form a feedback loop, which leads to recommender systems suffering from popularity bias. To solve this problem, we propose a novel dual disentanglement of user–item interaction for recommendation with causal embedding (DDCE). Different from the existing work, our innovation is we take into account double-end popularity bias from the user-side and the item-side. Firstly, we perform a causal analysis of the reasons for user–item interaction and obtain the causal embedding representation of each part according to the analysis results. Secondly, on the item-side, we consider the influence of item attributes on popularity to improve the reliability of the item popularity. Then, on the user-side, we consider the effect of the time series when obtaining users’ interest. We model the contrastive learning task to disentangle users’ long–short-term interests, which avoids the bias of long–short-term interests overlapping, and use the attention mechanism to realize the dynamic integration of users’ long–short-term interests. Finally, we realize the disentanglement of user–item interaction reasons by decoupling user interest and item popularity. We experiment on two real-world datasets (Douban Movie and KuaiRec) to verify the significance of DDCE, the average improvement of DDCE in three evaluation metrics (NDCG, HR, and Recall) compared to the state-of-the-art model are 5.1106% and 4.1277% (MF as the backbone), 3.8256% and 3.2790% (LightGCN as the backbone), respectively.     

毛奇龄“推移”说与清代汉易复兴

毛奇龄提出易有变易、交易、转易、对易、移易,演《易》系辞用移易,并证之以经文。他从《系辞传》"乾坤成列"、"刚柔相推"、"方以类聚物以群分"出发,整合了易学史上以卦变为核心的易象说,提出移易说,以此详细推演了六十四卦之间的关系。毛氏的移易说以全新的易学象数视域解读《周易》经文和《易传》,从中提炼和抽绎出"移易"、"聚卦"、"分卦"等概念,进而解构和重建六十四卦符号系统,以此探索卦与卦之间的内在联系,从而形成与以往卦变说不同的、以移易为核心的符号系统。他以移易之象和其他象解《易》,成为清初复兴汉易最重要的标志。同时,他运用考据的方法对于宋代图书之学进行全面的检讨和批判,开清初考据学之学风,对于清中后期乾嘉学派形成和汉易鼎盛产生重大的影响。     

Causal attribution and emotion in the days following competition

Abstract

We examined the extent to which attributions are consistent in the days following competition and how attributions made immediately after competition may influence the temporal patterning of emotions experienced in response to competition. A sample of 60 adult female golfers completed measures of performance satisfaction, causal attribution, and emotion immediately after competition, 5 h after competition, and 2 days after competition. Repeated-measures multivariate analysis of covariance indicated that attributions did not change over this period. Emotions showed a significant decrease in intensity over the 5-h post-competition period. Regression analyses indicated that changes in anger and dejection were more likely in the case of less successful performances. For anger, attributions moderated this level of change. Golfers experienced anger for a longer period when they identified the cause of poor performance as stable rather than unstable. Thus, in the present sample although attributions did not change over 2 days, the longevity of anger depended on the attributions made immediately after competition.