A Propensity Score Adjustment for Multiple Group Structural Equation Modeling

In the behavioral and social sciences, quasi-experimental and observational studies are used due to the difficulty achieving a random assignment. However, the estimation of differences between groups in observational studies frequently suffers from bias due to differences in the distributions of covariates. To estimate average treatment effects when the treatment variable is binary, Rosenbaum and Rubin (1983a) proposed adjustment methods for pretreatment variables using the propensity score. However, these studies were interested only in estimating the average causal effect and/or marginal means. In the behavioral and social sciences, a general estimation method is required to estimate parameters in multiple group structural equation modeling where the differences of covariates are adjusted. We show that a Horvitz–Thompson-type estimator, propensity score weighted M estimator (PWME) is consistent, even when we use estimated propensity scores, and the asymptotic variance of the PWME is shown to be less than that with true propensity scores. Furthermore, we show that the asymptotic distribution of the propensity score weighted statistic under a null hypothesis is a weighted sum of independent χ² ₁ variables. We show the method can compare latent variable means with covariates adjusted using propensity scores, which was not feasible by previous methods. We also apply the proposed method for correlated longitudinal binary responses with informative dropout using data from the Longitudinal Study of Aging (LSOA). The results of a simulation study indicate that the proposed estimation method is more robust than the maximum likelihood (ML) estimation method, in that PWME does not require the knowledge of the relationships among dependent variables and covariates.     

Recent Debates Over Structural Realism

Summary  In recent years Structural Realism has been revived as a compromise candidate to resolve the long-standing question of scientific realism. Recent debate over structural realism originates with Worrall’s (1989) paper “Structural Realism: The best of Both Worlds”. However, critics such as Psillos contend that structural realism incorporates an untenable distinction between structure and nature, and is therefore unworkable. In this paper I consider three versions of structural realism that purport to avoid such criticism. The first is Chakravartty’s “semirealism” which proceeds by trying to show that structural realism and entity realism entail one another. I demonstrate that this position will not work, but follow Chakravartty’s contention that structural realism need not imply that scientific knowledge can only be of mathematical structure. I advance from this conclusion to sketch a version of structural realism that is consistent with recent deflationary approaches to the scientific realism question. Finally, I consider a third approach to structural realism Ladyman’s “metaphysical structural realism” which tries to avoid the difficulties of earlier versions by taking structure to be ontologically primary. I show that the deflationary approach to structural realism undermines the rationale behind Ladyman’s approach.     

Alternative Multiple Imputation Inference for Categorical Structural Equation Modeling

The use of item responses from questionnaire data is ubiquitous in social science research. One side effect of using such data is that researchers must often account for item level missingness. Multiple imputation is one of the most widely used missing data handling techniques. The traditional multiple imputation approach in structural equation modeling has a number of limitations. Motivated by Lee and Cai’s approach, we propose an alternative method for conducting statistical inference from multiple imputation in categorical structural equation modeling. We examine the performance of our proposed method via a simulation study and illustrate it with one empirical data set.     

bootstrap法在合成分数信度区间估计中的应用

在介绍bootstrap法原理的基础上,本文以一个同质测量模式的模拟数据为例,对结构方程模型下使用bootstrap法对合成分数信度的区间估计的应用中进行了演示。     

实体和初级关系（FOR）匹配的分离与结合对故事类比通达的影响

采用移动窗口阅读技术深入探讨实体和初级关系（FOR）匹配的分离与结合对故事类比通达的影响,对表面特征和结构特征在类比通达中的作用进行检验。实验1探讨系统变化目标故事和线索故事之间实体匹配数目与实体和初级关系（FOR）匹配的分离与结合是否影响读线索故事时对目标故事的回想。实验2探讨系统变化目标故事和线索故事之间初级关系（FOR）匹配的数目与实体和初级关系（FOR）匹配的分离与结合是否影响读线索故事时对目标故事的回想。实验3探讨实体和初级关系（FOR）匹配的分离与结合对目标故事的通达是否与高级关系（HOR）的匹配有关。结果表明,实体匹配独立影响通达,实体和初级关系（FOR）分离与结合影响故事类比通达,高级的结构关系（HOR）是实体和初级关系（FOR）影响故事类比通达的必要条件。     

A structural model of acculturation and mental health status among Chinese Americans

This study examined the role of acculturation and its direct and indirect impact on depressive symptom severity through various correlates, including socioeconomic status (SES), stress, social support, personality negativity, and physical health perception. Using structural equation modeling, the proposed model was tested with 983 employed Chinese Americans from a representative community sample, the majority of whom were immigrants. The results demonstrated that acculturation, correlated with SES, contributed to depressive symptom severity only through indirect pathways. Higher acculturation was found associated with higher stress that in turn contributed to more elevated depressive symptoms. On the other hand, higher acculturation was also found strongly correlated with higher SES, which was associated with lower depressive symptoms directly or indirectly through several mediators. Better support, lower personality negativity, better health perception, and lower stress were found mediating the relationship between higher SES and lower depressive symptom severity. The simultaneous multigroup analysis showed that the final model was comparable for both men and women with very few differences.