Doubly Robust-Type Estimation for Covariate Adjustment in Latent Variable Modeling

Due to the difficulty in achieving a random assignment, a quasi-experimental or observational study design is frequently used in the behavioral and social sciences. If a nonrandom assignment depends on the covariates, multiple group structural equation modeling, that includes the regression function of the dependent variables on the covariates that determine the assignment, can provide reasonable estimates under the condition of correct specification of the regression function. However, it is usually difficult to specify the correct regression function because the dimensions of the dependent variables and covariates are typically large. Therefore, the propensity score adjustment methods have been proposed, since they do not require the specification of the regression function and have been applied to several applied studies. However, these methods produce biased estimates if the assignment mechanism is incorrectly specified. In order to make a more robust inference, it would be more useful to develop an estimation method that integrates the regression approach with the propensity score methodology. In this study we propose a doubly robust-type estimation method for marginal multiple group structural equation modeling. This method provides a consistent estimator if either the regression function or the assignment mechanism is correctly specified. A simulation study indicates that the proposed estimation method is more robust than the existing methods. This research was partially supported by the Ministry of Education, Science, Sports and Culture, Grant-in-Aid for Young Scientists (B), 187-30406.     

项目反应理论框架下的新等值方法——对数对比等值法

项目反应理论有一些以除法形式给出的多级评分模型,若采用Haebara等值法、Stocking_Lord等值法或对称相对熵等值法进行测验等值,都因其对初值有较高要求而可能导致失败。针对这一类模型,我们给出了一种新的等值方法——对数对比等值法。这种方法收敛快,对迭代初值要求低,所得结果精度较高,可以为其他等值方法提供良好的初值。研究表明,对数对比等值法还改进和推广了0-1评分的两参数Logistic模型的Logit变换等值法     

Comparing score tests and other local dependence diagnostics for the graded response model

Score tests for identifying locally dependent item pairs have been proposed for binary item response models. In this article, both the bifactor and the threshold shift score tests are generalized to the graded response model. For the bifactor test, the generalization is straightforward; it adds one secondary dimension associated only with one pair of items. For the threshold shift test, however, multiple generalizations are possible: in particular, conditional, uniform, and linear shift tests are discussed in this article. Simulation studies show that all of the score tests have accurate Type I error rates given large enough samples, although their small‐sample behaviour is not as good as that of Pearson's Χ² and M₂ as proposed in other studies for the purpose of local dependence (LD) detection. All score tests have the highest power to detect the LD which is consistent with their parametric form, and in this case they are uniformly more powerful than Χ² and M₂; even wrongly specified score tests are more powerful than Χ² and M₂ in most conditions. An example using empirical data is provided for illustration.     

Dual imputation model for incomplete longitudinal data

Missing values are a practical issue in the analysis of longitudinal data. Multiple imputation (MI) is a well‐known likelihood‐based method that has optimal properties in terms of efficiency and consistency if the imputation model is correctly specified. Doubly robust (DR) weighing‐based methods protect against misspecification bias if one of the models, but not necessarily both, for the data or the mechanism leading to missing data is correct. We propose a new imputation method that captures the simplicity of MI and protection from the DR method. This method integrates MI and DR to protect against misspecification of the imputation model under a missing at random assumption. Our method avoids analytical complications of missing data particularly in multivariate settings, and is easy to implement in standard statistical packages. Moreover, the proposed method works very well with an intermittent pattern of missingness when other DR methods can not be used. Simulation experiments show that the proposed approach achieves improved performance when one of the models is correct. The method is applied to data from the fireworks disaster study, a randomized clinical trial comparing therapies in disaster‐exposed children. We conclude that the new method increases the robustness of imputations.     

Independence and dependence in human causal reasoning

Causal graphical models (CGMs) are a popular formalism used to model human causal reasoning and learning. The key property of CGMs is the causal Markov condition, which stipulates patterns of independence and dependence among causally related variables. Five experiments found that while adult’s causal inferences exhibited aspects of veridical causal reasoning, they also exhibited a small but tenacious tendency to violate the Markov condition. They also failed to exhibit robust discounting in which the presence of one cause as an explanation of an effect makes the presence of another less likely. Instead, subjects often reasoned “associatively,” that is, assumed that the presence of one variable implied the presence of other, causally related variables, even those that were (according to the Markov condition) conditionally independent. This tendency was unaffected by manipulations (e.g., response deadlines) known to influence fast and intuitive reasoning processes, suggesting that an associative response to a causal reasoning question is sometimes the product of careful and deliberate thinking. That about 60% of the erroneous associative inferences were made by about a quarter of the subjects suggests the presence of substantial individual differences in this tendency. There was also evidence that inferences were influenced by subjects’ assumptions about factors that disable causal relations and their use of a conjunctive reasoning strategy. Theories that strive to provide high fidelity accounts of human causal reasoning will need to relax the independence constraints imposed by CGMs.     

血清CHO、D-二聚体、FDP与Child-Pugh分级的关系研究

为了探讨血清胆固醇（CHO）、D-二聚体、纤维蛋白降解产物（FDP）与肝硬化Child-Pugh分级的关系,选取73例肝硬化患者按Child-pugh评分分为A、B、C级,与30例健康对照组比较,肝硬化组CHO显著降低,D-二聚体和FDP显著升高,且A级与B级、B级与C级之间均有差异。因此,联合检测肝硬化患者CHO、D-二聚体、FDP含量,对肝脏受损的严重程度、肝病的疗效观察及预后具有重要的临床意义。     

Toward Increasing Fairness in Score Scale Calibrations Employed in International Large-Scale Assessments

In this article, we investigate the creation of comparable score scales across countries in international assessments. We examine potential improvements to current score scale calibration procedures used in international large-scale assessments. Our approach seeks to improve fairness in scoring international large-scale assessments, which often ignore item misfit in score scale calibrations. We also seek to obtain improved model-data fit estimates when calibrating international score scales. To this end, we examine the use of two alternative score scale calibration procedures: (a) a language-based score scale and (b) a more parsimonious international scale wherein a large proportion of international parameters are used with a subset of country-based parameters for items that misfit in the international scale. In our analyses, we used data from all 40 countries participating in the Progress in International Reading Literacy Study. Our findings revealed that current score scale calibration procedures yield large numbers of misfitting items (higher than 25% for some countries). Our proposed approach diminished the effects of proportion of item misfit on score scale calibrations and also yielded enhanced model-data fit estimates. These results lead to enhancing confidence in measurements obtained from international large-scale assessments.     

The Effect of Propensity Scores on DIF Analysis: Inference on the Potential Cause of DIF

Differential item functioning (DIF) analysis is important in terms of test fairness. While DIF analyses have mainly been conducted with manifest grouping variables, such as gender or race/ethnicity, it has been recently claimed that not only the grouping variables but also contextual variables pertaining to examinees should be considered in DIF analyses. This study adopted propensity scores to incorporate the contextual variables into the gender DIF analysis. In this study, propensity scores were used to control for the contextual variables that potentially affect the gender DIF. Subsequent DIF analyses with the Mantel-Haenszel (MH) procedure and the Logistic Regression (LR) model were run with the propensity score applied reference (males) and focal groups (females) through propensity score matching. The propensity score embedded MH model and LR model detected fewer number of gender DIF than the conventional MH and LR models. The propensity score embedded models, as a confirmatory approach in DIF analysis, could contribute to hypothesizing an inference on the potential cause of DIF. Also, salient advantages of propensity score embedded DIF analysis models are discussed.