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.     

Visual asymmetry revisited: Mind wandering preferentially disrupts processing in the left visual field

An emerging theory proposes that visual attention operates in parallel at two distinct time scales – a shorter one (<1 s) associated with moment-to-moment orienting of selective visuospatial attention, and a longer one (>10 s) associated with more global aspects of attention-to-task. Given their parallel nature, here we examined whether these comparatively slower fluctuations in task-related attention show the same visual field asymmetry – namely, a right visual field bias – as often reported for selective visual–spatial attention. Participants performed a target detection task at fixation while event-related potentials (ERP) time-locked to task-irrelevant visual probes presented in the left and right visual fields were recorded. At random intervals, participants were asked to report whether they were “on-task” or “mind wandering”. Our results demonstrated that sensory attenuation during periods of “mind wandering” relative to “on-task”, as measured by the visual P1 ERP component at electrodes sites contralateral to the stimulus, was only observed for probes presented in the left visual field. In contrast, the magnitude of sensory gain in the right visual field was insensitive to whether participants were “on-task” or “mind wandering”. Taken together, our results support the notion that task-related attention at longer time scales and spatial attention at shorter time scales affect the same underlying mechanism in visual cortex.     

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.     

Developmental dynamics between reading and math in elementary school

Reading and math attainment develop during elementary grades. Questions remain, though, about the co‐developmental nature of the relation between reading and math. This study examined dynamic, longitudinal pathways between reading and math in first through fourth grades. Participants of the study were 554 academically at‐risk children (M_age at the first assessment point = 6.57 years; SD = 0.38) from Texas Project Achieve. Children were assessed utilizing the Woodcock‐Johnson–III reading and math measures. Results from dynamic bivariate latent change score models indicated unidirectional longitudinal coupling effects from reading to math. Specifically, average and high levels of reading performance were associated with subsequent gains in math growth, in particular for below average performing children in math. In contrast, low levels of reading performance had negligible or no amplifying influences on change in math growth. The nature of the dynamics was replicated even when controlling for nonverbal cognitive abilities. Results demonstrated that good reading skills pave the way for children to develop their math skills. Such findings underscore the importance of considering reading performance in treating math difficulties.     

Scale Alignment in the Between-Item Multidimensional Partial Credit Model

In between-item multidimensional item response models, it is often desirable to compare individual latent trait estimates across dimensions. These comparisons are only justified if the model dimensions are scaled relative to each other. Traditionally, this scaling is done using approaches such as standardization—fixing the latent mean and standard deviation to 0 and 1 for all dimensions. However, approaches such as standardization do not guarantee that Rasch model properties hold across dimensions. Specifically, for between-item multidimensional Rasch family models, the unique ordering of items holds within dimensions, but not across dimensions. Previously, Feuerstahler and Wilson described the concept of scale alignment, which aims to enforce the unique ordering of items across dimensions by linearly transforming item parameters within dimensions. In this article, we extend the concept of scale alignment to the between-item multidimensional partial credit model and to models fit using incomplete data. We illustrate this method in the context of the Kindergarten Individual Development Survey (KIDS), a multidimensional survey of kindergarten readiness used in the state of Illinois. We also present simulation results that demonstrate the effectiveness of scale alignment in the context of polytomous item response models and missing data.