The role of collateral information about examinees in item parameter estimation

Standard procedures for estimating item parameters in item response theory (IRT) ignore collateral information that may be available about examinees, such as their standing on demographic and educational variables. This paper describes circumstances under which collateral information about examineesmay be used to make inferences about item parameters more precise, and circumstances under which itmust be used to obtain correct inferences.This work was supported by Contract No. N00014-85-K-0683, project designation NR 150-539, from the Cognitive Science Program, Cognitive and Neural Sciences Division, Office of Naval Research. Reproduction in whole or in part is permitted for any purpose of the United States Government. We are indebted to Tim Davey, Eugene Johnson, and three anonymous referees for their comments on earlier versions of the paper.     

Randomization-based inference about latent variables from complex samples

Standard procedures for drawing inferences from complex samples do not apply when the variable of interest cannot be observed directly, but must be inferred from the values of secondary random variables that depend on stochastically. Examples are proficiency variables in item response models and class memberships in latent class models. Rubin's multiple imputation techniques yield approximations of sample statistics that would have been obtained, had been observable, and associated variance estimates that account for uncertainty due to both the sampling of respondents and the latent nature of. The approach is illustrated with data from the National Assessment for Educational Progress.This research was supported by Grant No. NIE-G-83-0011 of the Office for Educational Research and Improvement, Center for Education Statistics, and Contract No. N00014-88-K-0304, R&T 4421552 from the Cognitive Sciences Program, Cognitive and Neural Sciences Division, Office of Naval Research. It does not necessarily reflect the views of either agency. I am grateful to R. Darrell Bock for calling my attention to the applicability of multiple imputation to the assessment setting; to Albert Beaton and Eugene Johnson for enlightening discussions on the topic; and to Henry Braun, Ben King, Debra Kline, Gary Phillips, Paul Rosenbaum, Don Rubin, John Tukey, Ming-Mei Wang, Kentaro Yamamoto, Rebecca Zwick, and two anonymous reviewers for comments on earlier drafts. Example 4 is based on the analysis of the 1984 National Assessment for Educational Progress reading survey, carried out at Educational Testing Service through the tireless efforts of too many people to mention by name, under the direction of Albert Beaton, Director of NAEP Data Analyses. David Freund, Bruce Kaplan, and Jennifer Nelson conducted additional analyses of the 1984 and 1988 data for the example.     

The dimensional effects of missing information on choice processing

This paper demonstrates that choice processing may be different in missing information situations than in full information situations depending on whether inferences are used to fill in missing values and the overlap of the missing information itself. It is shown that when individuals do not form inferences to fill in missing values, fewer full attribute-based processes and more processes which accommodate for missing attribute values, alternative-based or given-dimension attribute-based, are used. It is also shown that when a processing shift due to missing information does occur, the overlap of the missing values will affect the type of shift that takes place. If overlap is high, a shift to given-dimension attribute-based processing is more likely, and when overlap is low, a shift to alternative-based processing is more likely. When individuals do form inferences to fill in missing values, processing is more similar to that in full information situations. Finally, it is shown that individuals will often partially fill in missing information, thus moderating the proposed effects.     

错失焦虑与大学生被动性社交网站使用的交叉滞后分析

为探讨大学生错失焦虑与被动性社交网站使用间的准因果关系,采用错失焦虑量表和被动性社交网站使用量表对403名大学生进行间隔8个月的两阶段追踪调查。结果发现：(1)错失焦虑和被动性社交网站使用的自回归路径系数显著,存在风险累积效应。(2)错失焦虑与被动性社交网站使用在8个月内能够互相预测,存在强化螺旋效应。(3)错失焦虑与被动性社交网站使用的交叉滞后效应性别差异不显著,存在跨性别趋同效应。结果支持了强化螺旋模型,表明错失焦虑与大学生被动性社交网站使用能够交互影响。     

2PL模型的两种马尔可夫蒙特卡洛缺失数据处理方法比较

马尔科夫蒙特卡洛（MCMC）是项目反应理论中处理缺失数据的一种典型方法。文章通过模拟研究比较了在不同被试人数,项目数,缺失比例下两种MCMC方法（M-H within Gibbs和DA-T Gibbs）参数估计的精确性,并结合了实证研究。研究结果表明,两种方法是有差异的,项目参数估计均受被试人数影响很大,受缺失比例影响相对更小。在样本较大缺失比例较小时,M-H within Gibbs参数估计的均方误差（RMSE）相对略小,随着样本数的减少或缺失比例的增加,DA-T Gibbs方法逐渐优于M-H within Gibbs方法     

行为决策中模糊规避研究的回顾与展望

模糊规避是指在相同奖赏的情况下,决策者会偏好有精确概率的事件而不是从主观上判断具有相同模糊概率的事件。自从Ellsberg提出模糊规避的概念以来,模糊规避已在行为决策研究的多个领域得到广泛验证。本文梳理了近五十年来关于模糊规避的研究文献,系统分析了模糊规避的研究范式、心理机制和影响因素,同时提出了未来的研究展望。     

LGM模型中缺失数据处理方法的比较:ML方法与Diggle-Kenward选择模型

追踪研究中缺失数据十分常见。本文通过Monte Carlo模拟研究,考察基于不同前提假设的Diggle-Kenward选择模型和ML方法对增长参数估计精度的差异,并考虑样本量、缺失比例、目标变量分布形态以及不同缺失机制的影响。结果表明:(1)缺失机制对基于MAR的ML方法有较大的影响,在MNAR缺失机制下,基于MAR的ML方法对LGM模型中截距均值和斜率均值的估计不具有稳健性。(2)DiggleKenward选择模型更容易受到目标变量分布偏态程度的影响,样本量与偏态程度存在交互作用,样本量较大时,偏态程度的影响会减弱。而ML方法仅在MNAR机制下轻微受到偏态程度的影响。     

社交网站支持对大学生社交网站成瘾的影响:有调节的中介模型

探讨社交网站支持对大学生社交网站成瘾的影响机制.采用社交网站支持量表、感觉寻求量表、错失恐惧量表和社交网站成瘾量表对501名大学生进行调查.结果发现:(1)社交网站支持正向预测大学生社交网站成瘾;(2)错失恐惧在社交网站支持与大学生社交网站成瘾间起部分中介作用;(3)感觉寻求调节了社交网站支持对大学生社交网站成瘾的影响...     

错失恐惧与社交网站成瘾：有调节的中介模型

为探讨错失恐惧、自我损耗及关系型自我构念对社交网站成瘾的影响及作用机制,本研究基于有限自制力模型,采用问卷法调查了526名微信朋友圈或QQ空间的大学生用户。结果发现：（1）在控制性别的条件下,错失恐惧能显著正向预测社交网站成瘾;（2）错失恐惧还通过自我损耗的中介作用间接预测社交网站成瘾;（3）关系型自我构念调节错失恐惧通过自我损耗预测社交网站成瘾的前半路径。具体来说,相比于低关系型自我构念个体,高关系型自我构念个体的错失恐惧更多地通过自我损耗影响其社交网站成瘾。     

Generalized canonical correlation analysis of matrices with missing rows: a simulation study

A method is presented for generalized canonical correlation analysis of two or more matrices with missing rows. The method is a combination of Carroll’s (1968) method and the missing data approach of the OVERALS technique (Van der Burg, 1988). In a simulation study we assess the performance of the method and compare it to an existing procedure called GENCOM, proposed by Green and Carroll (1988). We find that the proposed method outperforms the GENCOM algorithm both with respect to model fit and recovery of the true structure. The research of Michel van de Velden was partly funded through EU Grant HPMF-CT-2000-00664. The authors would like to thank the associate editor and three anonymous referees for their constructive comments and suggestions that led to a considerable improvement of the paper.