Identifying Variables Responsible for Data not Missing at Random

When data are not missing at random (NMAR), maximum likelihood (ML) procedure will not generate consistent parameter estimates unless the missing data mechanism is correctly modeled. Understanding NMAR mechanism in a data set would allow one to better use the ML methodology. A survey or questionnaire may contain many items; certain items may be responsible for NMAR values in other items. The paper develops statistical procedures to identify the responsible items. By comparing ML estimates (MLE), statistics are developed to test whether the MLEs are changed when excluding items. The items that cause a significant change of the MLEs are responsible for the NMAR mechanism. Normal distribution is used for obtaining the MLEs; a sandwich-type covariance matrix is used to account for distribution violations. The class of nonnormal distributions within which the procedure is valid is provided. Both saturated and structural models are considered. Effect sizes are also defined and studied. The results indicate that more missing data in a sample does not necessarily imply more significant test statistics due to smaller effect sizes. Knowing the true population means and covariances or the parameter values in structural equation models may not make things easier either. The research was supported by NSF grant DMS04-37167, the James McKeen Cattell Fund.     

Estimation of the Nonlinear Random Coefficient Model when Some Random Effects Are Separable

A method is presented for marginal maximum likelihood estimation of the nonlinear random coefficient model when the response function has some linear parameters. This is done by writing the marginal distribution of the repeated measures as a conditional distribution of the response given the nonlinear random effects. The resulting distribution then requires an integral equation that is of dimension equal to the number of nonlinear terms. For nonlinear functions that have linear coefficients, the improvement in computational speed and accuracy using the new algorithm can be dramatic. An illustration of the method with repeated measures data from a learning experiment is presented.     

SINGLE‐SAMPLE DISCRIMINATION OF DIFFERENT SCHEDULES' REINFORCED INTERRESPONSE TIMES

Food‐deprived rats in Experiment 1 responded to one of two tandem schedules that were, with equal probability, associated with a sample lever. The tandem schedules' initial links were different random‐interval schedules. Their values were adjusted to approximate equality in time to completing each tandem schedule's response requirements. The tandem schedules differed in their terminal links: One reinforced short interresponse times; the other reinforced long ones. Tandem‐schedule completion presented two comparison levers, one of which was associated with each tandem schedule. Pressing the lever associated with the sample‐lever tandem schedule produced a food pellet. Pressing the other produced a blackout. The difference between terminal‐link reinforced interresponse times varied across 10‐trial blocks within a session. Conditional‐discrimination accuracy increased with the size of the temporal difference between terminal‐link reinforced interresponse times. In Experiment 2, one tandem schedule was replaced by a random ratio, while the comparison schedule was either a tandem schedule that only reinforced long interresponse times or a random‐interval schedule. Again, conditional‐discrimination accuracy increased with the temporal difference between the two schedules' reinforced interresponse times. Most rats mastered the discrimination between random ratio and random interval, showing that the interresponse times reinforced by these schedules can serve to discriminate between these schedules.     

Brainstorming rules as assigned goals: Does brainstorming really improve idea quantity?

In two experiments (n = 264 and 339), I treat brainstorming rules as assigned goals and compare their effectiveness to that of quantity goals as interventions to improve the number of ideas generated by individuals. Controlling for goal commitment, I find that brainstorming rules alone do not convey an advantage over even a vague quantity goal presented alone for enhancing the number of ideas generated in two common tasks. Detailed contrasts revealed that specific, difficult goals were only partially effective on their own, as expected when goal commitment is moderate. However, I find evidence in both studies that brainstorming rules are useful adjuncts to specific, difficult quantity goals. Importantly, their combination was the only consistently effective improvement over both vague quantity goals and brainstorming rules presented alone. I discuss implications for future research adopting a goal-based view of intervention in idea generation.

Changes in brain activation induced by the training of hypothesis generation skills: an fMRI study

The aim of the present study is to investigate the learning-related changes in brain activation induced by the training of hypothesis generation skills regarding biological phenomena. Eighteen undergraduate participants were scanned twice with functional magnetic resonance imaging (fMRI) before and after training over a period of 2 months. The experimental group underwent eight biological hypothesis generation training programs, but the control group was not given any during the 2-month period. The results showed that the left frontal gyri, the cingulate gyrus, and the cuneus were activated during hypothesis generation. In addition, the brain activation of the trained group increased in the left inferior and the superior frontal gyri, which are related to working memory load and higher-order inferential processes. However, the activation after training decreased in the occipito-parietal route, which is associated with the perception and the analysis processes of visual information. Furthermore, the results have suggested that the dorsolateral prefrontal cortex (DLPFC) region is the critical area in the training of hypothesis generation skills.     

计算机自动化项目生成概述

近年来,计算机自动化项目生成作为应对测验项目曝光问题的很具前景的一项技术,逐渐得到越来越多研究者的重视。这种技术是以认知心理学和心理测量学为基础,在测试过程中借助计算机自动生成目标难度水平的项目。该文首先简要介绍了这种技术的理论基础和两种具体方法,然后以项目设计系统法为例说明自动化项目生成研究的具体步骤与优点,最后评述了这种技术的局限性及未来的发展趋势     

瑞文测验项目认知难度因素分析及LLTM拟合验证

该文在以认知和测量相结合为特征的新一代测验理论的指导下,针对项目刺激特征,对影响瑞文测验项目认知难度的因素进行了分析,提取出图块繁简度、整体布局、变化维度和题序等4个因素,然后应用线性逻辑潜在特质模型（LLTM）对这四个因素进行了拟合检验,结果证明提取出的四个因素是有效的。文章还进一步探讨了研究结果在智力测验中的应用。     

关键词对精加工推理的影响作用初探

本实验研究在文本中突出关键词对精加工推理的产生水平和产生阶段的影响。研究采用了让被试阅读后做测试题的方法,通过分析测试题的成绩得出的结果表明：在文章中突出关键词有助于产生近推理和内涵推理,且使这两类推理更有可能在阅读过程中产生。本研究补充了McDaniel和Donnelly 1996年的研究结果。     

Use of current explanations in multicausal abductive reasoning

In multicausal abductive tasks a person must explain some findings by assembling a composite hypothesis that consists of one or more elementary hypotheses. If there are n elementary hypotheses, there can be up to 2ⁿ composite hypotheses. To constrain the search for hypotheses to explain a new observation, people sometimes use their current explanation—the previous evidence and their present composite hypothesis of that evidence; however, it is unclear when and how the current explanation is used. In addition, although a person's current explanation can narrow the search for a hypothesis, it can also blind the problem solver to alternative, possibly better, explanations. This paper describes a model of multicausal abductive reasoning that makes two predictions regarding the use of the current explanation. The first prediction is that the current explanation is not used to explain new evidence if there is a simple (i.e., nondisjunctive, concrete) hypothesis to account for that evidence. The second prediction is that the current explanation is used when attempting to discriminate among several alternative hypotheses for new evidence. These hypotheses were tested in three experiments. The results are consistent with the second prediction: the current explanation is used when discriminating among alternative hypotheses. However, the first prediction—that the current explanation is not used when a simple hypothesis can account for new data—received only limited support. Participants used the current explanation to constrain their interpretation of new data in 46.5% of all trials. This suggests that context-independent strategies compete with context-dependent ones—an interpretation that is consistent with recent work on strategy selection during problem solving.