A Thurstonian Analysis of Preference Change

This paper presents a Thurstonian model for the analysis of preference change. Preferences are expressed in the form of rankings, possibly with ties. A vector-autoregression framework is used to investigate relationships between past and current rankings. It is shown that this approach yields a parsimonious and easily interpretable representation of individual preference differences in time-dependent ranking data. A detailed analysis of the 1992 National Election Study illustrates the proposed approach.     

The Relationship Between Applicant Reactions, the Likelihood of Complaints, and Organization Attractiveness

This study examined the effect of applicants' reactions to selection measures on complaint intentions and perceptions of organizational attractiveness. Despite recent challenges to selection tests for invasion of privacy, limited research has examined the link between specific reactions and potential legal challenges. As predicted, perceived invasiveness was significantly related to perceived likelihood of complaints for three selection measures. Further, perceived job relatedness was negatively related to likelihood of complaints for a test battery and a math test, but not for an integrity test. On the other hand, neither job relatedness nor invasiveness was significantly related to organizational attractiveness for any of the tests. Limitations and implications for future research are discussed.     

个人目标相关性对非临床抑郁者未来想象的影响

通过两个实验考查了非临床抑郁者未来想象的异常是否受到个人目标相关性的调节。实验1采用未来想象任务, 实验2采用可能性评估范式, 两个实验一致发现, 抑郁倾向者想象未来积极事件的异常, 受到了与个人目标相关性的调节：相对于非抑郁倾向者, 抑郁倾向者对未来与个人目标相关的积极事件的预期减弱, 而对未来与个人目标无关的积极事件的预期则没有表现出异常; 同时还发现, 抑郁倾向者表现出了对未来消极预期的普遍增强, 不受与个人目标相关性的影响。     

Efficient nonparametric approaches for estimating the operating characteristics of discrete item responses

Rationale and the actual procedures of two nonparametric approaches, called Bivariate P.D.F. Approach and Conditional P.D.F. Approach, for estimating the operating characteristic of a discrete item response, or the conditional probability, given latent trait, that the examinee's response be that specific response, are introduced and discussed. These methods are featured by the facts that: (a) estimation is made without assuming any mathematical forms, and (b) it is based upon a relatively small sample of several hundred to a few thousand examinees.Some examples of the results obtained by the Simple Sum Procedure and the Differential Weight Procedure of the Conditional P.D.F. Approach are given, using simulated data. The usefulness of these nonparametric methods is also discussed.This research was mostly supported by the Office of Naval Research (N00014-77-C-0360, N00014-81-C-0569, N00014-87-K-0320, N00014-90-J-1456).     

A caveat on the Savage–Dickey density ratio: The case of computing Bayes factors for regression parameters

The Savage–Dickey density ratio is a simple method for computing the Bayes factor for an equality constraint on one or more parameters of a statistical model. In regression analysis, this includes the important scenario of testing whether one or more of the covariates have an effect on the dependent variable. However, the Savage–Dickey ratio only provides the correct Bayes factor if the prior distribution of the nuisance parameters under the nested model is identical to the conditional prior under the full model given the equality constraint. This condition is violated for multiple regression models with a Jeffreys–Zellner–Siow prior, which is often used as a default prior in psychology. Besides linear regression models, the limitation of the Savage–Dickey ratio is especially relevant when analytical solutions for the Bayes factor are not available. This is the case for generalized linear models, non-linear models, or cognitive process models with regression extensions. As a remedy, the correct Bayes factor can be computed using a generalized version of the Savage–Dickey density ratio.     

The test taker's fallacy: How students guess answers on multiple‐choice tests

Many students and applicants take multiple‐choice tests to demonstrate their competence and achievement. When they are unsure, they guess the most likely answer to maximize their score. Despite the impact of guessing on test reliability and individual performance, studies have not examined how patterns of answer sequences in multiple‐choice tests affect guessing. This research presents the test taker's fallacy, which refers to an individual's tendency to expect a different answer to appear for the next question given a run of the same answer choices. The test taker's fallacy exhibits negative recency, similar to the gambler's fallacy. However, extending the sequential judgment literature, the test taker's fallacy shows that negative recency arises even when sequences may or may not be randomly generated. In three studies, including a survey and experiments, the test taker's fallacy is robustly observed. The test taker's fallacy is consistent with the operation of the representativeness heuristic. This research explains what and how test takers guess given a streak of answers and extends judgment under uncertainty to the test‐taking context.     

Estimating Incremental Validity Under Missing Data

A common form of missing data is caused by selection on an observed variable (e.g., Z). If the selection variable was measured and is available, the data are regarded as missing at random (MAR). Selection biases correlation, reliability, and effect size estimates when these estimates are computed on listwise deleted (LD) data sets. On the other hand, maximum likelihood (ML) estimates are generally unbiased and outperform LD in most situations, at least when the data are MAR. The exception is when we estimate the partial correlation. In this situation, LD estimates are unbiased when the cause of missingness is partialled out. In other words, there is no advantage of ML estimates over LD estimates in this situation. We demonstrate that under a MAR condition, even ML estimates may become biased, depending on how partial correlations are computed. Finally, we conclude with recommendations about how future researchers might estimate partial correlations even when the cause of missingness is unknown and, perhaps, unknowable.     

Computational exploration of metaphor comprehension processes using a semantic space model

Recent metaphor research has revealed that metaphor comprehension involves both categorization and comparison processes. This finding has triggered the following central question: Which property determines the choice between these two processes for metaphor comprehension? Three competing views have been proposed to answer this question: the conventionality view ( Bowdle & Gentner, 2005 ), aptness view ( Glucksberg & Haught, 2006b ), and interpretive diversity view ( Utsumi, 2007 ); these views, respectively, argue that vehicle conventionality, metaphor aptness, and interpretive diversity determine the choice between the categorization and comparison processes. This article attempts to answer the question regarding which views are plausible by using cognitive modeling and computer simulation based on a semantic space model. In the simulation experiment, categorization and comparison processes are modeled in a semantic space constructed by latent semantic analysis. These two models receive word vectors for the constituent words of a metaphor and compute a vector for the metaphorical meaning. The resulting vectors can be evaluated according to the degree to which they mimic the human interpretation of the same metaphor; the maximum likelihood estimation determines which of the two models better explains the human interpretation. The result of the model selection is then predicted by three metaphor properties (i.e., vehicle conventionality, aptness, and interpretive diversity) to test the three views. The simulation experiment for Japanese metaphors demonstrates that both interpretive diversity and vehicle conventionality affect the choice between the two processes. On the other hand, it is found that metaphor aptness does not affect this choice. This result can be treated as computational evidence supporting the interpretive diversity and conventionality views.     

计算机化自适应测验中原始题项目参数的估计

计算机化自适应测验(Computerized Adaptive Testing, 简称CAT)其安全性面临着新的挑战, 小题库的安全更受威胁。如何建设一个大型、优质的题库成为CAT研究中一个非常重要的课题。目前CAT题库的建设存在一些问题, 如成本高且保密性较差。尤其是等值技术较复杂且锚题重复使用容易造成泄露。如能在实施CAT过程中插入未经过参数估计的项目(原始题), 同时对原始题项目参数进行估计, 这对建设大型、优质的CAT题库来说其意义是不言而喻的。本文基于1PLM和2PLM对此进行研究, 提出了原始题在线估计的新方法以及推导出了求区分度参数a迭代初值的计算公式。研究结果表明：无论是模拟研究还是实证研究, 原始题被作答的次数对项目参数估计结果都会产生不同的影响, 并且原始题作答人数越多项目参数估计精度也越高。