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.     

Psychometric and information processing properties of selected response time models

This paper discusses the compatibility of some response time (RT) models with psychometric and with information processing approaches to response times. First, three psychometrically desirable properties of probabilistic models for binary data, related to the principle of specific objectivity, are adapted to the domain of RT models. One of these is the separability of item and subject parameters, and another is double monotonicity. Next, the compatibility of these psychometric properties with one very popular information processing approach, the serial-additive model, is discussed. Finally, five RT models are analyzed with respect to their compatibility with the psychometric properties, with serial-additive processing and with some alternative types of processing. It is concluded that (a) current psychometric models each satisfy one or more of the psychometric properties, but are not (easily) compatible with serial-additive processing, (b) at least one serial-additive processing model satisfies separability of item and subject parameters, and (c) RT models will more easily satisfy double monotonicity than the other two psychometric properties.     

Memory in schizophrenia and affective disorders*

The purpose of the present study was to investigate the stability of memory and information processing in schizophrenics and affectively disturbed patients. Twenty-two schizophrenics, eight affectives and 14 normal controls were tested twice, with an interval of approximately one year. Results showed that normals performed better than schizophrenics and affectively disturbed patients on almost all of the cognitive measures. The differences reached a level of significance on a reaction time test, a vigilance task and a long-term memory task. The only measure where the difference did not reach a level of significance was on a short-term memory test. Groups' performance to some extent changed over time on most of the measures, but the changes were not significantly different for different groups.     

项目难度与被试能力分布最优匹配的模拟研究

该文运用蒙特卡罗方法对被测试能力分布与测验项目难度分布的匹配问题进行模拟分析,分析表明当能力分布为正态分布正偏态分布和负偏态分布时分别与测验项目难度分布与为正态分布,正偏态分布和负偏态分布匹配,比别的匹配有更高测验期望信息值,测验最大信息测验 系数,并且测验信息曲线最大值的能力点与能力分布的众数愈相一致,测验项目参数估计值性真实值的相关也更高。     

Evaluation of job candidates with missing information: Effects of attribute importance and interattribute correlation

How do people make evaluations when important items of information are missing? In the context of personnel evaluations or product evaluations, researchers have proposed that decision makers may predict the missing attribute based on its assumed relationship to attributes that are present. In addition, some researchers have suggested that there is a penalty for missing information. An experiment was conducted to investigate the influence of the importance of the missing attribute on the hypothesized inference effect. Hypothetical job candidates were evaluated based on one or two attributes. The correlation between the attributes was varied between groups of subjects as was the importance of one of the attributes. The pattern of ratings of candidates with missing information varied significantly with the correlation condition when the missing attribute was very important, but did not vary as much when the missing attribute was less important. The results were generally consistent with the predictions of the Inferred Information Model (Johnson and Levin, 1985). On average, candidates with missing information were rated lower than comparable candidates with complete information and the missing attribute at an average level.     

Is the association between childhood maltreatment and aggressive behavior mediated by hostile attribution bias in women? A discordant twin and sibling study

Understanding the mechanisms behind aggressive behavior (AGG) is vital so that effective prevention and intervention strategies can be developed. Maltreated children are hypothesized to be prone to social information processing biases, such as hostile attribution bias (HAB), which, in turn, may increase the likelihood of behaving aggressively. The first aim of the present study was to replicate findings regarding associations between childhood maltreatment (CM), HAB, and aggression in a population‐based sample of Finnish female twins and their sisters (N = 2,167). However, these associations might not be causal but instead confounded by familial factors, shared between the variables. The second aim was, thus, to test the associations when potential confounding by familial (genetic or common environmental) effects were controlled for using a multilevel discordant twin and sibling design within (a) 379 pairs of twins (n_pairs = 239) or siblings (n_pairs = 140), and (b) within the 131 monozygotic (MZ) twin pairs. Consistent with previous studies, HAB mediated the association between CM and AGG when familial confounding was uncontrolled. No support was found for the mediation when controlling for familial confounding. Between‐pair associations were found between CM and AGG, and between CM and HAB. In addition, within‐pair associations were found between HAB and AGG, and between CM and AGG, however, these were nonsignificant in the discordant MZ analysis, offering the most stringent control of familial confounding. The results indicate the necessity of taking familial confounding into account when investigating the development of AGG.     

手机成瘾与抑郁：社交焦虑和负性情绪信息注意偏向的多重中介作用

为考察手机成瘾与抑郁的关系, 研究同时整合情绪和认知两方面因素, 探讨了社交焦虑和负性情绪信息注意偏向在手机成瘾和抑郁之间的多重中介作用。研究1共有545名大学生完成了大学生手机成瘾倾向量表、贝克抑郁量表和社会交往焦虑量表。研究2选取51名大学生采用问卷法和2(配对面孔表情的情绪类型: 负性、中性) × 2(探测点位置: 与负性情绪面孔表情同侧、与负性情绪面孔表情异侧)的被试内实验设计。结果表明: (1)手机成瘾、社交焦虑和抑郁两两间存在显著正相关, 且社交焦虑在手机成瘾和抑郁之间起完全中介作用; (2)社交焦虑和负性情绪信息注意偏向在手机成瘾与抑郁的关系中起链式中介作用, 而负性情绪信息注意偏向在手机成瘾与抑郁间的单独中介效应不显著。具体而言, 手机成瘾通过两条路径影响抑郁: 一是社交焦虑的单独中介作用; 二是社交焦虑→负性情绪信息注意偏向的链式中介作用。     

A Gibbs sampler for the multidimensional four-parameter logistic item response model via a data augmentation scheme

The four-parameter logistic (4PL) item response model, which includes an upper asymptote for the correct response probability, has drawn increasing interest due to its suitability for many practical scenarios. This paper proposes a new Gibbs sampling algorithm for estimation of the multidimensional 4PL model based on an efficient data augmentation scheme (DAGS). With the introduction of three continuous latent variables, the full conditional distributions are tractable, allowing easy implementation of a Gibbs sampler. Simulation studies are conducted to evaluate the proposed method and several popular alternatives. An empirical data set was analysed using the 4PL model to show its improved performance over the three-parameter and two-parameter logistic models. The proposed estimation scheme is easily accessible to practitioners through the open-source IRTlogit package.     

Maximum information per time unit designs for continuous online item calibration

Previous designs for online calibration have only considered examinees’ responses to items. However, the use of response time, a useful metric that can easily be collected by a computer, has not yet been embedded in calibration designs. In this article we utilize response time to optimize the assignment of new items online, and accordingly propose two new adaptive designs. These are the D-optimal per expectation time unit design (D-ET) and the D-optimal per time unit design (D-T). The former method uses the conditional maximum likelihood estimation (CMLE) method to estimate the expected response times, while the latter employs the nonparametric k-nearest-neighbour method to predict the response times. Simulations were conducted to compare the two new designs with the D-optimal online calibration design (D design) in the context of continuous online calibration. In addition, a preliminary study was carried out to evaluate the performance of CMLE prior to its application in D-ET. The results showed that, compared to the D design, the D-ET and D-T designs saved response time and accrued larger calibration information per time unit, without sacrificing item calibration precision.